CN114939276B - Game operation data analysis method, system and storage medium - Google Patents
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Abstract
The invention discloses a game operation data analysis method, a game operation data analysis system and a storage medium. The game operation data analysis method comprises the steps of obtaining the number of registered users corresponding to a game platform and basic registration information corresponding to each registered user; extracting feedback data corresponding to each registered user from the game platform background; setting optimization weights of all preset optimization directions corresponding to the game platform; analyzing each optimizing stage and each preset optimizing direction corresponding to the game platform; the method effectively solves the problem that the prior art does not analyze from the user experience sense level, and highlights the problems of the current game platform, thereby providing reliable reference basis and clear direction for the game platform to perfection, greatly improving the viscosity and the loyalty of the user and the game platform, greatly improving the game experience sense corresponding to the user, and improving the operation efficiency of the game platform to a certain extent.
Description
Technical Field
The invention belongs to the technical field of game operation data analysis, and relates to a game operation data analysis method, a game operation data analysis system and a storage medium.
Background
With the rapid development of internet technology, the electronic game industry has grown, and the electronic game becomes one of the first choice entertainment modes of the current young according to the characteristics of interest, interactivity, content richness and the like, so that in order to improve the operation efficiency of the electronic game platform, the game operation data needs to be analyzed.
The analysis of the current game operation data mainly concentrates on the analysis of a user management layer, such as the analysis of the data of the playing data of the user, the user charge rate, the playing time length of the user and the like, and obviously, the analysis mode of the current game operation data has the following problems:
1. the main influencing factor of the audience rate of the game platform is user game experience sense, the current game operation data analysis mode does not analyze from the user experience sense level, has certain limitation, cannot effectively highlight the problems existing in the current game platform, and further cannot provide reliable reference for the improvement of the game platform;
2. the operation efficiency of the game platform is mainly reflected on the viscosity of the platform and the user, feedback data of the user is not analyzed at present, subjective feeling of the user on the platform cannot be reflected, and further a clear optimization direction cannot be provided for the game platform, so that the viscosity of the user and the game platform cannot be effectively improved, and meanwhile, the fidelity of the user cannot be effectively improved;
3. the experience of people in different game stages is different, the operation data of the game platform is not analyzed according to the game stage types of the user at present, the game requirements corresponding to the different game stages cannot be reflected effectively, the game experience corresponding to the different game stages cannot be improved effectively, and meanwhile, decision-making reference data cannot be provided for positioning of the game platform optimizing stage.
Disclosure of Invention
In view of this, in order to solve the problems set forth in the background art, a method, a system, and a storage medium for analyzing game operation data are provided;
the aim of the invention can be achieved by the following technical scheme:
the first aspect of the present invention provides a game operation data analysis method, comprising the steps of:
step 1, acquiring user registration information: acquiring the number of registered users corresponding to the game platform and basic registration information corresponding to each registered user, numbering each registered user according to a preset sequence, and marking the numbers as 1, 2.
Step 2, extracting user feedback data: extracting feedback data corresponding to each registered user from the background of the game platform, wherein the feedback data specifically comprises feedback problem times corresponding to each registered user and feedback information corresponding to each registered user when each feedback problem occurs;
step 3, optimizing direction weight setting: acquiring each preset optimizing direction corresponding to the game platform, setting optimizing weights according to each preset optimizing direction corresponding to the game platform, numbering each preset optimizing direction corresponding to the game platform according to a preset sequence, and marking the preset optimizing directions as 1, 2.
Step 4, platform optimization information analysis: analyzing each optimizing stage and each preset optimizing direction corresponding to the game platform based on basic registering information corresponding to each registering user and feedback data corresponding to each registering user, respectively counting the optimizing value index corresponding to each optimizing stage and the optimizing value index corresponding to each preset optimizing direction of the game platform, matching and comparing the optimizing value index corresponding to each optimizing stage of the game platform with a set standard optimizing value index, taking the optimizing stage as a key optimizing stage corresponding to the game platform if the optimizing value index corresponding to one optimizing stage in the game platform reaches the set standard optimizing value index, simultaneously matching and comparing the optimizing value index corresponding to each preset optimizing direction of the game platform with the set standard optimizing value index, and taking the preset optimizing direction as a key optimizing direction corresponding to the game platform if the optimizing value index corresponding to one preset optimizing direction in the game platform reaches the set standard optimizing value index;
step 5, analyzing result feedback: and feeding back the key optimization stage and the key optimization direction corresponding to the game platform background.
In one possible design, the basic registration information corresponding to each registered user includes a registration duration and registration account information, where the registration account information includes a type corresponding to the registration account and a game stage where the registration account is located, the registration account type includes a normal type and a member type, and the game stage includes an early stage, a middle stage and a later stage.
