CN117679749A - Game application data processing method, device, equipment and medium - Google Patents
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Abstract
The invention relates to the technical field of game application data processing, in particular to a method, a device, equipment and a medium for processing game application data, wherein the method specifically comprises the following steps: simultaneously running a plurality of cloud mobile phones based on a multithreading technology, wherein the cloud mobile phones are mobile phone simulators arranged on cloud services; respectively logging in at least one application store according to each cloud mobile phone, extracting game application data of each application store through a preset data acquisition model, and then pushing the game application data to a message queue; and acquiring different game application data from the message queue according to the topic classification for analysis, and generating a game application data analysis result. The invention realizes the multi-thread parallel mobile phone simulation and automatically extracts the latest game application data of each application market.
Description
Technical Field
The present invention relates to the field of game application data processing technologies, and in particular, to a method, an apparatus, a device, and a medium for processing game application data.
Background
In the traditional game operation, for new games, even first hand data of the bidding products, attention, follow-up and analysis are often carried out manually, the manual process needs to switch APP of a plurality of application markets, timing follow-up of the new games is carried out by virtue of Excel documents, the whole data chain has larger instability, meanwhile, certain subjectivity and error exist in a manual operation mode, information delay, error and inconsistency are easy to cause, and meanwhile, large-scale data management and analysis are inconvenient.
If these data are obtained by an existing crawler model, the following problems also exist: each application market has own data structure and interface, the application market can also irregularly change the structure of the APP internal page, and when encountering the structure change, technicians are required to carry out re-adaption. Even in the absence of updates to the APP version, element adjustments and changes to the literal description may occur, as well as requiring timely adaptation to those changes, which may result in inaccurate or failed data acquisition. In addition, since the data of the respective application stores is different, the difficulty of unified analysis for each game application is high, and only individual analysis can be performed for each application store.
Disclosure of Invention
The invention aims to provide a processing method, a device, equipment and a medium for game application data, which realize multi-thread parallel mobile phone simulation and automatically extract the latest game application data of each application market so as to solve at least one of the problems in the prior art.
In a first aspect, a method for processing game application data, the method specifically includes:
simultaneously running a plurality of cloud mobile phones based on a multithreading technology, wherein the cloud mobile phones are mobile phone simulators arranged on cloud services;
respectively logging in at least one application store according to each cloud mobile phone, extracting game application data of each application store through a preset data acquisition model, and then pushing the game application data to a message queue;
and acquiring different game application data from the message queue according to the topic classification for analysis, and generating a game application data analysis result.
Further, the method for simultaneously operating a plurality of cloud mobile phones based on the multithreading technology specifically comprises the following steps:
setting a Python multithreading program;
and connecting and simulating to operate at least one cloud mobile phone through an Airtest module in each thread of the Python multithreading program.
Further, the setting Python multithreading program specifically includes:
creating, starting, ending and synchronizing a plurality of sub-thread objects according to a threading module of Python, wherein the plurality of sub-thread objects comprise at least one main thread object and a plurality of sub-thread objects;
creating a plurality of task queues according to a queue module of Python, and scheduling execution tasks of the thread objects through the task queues;
setting the maximum retry times and retry intervals of each thread object according to the time module of Python;
and recording the task execution condition of each thread object according to the logging module of the Python.
Further, the connecting and simulating operation of the at least one cloud mobile phone through the Airtest module specifically includes:
when the cloud mobile phone is an android version cloud mobile phone, connecting and simulating to operate at least one cloud mobile phone of security Zhuo Banben according to an airtest.core.api component of the Airtest module;
when the cloud mobile phone is an iOS version cloud mobile phone, connecting and simulating to operate at least one iOS version cloud mobile phone through a WDA library according to the Airtest module.
