Data processing method, device and equipment and computer readable storage medium

Abstract

The invention discloses a data processing method, device and equipment and a computer readable storage medium, and the method comprises the steps: generating a plurality of initial lineup sets according to B lineup composition basic conditions respectively comprised by A lineup composition targets; wherein one initial lineup set is generated according to one lineup composition basic condition; performing merging processing on the initial lineup sets containing the same number of roles in the plurality of initial lineup sets to obtain B to-be-optimized lineup sets, and performing optimization processing on the B to-be-optimized lineup sets to obtain B optimized lineup sets; and according to the lineup intensity of the optimized lineup included in each optimized lineup set, determining a lineup ladder corresponding to each optimized lineup set, and according to the B lineup ladders, performing evaluation processing on the A lineup composition targets. By adopting the method and the device, not only can the game numerical value be accurately adjusted and the balance of the lineup intensity among different lineups be improved, but also the adjustment efficiency of the game numerical value can be improved.

Description

Technical Field

[0001] The present application relates to the field of Internet technology, and in particular to a data processing method, apparatus, device, and computer-readable storage medium. Background Art

[0002] With the development of information technology, online and mobile games have also grown. Many games rely on matching and playing with specific lineups. For example, the game Auto Battler (or Auto Chess) is a subgenre of strategy games. During the preparation phase, players assemble a lineup using multiple characters. They then engage in virtual battles with the opponent's lineup, without direct control.

[0003] Due to the wide variety of roles and bond combinations (also referred to as linkage relationships in this application) in the game, it is very important to analyze the balance of lineup strength between different lineups. The existing technology is to determine the lineup strength of each lineup based on the virtual battles in which players participate after the game is launched, and then the game planners adjust the game values ​​according to the lineup strength of each lineup; however, the collection of game data related to the virtual battles in which players participate can only be obtained after the game is launched, resulting in the need to adjust the game values ​​after the game is launched, thus reducing the efficiency of game value adjustment; in addition, the game data related to the virtual battles in which players participate has limitations. For example, players generally choose the strongest lineup for virtual battles. Therefore, it may not be possible to accurately adjust the game values ​​based on the game data related to the virtual battles in which players participate, thereby reducing the balance of lineup strength between different lineups. Summary of the Invention

[0004] The embodiments of the present application provide a data processing method, apparatus, device, and computer-readable storage medium, which can not only accurately adjust game values ​​and improve the balance of lineup strength between different lineups, but also improve the adjustment efficiency of game values.

[0005] On the one hand, an embodiment of the present application provides a data processing method, including:

[0006] Get A lineup composition targets; A is a positive integer; A lineup composition targets each include B lineup composition basic conditions, and the number of characters included in the B lineup composition basic conditions belonging to a lineup composition target is different from each other; A lineup composition targets each include the same number of characters; B is a positive integer greater than 1; a lineup composition basic condition is used to indicate the basic conditions for generating a type of lineup;

[0007] Generate multiple initial lineup sets based on the B basic lineup composition conditions included in the A lineup composition targets; wherein one initial lineup set is generated based on one basic lineup composition condition in one lineup composition target;

[0008] Merge multiple initial lineup sets containing the same number of characters to obtain B lineup sets to be optimized, and optimize each of the B lineup sets to be optimized to obtain B optimized lineup sets;

[0009] According to the lineup strength of the optimized lineups included in each optimized lineup set, the lineup ladder corresponding to each optimized lineup set is determined, and according to the B lineup ladders, the A lineup composition targets are evaluated and processed respectively.

[0010] In one aspect, an embodiment of the present application provides a data processing device, including:

[0011] An acquisition module is used to acquire A lineup composition targets; A is a positive integer; A lineup composition targets respectively include B lineup composition basic conditions, and the B lineup composition basic conditions belonging to a lineup composition target respectively include different numbers of characters; A lineup composition targets respectively include the same number of characters; B is a positive integer greater than 1; a lineup composition basic condition is used to indicate the basic conditions for generating a type of lineup;

[0012] A generation module, configured to generate a plurality of initial lineup sets based on the B basic lineup composition conditions respectively included in the A lineup composition objectives; wherein an initial lineup set is generated based on one basic lineup composition condition in one lineup composition objective;

[0013] An optimization module is used to merge initial lineup sets containing the same number of characters from multiple initial lineup sets to obtain B lineup sets to be optimized, and optimize the B lineup sets to be optimized respectively to obtain B optimized lineup sets;

[0014] The evaluation module is used to determine the lineup ladder corresponding to each optimized lineup set according to the lineup strength of the optimized lineup included in each optimized lineup set, and evaluate the A lineup composition targets respectively according to the B lineup ladders.

[0015] In a possible implementation, A lineup composition goals include C lineup composition basic conditions, where C equals A*B; the C lineup composition basic conditions include a first lineup composition basic condition; the first lineup composition basic condition is one of the C lineup composition basic conditions;

[0016] The generation module generates multiple initial lineup sets based on the B lineup composition basic conditions included in the A lineup composition targets, and is used to perform the following operations:

[0017] According to the basic conditions for forming the first lineup, a core role is obtained from the candidate role set; the candidate role set includes all roles in the role configuration information; the role configuration information includes detailed configuration information corresponding to all roles; A lineup formation target meets the role configuration information;

[0018] Subtract the number of characters corresponding to the basic conditions of the first lineup from the number of core characters to get the filling number;

[0019] According to the filling quantity, F filling role combinations are obtained from the candidate role set; F is a positive integer; wherein the number of roles included in a filling role combination is the filling quantity;

[0020] Based on the F filler role combinations and the core roles, generate an initial lineup set corresponding to the basic conditions for the first lineup;

[0021] The initial lineup sets corresponding to the C basic lineup composition conditions are determined as multiple initial lineup sets.

[0022] In one possible implementation, the generation module obtains F filling role combinations from the candidate role set according to the filling quantity, and performs the following operations:

[0023] If the basic conditions for the first lineup include the linkage relationship of the core characters, then the linkage filling quantity corresponding to the linkage relationship is determined; the linkage relationship has the function of additional virtual combat power;

[0024] According to the linkage filling number, I linkage role combinations are obtained from the candidate role set; I is a positive integer; the number of roles included in a linkage role combination is the linkage filling number; I linkage role combination includes linkage role combination J k , k is a positive integer, and k is less than or equal to 1;

[0025] Subtract the filling quantity and the linkage filling quantity to obtain the remaining filling quantity;

[0026] According to the remaining number of fills, select the linkage role combination J from the candidate role set k Get one or more remaining role combinations, and combine the obtained one or more remaining role combinations with the linkage role combination J k Combine them separately to get the linkage role combination J k a corresponding combination of one or more filler roles;

[0027] The filling role combinations corresponding to the I linkage role combinations are determined to be F filling role combinations; F is equal to or greater than I.

[0028] In one possible implementation, the generation module generates an initial lineup set corresponding to the basic conditions for forming the first lineup based on the F filler role combinations and the core role, and performs the following operations:

[0029] Combine F filler role combinations with the core roles respectively to obtain F lineups to be allocated; wherein one lineup to be allocated includes one filler role combination;

[0030] Obtain resource allocation standard information based on the number of characters corresponding to the basic conditions for the first lineup;

[0031] According to the resource allocation standard information, resource allocation processing is performed on F lineups to be allocated, and F initial lineups are obtained; wherein an initial lineup is obtained by performing resource allocation processing on one lineup to be allocated;

[0032] The F initial lineups each carrying a condition identifier indicating the basic condition for forming the first lineup are determined as the initial lineup set corresponding to the basic condition for forming the first lineup.

[0033] In a possible implementation, the B lineup sets to be optimized include the lineup set L to be optimized. m , m is a positive integer, and m is less than or equal to B;

[0034] The optimization module optimizes each of the B lineup sets to be optimized to obtain B optimized lineup sets, which are used to perform the following operations:

[0035] The lineup to be optimized is set to L m The lineups to be optimized with the same lineup characteristics are divided into the same lineup population to be optimized, and N lineup populations to be optimized are obtained; N is a positive integer;

[0036] Optimize N lineup populations to be optimized respectively to obtain N optimized lineup populations; wherein, one optimized lineup population is obtained by optimizing one lineup population to be optimized;

[0037] Determine the N optimized lineup populations as the lineup set to be optimized L m The corresponding optimized lineup set.

[0038] In a possible implementation, the N lineup populations to be optimized include a first lineup population to be optimized; the first lineup population to be optimized is one of the N lineup populations to be optimized; the first lineup population to be optimized includes Q lineups to be optimized; Q is a positive integer;

[0039] The optimization module optimizes the N to-be-optimized lineup populations separately to obtain N optimized lineup populations, which are used to perform the following operations:

[0040] Perform strength evaluation processing on each of the Q lineups to be optimized to obtain the lineup strengths corresponding to each of the Q lineups to be optimized; wherein the lineup strength of a lineup to be optimized is obtained by performing strength evaluation processing on the lineup to be optimized;

[0041] Adjust the Q lineups to be optimized respectively to obtain Q adjusted lineups, and generate a first new lineup with the same lineup characteristics as the lineup characteristics corresponding to the Q lineups to be optimized respectively;

[0042] Perform a strength evaluation process on the first newly added lineup to obtain the lineup strength corresponding to the first newly added lineup, and perform a strength evaluation process on each of the Q adjusted lineups to obtain the lineup strength corresponding to each of the Q adjusted lineups;

[0043] According to the lineup strengths corresponding to the Q lineups to be optimized, the lineup strengths corresponding to the Q adjusted lineups, the lineup strength corresponding to the first newly added lineup, the Q adjusted lineups, and the first newly added lineup, a lineup update process is performed on the first lineup population to be optimized to obtain an optimized lineup population corresponding to the first lineup population to be optimized;

[0044] The optimized lineup populations corresponding to the N lineup populations to be optimized are determined as N optimized lineup populations.

