CN111185015B - Method for optimizing ten-player online competitive game matching mechanism - Google Patents
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/70—Game security or game management aspects
- A63F13/79—Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories
- A63F13/795—Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories for finding other players; for building a team; for providing a buddy list
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/45—Controlling the progress of the video game
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/70—Game security or game management aspects
- A63F13/79—Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories
- A63F13/798—Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories for assessing skills or for ranking players, e.g. for generating a hall of fame
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
- A63F2300/50—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by details of game servers
- A63F2300/55—Details of game data or player data management
- A63F2300/5546—Details of game data or player data management using player registration data, e.g. identification, account, preferences, game history
- A63F2300/5566—Details of game data or player data management using player registration data, e.g. identification, account, preferences, game history by matching opponents or finding partners to build a team, e.g. by skill level, geographical area, background, play style
Abstract
The invention provides a method for optimizing a matching mechanism of ten-player online competitive games, and relates to a game matching mechanism optimization technology. The method collects the game information of the nearest 30 fields of each player entering between matching time of 5 seconds to 60 seconds, enables the average level of one player to be in a set interval through algorithms such as 1-D convolution, maximum function, random sampling, function mapping and arithmetic matching, and reduces the probability of conflict of professional selection of one player to the maximum. The method of the invention increases the game experience of the player, fully utilizes the limited game time of the player and realizes the aim of the game service for entertainment. On the other hand, the application of the invention promotes the development and growth of ten-player online competitive games in the game market, has certain promotion significance for the game industry, and has positive significance for the harmonious development of the human society from a deeper layer.
Description
Technical Field
The invention relates to the field of game matching mechanism optimization.
Technical Field
With the acceleration of the rhythm of life, the fast-paced online competitive game becomes a part of the daily life of people, particularly teenagers, and the proper game is beneficial to physical and psychological health. However, the current matching mechanism is imperfect, and the problems that the game levels of the players of the two parties are often in a large gap and the players compete for occupation of the characters exist, so that the countercheck experience is poor, the purpose of the game for serving life is deviated, and the advance of the game industry is hindered.
At present, the online competitive game has good development prospect, and the crowd base number must be increased rapidly along with the time. Therefore, how to optimize the matching mechanism, solve the phenomenon that the difference between the strength of the players of the two parties is large, reduce the probability of professional selection conflict of the player characters, and improve the game experience of the players becomes a problem which needs to be solved urgently by game developers.
Disclosure of Invention
The invention aims to provide an algorithm method for optimizing a matching mechanism of a ten-player online competitive game, which can effectively improve the confrontation experience of players, increase the interestingness of the competitive game and promote the development of the game industry.
In order to achieve the purpose, the invention adopts the technical scheme that:
an algorithmic method for optimizing a ten player online competitive game matching mechanism, comprising the steps of:
1) collection of player gaming information
Collecting the game information of the nearest N fields of each player entering at a certain time, wherein the game information refers to two aspects, namely scoring of the nearest N fields of games and professional selection of roles of the nearest N fields of games; supplied to step 2);
2) scored 1-D convolution
Carrying out convolution operation on the N field scores score of each player by using 1 row, N columns and 1-D templates respectively, wherein the coefficients of the templates correspond to the N field scores score of each player one by one, so as to obtain convolution operation results equal to the number of matched people in the step 1), and recording the convolution results as G; after all players perform the same operation, each matched player in the step 1) has a G value; provided to step 3), step 6);
3) section setting of score
Randomly sampling all matched players in the step 2) to obtain G values of ten players, namely extracting ten G values, carrying out 1-D convolution on the ten G values by utilizing the template in the figure 4, recording the convolution result as A, and setting A +/-20% as a scoring interval; supplied to step 6);
4) function mapping for role occupations
Performing the same operation on all the players matched in the step 1), performing function mapping on the selection of N game role occupations of each player in the step 1), wherein the mapping function is in a form of t-E (role occupations), wherein E is a functional relation, t is a mapping result, t of each of five types of occupations has uniqueness, counting the same t, and obtaining the number d corresponding to each t, namely a table shown in a figure 5t-d, wherein all the players matched in the step 1) have a respective table shown in a figure 5 t-d; supplied to step 5);
5) generation of player combinations
Obtaining the maximum value d of the d (d1, d2, d3, d4, d5) value of each player in the step 4) through a maximum value functionmaxAnd t is one of 000, 001, 010, 011 and 100 and is marked as tdmax(ii) a Arithmetic matching five differencestdmaxTraversing all matched players in the step 1) for one player combination to generate a player combination of one group of five persons, namely obtaining a primary player combination, and providing the primary player combination for the step 6) for screening;
6) final player combination derivation
Recording the G value of each group of players in the preliminary player combination obtained in the step 5) as G1, G2, G3, G4 and G5, and calculating Gave(G1+ G2+ G3+ G4+ G5)/5, if GaveIn step 3), the interval is set, and the set of player combinations is the final player combination.