Further, the data acquisition model is a Poco image recognition module; the game application data of each application store is extracted through a preset data acquisition model, specifically: extracting game application data of each application store through the Poco image recognition module in each thread of the Python multithreading program;
the extracting the game application data of each application store through the Poco image recognition module specifically comprises the following steps:
acquiring an application entry of each application store according to a get_position method of the Poco image recognition module;
acquiring screen shots of at least one game application of each application store according to a snapshot method of the Pogo image recognition module;
and acquiring game application data on the screen shot according to a get_text method of the Pogo image recognition module.
Further, the step of obtaining different game application data from the message queue according to the topic classification for analysis to generate a game application data analysis result specifically includes:
acquiring game application data of all application stores of each game from each theme of the message queue;
obtaining a value score of each game at each application store respectively through a principal component analysis method based on game application data of each application store of each game;
and obtaining the value score analysis results of all games according to the value score of each game in each application store.
Further, the game application data of each application store of each game is based on, and the value score of each game in each application store is obtained through a principal component analysis method, which specifically comprises the following steps:
obtaining all grading indexes of each game in each application store, and obtaining a covariance matrix of each game in each application store according to a covariance formula through the grading indexes, wherein the covariance formula meets the following requirementsWherein cov (X) i ,X j ) Representing the covariance between any two scoring indices, X i And X j Any two scoring indexes in all scoring indexes are represented, and n represents the number of the scoring indexes;
decomposing the covariance matrix to obtain a plurality of eigenvalues and corresponding eigenvectors;
sorting a plurality of characteristic values from large to small to obtain characteristic vectors corresponding to the first k characteristic values;
according to the feature vectors of each scoring index corresponding to the first k feature values, obtaining the value score of each game in each application store, wherein the value score satisfies P=P 1 +P 2 +...+P n ，P n ＝X n *v 1 +X n *v 2 +...+X n *v k Wherein P represents the value score of any game at any application store, P n Value score, X, representing any score indicator of any game at any application store n Any scoring index, v, representing any application store k Any one of the feature vectors corresponding to the first k feature values is represented.
In a second aspect, the present invention provides a processing device for game application data, where the device specifically includes:
the cloud mobile phone simulator comprises a first processing module, a second processing module and a third processing module, wherein the first processing module is used for simultaneously running a plurality of cloud mobile phones based on a multithreading technology, and the cloud mobile phones are mobile phone simulators arranged on cloud services;
the second processing module is used for logging in at least one application store according to each cloud mobile phone respectively, extracting game application data of each application store through a preset data acquisition model, and then pushing the game application data to a message queue;
and the third processing module is used for acquiring different game application data from the message queue according to the topic classification for analysis and generating a game application data analysis result.
In a third aspect, the present invention provides a computer device comprising: memory and processor and computer program stored on the memory, which when executed on the processor, implements a method of processing game application data as described in any of the above methods.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of processing game application data as described in any of the above methods.
Compared with the prior art, the invention has at least one of the following technical effects:
1. the method solves the problem that the traditional data acquisition means is difficult to uniformly acquire the game application data from various game application markets.
2. The cloud mobile phone can be infinitely extended, and the task of simultaneously carrying out mobile phone simulation is unlimited.
3. And uniformly formatting and presenting the scattered new game data.
4. The analysis of the whole new game data and the message pushing after the new game is found to be online can be carried out.
5. The game value scores of all application shops are comprehensively evaluated through principal component analysis, so that the comprehensive condition of each game can be more objectively evaluated.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for processing game application data according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for processing game application data according to another embodiment of the present invention;
FIG. 3 is a schematic diagram of a processing device for game application data according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
In the traditional game operation, for new games, even first hand data of the bidding products, attention, follow-up and analysis are often carried out manually, the manual process needs to switch APP of a plurality of application markets, timing follow-up of the new games is carried out by virtue of Excel documents, the whole data chain has larger instability, meanwhile, certain subjectivity and error exist in a manual operation mode, information delay, error and inconsistency are easy to cause, and meanwhile, large-scale data management and analysis are inconvenient.