In one possible design, the optimizing weight setting is performed according to each preset optimizing direction corresponding to the game platform, and the specific setting process is as follows:
acquiring each preset optimizing direction corresponding to the game platform, wherein the preset optimizing directions comprise user experience, story flows, interactive operation, game performance and game memory;
marking the optimization weight corresponding to the user experience in the game platform as epsilon 1, marking the optimization weight corresponding to the story flow as epsilon 2, marking the optimization weight corresponding to the interactive operation as epsilon 3, and playing the gameThe optimization weight corresponding to the performance is marked as epsilon 4, the optimization weight corresponding to the game memory is marked as epsilon 5, and the optimization weights corresponding to each preset optimization direction in the game platform are respectively obtained in this way and marked as epsilon j J represents the number corresponding to each preset optimization direction, j=1, 2.
In one possible design, the parsing in step 4 is performed on each optimization stage corresponding to the game platform, and the specific parsing process includes the following steps:
extracting registration time length from basic registration information corresponding to each registered user, comparing the registration time length corresponding to each registered user with a set platform reference registration time length, calculating to obtain a registration time length influence weight corresponding to each registered user, and recording as eta i I is a number corresponding to each registered user, i=1, 2,..;
extracting registration account information from basic registration information corresponding to each registration user, extracting the type corresponding to the registration account from the registration account information corresponding to each registration user, marking the account influence weight corresponding to the registration user as sigma 1 if the registration account type corresponding to the registration user is a common type, marking the account influence weight corresponding to the registration user as sigma 2 if the account type corresponding to the registration user is a member type, thereby respectively obtaining the account type influence weight corresponding to each registration user and marking the account influence weight as sigma i Wherein σ is i The value is sigma 1 or sigma 2, and sigma 2 is more than sigma 1;
extracting game stages of the registered accounts from the registered account information corresponding to each registered user, further obtaining the game stages of the accounts corresponding to each registered user, mutually comparing the game stages of the accounts corresponding to each registered user, counting the number of the registered users corresponding to each game stage, taking each game stage as an optimization stage, and extracting the registration duration influence weight and the account type influence weight corresponding to each registered user in each optimization stage;
based on the feedback problem times corresponding to the registered users and the number of the registered users corresponding to the optimizing stages, the comprehensive feedback problem times corresponding to the optimizing stages are obtained through accumulation, and the comprehensive feedback problem times corresponding to the optimizing stages are calculated to obtain the optimizing value index corresponding to the optimizing stages of the game platform.
In one possible design, the optimization value index calculation formula corresponding to each optimization stage of the game platform is as followsDelta shown in the formula w Expressed as an optimized value index corresponding to each optimized stage of the game platform, w represents each optimized stage, w=a1 or a2 or a3, a1, a2, a3 are respectively expressed as an early stage, a middle stage and a later stage, eta' w i′ Expressed as the influence weight of the registration duration corresponding to each registered user in each optimization stage, i 'expressed as the number of each registered user corresponding to each optimization stage, i' =1 ',2',..>Representing account type influence weights corresponding to registered users in each optimization stage>
In one possible design, the parsing in step 4 is performed on each preset optimization direction corresponding to the game platform, and the specific parsing process includes the following steps:
acquiring feedback information corresponding to each registered user in the game platform during each feedback problem, and locating a feedback mode corresponding to each registered user during each feedback problem from the feedback information, wherein the feedback modes comprise a voice feedback mode and a text feedback mode, and the influence weight corresponding to the voice feedback mode is recorded asThe influence weight corresponding to the text feedback mode is marked as +.>Thereby respectively obtaining the influence weights corresponding to the feedback modes of each registered user during each feedback problem, andmarked as->The value is +.>And-> t is denoted as the number corresponding to each feedback problem, t=1, 2.
Identifying feedback information corresponding to each registered user in each feedback problem to obtain each feedback keyword corresponding to each registered user in each feedback problem, extracting a related keyword set corresponding to each preset optimization direction from a game optimization information base based on each preset optimization direction corresponding to a game platform, and analyzing to obtain the matching degree of each feedback problem of each registered user and each preset optimization direction;
based on the matching degree of each feedback problem of each registered user and each preset optimizing direction, comparing the matching degree with a preset standard matching degree, and if the matching degree of a certain feedback problem of a certain registered user and a certain preset optimizing direction is larger than or equal to the preset standard matching degree, taking the preset optimizing direction as the matching preset optimizing direction of the registered user corresponding to the feedback problem, so as to respectively acquire the matching preset optimizing direction of each registered user corresponding to each feedback problem;
and analyzing and obtaining the optimization value index corresponding to each preset optimization direction based on the corresponding matching preset optimization direction of each registered user during each feedback problem.