[0045] In one possible implementation, the acquisition module acquires A lineup composition targets and performs the following operations:

[0046] Get role configuration information; role configuration information includes detailed configuration information corresponding to all roles;

[0047] Generate A lineup composition targets that all meet the character configuration information;

[0048] Generate a reference lineup that meets the role configuration information; the number of roles included in the reference lineup is the same as the lineup set L to be optimized m same;

[0049] Then the Q lineups to be optimized include the first lineup to be optimized; the first lineup to be optimized is one of the Q lineups to be optimized;

[0050] The optimization module performs strength evaluation on each of the Q lineups to be optimized, and obtains the lineup strength corresponding to each of the Q lineups to be optimized, which is used to perform the following operations:

[0051] Determine the second lineup to be optimized, excluding the first lineup to be optimized, and the reference lineup from the N lineup populations to be optimized as the first lineup to be optimized;

[0052] Performing virtual battles on the first lineup to be optimized and each of the first battle lineups, respectively, to obtain virtual battle results corresponding to the first lineup to be optimized and each of the first battle lineups;

[0053] The lineup strength of the first lineup to be optimized is determined according to the virtual battle results corresponding to the first lineup to be optimized and each competing lineup in the first competing lineup.

[0054] In a possible implementation, the Q lineups to be optimized include a first lineup to be optimized; the first lineup to be optimized is one of the Q lineups to be optimized;

[0055] The optimization module adjusts the Q lineups to be optimized respectively to obtain Q adjusted lineups, which are used to perform the following operations:

[0056] Performing an adjustment process on the first lineup to be optimized to obtain a lineup to be confirmed, and obtaining an adjustment type of the adjustment process;

[0057] If the adjustment type is a target adjustment type, then the condition identifier carried by the first lineup to be optimized is obtained; the condition identifier carried by the first lineup to be optimized is used to indicate the basic conditions of the lineup composition corresponding to the first lineup to be optimized;

[0058] If the lineup to be confirmed meets the basic lineup composition conditions corresponding to the first lineup to be optimized, the lineup to be confirmed will be determined as the adjusted lineup corresponding to the first lineup to be optimized;

[0059] If the lineup to be confirmed does not meet the basic lineup composition conditions corresponding to the first lineup to be optimized, the lineup to be confirmed is adjusted. If the lineup after the adjustment meets the basic lineup composition conditions corresponding to the first lineup to be optimized, the lineup after the adjustment is determined as the adjusted lineup corresponding to the first lineup to be optimized;

[0060] If the adjustment type is not the target adjustment type, the lineup to be confirmed will be determined as the adjustment lineup corresponding to the first lineup to be optimized.

[0061] In a possible implementation, the Q adjusted lineups include a first adjusted lineup; the first adjusted lineup is one of the Q adjusted lineups;

[0062] The optimization module performs strength evaluation on each of the Q adjusted lineups to obtain the lineup strength corresponding to each of the Q adjusted lineups, which is used to perform the following operations:

[0063] Obtaining a second lineup population to be optimized other than the first lineup population to be optimized from the N lineup populations to be optimized;

[0064] Obtaining a second adjusted lineup obtained by adjusting the lineup to be optimized in the second population of lineups to be optimized;

[0065] The third adjusted lineup, the second adjusted lineup, the reference lineup, the first newly added lineup, and the second newly added lineup among the Q adjusted lineups, excluding the first adjusted lineup, are determined as the second match lineup of the first adjusted lineup; the second newly added lineup refers to the lineup generated for the second lineup population to be optimized, and the lineup characteristics corresponding to the second newly added lineup are the same as the lineup characteristics corresponding to the second lineup population to be optimized;

[0066] Performing virtual battles on each of the first adjusted lineup and the second battle lineup, respectively, to obtain virtual battle results corresponding to each of the first adjusted lineup and the second battle lineup;

[0067] The lineup strength of the first adjusted lineup is determined according to the virtual battle results corresponding to each of the first adjusted lineup and the second battle lineup.

[0068] In one possible implementation, the optimization module performs lineup update processing on the first lineup population to be optimized based on the lineup strengths corresponding to the Q lineups to be optimized, the lineup strengths corresponding to the Q adjusted lineups, the lineup strength corresponding to the first newly added lineup, the Q adjusted lineups, and the first newly added lineup to obtain an optimized lineup population corresponding to the first lineup population to be optimized, and performs the following operations:

[0069] The Q lineups to be optimized, the Q adjusted lineups, and the first newly added lineup are determined as the population to be deduplicated, and multiple lineups with the same non-core characters are obtained from the population to be deduplicated;

[0070] Obtain the lineup strengths corresponding to the multiple lineups from the lineup strengths corresponding to the Q lineups to be optimized, the lineup strengths corresponding to the Q adjusted lineups, and the lineup strength corresponding to the first newly added lineup;

[0071] Determine a first lineup with the greatest lineup strength among multiple lineups, delete the second lineup from the population to be deduplicated, and obtain a deduplicated lineup population; the second lineup includes the lineups among the multiple lineups except the first lineup;

[0072] Determining an updated lineup population generated by a first iterative operation of the first lineup population to be optimized based on the first number of lineups in the deduplicated lineup population and the second number of lineups to be optimized in the first lineup population to be optimized;

[0073] According to the updated lineup population, the optimized lineup population corresponding to the first lineup population to be optimized is determined.

[0074] In one possible implementation, the optimization module determines, based on the first number of lineups in the deduplicated lineup population and the second number of lineups to be optimized in the first lineup population to be optimized, an updated lineup population generated by a first iteration of the first lineup population to be optimized, and performs the following operations:

[0075] If the first number of lineups in the deduplicated lineup population is greater than the second number of lineups to be optimized in the first lineup population to be optimized, obtaining a second lineup composition basic condition associated with the first lineup population to be optimized;

[0076] In the deduplicated lineup population, obtain a third lineup that meets the basic conditions for forming the second lineup; the lineup strength of the third lineup is greater than or equal to the lineup strength of the fourth lineup; the fourth lineup includes lineups in the deduplicated lineup population that meet the basic conditions for forming the second lineup except the third lineup;

[0077] Determine the difference between the second number and the fourth number of the third lineup, and obtain a fifth lineup that does not meet the basic conditions for forming the second lineup from the deduplicated lineup population; the number of the fifth lineup is the difference; the lineup strength of the fifth lineup is greater than or equal to the lineup strength of the sixth lineup; the sixth lineup includes lineups in the deduplicated lineup population that do not meet the basic conditions for forming the second lineup except the fifth lineup;

[0078] The third lineup and the fifth lineup are determined as the updated lineup population generated by the first iteration operation of the first lineup population to be optimized;

[0079] If the first number is less than or equal to the second number, the deduplicated lineup population is determined to be the updated lineup population generated by the first iterative operation on the first lineup population to be optimized.

[0080] In one possible implementation, the optimization module determines the optimized lineup population corresponding to the first lineup population to be optimized based on the updated lineup population, and performs the following operations:

[0081] Get the iterative operation count threshold corresponding to the iterative operation; the iterative operation includes lineup addition operation, lineup adjustment operation, lineup evaluation operation and population update operation;

[0082] If the iterative operation number threshold is equal to 1, the updated lineup population is determined to be the optimized lineup population corresponding to the first lineup population to be optimized;

[0083] If the iterative operation number threshold is greater than 1, a second iterative operation is performed by updating the lineup population. When the iterative operation number is equal to the iterative operation number threshold, the lineup population generated by the iterative operation is determined as the optimized lineup population corresponding to the first lineup population to be optimized.

[0084] In a possible implementation, the B optimized lineup sets include the optimized lineup set X y ; y is a positive integer, and y is less than or equal to B;

[0085] The evaluation module determines the lineup ladder corresponding to each optimized lineup set based on the lineup strength of the optimized lineups included in each optimized lineup set, and is used to perform the following operations:

[0086] In the optimized lineup set X y Get the first optimized lineup; the first optimized lineup is the optimized lineup set X y An optimized lineup in

[0087] The first optimized lineup and the second optimized lineup are virtual battled to obtain the lineup strength of the first optimized lineup; the second optimized lineup includes the optimized lineup set X y The optimized lineup except the first optimized lineup;

[0088] According to the optimized lineup set X y The optimized lineups in the corresponding lineup strengths are the same as the optimized lineup set X. y Sorting the optimized lineup in the , and getting the optimized lineup set X y The corresponding lineup ladder to be optimized;

[0089] According to the optimized lineup set X y The corresponding lineup ladder to be optimized generates the optimized lineup set X y Corresponding lineup ladder.

[0090] In a possible implementation, the evaluation module optimizes the lineup set X y The corresponding lineup ladder to be optimized generates the optimized lineup set X y The corresponding lineup ladder is used to perform the following operations:

[0091] In the B basic conditions of lineup composition included in the A lineup composition goals, obtain the optimized lineup set X y The corresponding A basic conditions for the formation of a lineup; the basic conditions for the formation of a lineup include the basic conditions for the formation of a lineup Z w , w is a positive integer, and w is less than or equal to A;

[0092] In the optimized lineup set X y In the corresponding lineup ladder to be optimized, obtain the basic conditions Z of the lineup composition w The third optimized lineup; the lineup strength of the third optimized lineup is greater than or equal to the lineup strength of the fourth optimized lineup; the fourth optimized lineup includes the optimized lineup set X y The corresponding lineup to be optimized in the ladder meets the basic conditions of lineup composition except the third optimized lineup Zw Optimized lineup;

[0093] In the optimized lineup set X y In the corresponding lineup ladder to be optimized, obtain a fifth optimized lineup that does not meet the basic conditions for A lineups, and determine V linkage relationships included in the fifth optimized lineup; V is a positive integer;

[0094] Determine the maximum levels corresponding to the V linkage relationships, and among the V maximum levels, obtain the maximum level that is greater than or equal to the level threshold as the effective level;

[0095] In the fifth optimized lineup, obtain the sixth optimized lineup for the effective level; the lineup strength of the sixth optimized lineup is greater than or equal to the lineup strength of the seventh optimized lineup; the seventh optimized lineup includes the optimized lineups in the fifth optimized lineup except the sixth optimized lineup;

[0096] Based on the third optimized lineup and the sixth optimized lineup, generate the optimized lineup set X y Corresponding lineup ladder.