If these data are obtained by an existing crawler model, the following problems also exist: each application market has own data structure and interface, the application market can also irregularly change the structure of the APP internal page, and when encountering the structure change, technicians are required to carry out re-adaption. Even in the absence of updates to the APP version, element adjustments and changes to the literal description may occur, as well as requiring timely adaptation to those changes, which may result in inaccurate or failed data acquisition. In addition, since the data of the respective application stores is different, the difficulty of unified analysis for each game application is high, and only individual analysis can be performed for each application store.
Referring to fig. 1, an embodiment of the present invention provides a method for processing game application data, where the method specifically includes:
s101: and simultaneously running a plurality of cloud mobile phones based on a multithreading technology, wherein the cloud mobile phones are mobile phone simulators arranged on cloud services.
In this embodiment, it is first necessary to select a suitable cloud service provider, such as Amazon Web Services (AWS), google Cloud Platform (GCP) or ali cloud, and then create multiple virtual machine instances on these cloud platforms, each of which will run a handset simulator that will act as our cloud handset, on which various applications can be run to perform various operations. Due to the use of multi-threading techniques, these simulators will run simultaneously, increasing efficiency.
In some embodiments, the method for simultaneously operating a plurality of cloud mobile phones based on the multithreading technology specifically includes:
setting a Python multithreading program;
and connecting and simulating to operate at least one cloud mobile phone through an Airtest module in each thread of the Python multithreading program.
In this embodiment, the program is a task generation and processing framework program developed based on Python multithreading. Firstly, the system has the characteristic of multithreading, namely a plurality of tasks can be operated simultaneously without mutual interference, so that the execution efficiency of the whole system is accelerated; secondly, task model design is carried out through Manager-workers, and one type of task has a Manager to schedule tasks of the workers below the Manager, so that concurrent operation can be carried out; furthermore, the frame itself has a mechanism of failure retry and failure restart, and forms a data processing frame integrating monitoring and self-healing by matching with error warning.
Airtest is an open source library for automated testing and operation that supports a variety of devices, including Android and iOS physics and simulators, and may be installed in a Python environment by pip commands. By using multi-threading technology, multiple cloud handsets can be connected and operated simultaneously, performing automated testing or operational tasks more efficiently, especially if multiple devices need to be handled simultaneously. Furthermore, since each device runs on its independent virtual machine, security may also be improved because the operation of each device is isolated.
Specifically, the method for setting the Python multithreading program specifically includes:
creating, starting, ending and synchronizing a plurality of sub-thread objects according to a threading module of Python, wherein the plurality of sub-thread objects comprise at least one main thread object and a plurality of sub-thread objects;
creating a plurality of task queues according to a queue module of Python, and scheduling execution tasks of the thread objects through the task queues;
setting the maximum retry times and retry intervals of each thread object according to the time module of Python;
and recording the task execution condition of each thread object according to the logging module of the Python.
In this embodiment, after the threading module is imported, a thread object is created by using a threading class, a start () method of the thread object is called to start a thread, a join () method of the threading class is used to wait for the thread to finish, the join () method blocks the current thread until the thread calling it finishes executing, and a lock object is created by using the threading class method to ensure that access to the shared resource is thread-safe.
After the Queue module is imported, a task Queue is created through the Queue class, and the access to the Queue in the multithreading environment is ensured to be thread-safe.
After the time module is imported, setting a parameter of the maximum retry number, setting a conditional circulation function by taking the parameter as a condition, executing a certain task in the conditional circulation function if the maximum number does not exceed the maximum retry number, adding 1 if the operation fails, and setting the time of the retry interval by a time.
After the logging module is imported, a log recorder object is obtained through logging.getLogger (), one or more processors are added to the recorder through logging.addHandler (), a formatter is created through the logging.formater, the formatter is associated with the processor through the hander.setFormat (), and log messages of different levels are recorded through the methods of logging (), logging.info (), logging.wave (), logging.error (), or logging.critical (). Each method corresponds to a different log level, and the global log level of the logger is set by a log-based.setlevel (level) method to filter the control log output.