In one possible design, the identifying feedback information corresponding to each feedback problem of each registered user includes identifying feedback information corresponding to each feedback problem in a voice feedback mode and identifying feedback information corresponding to each feedback problem in a text feedback mode.
In one possible design, the analysis obtains an optimal value index corresponding to each preset optimization direction, and the specific analysis process comprises the following steps:
based on the matching preset optimization directions corresponding to the registered users in the feedback problems, mutually comparing the matching preset optimization directions corresponding to the registered users in the feedback problems, and screening to obtain the comprehensive feedback times corresponding to the preset optimization directions;
based on the feedback mode corresponding to each feedback problem of each registered user and the matching preset optimization direction corresponding to each feedback problem, obtaining the feedback mode corresponding to the matching preset optimization direction when each registered user feeds back the problem each time;
comparing feedback modes corresponding to the corresponding matching preset optimizing directions when each registered user feeds back the problems for each time, screening to obtain comprehensive feedback times corresponding to each feedback mode in each preset optimizing direction, analyzing to obtain main body feedback modes corresponding to each preset optimizing direction based on the comprehensive feedback times corresponding to each feedback mode in each preset optimizing direction, obtaining influence weights corresponding to the main body feedback modes of each preset optimizing direction based on the influence weights corresponding to each feedback mode, and recording as
Calculating an optimization value index corresponding to each preset optimization direction by using a calculation formula, wherein the calculation formula is thatWherein, lambda is shown in the formula j Expressed as an optimal value index corresponding to the jth preset optimal direction, R j The comprehensive feedback times corresponding to the j-th preset optimization direction are represented as R j ' is expressed as the set reference feedback number corresponding to the jth preset optimizing direction.
A second aspect of the present invention provides a game operation number analysis system, comprising:
the user basic registration information acquisition module is used for acquiring the number of registered users corresponding to the game platform and basic registration information corresponding to each registered user;
the user feedback data extraction module is used for extracting feedback data corresponding to each registered user from the background of the game platform, wherein the feedback data corresponding to each registered user comprises feedback problem times corresponding to each registered user and feedback information corresponding to each registered user when each feedback problem occurs;
the optimizing weight setting module is used for obtaining the preset optimizing directions corresponding to the game platform and setting the optimizing weight according to the preset optimizing directions corresponding to the game platform;
the operation optimization analysis module is used for respectively analyzing each optimization stage and each preset optimization direction corresponding to the game platform based on the basic registration information corresponding to each registered user and the feedback data corresponding to each registered user, and outputting a key optimization stage and a key optimization direction corresponding to the game platform;
the game optimization information base is used for storing associated keyword sets corresponding to all preset optimization directions;
and the feedback terminal is used for feeding back the key optimization stage and the key optimization direction corresponding to the game platform background.
A third aspect of the present invention provides a storage medium having a computer program recorded thereon, the computer program implementing the method of the present invention when running in the memory of a server.
Compared with the prior art, the invention has the following beneficial effects:
according to the game operation data analysis method provided by the invention, the key optimization stage and the key optimization direction corresponding to the game platform are analyzed based on the basic registration information corresponding to each registered user and the feedback data corresponding to each registered user in the game platform, so that on one hand, the problem that the current technology does not analyze from the user experience level is effectively solved, the problem existing in the current game platform is highlighted, and a reliable reference basis is provided for perfecting the game platform; on one hand, the subjective feeling of the user on the platform is intuitively displayed by analyzing the feedback data corresponding to each registered user, a definite direction is provided for optimizing the game platform, and the viscosity and the fidelity of the user and the game platform are greatly improved; on the other hand, through analyzing the game stages where the users are located, the game requirements corresponding to the users in different game stages are reflected, the game experience of the users in different game stages is greatly improved, and decision-making reference data is provided for positioning of the game platform optimizing stage, so that the operation efficiency of the game platform is effectively improved to a certain extent.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of 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 invention, 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 the steps of the method of the present invention;
FIG. 2 is a schematic diagram of the connection of the modules of the method system of the present invention.
Detailed Description
The foregoing is merely illustrative of the principles of the invention, and various modifications, additions and substitutions for those skilled in the art will be apparent to those having ordinary skill in the art without departing from the principles of the invention or from the scope of the invention as defined in the accompanying claims.