[0097] In a possible implementation, the evaluation module generates an optimized lineup set X based on the third optimized lineup and the sixth optimized lineup. y The corresponding lineup ladder is used to perform the following operations:

[0098] Performing a virtual battle between the third optimized lineup and the sixth optimized lineup to obtain a result of the virtual battle between the third optimized lineup and the sixth optimized lineup;

[0099] According to the virtual battle results between the third optimized lineup and the sixth optimized lineup, the third optimized lineup and the sixth optimized lineup are sorted to obtain the optimized lineup set X. y Corresponding lineup ladder.

[0100] In a possible implementation, the A lineup composition targets include a first lineup composition target, and the first lineup composition target is one of the A lineup composition targets;

[0101] The evaluation module evaluates the A lineup composition targets based on the B lineup ladders and performs the following operations:

[0102] In each of the B lineup ladders, obtain a lineup that meets the first lineup composition goal and has the greatest lineup strength;

[0103] Based on the lineup strengths corresponding to each of the B lineups, determine the strength trend of the first lineup composition target with respect to the number of B characters;

[0104] According to the intensity trend, the first lineup composition target is evaluated and processed to obtain a first evaluation result; the first evaluation result is used to adjust the first lineup composition target.

[0105] In a possible implementation, the A lineup composition targets include the second lineup composition target, and the second lineup composition target is one of the A lineup composition targets; the B lineup ladder includes the lineup ladder O p , p is a positive integer, and p is less than or equal to B; the number of B characters includes the lineup ladder O p The corresponding number of roles G p ;

[0106] The evaluation module evaluates the A lineup composition targets based on the B lineup ladders and performs the following operations:

[0107] In the lineup ladder O p In the second lineup formation goal, obtain U lineups; U is a positive integer greater than 1; the lineup strength corresponding to each of the U lineups is greater than or equal to the lineup strength of the remaining lineups; the remaining lineups include the lineup ladder O p The lineup that meets the goal of forming the second lineup except for the U lineup;

[0108] Perform variance processing on the lineup strength corresponding to each of the U lineups to obtain the second lineup composition target for the number of characters G p The variance of lineup strength;

[0109] Determine the lineup strength variance corresponding to the number of B characters in the second lineup composition target;

[0110] Based on the strength variance of B lineups, determine the strength variance trend of the second lineup composition target for the number of B characters;

[0111] According to the strength variance trend, the second lineup composition target is evaluated and processed to obtain a second evaluation result; the second evaluation result is used to adjust the second lineup composition target.

[0112] On one hand, the present application provides a computer device, including: a processor, a memory, and a network interface;

[0113] The above-mentioned processor is connected to the above-mentioned memory and the above-mentioned network interface, wherein the above-mentioned network interface is used to provide data communication function, the above-mentioned memory is used to store computer programs, and the above-mentioned processor is used to call the above-mentioned computer program so that the computer device executes the method in the embodiment of the present application.

[0114] On one hand, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. The computer program is suitable for being loaded by a processor and executing the method in the embodiment of the present application.

[0115] On the one hand, an embodiment of the present application provides a computer program product, which includes a computer program stored in a computer-readable storage medium; a processor of a computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the method in the embodiment of the present application.

[0116] In an embodiment of the present application, a computer device obtains A lineup composition targets; wherein, the A lineup composition targets respectively include B lineup composition basic conditions, and the B lineup composition basic conditions belonging to one lineup composition target respectively include different numbers of characters; the A lineup composition targets respectively include the same number of characters; further, according to the B lineup composition basic conditions respectively included in the A lineup composition targets, multiple initial lineup sets can be generated; wherein, an initial lineup set is generated according to one lineup composition basic condition in one lineup composition target; the initial lineup sets containing the same number of characters in the multiple initial lineup sets are merged to obtain B lineup sets to be optimized, and the B lineup sets to be optimized are respectively optimized to obtain B optimized lineup sets; further, according to the lineup strength of the optimized lineup included in each optimized lineup set, the lineup ladder corresponding to each optimized lineup set can be determined; according to the B lineup ladders, the A lineup composition targets can be evaluated and processed respectively. As can be seen from the above, the embodiments of the present application propose a lineup evaluation method that meets lineup composition goals. This method can evaluate the changes in lineup strength under different numbers of characters for a lineup composition goal, and can also evaluate the differences in lineup strength under the same number of characters for different lineup composition goals. Therefore, it can provide accurate data for game planners to adjust game values, thereby improving the balance of lineup strength between different lineups in the game. In addition, this method can be applied before the game goes online, so the embodiments of the present application can improve the efficiency of adjusting game values. BRIEF DESCRIPTION OF THE DRAWINGS

[0117] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0118] Figure 1 This is a schematic diagram of a system architecture provided by an embodiment of the present application;

[0119] Figure 2 This is a flow diagram of a data processing method provided in an embodiment of the present application. Figure 1 ;

[0120] Figure 3 This is an example diagram of a lineup composition target provided by an embodiment of the present application;

[0121] Figure 4 This is a flow chart of a method for optimizing a population of a lineup to be optimized provided in an embodiment of the present application;

[0122] Figure 5 This is a schematic diagram of intensity trend changes of targets composed of different lineups provided in an embodiment of the present application;

[0123] Figure 6 This is a schematic diagram of the intensity variance trend change of targets with different lineup components provided by an embodiment of the present application;

[0124] Figure 7 This is a flow diagram of a data processing method provided in an embodiment of the present application. Figure 2 ;

[0125] Figure 8 This is a flow diagram of a data processing method provided in an embodiment of the present application. Figure 3 ;

[0126] Figure 9 This is a flow chart of a lineup generation method that meets lineup composition objectives, provided by an embodiment of the present application;

[0127] Figure 10 is a structural diagram of a data processing device provided in an embodiment of the present application;

[0128] Figure 11 It is a structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0129] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0130] See Figure 1 , Figure 1 This is a schematic diagram of a system architecture provided by an embodiment of the present application. Figure 1As shown, the system may include a service server 100 and a terminal device cluster. The terminal device cluster may include: terminal device 200a, terminal device 200b, terminal device 200c, ..., terminal device 200n. It is understandable that the above system may include one or more terminal devices, and this application does not limit the number of terminal devices.

[0131] Among them, there can be communication connections between terminal device clusters, for example, there is a communication connection between terminal device 200a and terminal device 200b, and there is a communication connection between terminal device 200a and terminal device 200c. At the same time, any terminal device in the terminal device cluster can have a communication connection with the business server 100, for example, there is a communication connection between terminal device 200a and business server 100. The above-mentioned communication connection is not limited to the connection method, and can be directly or indirectly connected through wired communication, directly or indirectly connected through wireless communication, or through other methods, and this application does not impose any restrictions on this.

[0132] like Figure 1 Each terminal device in the terminal device cluster shown can run an operation management client of a strategy game, which can be an auto chess game that supports terminal users to use characters (in the game, they can also be called chess pieces or heroes) to build a lineup and conduct virtual battles based on the lineup. The terminal users of the operation management client of the strategy game are the developers of the strategy game, or game staff such as operation managers; for the strategy game, the game staff can trigger the operation of evaluating the lineup composition target through the operation management client. Server 100 can be an operation management server for the strategy game, which is used to execute the data processing method provided in the embodiment of the present application to generate a basis for the game staff to adjust the game values ​​before the strategy game goes online.

[0133] To facilitate subsequent understanding and explanation, the embodiments of the present application can be Figure 1 An example of a terminal device is selected from the terminal device cluster shown for description, for example, terminal device 200a is used as an example for description. When the operation management client of the strategy game obtains A lineup composition targets and receives an evaluation instruction for the A lineup composition targets, the terminal device 200a can generate an evaluation request for the A lineup composition targets and send the evaluation request to the business server 100. The embodiment of the present application does not limit the number of lineup composition targets, and can be one or more. The lineup composition target is a pre-achieved lineup under different numbers of characters in the strategy game (which can be called the population number in the game). It is a set of planned and designed specific gameplay, which can be a gameplay that meets certain skills, or a gameplay that includes certain specific chess pieces, that is, a conception of a lineup type.

[0134] After the business server 100 receives the evaluation request sent by the terminal device 200a, it can obtain A lineup composition targets. This embodiment of the present application does not limit the manner in which the business server 100 obtains the A lineup composition targets. One feasible acquisition method is that the terminal device 200a generates an evaluation request including the A lineup composition targets, so the business server 100 can obtain the A lineup composition targets from the evaluation request. Another feasible acquisition method is that the evaluation request sent by the terminal device 200a carries the storage address of the A lineup composition targets, so the business server 100 can obtain the A lineup composition targets based on the storage address.

[0135] Specifically, a lineup composition target can include B basic lineup composition conditions, where B is a positive integer greater than 1. Each basic lineup composition condition corresponds to one character number, and the B character numbers are different. A character number refers to the number of characters included in a lineup, and a basic lineup composition condition is used to indicate the basic conditions for forming a lineup with a certain number of characters. A lineup composition target each includes the same number of B characters.

[0136] For example, if A is equal to 2, the number of lineup composition targets is 2, namely lineup composition target 1 and lineup composition target 2; lineup composition target 1 includes 3 basic lineup composition conditions, namely lineup composition basic condition 11, lineup composition basic condition 12 and lineup composition basic condition 13; among them, the number of characters corresponding to lineup composition basic condition 11 is 4, namely, lineup composition basic condition 11 indicates that the pre-achieved lineup includes 4 characters; the number of characters corresponding to lineup composition basic condition 12 is 6, namely, lineup composition basic condition 12 indicates that the pre-achieved lineup includes 6 characters; the number of characters corresponding to lineup composition basic condition 13 is 8, namely, lineup composition basic condition 13 indicates that the pre-achieved lineup includes 8 characters. It should be emphasized that in a lineup composition target, the differences between the basic lineup composition conditions corresponding to different numbers of characters may include not only the number of characters, but also the composition of the lineup. Please see below for details. Figure 2 The description of step S101 in the corresponding embodiment.

