CN111185015B - Method for optimizing ten-player online competitive game matching mechanism - Google Patents

Abstract

The invention provides a method for optimizing the matching mechanism of a ten-player online competitive game, and relates to the optimization technology of the game matching mechanism. This method collects the last 30 game information of each player who has entered between 5 seconds and 60 seconds of matching time, and uses algorithms such as 1‑D convolution, maximum function, random sampling, function mapping, and arithmetic matching to make one player's The average level is in a set interval, and the probability of conflict between one player's career choice is minimized. Using the method of the present invention, on the one hand, the game experience of the player is increased, the limited game time of the player is fully utilized, and the purpose of the game serving entertainment is realized. On the other hand, the application of the present invention promotes the development and growth of the ten-player online competitive game in the game market, which has certain promotion significance for the game industry, and also has positive significance for the harmonious development of human society at a deeper level.

Description

A method for optimizing the matching mechanism of a ten-player online competitive game

technical field

The invention relates to the field of game matching mechanism optimization.

technical background

With the accelerated pace of life, fast-paced online competitive games have become a part of people's daily lives, especially teenagers. Appropriate games are beneficial to physical and mental health. However, the current matching mechanism is not perfect, and there is a problem that the game level of the players on both sides is often quite different and players compete for character occupations, resulting in a very bad confrontation experience, which deviates from the purpose of the game serving life, and also hinders the game. The pace of advancement of the industry.

At present, the development prospects of online competitive games are good, and the crowd base will definitely increase rapidly with the passage of time. Therefore, how to optimize the matching mechanism, solve the phenomenon of the large gap in the strength of the players on both sides, reduce the probability of conflict between the player's role and career choice, and improve the player's game experience has become an urgent problem for game developers to solve.

SUMMARY OF THE INVENTION

The purpose of the present invention is an algorithm method for optimizing the matching mechanism of a ten-player online competitive game, which can effectively improve the player's confrontation experience, increase the fun of the competitive game, and promote the development of the game industry.

In order to achieve the above purpose, the technical scheme adopted in the present invention is:

An algorithm method for optimizing a ten-player online competitive game matching mechanism, characterized in that it comprises the following steps:

1) Collection of player game information

Collect the game information of each player who entered at a certain time in the last N games, where the game information refers to two aspects, one is the score of the last N games, and the other is the character occupation selection of the last N games; provide it to step 2 );

2) 1-D convolution for scoring

Convolution operation is performed on each player's N-field rating score with a 1-row N-column 1-D template, the coefficients of the template are in one-to-one correspondence with each player's N-field rating score, and it is drawn that matches in step 1). The result of the convolution operation with the same number of people, denote the result of the convolution as G; after all players perform the same operation, each matching player in step 1) has a G value; provide it to step 3), step 6);

3) The interval setting of the score

The G values of ten players are randomly sampled from all matching players in step 2), that is, ten G values are extracted, and the ten G values are subjected to 1-D convolution using the template in Figure 4, and the convolution result is denoted as A, Set A±20% as the scoring interval; provide to step 6);

4) Function mapping of role occupation

Perform the same operation on all players matched in step 1) respectively, and perform function mapping on the choice of each player's role occupation in N games in step 1). The mapping function is in the form of t=E (role occupation), where E is the functional relationship , t is the mapping result, the t of each occupation in the five occupations is unique, and the same t is counted to obtain the number d corresponding to each t, that is, the table t-d in Figure 5, all players matched in step 1) have a respective Figure 5t-d table; provided to step 5);

5) Generation of player combinations

Obtain the maximum value d _max of each player's d (d1, d2, d3, d4, d5) value in step 4) through the maximum value function, and its corresponding t is one of 000, 001, 010, 011, and 100, denoted Be t _dmax ; Arithmetic matching five different t _dmax is a player combination, traverse step 1) all matching players, generate the player combination of a group of five, i.e. obtain preliminary player combination, provide step 6) screening;

6) The final player combination is obtained

Denote the G value of each group of players in the preliminary player combination obtained in step 5) as G1, G2, G3, G4, G5, and calculate _Gave = (G1+G2+G3+G4+G5)/5, if _Gave is in the middle interval of step 3), then this group of players is the final player combination.