Example 1
Referring to fig. 1, the present invention provides a game operation data analysis method, which includes the following steps:
step 1, acquiring user registration information: acquiring the number of registered users corresponding to the game platform and basic registration information corresponding to each registered user, numbering each registered user according to a preset sequence, and marking the numbers as 1, 2.
Specifically, the basic registration information corresponding to each registered user includes registration duration and registration account information, wherein the registration account information includes a type corresponding to the registration account and a game stage where the registration account is located, the registration account type includes a common type and a member type, and the game stage includes an early stage, a middle stage and a later stage.
Step 2, extracting user feedback data: extracting feedback data corresponding to each registered user from the background of the game platform, wherein the feedback data specifically comprises feedback problem times corresponding to each registered user and feedback information corresponding to each registered user when each feedback problem occurs;
it should be noted that the feedback information includes a feedback manner and feedback content.
Step 3, optimizing direction weight setting: acquiring each preset optimizing direction corresponding to the game platform, setting optimizing weights according to each preset optimizing direction corresponding to the game platform, numbering each preset optimizing direction corresponding to the game platform according to a preset sequence, and marking the preset optimizing directions as 1, 2.
The optimizing weight setting is performed according to each preset optimizing direction corresponding to the game platform, and the specific setting process is as follows:
acquiring each preset optimizing direction corresponding to the game platform, wherein the preset optimizing directions comprise user experience, story flows, interactive operation, game performance and game memory;
the optimization weight corresponding to the user experience in the game platform is marked as epsilon 1, the optimization weight corresponding to the story flow is marked as epsilon 2, the optimization weight corresponding to the interactive operation is marked as epsilon 3, the optimization weight corresponding to the game performance is marked as epsilon 4, the optimization weight corresponding to the game memory is marked as epsilon 5, and the optimization weights corresponding to each preset optimization direction in the game platform are respectively obtained in this way and marked as epsilon j J represents the number corresponding to each preset optimization direction, j=1, 2.
Step 4, platform optimization information analysis: analyzing each optimizing stage and each preset optimizing direction corresponding to the game platform based on basic registering information corresponding to each registering user and feedback data corresponding to each registering user, respectively counting the optimizing value index corresponding to each optimizing stage and the optimizing value index corresponding to each preset optimizing direction of the game platform, matching and comparing the optimizing value index corresponding to each optimizing stage of the game platform with a set standard optimizing value index, taking the optimizing stage as a key optimizing stage corresponding to the game platform if the optimizing value index corresponding to one optimizing stage in the game platform reaches the set standard optimizing value index, simultaneously matching and comparing the optimizing value index corresponding to each preset optimizing direction of the game platform with the set standard optimizing value index, and taking the preset optimizing direction as a key optimizing direction corresponding to the game platform if the optimizing value index corresponding to one preset optimizing direction in the game platform reaches the set standard optimizing value index;
illustratively, the parsing each optimizing stage of the game platform includes the following steps:
p1, extracting registration time length from basic registration information corresponding to each registered user, comparing the registration time length corresponding to each registered user with a set platform reference registration time length, calculating to obtain a registration time length influence weight corresponding to each registered user, and marking as eta i I is the number corresponding to each registered user, i=1, 2, the term "m", wherein, the liquid crystal display device comprises a liquid crystal display device,t in the formula i Expressed as the registration duration corresponding to the ith registered user, T Reference to The set platform reference registration duration is expressed;
p2, extracting registration account information from basic registration information corresponding to each registration user, further extracting the type corresponding to the registration account from the registration account information corresponding to each registration user, if the registration account type corresponding to the registration user is a common type, marking the account influence weight corresponding to the registration user as sigma 1, if the account type corresponding to the registration user is a member type, marking the account influence weight corresponding to the registration user as sigma 2, thereby respectively obtaining the account type influence weight corresponding to each registration user, and marking as sigma i Wherein σ is i The value is sigma 1 or sigma 2, and sigma 2 is more than sigma 1;
p3, extracting game stages of the registered accounts from the registered account information corresponding to each registered user, further obtaining the game stages of the accounts corresponding to each registered user, mutually comparing the game stages of the accounts corresponding to each registered user, counting the number of the registered users corresponding to each game stage, taking each game stage as an optimization stage, and extracting the registration duration influence weight and the account type influence weight corresponding to each registered user in each optimization stage;
p4, based on the feedback problem times corresponding to all registered users and the number of registered users corresponding to all optimization stages, accumulating to obtain the comprehensive feedback problem times corresponding to all optimization stages, and calculating the comprehensive feedback problem times corresponding to all optimization stages to obtain the optimization value index corresponding to all optimization stages of the game platform, wherein the specific calculation formula is as follows
Delta is shown in the formula w Expressed as an optimal value index corresponding to each optimizing stage of the game platform, w represents each optimizing stage, w=a1 or a2 or a3, a1, a2, a3 are respectively expressed as an early stage, a middle stage and a later stage,expressed as the influence weight of the registration duration corresponding to each registered user in each optimization stage, i 'expressed as the number of each registered user corresponding to each optimization stage, i' =1 ',2',..>Representing account type influence weights corresponding to registered users in each optimization stage>
Still another exemplary embodiment of the present invention provides a method for analyzing each preset optimization direction corresponding to a game platform, where a specific analysis process includes the following steps:
y1, acquisition ofFeedback information corresponding to each registered user in the game platform when each feedback problem is fed back is located, and corresponding feedback modes corresponding to each registered user when each feedback problem is fed back are located, wherein the feedback modes comprise a voice feedback mode and a text feedback mode, and influence weights corresponding to the voice feedback modes are recorded asThe influence weight corresponding to the text feedback mode is marked as +.>Thereby, the corresponding influence weight of the feedback mode of each registered user during each feedback problem is obtained and is marked as +.> The value is +.>And-> t is denoted as the number corresponding to each feedback problem, t=1, 2.