[0137] Lineup composition goal 2 also includes 3 basic conditions for lineup composition, and the number of characters corresponding to the 3 basic conditions for lineup composition is 4, 6, and 8 respectively.

[0138] Furthermore, based on the B basic lineup composition conditions respectively included in the A lineup composition targets, the business server 100 generates multiple initial lineup sets; wherein, an initial lineup set is generated based on a basic lineup composition condition in a lineup composition target, so the number of initial lineup sets is equal to A*B. For example, the business server 100 obtains 2 lineup composition targets, each of which includes 3 basic lineup composition conditions (for example, corresponding to the number of characters of 4, 6, and 8, respectively), then the business server 100 can generate 2*3 initial lineup sets. Since an initial lineup set is generated based on a basic lineup composition condition in a lineup composition target, for example, initial lineup set 111 is generated based on lineup composition basic condition 11, then all initial lineups in initial lineup set 111 meet lineup composition basic condition 11. Among them, a lineup can also be called a team, a virtual battle combination composed of one or more characters.

[0139] The business server 100 merges the initial lineup sets containing the same number of characters in the multiple initial lineup sets to obtain B lineup sets to be optimized, and optimizes the B lineup sets to be optimized to obtain B optimized lineup sets. The optimization process of the lineup sets to be optimized will not be described here, please refer to the following Figure 2 The description of step S103 in the corresponding embodiment. The initial lineup and the lineup to be optimized in the embodiment of the present application refer to the lineup that meets the lineup composition target, and the optimized lineup refers to the lineup after the lineup to be optimized is optimized, which may or may not meet the lineup composition target.

[0140] Based on the strength of the optimized lineups included in each optimized lineup set, the service server 100 can determine a lineup ladder corresponding to each optimized lineup set. The lineup strength of a lineup indicates the winning or losing situation of that lineup in a virtual battle. A lineup ladder includes optimized lineups sorted from high to low based on lineup strength, and all optimized lineups in a lineup ladder have the same number of characters.

[0141] Further, based on the B lineup ladders, the business server 100 can evaluate the A lineup composition targets respectively to obtain the evaluation results corresponding to the A lineup composition targets. The evaluation process of the lineup composition targets will not be described here, please refer to the following Figure 2 The description of step S104 in the corresponding embodiment.

[0142] Subsequently, the service server 100 sends the A evaluation results to the terminal device 200a. The terminal device 200a can display the A evaluation results on its corresponding screen, which can provide effective reference data for game planners to adjust game values.

[0143] Optionally, the business server 100 may send B lineup ladders to the terminal device 200a, and the terminal device 200a will evaluate and process the A lineup composition targets respectively according to the B lineup ladders. The subsequent processing is the same as described above, so it will not be repeated.

[0144] Optionally, the business server 100 can return the lineup strength of the optimized lineups included in the B optimized lineup sets to the terminal device 200a, and the terminal device 200a can determine the lineup ladder corresponding to each optimized lineup set based on the lineup strength of the optimized lineup included in each optimized lineup set. The subsequent processing is the same as the above description, so it will not be repeated.

[0145] Optionally, the business server 100 may return B optimized lineup sets to the terminal device 200a, and the terminal device 200a may generate the lineup strength of the optimized lineups included in the B optimized lineup sets. The subsequent processing is the same as the above description, so it will not be repeated.

[0146] Optionally, the business server 100 returns the B lineup sets to be optimized to the terminal device 200a, and the terminal device 200a optimizes the B lineup sets to be optimized respectively. The subsequent processing is the same as the above description, so it is not repeated here.

[0147] Optionally, the business server 100 returns multiple initial lineup sets to the terminal device 200a, and the terminal device 200a can merge the initial lineup sets containing the same number of characters in the multiple initial lineup sets to obtain B lineup sets to be optimized. The subsequent processing is the same as the above description, so it will not be repeated.

[0148] Optionally, when obtaining evaluation instructions for A lineup composition targets, the terminal device 200a generates multiple initial lineup sets according to the B lineup composition basic conditions respectively included in the A lineup composition targets. The subsequent processing is the same as the above description, so it will not be repeated.

[0149] Among them, the data involved in the specific implementation of this application will comply with relevant provisions of laws and regulations when acquired or used.

[0150] As can be seen from the above, the embodiments of the present application propose a lineup evaluation method that meets lineup composition goals. This method can evaluate the changes in lineup strength under different numbers of characters for a lineup composition goal, and can also evaluate the differences in lineup strength under the same number of characters for different lineup composition goals. Therefore, it can provide accurate data for game planners to adjust game values, thereby improving the balance of lineup strength between different lineups in the game. In addition, this method can be applied before the game goes online, so the embodiments of the present application can improve the efficiency of adjusting game values.

[0151] It should be noted that the above-mentioned business server 100, terminal device 200a, terminal device 200b, terminal device 200c..., terminal device 200n can all be blockchain nodes in the blockchain network, and the data described in the full text (for example, A lineup composition goals and the evaluation results corresponding to A lineup composition goals) can be stored. The storage method can be a method in which the blockchain node generates blocks based on the data and adds the blocks to the blockchain for storage.

[0152] Blockchain is a novel application model of computer technologies, integrating distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. It primarily organizes data in chronological order and encrypts it into a ledger, rendering it tamper-proof and forgery-proof. It also enables data verification, storage, and updates. Blockchain is essentially a decentralized database, where each node stores an identical blockchain. Blockchain networks categorize nodes as core nodes, data nodes, and light nodes. Core nodes, data nodes, and light nodes collectively constitute blockchain nodes. Core nodes are responsible for consensus across the entire blockchain network, effectively serving as consensus nodes within the blockchain network.

[0153] The process of writing transaction data in the blockchain network into the ledger can be as follows: the data node or light node in the blockchain network obtains the transaction data and passes the transaction data in the blockchain network (that is, the node passes the transaction data in a relay manner) until the consensus node receives the transaction data. The consensus node then packages the transaction data into a block, performs consensus on the block, and writes the transaction data into the ledger after the consensus is completed. Here, the transaction data of A lineup composition goals and the evaluation results corresponding to A lineup composition goals are used as examples. After reaching a consensus on the transaction data, the business server 100 (blockchain node) generates a block based on the transaction data and stores the block in the blockchain network; and for reading the transaction data (that is, A lineup composition goals and the evaluation results corresponding to A lineup composition goals), the blockchain node can obtain the block containing the transaction data in the blockchain network, and further obtain the transaction data in the block.

[0154] It is understandable that the method provided in the embodiments of the present application can be executed by a computer device, including but not limited to a terminal device or a business server. Among them, the business server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud databases, cloud services, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. Terminal devices include but are not limited to mobile phones, computers, intelligent voice interaction devices, smart home appliances, vehicle-mounted terminals, aircraft, etc. Among them, the terminal device and the business server can be directly or indirectly connected by wired or wireless means, and the embodiments of the present application are not limited here.

[0155] Further, see Figure 2 , Figure 2 This is a flow diagram of a data processing method provided in an embodiment of the present application. Figure 1 The embodiments of the present application can be applied to various scenarios, including but not limited to cloud technology, artificial intelligence, smart transportation, assisted driving, autonomous driving, etc. The embodiments of the present application can be applied to business scenarios such as lineup recommendation scenarios, lineup distribution scenarios, and lineup combination scenarios. Specific business scenarios will not be listed one by one here.

[0156] The data processing method can be performed by a business server (for example, Figure 1 The service server 100 shown in FIG. 1 may also be executed by a terminal device (for example, the above Figure 1 The terminal device 200a shown in the figure can also be executed by the business server and the terminal device interactively. For ease of understanding, the embodiment of the present application takes the method executed by the business server as an example for explanation, that is, the business server executes the data processing method as a computer device. Figure 2 As shown, the data processing method may at least include the following steps S101 to S104.

[0157] Step S101, obtain A lineup composition targets; A is a positive integer; A lineup composition targets respectively include B lineup composition basic conditions, and the number of characters included in the B lineup composition basic conditions belonging to one lineup composition target is different from each other; the number of characters included in A lineup composition targets is the same; B is a positive integer greater than 1; one lineup composition basic condition is used to indicate the basic conditions for generating a type of lineup.

[0158] Specifically, for easier understanding and description, let's first explain the terminology of strategy games such as Auto Chess:

[0159] Star rating: In Auto Chess, the same piece is usually divided into 1 to 3 stars. Usually, 3 identical pieces with a lower star rating can be combined into a piece with a higher star rating. The star rating is used to identify the overall strength of the piece, such as the upper limit of attribute values, the upper limit of skill values, etc.

[0160] Piece Quality: In Auto Chess, pieces are usually classified into qualities 1 to 5, and the quality is related to the cost of purchasing the piece.

[0161] Piece Value: The total purchase cost required to synthesize the piece.

[0162] C position: the core piece of the lineup.

[0163] In strategy games such as Auto Chess, chess pieces (referred to as characters in this application) usually have bonds (referred to as linkage relationships in this application), virtual equipment, positions, etc. Different population levels (referred to as the number of characters in this application) determine the number of chess pieces that can be put on the field, the quality of chess pieces that can be purchased, and usually also determine the number of equipment that appears. At the same time, more chess pieces also mean more levels of bonds that can be activated and more bonds, which will make the lineup stronger. Game players use multiple chess pieces to form different bond combinations and match them with equipment to fight automatically. At the same time, they gradually increase the population level to enhance the strength of the lineup. There is a certain correlation between each population level. This gameplay provides game players with a diversified combat method with strong playability.