In step 5), described arithmetic matching algorithm realizes the combination of five different t _dmax , is realized by the following steps:

Step 5.2.1, form a queue with the t _dmax of all players matched in 1), the length of the queue is the same as the number of players, and set a flag p to point to the t _dmax of the first player in the queue.

Step 5.2.2, use p to take out the t _dmax of the first player, record it as t1, keep t1, the t _dmax of the first player disappears from the queue, and then p moves backward one place, pointing to the t of the second player _dmax .

Step 5.2.3, use p again to take out the t _dmax of the second player. If t1 is different from the t _dmax of the second player, keep the t _dmax of the second player and record it as t2. t2 and t1 form a combination. The second player's t _dmax is also removed from the queue; otherwise, the second player's t _dmax is returned to its original position in the queue. Shift p back one place.

Step 5.2.4, repeat step 5.2.3 to get t3, t4, t5, then 5 players corresponding to t1, t2, t3, t4, t5 are a group of players.

Step 5.2.4-1 If p points to the t _dmax of the last player in the queue, that is, at the end of the first traversal, it is found that a group of players cannot be formed, then the players that have been taken out, the remaining players in the queue and the next batch The players who have entered the match re-execute all the processes of the present invention, and do not execute steps 5.2.5, 5.2.6 and 6); if at least one group of player combinations is generated after the first traversal, continue to execute steps 5.2.5 and 5.2.5. 5.2.6 and step 6).

Step 5.2.5, move p again to point to the t _dmax of the first player among the remaining players in the queue, and repeat the above steps 5.2.1 to 5.2.4 to achieve the goal of traversing all players.

Step 5.2.6, when p points to the t _dmax of the last player in the queue, it is found that a group of t1, t2, t3, t4, t5 combinations can no longer be obtained, then the players that have been taken out, the remaining players in the queue, and the steps In 6), the combination of players that do not meet the scoring interval in step 3) and the next batch of players who enter the matching process re-execute all the processes of the present invention.

The invention can effectively reduce the difference between the average game levels of the two players, and reduce the probability of conflict between one player's role choice and occupation, and improve the game experience of the two players. Significance of the present invention: On the one hand, for individual players, they can relax their mind and body more fully in a limited time, increase the player's game experience, conform to the industrial purpose that games serve life, and also speed up the development of a harmonious society to a certain extent. Construction pace. On the other hand, the crowd base of multiplayer online competitive games must continue to expand over time. The use of the present invention promotes the development and expansion of such games in the game market, and indirectly promotes the development of the entire game industry.

Description of drawings

Fig. 1 is the operation flow schematic diagram of the present invention

Fig. 2 is a 1-D template coefficient diagram of 1 row and 30 columns according to the embodiment

3 is a schematic diagram of a 1-D template with 1 row and 30 columns of an embodiment

FIG. 4 is a filter template of 1 row and 10 columns of an embodiment

Fig. 5 is the t-d schematic diagram of the embodiment

Fig. 6 is the present invention step 5) schematic flow chart

Detailed ways

Example

An algorithm method for optimizing a ten-player online competitive game matching mechanism, characterized in that it comprises the following steps:

1) Collection of player game information

Collect the game information of each player who entered between a certain matching time (5S-60S in this embodiment) in the most recent N games (30 games in this embodiment), where game information refers to two aspects, one is the most recent N games. The score of the game (30 games in the embodiment), and the second is the role career choice of the most recent N games (30 games in the embodiment); provided to step 2);

2) 1-D convolution for scoring

Use the 1-D template of 1 row and 30 columns in Fig. 3 to carry out convolution operation on each player's 30 field scoring scores respectively, and the coefficients of the template correspond to each player's 30 field scoring scores one-to-one, and the steps are as follows: 1) The result of the convolution operation that matches the number of people is equal, and the convolution result is denoted as G; after all players perform the same operation, each matching player in step 1) has a G value; provide to step 3), step 6);

3) The interval setting of the score

The G values of ten players are randomly sampled from all matching players in step 2), that is, ten G values are extracted, and the ten G values are subjected to 1-D convolution using the template in Figure 4, and the convolution result is denoted as A, Set A±20% as the scoring interval; provide to step 6);