Y2, identifying feedback information corresponding to each registered user in each feedback problem, obtaining each feedback keyword corresponding to each registered user in each feedback problem, extracting a related keyword set corresponding to each preset optimization direction from a game optimization information base based on each preset optimization direction corresponding to a game platform, and analyzing to obtain the matching degree of each feedback problem of each registered user and each preset optimization direction;
it should be noted that, identifying feedback information corresponding to each feedback problem of each registered user includes identifying feedback information corresponding to each feedback problem in a voice feedback mode and identifying feedback information corresponding to each feedback problem in a text feedback mode, when the feedback mode corresponding to a certain registered user in a certain feedback problem is a voice feedback mode, converting feedback content corresponding to the certain feedback problem into a text mode by using a voice recognition technology, and then carrying out keyword recognition by combining a text recognition technology and a keyword extraction technology to obtain each feedback keyword corresponding to the certain feedback problem of the registered user, when the feedback mode corresponding to the certain feedback problem of the certain registered user is a text feedback mode, obtaining each feedback keyword corresponding to the certain feedback problem of the registered user by using a text recognition technology and combining a keyword extraction technology;
it should be further noted that the voice recognition technology, the text recognition technology and the keyword extraction technology described in the foregoing are existing mature technologies, and specific recognition and operation processes thereof are not described herein.
Further, the specific analysis process of the matching degree between each feedback problem of each registered user and each preset optimization direction is as follows:
y2-1, based on each feedback keyword corresponding to each registered user in each feedback problem, constructing a feedback keyword set corresponding to each registered user in each feedback problem, and marking as F i t ＝{F i t 1,F i t 2,...F i t s,...F i t p}，F i t s represents an s-th feedback keyword corresponding to an i-th user in the t-th feedback, s represents a number corresponding to each feedback keyword, s=1, 2, & gt.
Y2-2, marking the associated keyword set corresponding to each preset optimization direction as H j ＝{H j 1,H j 2,...H j u,...H j v}，H j u represents a corresponding u-th associated keyword in a j-th preset optimization direction, u represents a corresponding number of each associated keyword, and u=1, 2.
Y2-3, based on the feedback keyword set corresponding to each registered user in each feedback problem and the associated keyword set corresponding to each preset optimization directionAnd analyzing the matching degree of each feedback problem of each registered user and each preset optimization direction, wherein the specific analysis formula is as followsAccording to the formula show in->And (5) representing the matching degree of the ith feedback problem of the ith registered user and j preset optimization directions.