[0164] However, different combinations of chess pieces will result in differences in lineup strength. If, at the same population level, the strength of different lineups varies greatly, players will generally choose the strongest lineup combination, which will greatly reduce the playability of the game and have a negative impact on the game. The same type of lineup will show completely different lineup strength differences at different populations. In addition, planners usually set a variety of different lineup composition goals at the beginning of game design. Different lineup composition goals will also show large differences in team (i.e. lineup) strength. Therefore, it is very necessary to test the trend changes in the strength of the lineup composition goals before the game goes online. Based on the trend changes, the planner will make multiple adjustments to ensure that different lineups reach a balance under the same population and a comprehensive balance under different populations.

[0165] In order to achieve a balance between different lineups in a strategy game, an embodiment of the present application proposes an automated analysis method based on the evaluation of the target strength of the lineup composition, and the specific process is as follows. The business server obtains the role configuration information, and the role configuration information includes the detailed configuration information corresponding to all the roles. For example, a strategy game includes 100 different chess pieces (also called heroes, referred to as characters in this application), and each chess piece contains the following contents: chess piece identification, chess piece name, chess piece bond information (i.e., linkage relationship), and chess piece basic categories (such as physical tanks, spell tanks, physical warriors, spell warriors, etc.). The role configuration information includes the detailed content corresponding to each of the 100 characters, i.e., the above-mentioned content.

[0166] Based on the role configuration information, the business server obtains A lineup composition targets, where A is a positive integer, so the lineup composition target can be one or more. A lineup composition target refers to a lineup that is expected to be achieved under different populations. For ease of understanding and description, please refer to Figure 3 , Figure 3 This is an example diagram of a lineup composition target provided by an embodiment of the present application. Figure 3 As shown, B is equal to 3. The lineup composition target includes three types of linkage relationships a under population, that is, the lineup composition target includes three basic conditions for lineup composition. The first basic condition for lineup composition is the basic condition under 4 population (that is, the number of characters is 4). This basic condition requires the activation of linkage relationship a and includes Hero 1 and Hero 2. There are two heroes to be determined, that is, they need to be completed; the second basic condition for lineup composition is the basic condition under 6 population. This basic condition requires the activation of linkage relationship a and includes Hero 1, Hero 2, Hero 3 and Hero 4. There are two heroes to be determined; the third basic condition for lineup composition is the basic condition under 8 population. This basic condition requires the activation of linkage relationship a with a level of 3 and includes Hero 1, Hero 2, Hero 3 and Hero 4.

[0167] The linkage relationship in the embodiments of this application can be understood as a bond in Auto Chess. In strategy games, a certain number of heroes of the same race or a certain number of heroes with the same mission can form a bond, which can bring combat effects beyond those of the heroes that form the bond. Bonds also have levels, and the higher the level, the stronger the additional combat effect. For example, two heroes of the human race can activate a level 1 human bond, which brings an additional combat effect of all friendly humans attacking enemies with a 20% chance of silencing the enemy for 4 seconds; four heroes of the human race can activate a level 2 human bond, which brings an additional combat effect of all friendly humans attacking enemies with a 25% chance of silencing the enemy for 4 seconds; six heroes of the human race can activate a level 3 human bond, which brings an additional combat effect of all friendly humans attacking enemies with a 30% chance of silencing the enemy for 4 seconds.

[0168] In an embodiment of the present application, when there are multiple lineup composition targets, the number of characters corresponding to the multiple lineup composition targets is the same. For example, if lineup composition target 1 includes the basic conditions for lineup composition under 4, 6, and 8 populations, then lineup composition target 2 also includes the basic conditions for lineup composition under 4, 6, and 8 populations. Lineup composition target 3 also includes the basic conditions for lineup composition under 4, 6, and 8 populations, and so on.

[0169] Step S102 , generating a plurality of initial lineup sets according to the B basic lineup composition conditions respectively included in the A lineup composition targets; wherein one initial lineup set is generated according to one basic lineup composition condition in one lineup composition target.

[0170] Specifically, A lineup composition targets include C lineup composition basic conditions, where C equals A*B; the C lineup composition basic conditions include the first lineup composition basic condition; the first lineup composition basic condition is one of the C lineup composition basic conditions. Based on the first lineup composition basic condition, a core role is obtained from a candidate role set; the candidate role set includes all roles in the role configuration information; the role configuration information includes detailed configuration information corresponding to all roles; A lineup composition targets meet the role configuration information; the number of roles corresponding to the first lineup composition basic condition and the number corresponding to the core role are subtracted to obtain a filling number; based on the filling number, F filling role combinations are obtained from the candidate role set; F is a positive integer; the number of roles included in a filling role combination is the filling number; based on the F filling role combinations and the core role, an initial lineup set corresponding to the first lineup composition basic condition is generated; the initial lineup sets corresponding to the C lineup composition basic conditions are determined as multiple initial lineup sets.

[0171] Wherein, according to the filling number, the specific process of obtaining F filling role combinations from the candidate role set may include: if the basic condition for the first lineup includes the linkage relationship of the core role, then determining the linkage filling number corresponding to the linkage relationship; the linkage relationship has the function of additional virtual combat power; according to the linkage filling number, obtaining I linkage role combinations from the candidate role set; I is a positive integer; wherein the number of roles included in a linkage role combination is the linkage filling number; I linkage role combination includes linkage role combination J k , k is a positive integer, and k is less than or equal to 1; the filling number and the linkage filling number are subtracted to obtain the remaining filling number; according to the remaining filling number, the linkage role combination J is selected from the candidate role set k Get one or more remaining role combinations, and combine the obtained one or more remaining role combinations with the linkage role combination J k Combine them separately to get the linkage role combination J kOne or more corresponding filling role combinations; the filling role combinations corresponding to I linkage role combinations are determined as F filling role combinations; F is equal to or greater than I.

[0172] Among them, the specific process of generating an initial lineup set corresponding to the basic conditions for the first lineup composition based on F filling role combinations and core roles can include: combining the F filling role combinations with the core roles respectively to obtain F lineups to be allocated; wherein, one lineup to be allocated includes one filling role combination; according to the number of roles corresponding to the basic conditions for the first lineup composition, obtaining resource allocation standard information; according to the resource allocation standard information, performing resource allocation processing on the F lineups to be allocated respectively to obtain F initial lineups; wherein, an initial lineup is obtained by performing resource allocation processing on a lineup to be allocated; and determining the F initial lineups that all carry condition identifiers for indicating the basic conditions for the first lineup composition as the initial lineup set corresponding to the basic conditions for the first lineup composition.

[0173] A lineup composition targets each include B basic lineup composition conditions, resulting in a total of A*B basic lineup composition conditions. The service server generates an initial lineup set based on one basic lineup composition condition in a lineup composition target. It should be understood that, with C being a positive integer greater than 1, the generation process for each of the C initial lineup sets is identical. Therefore, the following description uses one of the C initial lineup sets as an example.

[0174] Among them, the basic condition for the first lineup is any one of the C basic conditions for the first lineup. For the sake of ease of understanding and description, the basic condition for the first lineup is exemplified here as Figure 3 The second basic condition for lineup composition is that linkage relationship a must be activated under 6 population, and 6 population must include Hero 1, Hero 2, Hero 3, and Hero 4. The business server obtains a set of candidate characters, which includes all characters (i.e., all heroes) in the strategy game. Furthermore, the business server obtains the core characters in the first basic condition for lineup composition from the candidate character set, such as Figure 3 The second lineup in the example consists of Hero 1, Hero 2, Hero 3, and Hero 4 in the basic conditions.

[0175] Figure 3 The basic condition for the second lineup in the example is 6 population, that is, the number of characters is 6, the number of core characters is 4, so there are 2 characters to be determined, that is, the number of fillers is equal to 2. Figure 3The second basic condition for the formation of the lineup in the example includes linkage relationship a. The business server needs to first determine the number of roles in the core role that can activate linkage relationship a. If the core role can already activate linkage relationship a, the linkage filling number is determined to be 0. At this time, the remaining filling number is the filling number. A filling role combination includes 2 pending roles. The 2 pending roles can be any two different roles in the candidate role set except the core role. It can be understood that if the candidate role set includes 100 roles, the number of filling role combinations F is equal to Optionally, if F has an upper limit, such as 1000, the business server can 1000 combinations of filler characters are randomly selected from the combinations.

[0176] If linkage relationship a is activated and 4 heroes with the same mission (or the same family) are required, and the core character has r (r is a positive integer less than 4 and equal to or greater than 2) heroes with the same mission (or the same family), then the core character has not yet activated linkage relationship a, and 4-r heroes with the same mission (or the same family) are required, where 4-r is a positive integer less than or equal to 2.

[0177] If r is equal to 2, 4-r is equal to 2, that is, the linkage filling number is equal to the filling number, and the remaining filling number is equal to 0, the linkage role combination is the filling role combination, then the business server needs to obtain two different roles other than the core role from the candidate role set that can activate the linkage relationship a. If the candidate role set includes 20 roles with linkage relationship a, then the number of filling role combinations F is equal to

[0178] If r is equal to 3 and 4-r is equal to 1, then the linkage filling quantity is equal to 1 and the remaining filling quantity is equal to 1. At this time, the business server needs to obtain 1 role that can activate linkage relationship a from the candidate role set, as well as any role that is different from the core role and the aforementioned role that can activate linkage relationship a. If the candidate role set includes 100 roles, and 20 roles carry linkage relationship a, then the number of linkage role combinations I is equal to The number of remaining role combinations corresponding to a linked role combination is equal to Therefore, the number of filling role combinations F is equal to 17*79. Similarly, if F has an upper limit value, such as 1000, the business server can randomly select 1000 filling role combinations from the 17*79 combinations.