Specifically, the connecting and simulating operation of at least one cloud mobile phone through the Airtest module specifically includes:
when the cloud mobile phone is an android version cloud mobile phone, connecting and simulating to operate at least one cloud mobile phone of security Zhuo Banben according to an airtest.core.api component of the Airtest module;
when the cloud mobile phone is an iOS version cloud mobile phone, connecting and simulating to operate at least one iOS version cloud mobile phone through a WDA library according to the Airtest module.
In this embodiment, the airtest.core.api is a core API module in the Airtest tool, and for the android phone simulator, the function of the airtest.core.api may be directly utilized to connect and simulate to operate at least one cloud phone of the ampere Zhuo Banben. For example, a connect_device function may be used to connect to a target device, a device ID or IP address may be input in the function, a click operation on a screen may be simulated using the touch function, a coordinate parameter representing a click position may be accepted, a slide operation on the screen may be simulated using the wipe function, a start coordinate and an end coordinate may be accepted, and a duration of the slide may be simulated using the keyvent function, a key event such as a return key, a menu key, etc., a disconnect_device function may be used to disconnect the device, and resources may be released.
WebDriverAgent (WDA) is an open source tool for connecting and operating iOS devices, is an important dependency library for Airtest to support automation testing of iOS devices, and can communicate with iOS devices through WDA libraries, simulate user operations, obtain device information, etc. For the iOS mobile phone simulator, the WDA library dependency is also required to be imported into the Python environment, and then at least one iOS version of cloud mobile phone is connected and simulated through the Airtest module. For example, a connection with the iOS device is created through the wda.client function, a command is sent, and a response is received, device information is acquired through the wda.device function, an operation is simulated, and the like.
By using different airest module components (i.e., airest. Core. Api and WDA libraries), the appropriate connectivity and analog operation may be selected according to the operating system type (android or iOS) of the cloud handset.
S102: and respectively logging in at least one application store according to each cloud mobile phone, extracting game application data of each application store through a preset data acquisition model, and then pushing the game application data to a message queue.
In this embodiment, for each cloud mobile phone, at least one application store needs to be logged in first. According to different cloud mobile phone types (android or iOS), corresponding Airtest module components or WDA libraries can be used for simulating user operation, and the process of logging in an application store is realized. For example, for a An Zhuoyun handset, an app store application may be launched using the device.launch_app ("app store") function of the airest module, and then the user name and password may be entered using the device.type_text ("user name") and device.type_text ("password") functions, enabling the login operation.
After logging into the application store, the game application data needs to be extracted next. To achieve this goal, a preset data acquisition model may be used. The model may be a predefined function or process for extracting game application data on application store pages, which may be adjusted according to different application store page structures and data formats.
After the game application data are extracted, the game application data can be pushed to a kafka queue, and theme classification can be set for the game application data according to different application stores or according to different games.
Through the embodiment, the game application data of different application stores can be automatically extracted and pushed to the message queue. The technical scheme can improve the efficiency and accuracy of data acquisition and can be expanded to other types of application stores and data acquisition tasks.
In some embodiments, the data acquisition model is a Poco image recognition module; the game application data of each application store is extracted through a preset data acquisition model, specifically: extracting game application data of each application store through the Poco image recognition module in each thread of the Python multithreading program;
the extracting the game application data of each application store through the Poco image recognition module specifically comprises the following steps:
acquiring an application entry of each application store according to a get_position method of the Poco image recognition module;
acquiring screen shots of at least one game application of each application store according to a snapshot method of the Pogo image recognition module;
and acquiring game application data on the screen shot according to a get_text method of the Pogo image recognition module.
In the embodiment, through using the Poco image recognition module, game application data of different application stores can be automatically extracted, game application elements on pages of the application stores can be rapidly positioned and recognized, time and energy of manual operation are greatly reduced, and data extraction efficiency is remarkably improved. Meanwhile, as the whole process is automatic, human intervention can be reduced, and operation risks are reduced.
S103: and acquiring different game application data from the message queue according to the topic classification for analysis, and generating a game application data analysis result.