Y3, comparing the matching degree of each feedback problem of each registered user with the preset standard matching degree based on the matching degree of each preset optimizing direction, and if the matching degree of a certain feedback problem of a certain registered user and a certain preset optimizing direction is larger than or equal to the preset standard matching degree, taking the preset optimizing direction as the matching preset optimizing direction corresponding to the feedback problem of the registered user, so as to respectively acquire the matching preset optimizing direction corresponding to each registered user in each feedback problem;
and Y4, analyzing and obtaining an optimized value index corresponding to each preset optimizing direction based on the matched preset optimizing direction corresponding to each registered user during each feedback problem, wherein the specific analysis process comprises the following steps of:
based on the matching preset optimization directions corresponding to the registered users in the feedback problems, mutually comparing the matching preset optimization directions corresponding to the registered users in the feedback problems, and screening to obtain the comprehensive feedback times corresponding to the preset optimization directions;
based on the feedback mode corresponding to each feedback problem of each registered user and the matching preset optimization direction corresponding to each feedback problem, obtaining the feedback mode corresponding to the matching preset optimization direction when each registered user feeds back the problem each time;
comparing feedback modes corresponding to the corresponding matching preset optimizing directions when each registered user feeds back the problems for each time, screening to obtain comprehensive feedback times corresponding to each feedback mode in each preset optimizing direction, and arranging the comprehensive feedback times corresponding to each feedback mode in each preset optimizing direction according to the sequence from big to smallExtracting the feedback mode of the first rank in each optimization direction, taking the feedback mode as the main body feedback mode corresponding to each optimization direction, obtaining the influence weight corresponding to the main body feedback mode of each preset optimization direction based on the influence weight corresponding to each feedback mode, and marking the influence weight as
Calculating an optimization value index corresponding to each preset optimization direction by using a calculation formula, wherein the calculation formula is thatWherein, lambda is shown in the formula j Expressed as an optimal value index corresponding to the jth preset optimal direction, R j The comprehensive feedback times corresponding to the j-th preset optimization direction are represented as R j ' is expressed as the set reference feedback number corresponding to the jth preset optimizing direction.
According to the embodiment of the invention, the key optimization stage and the key optimization direction corresponding to the game platform are analyzed based on the basic registration information corresponding to each registered user and the feedback data corresponding to each registered user in the game platform, so that on one hand, the problem that the current technology does not analyze from the user experience sense level is effectively solved, the problem existing in the current game platform is highlighted, and a reliable reference basis is provided for the perfection of the game platform; on one hand, the subjective feeling of the user on the platform is intuitively displayed by analyzing the feedback data corresponding to each registered user, a definite direction is provided for optimizing the game platform, and the viscosity and the fidelity of the user and the game platform are greatly improved; on the other hand, through analyzing the game stages where the users are located, the game requirements corresponding to the users in different game stages are reflected, the game experience of the users in different game stages is greatly improved, and decision-making reference data is provided for positioning of the game platform optimizing stage, so that the operation efficiency of the game platform is effectively improved to a certain extent.
Step 5, analyzing result feedback: and feeding back the key optimization stage and the key optimization direction corresponding to the game platform background.
Example two
Referring to fig. 2, the invention provides a game operation data analysis system, which comprises a user basic registration information acquisition module, a user feedback data extraction module, an optimization weight setting module, an operation optimization analysis module, a game optimization information base and a feedback terminal;
according to the connection relation in the figure, the operation optimization analysis module is respectively connected with the user basic registration information acquisition module, the user feedback data extraction module, the optimization weight setting module, the game optimization information base and the feedback terminal;
the user basic registration information acquisition module is used for acquiring the number of registered users corresponding to the game platform and basic registration information corresponding to each registered user;
the user feedback data extraction module is used for extracting feedback data corresponding to each registered user from the background of the game platform, wherein the feedback data corresponding to each registered user comprises feedback problem times corresponding to each registered user and feedback information corresponding to each registered user when each feedback problem occurs;
the optimizing weight setting module is used for obtaining the preset optimizing directions corresponding to the game platform and setting the optimizing weight according to the preset optimizing directions corresponding to the game platform;
the operation optimization analysis module is used for respectively analyzing each optimization stage and each preset optimization direction corresponding to the game platform based on the basic registration information corresponding to each registered user and the feedback data corresponding to each registered user, and outputting a key optimization stage and a key optimization direction corresponding to the game platform;
the game optimization information base is used for storing associated keyword sets corresponding to all preset optimization directions;
and the feedback terminal is used for feeding back the key optimization stage and the key optimization direction corresponding to the game platform background.
Example III
The invention also provides a computer storage medium, wherein the storage medium is burnt with a computer program, and the computer program realizes the method of the invention when running in the memory of the server.
The foregoing is merely illustrative and explanatory of the principles of the invention, as various modifications and additions may be made to the specific embodiments described, or similar thereto, by those skilled in the art, without departing from the principles of the invention or beyond the scope of the appended claims.
Claims (8)
1. A game operation data analysis method, comprising:
step 1, acquiring user registration information: acquiring the number of registered users corresponding to the game platform and basic registration information corresponding to each registered user, numbering each registered user according to a preset sequence, and marking the numbers as 1, 2.
Step 2, extracting user feedback data: extracting feedback data corresponding to each registered user from the background of the game platform, wherein the feedback data specifically comprises feedback problem times corresponding to each registered user and feedback information corresponding to each registered user when each feedback problem occurs;
step 3, optimizing direction weight setting: acquiring each preset optimizing direction corresponding to the game platform, setting optimizing weights according to each preset optimizing direction corresponding to the game platform, numbering each preset optimizing direction corresponding to the game platform according to a preset sequence, and marking the preset optimizing directions as 1, 2.