[0179] After generating F combinations of filling roles, the business server combines the F combinations of filling roles with the core roles respectively to obtain F lineups to be allocated. As mentioned above, a hero (i.e., a role) includes basic information, such as star rating, virtual equipment, and position, so the business server needs to perform resource allocation processing on each role in each lineup to be allocated. The embodiment of the present application determines unified resource allocation standard information for the same number of characters, that is, a unified cost and star allocation strategy is used under a population to ensure that the random generation of the initial lineup is within a reasonable range, specifically one number of characters corresponds to one resource allocation standard information. For example, the resource allocation standard information corresponding to a population of 6 is as follows: characters with a quality of 4 or above shall not appear, the number of characters with a quality of 1 shall not exceed 1, the star rating of the remaining characters shall not exceed 2, and the number of all virtual equipment in an initial lineup shall not exceed 3 pieces.

[0180] Here is an example of the basic conditions for the first lineup: Figure 3 The second basic lineup formation condition in [1] is that the number of characters is equal to 6. The business server obtains the resource allocation standard information corresponding to the number of characters 6. Based on this resource allocation standard information, the business server performs resource allocation processing on each of the F to-be-allocated lineups, generating F initial lineups. The business server adds a conditional identifier to each of the F generated initial lineups. This conditional identifier is used to indicate whether the initial lineup meets the first basic lineup formation condition.

[0181] According to the above process, the service server can generate an initial lineup set corresponding to the basic conditions for the first lineup composition. Similarly, the service server can generate an initial lineup set corresponding to the basic conditions for the remaining lineup compositions.

[0182] To evaluate the strength of the initial lineup, the business server generates a reference lineup containing the same number of characters as the initial lineup. For example, if the target lineups A include 4, 6, and 8 populations, the business server generates reference lineups with populations of 4, 6, and 8, respectively. The business server selects each character in the candidate set as a core character and the bonds possessed by the core characters as primary bonds, generating a large number of lineups with the same population that meet the character configuration requirements. Furthermore, the initial lineup and reference lineups with the same population share the same resource allocation criteria.

[0183] Step S103 , merging the initial lineup sets containing the same number of characters in the multiple initial lineup sets to obtain B lineup sets to be optimized, and optimizing the B lineup sets to be optimized respectively to obtain B optimized lineup sets.

[0184] Specifically, the B lineup sets to be optimized include the lineup set to be optimized L m, m is a positive integer, and m is less than or equal to B; the lineup set to be optimized L m The lineups to be optimized with the same lineup characteristics are divided into the same lineup population to be optimized, and N lineup populations to be optimized are obtained; N is a positive integer; the N lineup populations to be optimized are optimized separately to obtain N optimized lineup populations; among them, an optimized lineup population is obtained by optimizing a lineup population to be optimized; the N optimized lineup populations are determined as the lineup set to be optimized L m The corresponding optimized lineup set.

[0185] As described above, the number of initial lineup sets is A*B. For ease of understanding and description, this step assumes that A equals 4 and B equals 3, meaning there are four lineup composition targets. Each lineup composition target includes three basic lineup composition conditions, and each lineup composition target corresponds to three different numbers of characters, for example, 4, 6, and 8 characters. Based on the above example, there are four initial lineup sets with 4 characters: initial lineup set 1a, initial lineup set 2a, initial lineup set 3a, and initial lineup set 4a. Each of these four initial lineup sets includes 4 characters. There are four initial lineup sets with 6 characters: initial lineup set 1b, initial lineup set 2b, initial lineup set 3b, and initial lineup set 4b. Each of these four initial lineup sets includes 6 characters. There are four initial lineup sets with 8 characters: initial lineup set 1c, initial lineup set 2c, initial lineup set 3c, and initial lineup set 4c. Each of these four initial lineup sets includes 8 characters.

[0186] The business server merges the initial lineup sets containing the same number of characters in the 12 initial lineup sets in the above example, and obtains 3 lineup sets to be optimized, among which the first lineup set to be optimized can include initial lineup set 1a, initial lineup set 2a, initial lineup set 3a and initial lineup set 4a; the second lineup set to be optimized can include initial lineup set 1b, initial lineup set 2b, initial lineup set 3b and initial lineup set 4b; the third optimized lineup set can include initial lineup set 1c, initial lineup set 2c, initial lineup set 3c and initial lineup set 4c.

[0187] For a lineup set to be optimized, the business server divides the initial lineups with the same lineup characteristics in the four initial lineup sets included in it into the same lineup population to be optimized, and obtains N lineup populations to be optimized. Among them, the number of lineups to be optimized in a lineup population to be optimized can have an upper limit value, such as 100, that is, a lineup population to be optimized includes 100 lineups to be optimized with the same lineup characteristics. Therefore, if the number of initial lineups with the same lineup characteristics in the four initial lineup sets (for example, 200) is greater than the upper limit value (for example, 100), they can be divided into different lineup populations to be optimized. The lineup characteristics may include core characters, or they may include core characters and main bonds, that is, main linkage relationships, but the target levels of the main bonds may be different.

[0188] Specifically, the business server divides the initial lineups with the same lineup characteristics in the initial lineup set 1a, initial lineup set 2a, initial lineup set 3a, and initial lineup set 4a of the above examples into the same lineup population to be optimized, and obtains one or more lineup populations to be optimized; similarly, the business server divides the initial lineups with the same lineup characteristics in the initial lineup set 1b, initial lineup set 2b, initial lineup set 3b, and initial lineup set 4b of the above examples into the same lineup population to be optimized, and obtains one or more lineup populations to be optimized; the business server divides the initial lineups with the same lineup characteristics in the initial lineup set 1c, initial lineup set 2c, initial lineup set 3c, and initial lineup set 4c of the above examples into the same lineup population to be optimized, and obtains one or more lineup populations to be optimized.

[0189] It is understandable that the optimization process of the lineup population to be optimized under different populations is the same, and the lineup population to be optimized under the same population is optimized independently. In the same lineup population to be optimized, the business server reasonably and randomly adjusts the chess pieces, star levels, positions, virtual equipment, etc. of the lineup to be optimized, and the lineups of different lineup populations to be optimized are pitted against each other to evaluate the strength of the lineup. Please refer to the lineup evolution process of the lineup population to be optimized under a population. Figure 4 , Figure 4 This is a flow chart of a method for optimizing a population of a lineup to be optimized provided in an embodiment of the present application. Figure 4 As shown, the method includes the following steps:

[0190] Step S11: Generate a lineup population to be optimized.

[0191] Specifically, the business server divides multiple initial lineup sets into lineup sets to be optimized with the same number of characters, such as the initial lineup set 1a, initial lineup set 2a, initial lineup set 3a, and initial lineup set 4a, all with 4 people, the initial lineup set 1b, initial lineup set 2b, initial lineup set 3b, and initial lineup set 4b, all with 6 people, and the initial lineup set 1c, initial lineup set 2c, initial lineup set 3c, and initial lineup set 4c, all with 8 people. Furthermore, under the same population, the lineups to be optimized with the same lineup characteristics are divided into the same lineup population to be optimized, thereby obtaining one or more lineup populations to be optimized.

[0192] For ease of understanding and description, Figure 4 For example, a service server obtains two lineup populations to be optimized for a certain population (e.g., 6 people). These two lineup populations can be referred to as lineup population 1 and lineup population 2. It is understood that if N is 1 or any other value, the service server's processing is the same as when N is 2, so this step is not detailed here.

[0193] Step S12: Perform strength evaluation on the lineup to be optimized to obtain the strength of the lineup to be optimized.

[0194] Specifically, the business server causes one of the lineups to be optimized in the lineup population 1 to be optimized to fight against the remaining lineups to be optimized in the lineup population 1, all the lineups to be optimized in the lineup population 2 to be optimized, and the reference lineup generated in step S101 (whose number of characters is the same as the number of characters in the lineup population 1 to be optimized). For example, the lineup population 1 to be optimized includes 100 lineups to be optimized, namely, lineup 1 to be optimized 100, and the lineup population 2 to be optimized includes 100 lineups to be optimized, namely, lineup 1 to be optimized 101 to be optimized 200, and the number of reference lineups is 200, namely, reference lineup 1 to reference lineup 200. The business server conducts a virtual battle between lineup 1 to be optimized and lineup 2 to be optimized. For example, after five virtual battles, if lineup 1 to be optimized has three wins and two losses, the business server can determine that the result of the virtual battle between lineup 1 to be optimized and lineup 2 to be optimized is a win for lineup 1 to be optimized. The business server conducts a virtual battle between the lineup to be optimized 1 and the lineup to be optimized 102. For example, after 5 virtual battles, the lineup to be optimized 1 has 2 wins and 3 losses. Therefore, the business server can determine that the result of the virtual battle between the lineup to be optimized 1 and the lineup to be optimized 102 is that the lineup to be optimized 1 loses. Similarly, the business server can obtain 399 win-loss results for the lineup to be optimized 1, for example, 220 wins and 179 losses. Then 220 / 399 can be used as the lineup strength of the lineup to be optimized 1. Figure 4 The lineup strength of a lineup to be optimized is referred to as the lineup strength to be optimized.

[0195] By analogy, the business server can obtain the lineup strength of each lineup to be optimized in the lineup population 1 to be optimized, and the lineup strength of each lineup to be optimized in the lineup population 2 to be optimized.

[0196] After completing the above steps S11 and S12, the business server performs an iterative operation on each lineup population to be optimized, and the lineup to be optimized and the strength of the lineup to be optimized serve as input data for the first iterative operation.

[0197] Step S13: Generate a new lineup.

[0198] Specifically, steps S13 through S19 constitute a single iterative operation. The input data for the first iterative operation is the lineup to be optimized and its strength. For each iterative operation, the service server generates a new lineup. The lineup to be optimized in the population of lineups to be optimized carries a conditional identifier indicating the basic conditions for lineup composition. The new lineup also satisfies the basic conditions for lineup composition corresponding to the population of lineups to be optimized. This new lineup generation process is identical to the process by which the service server generates the lineup to be optimized in step S102, so it will not be described in detail here.

[0199] Step S14: Adjust the lineup to be optimized.

[0200] Step S15: Check whether it is a target adjustment type.

[0201] Step S16: whether the basic conditions for lineup formation are met.