4) Function mapping of role occupation

Perform the same operation on all players matched in step 1) respectively, and perform function mapping on the choice of each player's 30-game character occupation in step 1). The mapping function is in the form of t=E (character occupation), where E is a function relationship, t is the mapping result, the t of each occupation in the five types of occupations is unique, and the same t is counted to obtain the number d corresponding to each t, that is, the table t-d in Figure 5, all matching in step 1) Players have a respective figure 5t-d table; provided to step 5);

5) Generation of player combinations

Obtain the maximum value d _max of each player's d (d1, d2, d3, d4, d5) value in step 4) through the maximum value function, and its corresponding t is one of 000, 001, 010, 011, and 100, denoted Be t _dmax ; Arithmetic matching five different t _dmax is a player combination, traverse step 1) all matching players, generate the player combination of a group of five, i.e. obtain preliminary player combination, provide step 6) screening;

6) The final player combination is obtained

Denote the G value of each group of players in the preliminary player combination obtained in step 5) as G1, G2, G3, G4, G5, and calculate _Gave = (G1+G2+G3+G4+G5)/5, if _Gave is in the middle interval of step 3), then this group of players is the final player combination.

Each step is detailed below

In step 2), the convolution operation of each player's score: the size of the template in Figure 3 should be consistent with the game session, that is, 30. In addition, according to the feature that the closer the game times in time are more relevant to the current game level, the one-to-one correspondence between the template coefficients in Figure 3 and the 30-game scores of each player in step 1) means that the template in Figure 3 is from large to The small coefficient corresponds to the 30-game score of each player from near to far in time.

The specific method is:

The G value obtained by convolution of the template in Figure 2 should reflect the current game level of each matched player in step 1). The closer the current game level and the time point are, the higher the correlation is, and the farther the time point is, the lower the correlation is, which is logical. In order to realize the above logic, the 30 game scores of each player in step 1) are recorded as the 1st game, the 2nd game...the nth game... The 30th field corresponds to the template coefficients f(1), f(2)...f(n)...f(30) in Fig. 3, and the value of f(n) is given by the following formula Sure:

f(n)=(1/2)f(n-1)+(1/4)f(n-2)+(1/8)f(n-3)+......+(1 /2 ^n-1 )f(1)+1(f(1)=1,n=2,3,4...30), and finally determine the value of the template coefficient in Fig. 2, that is, Fig. 3.

score(n) represents the score of the nth game, then for each matching player in step 1) G=(1/(f(1)+f(2)+...+f(30)))* (f(1)*score(1)+f(2)*score(2)+...+f(30)*score(30)).

Step 3) Meaning:

The convolution result A in step 3) represents the average level of all matched players in step 1). To achieve this, the following steps can be performed:

Step 3.1, record step 3) The G values of the ten players are G0, G1,...G9, and the coefficients of the template in Figure 4 are A0, A1, A2...A9;

Step 3.2, G0 and A0, G1 and A1...G9 and A9 correspond one by one;

Step 3.3, normalize the template coefficients in Figure 4: mark T=G0+G1+...+G9,

Determine the template coefficient of Figure 4 by calculating A0=G0/T, A1=G1/T, ...... A9=G9/T;

Step 3.4, after which A is determined by the following formula: A=G0*A0+G1*A1+...+G9*A9.

Step 4):

The character occupations can be generally divided into five categories, which is consistent with the number of players on one side of the ten-player online competitive game. This embodiment is proposed for a ten-player online competitive game. Players are divided into two camps, and each camp has five players, each of whom has a game task corresponding to five different occupations.

In step 4), t adopts a three-bit binary code, and five different t correspond to 000, 001, 010, 011, and 100, respectively, and correspond to d1, d2, d3, d4, and d5.

t uses five three-digit binary numbers of 000 001 010 011 100 for coding, which correspond to the five roles and occupations one-to-one, so the corresponding relationship E between the independent variable of the function and the dependent variable is one-to-one.

All players matched in step 1) each have a t-d table for matching themselves as shown in Figure 5, which exactly reflects the frequency of players using each occupation in the last 30 games. The larger the d value, the more the player prefers the corresponding occupation. The smaller the d value, the more resistance the player is to the corresponding profession.

Step 5):

The arithmetic matching is characterized in that five different t _dmax can be matched as a group of player combinations from all t _dmax that are consistent with the number of players matched in step 1) until the remaining players cannot satisfy the five different t dmax. t _dmax until this condition.