In step 5), the arithmetic matching algorithm realizes five different tdmaxBy the following steps:
step 5.2.1, matching t of all players matched in 1)dmaxForming a queue, the length of the queue is consistent with the number of players, setting a mark p to point to t of the first player in the queuedmax。
Step 5.2.2, take out t of the first player by pdmaxT of the first player, denoted t1, reserved t1dmaxDisappear from the queue, then p moves one bit backwards, pointing to t of the second playerdmax。
Step 5.2.3, take t of the second player again with pdmaxIf t1 is equal to t of the second playerdmaxOtherwise, t of the second player is reserveddmaxT of the second player, denoted as t2, t2 and t1 in combinationdmaxAlso disappear from the queue; otherwise, t of the second player is setdmaxThe home position of the queue is retired. P is shifted back by one bit.
And 5.2.4, repeating the step 5.2.3 to obtain t3, t4 and t5, wherein 5 players corresponding to t1, t2, t3, t4 and t5 are a group of player combinations.
Step 5.2.4-1 if p points to t of the last player in the queuedmaxI.e. the first traversal is finished, it is found that a group of player combinations cannot be formed, then all the processes of the present invention are executed again by the player who has been taken out, the remaining players in the queue and the next player who enters the matching, and step 5.2.5, step 5.2.6 and step 6 are not executed); if at least one is generated after the first traversalThe group player combination continues with step 5.2.5, step 5.2.6 and step 6).
Step 5.2.5, now again point p move to t of the first player of the remaining players in the queuedmaxAnd repeating the steps 5.2.1 to 5.2.4 to achieve the aim of traversing all players.
Step 5.2.6, when p points to t of the last player in the queuedmaxThen, it is found that a group of t1, t2, t3, t4 and t5 combinations is no longer available, and all the processes of the present invention are executed again for the player who has taken out, the players who remain in the queue, the player combination which does not meet the scoring interval in step 3) in step 6) and the next group of players who enter matching.
The invention can effectively reduce the difference of the average game level of the players of the two parties, reduce the probability of conflict of professional choices of the role of the player of one party and improve the game experience of the players of the two parties. The significance of the invention is as follows: on one hand, for the individual player, the body and mind are more fully relaxed in a limited time, the game experience of the player is increased, the industrial purpose of the game for serving life is met, and the construction pace of the harmonious society is accelerated to a certain extent. On the other hand, the crowd base of the multiplayer online competitive game is necessarily and continuously expanded along with the time. The use of the invention promotes the development and growth of the games in the game market and indirectly promotes the development of the whole game industry.
Drawings
FIG. 1 is a schematic flow chart of the operation of the present invention
FIG. 2 is a 1-D template coefficient plot for row 1 and column 30 of an embodiment
FIG. 3 is a schematic diagram of a 1-row 30-column 1-D template of an embodiment
FIG. 4 is a 1-row 10-column filter template of an embodiment
FIG. 5 is a schematic diagram of t-d of an embodiment
FIG. 6 is a schematic flow chart of step 5) of the present invention
Detailed Description
Examples
An algorithmic method for optimizing a ten player online competitive game matching mechanism, comprising the steps of:
1) collection of player gaming information
Collecting game information of a plurality of N fields (in the embodiment, 30 fields are set for N) which enter each player between certain matching time (in the embodiment, 5S-60S), wherein the game information refers to two aspects, namely scoring score of the game of the N fields (in the embodiment, 30 fields) and character occupation selection condition of the game of the N fields (in the embodiment, 30 fields); supplied to step 2);
2) scored 1-D convolution
Carrying out convolution operation on 30 field scores score of each player by using 1 row and 30 columns of 1-D templates in the figure 3, wherein the coefficients of the templates correspond to the 30 field scores score of each player one by one, so as to obtain a convolution operation result equal to the number of matched people in the step 1), and recording the convolution result as G; after all players perform the same operation, each matched player in the step 1) has a G value; provided to step 3), step 6);
3) section setting of score