Step 4, platform optimization information analysis: analyzing each optimizing stage and each preset optimizing direction corresponding to the game platform based on basic registering information corresponding to each registering user and feedback data corresponding to each registering user, respectively counting the optimizing value index corresponding to each optimizing stage and the optimizing value index corresponding to each preset optimizing direction of the game platform, matching and comparing the optimizing value index corresponding to each optimizing stage of the game platform with a set standard optimizing value index, taking the optimizing stage as a key optimizing stage corresponding to the game platform if the optimizing value index corresponding to one optimizing stage in the game platform reaches the set standard optimizing value index, simultaneously matching and comparing the optimizing value index corresponding to each preset optimizing direction of the game platform with the set standard optimizing value index, and taking the preset optimizing direction as a key optimizing direction corresponding to the game platform if the optimizing value index corresponding to one preset optimizing direction in the game platform reaches the set standard optimizing value index;
in the step 4, analyzing each preset optimization direction corresponding to the game platform, wherein the specific analysis process comprises the following steps:
acquiring feedback information corresponding to each registered user in the game platform during each feedback problem, and locating a feedback mode corresponding to each registered user during each feedback problem from the feedback information, wherein the feedback modes comprise a voice feedback mode and a text feedback mode, and the influence weight corresponding to the voice feedback mode is recorded asThe influence weight corresponding to the text feedback mode is marked as +.>Thereby, the influence weight corresponding to the feedback mode of each registered user during each feedback problem is obtained and marked as +.>Take the value ofAnd->t is denoted as the number corresponding to each feedback problem, t=1, 2.
Identifying feedback information corresponding to each registered user in each feedback problem to obtain each feedback keyword corresponding to each registered user in each feedback problem, extracting a related keyword set corresponding to each preset optimization direction from a game optimization information base based on each preset optimization direction corresponding to a game platform, and analyzing to obtain the matching degree of each feedback problem of each registered user and each preset optimization direction;
based on the matching degree of each feedback problem of each registered user and each preset optimizing direction, comparing the matching degree with a preset standard matching degree, and if the matching degree of a certain feedback problem of a certain registered user and a certain preset optimizing direction is larger than or equal to the preset standard matching degree, taking the preset optimizing direction as the matching preset optimizing direction of the registered user corresponding to the feedback problem, so as to respectively acquire the matching preset optimizing direction of each registered user corresponding to each feedback problem;
based on the corresponding matching preset optimization directions of each registered user in each feedback problem, analyzing and obtaining the corresponding optimization value index of each preset optimization direction;
the analysis obtains the optimization value index corresponding to each preset optimization direction, and the specific analysis process comprises the following steps:
based on the matching preset optimization directions corresponding to the registered users in the feedback problems, mutually comparing the matching preset optimization directions corresponding to the registered users in the feedback problems, and screening to obtain the comprehensive feedback times corresponding to the preset optimization directions;
based on the feedback mode corresponding to each feedback problem of each registered user and the matching preset optimization direction corresponding to each feedback problem, obtaining the feedback mode corresponding to the matching preset optimization direction when each registered user feeds back the problem each time;
comparing feedback modes corresponding to the corresponding matching preset optimizing directions when each registered user feeds back the problems for each time, screening to obtain comprehensive feedback times corresponding to each feedback mode in each preset optimizing direction, analyzing to obtain main body feedback modes corresponding to each preset optimizing direction based on the comprehensive feedback times corresponding to each feedback mode in each preset optimizing direction, obtaining influence weights corresponding to the main body feedback modes of each preset optimizing direction based on the influence weights corresponding to each feedback mode, and recording as
Calculating an optimization value index corresponding to each preset optimization direction by using a calculation formula, wherein the calculation formula is thatWherein, lambda is shown in the formula j Expressed as an optimal value index corresponding to the jth preset optimal direction, R j The comprehensive feedback times corresponding to the j-th preset optimization direction are represented as R j ' represents the set reference feedback times corresponding to the jth preset optimizing direction;
step 5, analyzing result feedback: and feeding back the key optimization stage and the key optimization direction corresponding to the game platform background.
2. A game operation data analysis method according to claim 1, wherein: the basic registration information corresponding to each registered user comprises registration time length and registration account information, wherein the registration account information comprises a type corresponding to the registration account and a game stage where the registration account is located, the registration account type comprises a common type and a member type, and the game stage comprises an early stage, a middle stage and a later stage.