In this embodiment, the server side extracts data from the Kafka queue to perform data attribution, and the main steps are: data extraction, data cleaning (regular into a uniform format), data storage (after storing MySql, automatic synchronization to Hologres is convenient for data analysis), hologres is an open-source distributed SQL data warehouse, high-performance and high-availability data storage and query functions are provided, and the requirements of large-scale data analysis can be met. The method can also be combined with the pandas library of Python to perform more flexible data processing and analysis operation, and the pandas library provides rich data processing functions including data screening, sorting, merging, conversion and the like, so that the data processing and analysis can be conveniently performed. The game operators can perform visual data analysis through the web pages, and can find out newly online games, track reservation data and attention of the operation games and know the data condition of the same type of competitive products.
In some embodiments, the obtaining different game application data from the message queue according to the topic classification for analysis, and generating a game application data analysis result specifically includes:
acquiring game application data of all application stores of each game from each theme of the message queue;
obtaining a value score of each game at each application store respectively through a principal component analysis method based on game application data of each application store of each game;
and obtaining the value score analysis results of all games according to the value score of each game in each application store.
In this embodiment, the kafka message queue aggregate contains a plurality of topics, each topic corresponding to a game, so that game application data of a game in a plurality of application stores, such as names, descriptions, scores, downloads, etc., are stored in each topic. Considering that the score index of each application store is different, the value score can be carried out on all the score indexes of the single application store of each game through a principal component analysis method, and after the value score of each game in each application store is obtained, the score indexes can be integrated together to obtain the value score analysis result of all the games. Wherein the scoring indicators include, but are not limited to, user ratings, download volume, pre-forms, number of reviews, etc. as numerical indicators.
Specifically, the value score of each game at each application store is obtained by a principal component analysis method based on the game application data of each application store of each game, and specifically includes:
obtaining all grading indexes of each game in each application store, and obtaining a covariance matrix of each game in each application store according to a covariance formula through the grading indexes, wherein the covariance formula meets the following requirementsWherein cov (X) i ,X j ) Representing the covariance between any two scoring indices, X i And X j Any two scoring indexes in all scoring indexes are represented, and n represents the number of the scoring indexes;
decomposing the covariance matrix to obtain a plurality of eigenvalues and corresponding eigenvectors;
sorting a plurality of characteristic values from large to small to obtain characteristic vectors corresponding to the first k characteristic values;
according to the feature vectors of each scoring index corresponding to the first k feature values, obtaining the value score of each game in each application store, wherein the value score satisfies P=P 1 +P 2 +...+P n ，P n ＝X n *v 1 +X n *v 2 +...+X n *v k Wherein P represents the value score of any game at any application store, P n Value score, X, representing any score indicator of any game at any application store n Any scoring index, v, representing any application store k Any one of the feature vectors corresponding to the first k feature values is represented.
In this embodiment, the covariance between any two indexes of each application store is calculated by a covariance formula to obtain a covariance matrixWherein X is 1 ,X 2 ,...,X n For different scoring indexes, the eigenvalues and their corresponding eigenvectors are then obtained by the formulas det (Σ - λi) =0 and (Σ - λi) v=0, where Σ represents the covariance matrix, λ represents the eigenvalues, v represents the eigenvector, I represents the identity matrix, the identity matrix is a square matrix with all elements on the main diagonal being 1, the remaining elements being 0, and if the covariance matrix is a second order matrix, the identity matrix is%>
Therefore, assuming that the covariance matrix is a second-order matrix, the specific value thereof isThen det (Σ - λi) =0 is +.>Simplified to lambda 2 -7λ+10=0, solving for two eigenvalues, λ 1 ＝2，λ 2 ＝5。
For each eigenvalue, a corresponding eigenvector is obtained by solving the system of equations (Σ - λi) v=0, e.g. for λ 1 =2, solve the equation set:
solving to v 11 ＝-1，v 21 =2, then eigenvalue λ 1 Corresponding feature vector
For lambda 2 =5, solve the equation set:
solving to v 12 ＝1，v 22 =2, then eigenvalue λ 2 Corresponding feature vector
Let k=2, the above derived v can be used 1 And v 2 Substitution formula p=p 1 +P 2 +...+P n ，P n ＝X n *v 1 +X n *v 2 +...+X n *v k Wherein a value score of each game at each application store is obtained, wherein X n The method comprises the steps of obtaining the value score of any scoring index through the weighted sum of the specific value of any scoring index and the previous k characteristic vectors, and obtaining the total value score of each game in any application store after the total addition of the value scores of each scoring index.