Randomly sampling all matched players in the step 2) to obtain G values of ten players, namely extracting ten G values, carrying out 1-D convolution on the ten G values by utilizing the template in the figure 4, recording the convolution result as A, and setting A +/-20% as a scoring interval; supplied to step 6);
4) functional mapping of role occupations
Respectively executing the same operation on all the players matched in the step 1), performing function mapping on the selection of thirty game role occupations of each player in the step 1), wherein the mapping function is in the form of t ═ E (role occupations), wherein E is a functional relation, t is a mapping result, t of each of the five occupations has uniqueness, counting the same t, and obtaining the number d corresponding to each t, namely a table of fig. 5t-d, wherein all the players matched in the step 1) have a respective table of fig. 5 t-d; supplied to step 5);
5) generation of player combinations
Obtaining the maximum value d of the d (d1, d2, d3, d4, d5) value of each player in the step 4) through a maximum value functionmaxAnd t is one of 000, 001, 010, 011 and 100 and is marked as tdmax(ii) a Arithmetic unitMatching five different tdmaxTraversing all matched players in the step 1) for one player combination to generate a player combination of one group of five persons, namely obtaining a primary player combination, and providing the primary player combination for the step 6) for screening;
6) final player combination derivation
Recording the G value of each group of players in the preliminary player combination obtained in the step 5) as G1, G2, G3, G4 and G5, and calculating Gave(G1+ G2+ G3+ G4+ G5)/5, if GaveIn step 3), the interval is set, and the set of player combinations is the final player combination.
Each step is described in detail below
In step 2), carrying out convolution operation on the scores of each player: the size of the template of fig. 3 should be consistent with the game field, i.e., 30. In addition, according to the characteristic that the correlation degree between the game field closer in time and the current game level is larger, the one-to-one correspondence between the coefficient of the template in fig. 3 and the 30-field score of each player in the step 1) refers to the one-to-one correspondence between the coefficient from large to small of the template in fig. 3 and the 30-field score of each player from near to far in time.
The specific method comprises the following steps:
the value of G resulting from the template convolution of FIG. 2 should reflect the current game level of each matching player of step 1). It is logical that the score correlation degree is higher when the current game level is closer to the time point, and the score correlation degree is lower when the current game level is farther from the time point. To implement the above logic, the 30-field score of each player in step 1) is recorded as 1 st field, 2 nd field, n.n.n.30 th field according to the sequence of time points from far to near, and corresponds to the template coefficients f (1), f (2).. f (n)... f (30) in fig. 3, and the value of f (n) is determined by the following formula:
f(n)＝(1/2)f(n-1)+(1/4)f(n-2)+(1/8)f(n-3)+......+(1/2n-1) f (1) +1(f (1) ═ 1, n ═ 2,3,4.. 30), the values of the template coefficients in fig. 2, i.e. in fig. 3, are finally determined.
score (n) represents the score of the nth field, then for step 1) each matched player G ═ (1/(f (1) + f (2) +. ·. · f (30))) (f (1) × score (1) + f (2) × score (2) +. ·.
Step 3) significance:
the convolution result a in step 3) represents the average level of all matched players in step 1), and to achieve this, the following steps can be performed:
step 3.1, recording step 3) the G values of the ten players are G0, G1, a.... G9, and the coefficients of the templates of fig. 4 are a0, a1, and a2.... a9, respectively;
3.2, G0 and A0 correspond to G1 and A1.... G9 and A9 one by one;
step 3.3, normalizing the template coefficients of FIG. 4: note that T is G0+ G1+. + G9,
determining the template coefficients of fig. 4 by calculating a0 ═ G0/T, a1 ═ G1/T.. a9 ═ G9/T;
step 3.4, after which a is determined by: a × G0 × a0+ G1 × a1+. + G9 × a 9.
Step 4):
the character professions can be divided into five types in total, which is consistent with the number of players on one side of a ten-player online competitive game. The embodiment is provided for ten-player online competitive games, players are divided into two camps, each of the camps is five, each player has a game task, and the game tasks correspond to five different professions.
In step 4), t adopts three-bit binary coding, and five different t respectively correspond to 000, 001, 010, 011 and 100 and correspond to d1, d2, d3, d4 and d 5.
t takes 000001010011100 five three-digit binary numbers to encode, and the five binary numbers correspond to five roles, so the corresponding relation E of the function independent variable and the dependent variable is single-to-single.