[0202] Specifically, as described in conjunction with steps S14 to S16, the business server can set an adjustment type for iterative operations. For example, if the threshold for the number of iterative operations is 10, the adjustment types corresponding to the 1st, 3rd, 5th, 7th, and 9th iterative operations are all target adjustment types, where the target adjustment type indicates that the adjusted lineup to be optimized must meet the basic lineup composition conditions indicated by the condition identifier carried by the adjusted lineup. The adjustment types corresponding to the 2nd, 4th, 6th, 8th, and 10th iterative operations are all non-target adjustment types, i.e., they are not target adjustment types.

[0203] For example, the lineup to be optimized 1 is based on Figure 3 The second lineup of the example is generated by the basic conditions. When the first iteration operation is performed on the optimized lineup 1, the adjusted lineup 1 needs to meet Figure 3 The second basic conditions for the formation of the lineup in the example; if after the first adjustment, the adjusted lineup 1 to be optimized does not meet the basic conditions for the formation, such as Figure 4As shown in step S16, the business server will execute step S14 again, that is, a lineup may be adjusted multiple times in one iterative operation; if after the first adjustment, the adjusted lineup to be optimized 1 meets the basic conditions for lineup composition, the business server executes step S17.

[0204] If the adjusted lineup 1 to be optimized is iterated for the second time, the second adjusted lineup 1 to be optimized may not satisfy Figure 3 The second lineup of the example consists of basic conditions, such as Figure 4 Therefore, the business server may perform step S17 on the lineup to be optimized 1 after the second adjustment.

[0205] To sum up, in each iterative operation, the business server can first randomly adjust the lineup to be optimized, including adjusting the characters, star levels, virtual equipment, and positions, etc., and then determine whether the adjustment type of the current iteration is the target adjustment type. If not, the business server can execute step S17. If so, the business server needs to determine whether the adjusted lineup to be optimized meets the basic conditions for lineup composition. On this basis, if the adjusted lineup to be optimized meets the basic conditions for lineup composition, step S17 is executed. If the adjusted lineup to be optimized does not meet the basic conditions for lineup composition, the business server executes step S14.

[0206] Among them, steps S14 to S16 are executed in sequence. The embodiment of the present application does not limit the execution order between step S14 and step S13. They can be executed synchronously, or step S13 can be executed first and then step S14, or step S14 can be executed first and then step S13.

[0207] Step S17: Evaluate the strength of the lineup to obtain the lineup strength.

[0208] Specifically, the lineup in step S17 includes the newly added lineup in step S13 and the adjusted lineup in steps S15 and S16. The embodiment of the present application does not limit the number of newly added lineups and can be set according to the needs of actual application scenarios.

[0209] The process for determining lineup strength in step S17 is the same as the process for determining the strength of the lineup to be optimized in step S12, the difference being that the data in the two steps is different. The first iteration operation will be used as an example for description. In the first iteration, the service server generates one or more new lineups 1 for lineup population 1 to be optimized, and one or more new lineups 2 for lineup population 2 to be optimized. For the aforementioned examples of lineups to be optimized 1-100 in lineup population 1, corresponding adjusted lineups are generated, referred to as adjusted lineups 1-100, respectively. For the aforementioned examples of lineups to be optimized 101-200 in lineup population 2, corresponding adjusted lineups are generated, referred to as adjusted lineups 101-200, respectively.

[0210] The business server determines the lineup strengths of adjusted lineups 1-adjusted lineup 100, adjusted lineups 101-adjusted lineup 200, one or more newly added lineups 1, and one or more newly added lineups 2 in the same manner, so the following description uses adjusted lineup 1 as an example. The business server determines the reference lineup generated in step S102, adjusted lineup 2-adjusted lineup 100, adjusted lineup 101-adjusted lineup 200, one or more newly added lineups 1, and one or more newly added lineups 2 as the battle lineups for adjusted lineup 1. The subsequent process is the same as the process of generating the lineup strength of lineup 1 to be optimized in step S12 above, so it will not be repeated here.

[0211] To sum up, during the iterative operation, the determination of the lineup strength of a lineup requires not only a virtual battle with the reference lineup, but also a virtual battle with the newly generated lineup (including new lineups and adjusted lineups) of the current iterative operation.

[0212] Step S18: performing lineup updating processing on the lineup population to be optimized to obtain an updated lineup population.

[0213] Specifically, following the principle of treating identical non-core characters as duplicate lineups, the business server deduplicates existing, newly added, and adjusted lineups within the population, retaining those with higher strength in the event of duplicate lineups. Deduplication does not consider a character's virtual equipment, star rating, or position, as characters are the fundamental building blocks of a lineup. The aforementioned existing lineup refers to the lineup to be optimized in the lineup population during the first iteration; in other iterations, it refers to the lineup in the updated lineup population from the previous iteration.

[0214] If, after deduplication, the number of lineups in the population exceeds the number of lineups in the previous population, for example, if the number of lineups to be optimized in the lineup population to be optimized is 100, then the number of lineups in the population generated at the completion of each iteration will also be 100. Therefore, if the number of lineups in the population after deduplication exceeds the number of lineups in the population before the iteration, low-strength lineups will be eliminated to ensure that the number of lineups in the population remains unchanged. It should be noted that when updating the population, the business server will retain lineups with high lineup strength that meet the basic lineup composition conditions corresponding to the lineup population to be optimized.

[0215] For example, lineup population 1 to be optimized corresponds to three basic lineup composition conditions: basic composition condition 1, basic composition condition 2, and basic composition condition 3. After ranking by lineup strength, lineups meeting basic composition condition 1 are ranked 1-10, lineups meeting basic composition condition 2 are ranked 11-25, and lineups meeting basic composition condition 3 are ranked 26-38. The higher the ranking, the stronger the lineup. Due to the large number of lineups, only five lineups corresponding to each basic composition condition are retained. The business server then selects the top five lineups from the lineups ranked 1-10, the top five lineups from the lineups ranked 11-25, and the top five lineups from the lineups ranked 26-38. The remaining lineups meeting the basic composition conditions are ranked 1-5, 11-15, and 26-30, respectively. Furthermore, during the lineup adjustment process, some lineups with high strength, i.e., lineups that do not meet the basic composition conditions, are obtained. The top ranked lineups among these lineups are also retained.

[0216] Step S19: Check whether the iteration is completed.

[0217] Specifically, the service server counts the number of iterations. If the number of iterations is less than the iteration threshold, the service server executes the next iteration, using the output data from the previous iteration as input, thereby updating the lineup population. If the number of iterations is equal to the iteration threshold, the service server executes step S20.

[0218] Step S20: obtaining the optimized lineup population.

[0219] Specifically, the optimized lineup population refers to the updated lineup population when the number of iteration operations is equal to the iteration operation number threshold.

[0220] Step S104: Determine the lineup ladder corresponding to each optimized lineup set based on the lineup strength of the optimized lineups included in each optimized lineup set, and evaluate the A lineup composition targets respectively based on the B lineup ladders.

[0221] Specifically, the B optimized lineup sets include the optimized lineup set X y ; y is a positive integer, and y is less than or equal to B; in the optimized lineup set X y Get the first optimized lineup; the first optimized lineup is the optimized lineup set X y An optimized lineup in ; a virtual battle is conducted between the first optimized lineup and the second optimized lineup to obtain the lineup strength of the first optimized lineup; the second optimized lineup includes the optimized lineup set X y The optimized lineup except the first optimized lineup; according to the optimized lineup set X y The optimized lineups in the corresponding lineup strengths are the same as the optimized lineup set X. y Sorting the optimized lineup in the , and getting the optimized lineup set X y The corresponding lineup ladder to be optimized; according to the optimized lineup set X y The corresponding lineup ladder to be optimized generates the optimized lineup set X y Corresponding lineup ladder.

[0222] Among them, according to the optimized lineup set X y The corresponding lineup ladder to be optimized generates the optimized lineup set X y The specific process of the corresponding lineup ladder may include: obtaining the optimized lineup set X from the B basic lineup conditions included in the A lineup composition goals y The corresponding A basic conditions for the formation of a lineup; the basic conditions for the formation of a lineup include the basic conditions for the formation of a lineup Z w , w is a positive integer, and w is less than or equal to A; in the optimized lineup set X y In the corresponding lineup ladder to be optimized, obtain the basic conditions Z of the lineup composition w The third optimized lineup; the lineup strength of the third optimized lineup is greater than or equal to the lineup strength of the fourth optimized lineup; the fourth optimized lineup includes the optimized lineup set X y The corresponding lineup to be optimized in the ladder meets the basic conditions of lineup composition except the third optimized lineup Z w The optimized lineup; in the optimized lineup set X y In the corresponding lineup ladder to be optimized, obtain the fifth optimized lineup that does not meet the basic conditions for the A lineup composition, and determine the V linkage relationships included in the fifth optimized lineup; V is a positive integer; determine the maximum levels corresponding to the V linkage relationships, and among the V maximum levels, obtain the maximum level greater than or equal to the level threshold as the effective level; in the fifth optimized lineup, obtain the sixth optimized lineup for the effective level; the lineup strength of the sixth optimized lineup is greater than or equal to the lineup strength of the seventh optimized lineup; the seventh optimized lineup includes the optimized lineups in the fifth optimized lineup except the sixth optimized lineup; based on the third optimized lineup and the sixth optimized lineup, generate an optimized lineup set Xy Corresponding lineup ladder.

[0223] Among them, based on the third optimized lineup and the sixth optimized lineup, the optimized lineup set X is generated y The specific process of the corresponding lineup ladder may include: conducting a virtual battle between the third optimized lineup and the sixth optimized lineup to obtain the virtual battle result between the third optimized lineup and the sixth optimized lineup; sorting the third optimized lineup and the sixth optimized lineup according to the virtual battle result between the third optimized lineup and the sixth optimized lineup to obtain the optimized lineup set X y Corresponding lineup ladder.