Suppose now that step 1) matches 101 players, and their respective d _max corresponds to their respective t _dmax (the t corresponding to d _max is recorded as t _dmax ), that is to say, there are 101 t _dmax in total,

At this time, in these 101 t _dmax , it may not even be possible to find five different t _dmax to form a group, or it is possible to find a group of two groups of three groups... But at most twenty groups can be found, because it is bound to be affected by The total number of people entering the venue is limited to 101. In any case, there will be at least one player left who cannot participate in the combination, and then it is only necessary to perform all the processes again with the players who cannot be combined and the players who are matched in the next round.

The number of players matched in step 1) is the same as the number of t _dmax generated in step 5). This arithmetic matching algorithm realizes the combination of five different t _dmax , which is realized by the following steps:

Step 5.2.1, form a queue with the t _dmax of all players matched in 1), the length of the queue is the same as the number of players, and set a flag p to point to the t _dmax of the first player in the queue.

Step 5.2.2, use p to take out the t _dmax of the first player, record it as t1, keep t1, the t _dmax of the first player disappears from the queue, and then p moves backward one place, pointing to the t of the second player _dmax .

Step 5.2.3, use p again to take out the t _dmax of the second player. If t1 is different from the t _dmax of the second player, keep the t _dmax of the second player and record it as t2. t2 and t1 form a combination. The second player's t _dmax is also removed from the queue; otherwise, the second player's t _dmax is returned to its original position in the queue. Shift p back one place.

Step 5.2.4, repeat step 5.2.3 to get t3, t4, t5, then 5 players corresponding to t1, t2, t3, t4, t5 are a group of players.

Step 5.2.4-1 If p points to the t _dmax of the last player in the queue, that is, at the end of the first traversal, it is found that a group of players cannot be formed, then the players that have been taken out, the remaining players in the queue and the next batch The players who have entered the match re-execute all the processes of the present invention, and do not execute steps 5.2.5, 5.2.6 and 6); if at least one group of player combinations is generated after the first traversal, continue to execute steps 5.2.5 and 5.2.5. 5.2.6 and step 6).

Step 5.2.5, move p again to point to the t _dmax of the first player among the remaining players in the queue, and repeat the above steps 5.2.1 to 5.2.4 to achieve the goal of traversing all players.

Step 5.2.6, when p points to the t _dmax of the last player in the queue, it is found that a group of t1, t2, t3, t4, t5 combinations can no longer be obtained, then the players that have been taken out, the remaining players in the queue, and the steps In 6), the combination of players that do not meet the scoring interval in step 3) and the next batch of players who enter the matching process re-execute all the processes of the present invention.

The maximum value function is in the form of d _max =F(d1, d2, d3, d4, d5), and the function is to obtain the maximum value or one of the maximum values among d1, d2, d3, d4, and d5 of each player. F is the function correspondence, and its algorithm consists of the following steps:

Step 5.1.1, first d _max = d1, ie d1 is assumed to be the maximum value or one of the maximum values.

Step 5.1.2, compare d _max with d2 , if d2 is greater than d _max , set d _max =d2, otherwise the value of d _max remains unchanged.

Step 5.1.2 is repeated, and d _max is compared with d3, d4, and d5 in turn, and finally the maximum value or one of the maximum values d _max among d1, d2, d3, d4, and d5 is obtained.

For example, a player has a td table as shown in Figure 5. There are 5 d values in this table, which are recorded as d1, d2, d3, d4, d5, the maximum or the maximum value among the 5 d values. One is denoted as d _max

So how should we determine d _max ?

Suppose the player's d values are 4 5 6 6 5, respectively,

Assuming d _max = 4, 4 is equivalent to d1, d _max is compared with d2, that is, 4 is compared with 5, because 5>4, so update d _max , let d _max =5; d _max is compared with d3, that is, 5 and 6 are compared (because d _max has been updated), because 6>5, continue to update d _max , d _max =6; compare d _max with d4, that is, compare 6 with 6, because 6=6, do not update d _max , d _max is still 6; d _max is compared with d5, that is, 6 is compared with 5, because 5<6, d _max is not updated, and d _max is still 6;

Therefore, d _max =6, that is, d3, and the corresponding t is 010.