As a summary of the foregoing embodiments, referring to fig. 2, a Python acquisition framework (i.e., the Python multithreading program described above) sets a plurality of threads through multithreading scheduling, each of which is responsible for application stores such as an application store, an OPPO application store, a VIVO application store, a millet application store, an application treasured, and the like, and each of which is connected to a cloud mobile phone a and a cloud mobile phone b.
Referring to fig. 3, an embodiment of the present invention provides a processing apparatus 3 for game application data, where the apparatus 3 specifically includes:
the first processing module 301 is configured to operate a plurality of cloud mobile phones simultaneously based on a multithreading technology, where the cloud mobile phones are mobile phone simulators arranged on a cloud service;
the second processing module 302 is configured to log in at least one application store according to each cloud mobile phone, extract game application data of each application store through a preset data acquisition model, and then push the game application data to a message queue;
and the third processing module 303 is configured to obtain different game application data from the message queue according to the topic classification for analysis, and generate a game application data analysis result.
It will be understood that the content of the embodiment of the method for processing game application data shown in fig. 1 is applicable to the embodiment of the apparatus for processing game application data, and the functions of the embodiment of the apparatus for processing game application data are the same as those of the embodiment of the method for processing game application data shown in fig. 1, and the advantages achieved are the same as those achieved by the embodiment of the method for processing game application data shown in fig. 1.
It should be noted that, because the content of information interaction and execution process between the above systems is based on the same concept as the method embodiment of the present invention, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the system is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
Referring to fig. 4, an embodiment of the present invention further provides a computer device 4, including: a memory 402, a processor 401 and a computer program 403 stored on the memory 402, which computer program 403, when executed on the processor 401, implements a method of processing game application data according to any of the above methods.
The computer device 4 may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The computer device 4 may include, but is not limited to, a processor 401, a memory 402. It will be appreciated by those skilled in the art that fig. 4 is merely an example of computer device 4 and is not intended to limit computer device 4, and may include more or fewer components than shown, or may combine certain components, or may include different components, such as input-output devices, network access devices, etc.
The processor 401 may be a central processing unit (Central Processing Unit, CPU), but the processor 401 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 402 may in some embodiments be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. The memory 402 may also be an external storage device of the computer device 4 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 4. Further, the memory 402 may also include both internal storage units and external storage devices of the computer device 4. The memory 402 is used to store an operating system, application programs, boot loader (BootLoader), data, and other programs, such as program code for the computer program. The memory 402 may also be used to temporarily store data that has been output or is to be output.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, implements a method for processing game application data according to any one of the above methods.
In this embodiment, the integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing device/terminal apparatus, recording medium, computer Memory, read-Only Memory (ROM), random access Memory (RAM, random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments disclosed in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Claims (10)
1. A method for processing game application data, the method comprising:
simultaneously running a plurality of cloud mobile phones based on a multithreading technology, wherein the cloud mobile phones are mobile phone simulators arranged on cloud services;
respectively logging in at least one application store according to each cloud mobile phone, extracting game application data of each application store through a preset data acquisition model, and then pushing the game application data to a message queue;
and acquiring different game application data from the message queue according to the topic classification for analysis, and generating a game application data analysis result.
2. The method of claim 1, wherein the operating a plurality of cloud handsets simultaneously based on a multi-threading technology specifically comprises:
setting a Python multithreading program;
and connecting and simulating to operate at least one cloud mobile phone through an Airtest module in each thread of the Python multithreading program.