All players matched in the step 1) are respectively provided with a t-d table matched with the players per se shown in fig. 5, the use frequency of each occupation of the players in the last thirty times is reflected exactly, the larger the value of d is, the more favored the players are in the corresponding occupation, and the smaller the value of d is, the more contradictory the players are in the corresponding occupation.
Step 5):
the arithmetic matching is characterized in that all t consistent with the number of matched players in the step 1) can be matcheddmaxIn matching five different tdmaxCombined as a group of players and up to the point where the remaining players fail to satisfy five different t' sdmaxThis condition is up to.
Suppose now that step 1) matches 101 players, each of them dmaxCorrespond to respective tdmax(dmaxThe corresponding t is denoted as tdmax) That is, there are 101 t in totaldmax，
At this time at these 101 tdmaxIn, there may not even be a way to find five different tdmaxOne group, it is also possible to find two and three groups but at most twenty groups, as this is necessarily limited by the total number of approaches 101. At least one player can not participate in the combination in any way, and then the players which cannot be combined and the next round of matched players are required to perform all processes again.
Number of players matched in step 1) and t generated in step 5)dmaxIs consistent, the arithmetic matching algorithm realizes five different tdmaxBy the following steps:
step 5.2.1, matching t of all players matched in 1)dmaxForming a queue, the length of the queue is consistent with the number of players, setting a mark p to point to t of the first player in the queuedmax。
Step 5.2.2, take out t of the first player by pdmaxT of the first player, denoted t1, reserved t1dmaxDisappear from the queue, then p moves one bit backwards, pointing to t of the second playerdmax。
Step 5.2.3, take t of the second player again with pdmaxIf t1 is equal to t of the second playerdmaxOtherwise, t of the second player is reserveddmaxT2 and t1 form a combination, denoted t2, for the second playerdmaxAlso disappear from the queue; otherwise, t of the second player is setdmaxThe home position of the queue is retired. P is shifted back by one bit.
And 5.2.4, repeating the step 5.2.3 to obtain t3, t4 and t5, wherein 5 players corresponding to t1, t2, t3, t4 and t5 are a group of player combinations.
Step 5.2.4-1 if p points to t of the last player in the queuedmaxI.e. first traversalWhen the player combination is found to be not capable of being formed into a group, all the processes of the invention are executed again by the player which is taken out, the players which are left in the queue and the next group of players which enter the matching, and the steps 5.2.5, 5.2.6 and 6 are not executed); if at least one set of player combinations is generated after the first traversal, then steps 5.2.5, 5.2.6, and 6) continue.
Step 5.2.5, now again point p move to t of the first player of the remaining players in the queuedmaxAnd repeating the steps 5.2.1 to 5.2.4 to achieve the aim of traversing all players.
Step 5.2.6, when p points to t of the last player in the queuedmaxThen, it is found that a group of t1, t2, t3, t4 and t5 combinations is no longer available, and all the processes of the present invention are executed again for the player who has taken out, the players who remain in the queue, the player combination which does not meet the scoring interval in step 3) in step 6) and the next group of players who enter matching.
The maximum function is of the form dmaxF (d1, d2, d3, d4, d5) for each player to obtain the maximum value or one of the maximum values among d1, d2, d3, d4, d 5. F is a function corresponding relation, and the algorithm of the function corresponding relation comprises the following steps:
step 5.1.1, first dmaxD1, i.e., assume that d1 is the maximum or one of the maxima.
Step 5.1.2, mixing dmaxIf d2 is greater than d, as compared to d2maxThen let dmaxD2 otherwisemaxThe value is unchanged.
Repeat step 5.1.2, dmaxComparing with d3, d4 and d5 in sequence to finally obtain the maximum value or one of the maximum values d of d1, d2, d3, d4 and d5max。
For example, a player has a t-d table as shown in FIG. 5, where 5 d values are recorded as d1, d2, d3, d4, d5, and the maximum value or one of the maximum values of the 5 d values is recorded as dmax
That should determine how to dmaxIs there?
Assuming that the player's d-value is 45665 respectively,
suppose dmax4, 4 corresponds to d1, dmaxComparison with d2, i.e. 4 and 5, since 5>4, so update dmaxLet dmax＝5；dmaxComparison with d3, i.e. 5 and 6 (because dmaxHas been updated) because 6>5, continuously updating dmax，dmax＝6；dmaxCompare with d4, i.e. 6 and 6, because 6 is 6, d is not updatedmax，dmaxIs still 6; dmaxComparison with d5, i.e. 6 and 5, since 5<6, do not update dmax，dmaxIs still 6;
thus dmaxWith 6, i.e. d3, corresponding to t being 010.