Claims (2)

1. a method for optimizing ten online competitive game matching mechanism, is characterized in that, comprises the following steps: 1) Collection of player game information Collect the game information of each player who entered at a certain time in the last N games, where the game information refers to two aspects, one is the score of the last N games, and the other is the character occupation selection of the last N games; provide it to step 2 ); 2) 1-D convolution for scoring Convolution operation is performed on each player's N-field rating score with a 1-row N-column 1-D template, the coefficients of the template are in one-to-one correspondence with each player's N-field rating score, and it is drawn that matches in step 1). The result of the convolution operation with the same number of people, denote the result of the convolution as G; after all players perform the same operation, each matching player in step 1) has a G value; provide it to step 3), step 6); 3) The interval setting of the score The G values of ten players are randomly sampled from all matching players in step 2), that is, ten G values are drawn, and the following template is used A0 A1 A2 A3 A4 A5 A6 A7 A8 A9 Perform 1-D convolution on the ten G values, denote the convolution result as A, and set A + 20% as the scoring interval; provide it to step 6); 4) Function mapping of role occupation Perform the same operation on all players matched in step 1) respectively, and perform function mapping on the choice of each player's role occupation in N games in step 1), the mapping function is in the form of t=E, E is the role occupation, and E is the function relationship, t is the mapping result, the t of each occupation in the five types of occupations is unique, and the same t is counted to obtain the quantity d corresponding to each t, that is, the following t-d table, t 000 001 010 011 100 d d1 d2 d3 d4 d5 All players matched in step 1) have a respective t-d table as follows; t 000 001 010 011 100 d d1 d2 d3 d4 d5 provided to step 5); 5) Generation of player combinations Obtain the maximum value d _max of each player's d (d1, d2, d3, d4, d5) value in step 4) through the maximum value function, and its corresponding t is one of 000, 001, 010, 011, and 100, denoted Be t _dmax ; Arithmetic matching five different t _dmax is a player combination, traverse step 1) all matching players, generate the player combination of a group of five, i.e. obtain preliminary player combination, provide step 6) screening; 6) The final player combination is obtained Denote the G value of each group of players in the preliminary player combination obtained in step 5) as G1, G2, G3, G4, G5, and calculate _Gave = (G1+G2+G3+G4+G5)/5, if _Gave is in the interval in step 3), then the player combination is the final player combination. 2. method as claimed in claim 1, is characterized in that, in step 5), The arithmetic matching algorithm realizes the combination of five different t _dmax by the following steps: Step 5.2.1, form a queue with the t _dmax of all players matched in 1), the length of the queue is the same as the number of players, and set a flag p to point to the t _dmax of the first player in the queue; Step 5.2.2, use p to take out the t _dmax of the first player, record it as t1, keep t1, the t _dmax of the first player disappears from the queue, and then p moves backward one place, pointing to the t of the second player _dmax ; Step 5.2.3, use p again to take out the t _dmax of the second player. If t1 is different from the t _dmax of the second player, keep the t _dmax of the second player and record it as t2. t2 and t1 form a combination. The t _dmax of the second player also disappears from the queue; otherwise, the t _dmax of the second player is returned to the original position of the queue; p is moved back one place; Step 5.2.4, repeat step 5.2.3 to get t3, t4, t5, then 5 players corresponding to t1, t2, t3, t4, t5 are a group of players; Step 5.2.4-1 If p points to the t _dmax of the last player in the queue, that is, at the end of the first traversal, it is found that a group of players cannot be formed, then the players that have been taken out, the remaining players in the queue and the next batch Players who have entered the match re-execute all the above steps, but do not execute steps 5.2.5, 5.2.6 and 6); if at least one set of player combinations is generated after the first traversal, continue to execute steps 5.2.5 and 5.2 .6 and step 6); Step 5.2.5, move p again to the t _dmax of the first player among the remaining players in the queue, and repeat the above steps 5.2.1 to 5.2.4 to achieve the goal of traversing all players; Step 5.2.6, when p points to the t _dmax of the last player in the queue, it is found that a group of t1, t2, t3, t4, t5 combinations can no longer be obtained, then the players that have been taken out, the remaining players in the queue, and the steps In 6), the combination of players who do not meet the scoring interval in step 3) and the next batch of players who enter the match will perform all the above steps again.

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