3. A game operation data analysis method according to claim 1, wherein: the optimization weight is set according to each preset optimization direction corresponding to the game platform, and the specific setting process is as follows:
acquiring each preset optimizing direction corresponding to the game platform, wherein the preset optimizing directions comprise user experience, story flows, interactive operation, game performance and game memory;
marking the optimization weight corresponding to the user experience in the game platform as epsilon 1, marking the optimization weight corresponding to the story flow as epsilon 2, marking the optimization weight corresponding to the interactive operation as epsilon 3, marking the optimization weight corresponding to the game performance as epsilon 4, marking the optimization weight corresponding to the game memory as epsilon 5, and respectively obtaining the game platform in this wayThe optimization weight corresponding to each preset optimization direction is marked as epsilon j J represents the number corresponding to each preset optimization direction, j=1, 2.
4. A game operation data analysis method according to claim 1, wherein: in the step 4, each optimizing stage corresponding to the game platform is analyzed, and the specific analyzing process comprises the following steps:
extracting registration time length from basic registration information corresponding to each registered user, comparing the registration time length corresponding to each registered user with a set platform reference registration time length, calculating to obtain a registration time length influence weight corresponding to each registered user, and recording as eta i I is a number corresponding to each registered user, i=1, 2,..;
extracting registration account information from basic registration information corresponding to each registration user, extracting the type corresponding to the registration account from the registration account information corresponding to each registration user, marking the account influence weight corresponding to the registration user as sigma 1 if the registration account type corresponding to the registration user is a common type, marking the account influence weight corresponding to the registration user as sigma 2 if the account type corresponding to the registration user is a member type, thereby respectively obtaining the account type influence weight corresponding to each registration user and marking the account influence weight as sigma i Wherein σ is i The value is sigma 1 or sigma 2, and sigma 2 is more than sigma 1;
extracting game stages of the registered accounts from the registered account information corresponding to each registered user, further obtaining the game stages of the accounts corresponding to each registered user, mutually comparing the game stages of the accounts corresponding to each registered user, counting the number of the registered users corresponding to each game stage, taking each game stage as an optimization stage, and extracting the registration duration influence weight and the account type influence weight corresponding to each registered user in each optimization stage;
based on the feedback problem times corresponding to the registered users and the number of the registered users corresponding to the optimizing stages, the comprehensive feedback problem times corresponding to the optimizing stages are obtained through accumulation, and the comprehensive feedback problem times corresponding to the optimizing stages are calculated to obtain the optimizing value index corresponding to the optimizing stages of the game platform.
5. The game operation data analysis method according to claim 4, wherein: the calculation formula of the optimizing value index corresponding to each optimizing stage of the game platform is thatDelta shown in the formula w The optimization value index is expressed as the corresponding optimization stages of the game platform, w represents the optimization stages, w=a1 or a2 or a3, a1, a2 and a3 are respectively expressed as early stage, medium stage and later stage,/and vice versa>Expressed as the influence weight of the registration duration corresponding to each registered user in each optimization stage, i 'expressed as the number of each registered user corresponding to each optimization stage, i' =1 ',2',..>Indicating the account type influence weight corresponding to each registered user in each optimizing stage,
6. a game operation data analysis method according to claim 1, wherein: the identifying of the feedback information corresponding to each feedback problem of each registered user in each feedback problem comprises identifying the feedback information corresponding to each feedback problem in a voice feedback mode and identifying the feedback information corresponding to each feedback problem in a text feedback mode.
7. A game play data analysis system for performing the method of claim 1, comprising:
the user basic registration information acquisition module is used for acquiring the number of registered users corresponding to the game platform and basic registration information corresponding to each registered user;
the user feedback data extraction module is used for extracting feedback data corresponding to each registered user from the background of the game platform, wherein the feedback data corresponding to each registered user comprises feedback problem times corresponding to each registered user and feedback information corresponding to each registered user when each feedback problem occurs;
the optimizing weight setting module is used for obtaining the preset optimizing directions corresponding to the game platform and setting the optimizing weight according to the preset optimizing directions corresponding to the game platform;
the operation optimization analysis module is used for respectively analyzing each optimization stage and each preset optimization direction corresponding to the game platform based on the basic registration information corresponding to each registered user and the feedback data corresponding to each registered user, and outputting a key optimization stage and a key optimization direction corresponding to the game platform;
the game optimization information base is used for storing associated keyword sets corresponding to all preset optimization directions;
and the feedback terminal is used for feeding back the key optimization stage and the key optimization direction corresponding to the game platform background.
8. A storage medium, characterized by: the storage medium has a computer program recorded thereon, which when run in the memory of a server implements the method of any of the preceding claims 1-6.
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