3. The method according to claim 2, wherein said setting Python multithreading program comprises:
creating, starting, ending and synchronizing a plurality of sub-thread objects according to a threading module of Python, wherein the plurality of sub-thread objects comprise at least one main thread object and a plurality of sub-thread objects;
creating a plurality of task queues according to a queue module of Python, and scheduling execution tasks of the thread objects through the task queues;
setting the maximum retry times and retry intervals of each thread object according to the time module of Python;
and recording the task execution condition of each thread object according to the logging module of the Python.
4. The method according to claim 2, wherein the connecting and simulating operation of the at least one cloud mobile phone by the Airtest module specifically comprises:
when the cloud mobile phone is an android version cloud mobile phone, connecting and simulating to operate at least one cloud mobile phone of security Zhuo Banben according to an airtest.core.api component of the Airtest module;
when the cloud mobile phone is an iOS version cloud mobile phone, connecting and simulating to operate at least one iOS version cloud mobile phone through a WDA library according to the Airtest module.
5. The method of claim 2, wherein the data acquisition model is a Poco image recognition module; the game application data of each application store is extracted through a preset data acquisition model, specifically: extracting game application data of each application store through the Poco image recognition module in each thread of the Python multithreading program;
the extracting the game application data of each application store through the Poco image recognition module specifically comprises the following steps:
acquiring an application entry of each application store according to a get_position method of the Poco image recognition module;
acquiring screen shots of at least one game application of each application store according to a snapshot method of the Pogo image recognition module;
and acquiring game application data on the screen shot according to a get_text method of the Pogo image recognition module.
6. The method according to claim 1, wherein the step of obtaining different game application data from the message queue according to the topic classification for analysis to generate a game application data analysis result specifically comprises:
acquiring game application data of all application stores of each game from each theme of the message queue;
obtaining a value score of each game at each application store respectively through a principal component analysis method based on game application data of each application store of each game;
and obtaining the value score analysis results of all games according to the value score of each game in each application store.
7. The method according to claim 6, wherein the obtaining the value score of each game at each application store by the principal component analysis method based on the game application data of each application store of each game, specifically comprises:
obtaining all grading indexes of each game in each application store, and obtaining a covariance matrix of each game in each application store according to a covariance formula through the grading indexes, wherein the covariance formula meets the following requirementsWherein cov (X) i ,X j ) Representing the covariance between any two scoring indices, X i And X j Any two scoring indexes in all scoring indexes are represented, and n represents the number of the scoring indexes;
decomposing the covariance matrix to obtain a plurality of eigenvalues and corresponding eigenvectors;
sorting a plurality of characteristic values from large to small to obtain characteristic vectors corresponding to the first k characteristic values;
according to the feature vectors of each scoring index corresponding to the first k feature values, obtaining the value score of each game in each application store, wherein the value score satisfies P=P 1 +P 2 +...+P n ，P n ＝X n *v 1 +X n *v 2 +...+X n *v k Wherein P represents the value score of any game at any application store, P n Value score, X, representing any score indicator of any game at any application store n Any scoring index, v, representing any application store k Any one of the feature vectors corresponding to the first k feature values is represented.
8. A game application data processing device, characterized in that the device specifically comprises:
the cloud mobile phone simulator comprises a first processing module, a second processing module and a third processing module, wherein the first processing module is used for simultaneously running a plurality of cloud mobile phones based on a multithreading technology, and the cloud mobile phones are mobile phone simulators arranged on cloud services;
the second processing module is used for logging in at least one application store according to each cloud mobile phone respectively, extracting game application data of each application store through a preset data acquisition model, and then pushing the game application data to a message queue;
and the third processing module is used for acquiring different game application data from the message queue according to the topic classification for analysis and generating a game application data analysis result.
9. A computer device, comprising: memory and processor and computer program stored on the memory, which when executed on the processor, implements a method of processing game application data according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method of processing game application data according to any one of claims 1 to 7.
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| CN202311725426.9A CN117679749A (en) | 2023-12-15 | 2023-12-15 | Game application data processing method, device, equipment and medium |
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