Claims (2)
1. A method for optimizing a matching mechanism of a ten-player online competitive game, characterized by comprising the steps of:
1) collection of player gaming information
Collecting the game information of the nearest N fields of each player entering at a certain time, wherein the game information refers to two aspects, namely the score of the nearest N fields of games and the role occupation selection condition of the nearest N fields of games; supplied to step 2);
2) scored 1-D convolution
Carrying out convolution operation on the N field scores score of each player by using 1 row, N columns and 1-D templates respectively, wherein the coefficients of the templates correspond to the N field scores score of each player one by one, so as to obtain convolution operation results equal to the number of matched people in the step 1), and recording the convolution results as G; after all players perform the same operation, each matched player in the step 1) has a G value; provided to step 3), step 6);
3) section setting of score
Randomly sampling all matched players in the step 2) to obtain G values of ten players, namely extracting ten G values, and utilizing the following template
Performing 1-D convolution on the ten G values, recording the convolution result as A, and setting A+20% is a scoring interval; supplied to step 6);
4) function mapping for role occupations
Respectively executing the same operation on all the players matched in the step 1), carrying out function mapping on the selection of N game role occupations of each player in the step 1), wherein the mapping function is in a form of t-E, E is a role occupation, E is a functional relation, t is a mapping result, t of each occupation in five categories of occupations has uniqueness, counting the same t to obtain the number d corresponding to each t, namely a lower t-d table,
all players matched in the step 1) have a respective t-d table as follows;
supplied to step 5);
5) generation of player combinations
Obtaining the maximum value d of the d (d1, d2, d3, d4, d5) value of each player in the step 4) through a maximum value functionmaxAnd t is one of 000, 001, 010, 011 and 100 and is marked as tdmax(ii) a Arithmetic matching five different tdmaxTraversing all matched players in the step 1) for one player combination to generate a player combination of one group of five persons, namely obtaining a primary player combination, and providing the primary player combination for the step 6) for screening;
6) final player combination derivation
Recording the G value of each group of players in the preliminary player combination obtained in the step 5) as G1, G2, G3, G4 and G5, and calculating Gave(G1+ G2+ G3+ G4+ G5)/5, if GaveIn step 3), the interval is set, and the player combination is the final player combination.
2. The method of claim 1, wherein, in step 5),
the arithmetic matching algorithm implements five different tdmaxBy the following steps:
step 5.2.1, matching t of all players matched in 1)dmaxForming a queue, the length of the queue is consistent with the number of players, setting a mark p to point to t of the first player in the queuedmax；
Step 5.2.2, take out t of the first player by pdmaxT of the first player, denoted t1, reserved t1dmaxDisappear from queue, then p moves one bit backwards, points toT of the second playerdmax；
Step 5.2.3, take t of the second player again with pdmaxIf t1 is equal to t of the second playerdmaxOtherwise, t of the second player is reserveddmaxT2 and t1 form a combination, denoted t2, for the second playerdmaxAlso disappear from the queue; otherwise, t of the second player is setdmaxReturning to the home position of the queue; p is shifted back by one bit;
step 5.2.4, repeating step 5.2.3 to obtain t3, t4 and t5, wherein 5 players corresponding to t1, t2, t3, t4 and t5 are a group of player combinations;
step 5.2.4-1 if p points to t of the last player in the queuedmaxI.e. the first traversal is finished, it is found that a group of player combinations cannot be formed, and then all the steps are executed again for the player which has been taken out, the remaining players in the queue and the next player which enters the matching, and step 5.2.5, step 5.2.6 and step 6 are not executed); if at least one set of player combinations is generated after the first traversal, continuing to perform step 5.2.5, step 5.2.6, and step 6);
step 5.2.5, now again point p move to t of the first player of the remaining players in the queuedmaxThe goal of traversing all players can be realized by repeating the steps from 5.2.1 to 5.2.4;
step 5.2.6, when p points to t of the last player in the queuedmaxThen, if it is found that a group of t1, t2, t3, t4 and t5 combinations is no longer available, all the above steps are executed again for the player who has taken out, the player who remains in the queue, the player who does not meet the scoring interval in step 3) in step 6), and the next player who enters matching.
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