[0224] Specifically, A lineup composition targets include a first lineup composition target, which is one of the A lineup composition targets; in B lineup ladders, lineups that meet the first lineup composition target and have the maximum lineup strength are obtained respectively; according to the lineup strengths corresponding to the B lineups, the strength trend of the first lineup composition target for the number of B characters is determined; according to the strength trend, the first lineup composition target is evaluated and processed to obtain a first evaluation result; the first evaluation result is used to adjust the first lineup composition target.

[0225] Specifically, the A lineup composition target includes the second lineup composition target, and the second lineup composition target is one of the lineup composition targets in the A lineup composition target; the B lineup ladder includes the lineup ladder O p , p is a positive integer, and p is less than or equal to B; the number of B characters includes the lineup ladder O p The corresponding number of roles G p ; In the lineup ladder O p In the second lineup formation goal, obtain U lineups; U is a positive integer greater than 1; the lineup strength corresponding to each of the U lineups is greater than or equal to the lineup strength of the remaining lineups; the remaining lineups include the lineup ladder O p The lineups that meet the second lineup composition goal except U lineups; perform variance processing on the lineup strength corresponding to U lineups, and obtain the second lineup composition goal for the number of characters G p The lineup strength variance is determined; the lineup strength variance corresponding to the second lineup composition target for B number of characters is determined; based on the B lineup strength variances, the strength variance trend of the second lineup composition target for B number of characters is determined; based on the strength variance trend, the second lineup composition target is evaluated to obtain a second evaluation result; the second evaluation result is used to adjust the second lineup composition target.

[0226] For ease of understanding and description, in this example, A equals 4 and B equals 3. Therefore, the business server can generate three lineup ladders for three population sizes, one lineup ladder for each population size. Assume that the four lineup composition targets are lineup composition target 1, lineup composition target 2, lineup composition target 3, and lineup composition target 4. Lineup composition target 1 includes three basic lineup composition conditions: lineup composition condition 11 for a specified population size of 4, lineup composition condition 12 for a specified population size of 6, and lineup composition condition 13 for a specified population size of 8. Lineup composition target 2 includes three basic lineup composition conditions: lineup composition condition 21 for a specified population size of 4, lineup composition condition 22 for a specified population size of 6, and lineup composition condition 23 for a specified population size of 8. Lineup composition target 3 includes three basic lineup composition conditions: lineup composition condition 31 for a specified population size of 4, lineup composition condition 32 for a specified population size of 6, and lineup composition condition 33 for a specified population size of 8.

[0227] The business server obtains the lineups that meet the lineup composition goal 1 and have the highest lineup strength in the three lineup ladders, that is, 3 lineups are obtained, namely lineup 111 corresponding to 4 population, lineup 121 corresponding to 6 population, and lineup 131 corresponding to 8 population. Based on the lineup strengths corresponding to the above three lineups, the business server can generate the strength trend of lineup composition goal 1 under 4, 6, and 8 populations. In the same way, the business server can generate the strength trend of lineup composition goal 2 under 4, 6, and 8 populations, as well as the strength trend of lineup composition goal 3 under 4, 6, and 8 populations. Please refer to Figure 5 , Figure 5 This is a schematic diagram of the intensity trend change of different lineup composition targets provided by the embodiment of the present application. Figure 5 As shown in the figure, with 4 populations, lineup composition target 1 has the greatest lineup strength, and lineup composition target 3 has the least lineup strength. With 6 populations, lineup composition target 1 has the greatest lineup strength, and lineup composition target 2 has the least lineup strength. With 8 populations, lineup composition target 2 has the greatest lineup strength, and lineup composition target 3 has the least lineup strength. Under different populations, the strength trend of lineup composition target 3 changes the least, and the strength trend of lineup composition target 2 changes the most.

[0228] For lineup composition target 1, the business server can obtain the top U lineups in the lineup ladder under 4 population, for example, U is equal to 5, and perform variance processing on the lineup strength corresponding to the top 5 lineups under 4 population, to obtain the lineup strength variance of lineup composition target 1 for 4 population; for lineup composition target 1, the business server can obtain the top 5 lineups in the lineup ladder under 6 population, and perform variance processing on the lineup strength corresponding to the top 5 lineups under 6 population, to obtain the lineup strength variance of lineup composition target 1 for 6 population; for lineup composition target 1, the business server can obtain the top 5 lineups in the lineup ladder under 8 population, and perform variance processing on the lineup strength corresponding to the top 5 lineups under 8 population, to obtain the lineup strength variance of lineup composition target 1 for 8 population.

[0229] Similarly, the business server can determine the lineup strength variance of lineup composition target 2 under different populations, and the lineup strength variance of lineup composition target 3 under different populations. Figure 6 , Figure 6 This is a schematic diagram of the intensity variance trend change of targets with different lineups provided in the embodiment of the present application. Figure 6 As shown in the figure, under a population of 4, lineup composition target 1 has the largest lineup strength variance, and lineup composition target 3 has the smallest lineup strength variance. Under a population of 6, lineup composition target 2 has the largest lineup strength variance, and lineup composition target 1 has the smallest lineup strength variance. Under a population of 8, lineup composition target 2 has the largest lineup strength variance, and lineup composition target 1 has the smallest lineup strength variance. Under different populations, the strength variance trend of lineup composition target 3 changes the least, and the strength variance trend of lineup composition target 1 changes the most.

[0230] As can be seen from the above, the embodiments of the present application propose a lineup evaluation method that meets lineup composition goals. This method can evaluate the changes in lineup strength under different numbers of characters for a lineup composition goal, and can also evaluate the differences in lineup strength under the same number of characters for different lineup composition goals. Therefore, it can provide accurate data for game planners to adjust game values, thereby improving the balance of lineup strength between different lineups in the game. In addition, this method can be applied before the game goes online, so the embodiments of the present application can improve the efficiency of adjusting game values.

[0231] Further, see Figure 7 , Figure 7 This is a flow diagram of a data processing method provided in an embodiment of the present application. Figure 2 .like Figure 7 As shown, the data processing method may at least include the following steps S1031-S1035, and steps S1031-S1035 are Figure 2 A specific embodiment of step S103 in the corresponding embodiment.

[0232] Step S1031 , performing strength evaluation processing on the Q lineups to be optimized respectively, and obtaining the lineup strengths corresponding to the Q lineups to be optimized respectively; wherein the lineup strength of a lineup to be optimized is obtained by performing strength evaluation processing on the lineup to be optimized.

[0233] Specifically, the N lineup populations to be optimized include a first lineup population to be optimized; the first lineup population to be optimized is a lineup population to be optimized among the N lineup populations to be optimized; the first lineup population to be optimized includes Q lineups to be optimized; Q is a positive integer.

[0234] Specifically, the Q lineups to be optimized include a first lineup to be optimized; the first lineup to be optimized is one of the Q lineups to be optimized; the second lineup to be optimized, excluding the first lineup to be optimized, and the reference lineup in the N lineup population to be optimized, are determined as the first battle lineup of the first lineup to be optimized; virtual battles are performed on the first lineup to be optimized and each battle lineup in the first battle lineup, respectively, to obtain virtual battle results corresponding to the first lineup to be optimized and each battle lineup in the first battle lineup; the lineup strength of the first lineup to be optimized is determined based on the virtual battle results corresponding to the first lineup to be optimized and each battle lineup in the first battle lineup.

[0235] Step S1032 : Adjust the Q lineups to be optimized respectively to obtain Q adjusted lineups, and generate a first new lineup whose lineup characteristics are the same as the lineup characteristics corresponding to the Q lineups to be optimized.

[0236] Specifically, the Q lineups to be optimized include a first lineup to be optimized; the first lineup to be optimized is one of the Q lineups to be optimized; the first lineup to be optimized is adjusted to obtain a lineup to be confirmed, and the adjustment type of the adjustment is obtained; if the adjustment type is a target adjustment type, the condition identifier carried by the first lineup to be optimized is obtained; the condition identifier carried by the first lineup to be optimized is used to indicate the basic conditions for the lineup composition corresponding to the first lineup to be optimized; if the lineup to be confirmed meets the basic conditions for the lineup composition corresponding to the first lineup to be optimized, the lineup to be confirmed is determined as the adjusted lineup corresponding to the first lineup to be optimized; if the lineup to be confirmed does not meet the basic conditions for the lineup composition corresponding to the first lineup to be optimized, the lineup to be confirmed is adjusted, and when the lineup after the adjustment meets the basic conditions for the lineup composition corresponding to the first lineup to be optimized, the lineup after the adjustment is determined as the adjusted lineup corresponding to the first lineup to be optimized; if the adjustment type is not a target adjustment type, the lineup to be confirmed is determined as the adjusted lineup corresponding to the first lineup to be optimized.

[0237] Step S1033: Perform strength evaluation processing on the first newly added lineup to obtain the lineup strength corresponding to the first newly added lineup, and perform strength evaluation processing on the Q adjusted lineups to obtain the lineup strengths corresponding to the Q adjusted lineups.

[0238] Specifically, the Q adjusted lineups include a first adjusted lineup; the first adjusted lineup is an adjusted lineup among the Q adjusted lineups; a second lineup population to be optimized is obtained from the N lineup populations to be optimized except the first lineup population to be optimized; a second adjusted lineup obtained by adjusting the lineup to be optimized in the second lineup population to be optimized is obtained; the third adjusted lineup, the second adjusted lineup, the reference lineup, the first newly added lineup and the second newly added lineup among the Q adjusted lineups except the first adjusted lineup are determined as the second battle lineup of the first adjusted lineup; the second newly added lineup refers to the lineup generated for the second lineup population to be optimized, and the lineup characteristics corresponding to the second newly added lineup are the same as the lineup characteristics corresponding to the second lineup population to be optimized; a virtual battle is performed on each battle lineup in the first adjustment lineup and the second battle lineup, and the virtual battle results corresponding to each battle lineup in the first adjustment lineup and the second battle lineup are obtained; the lineup strength of the first adjusted lineup is determined according to the virtual battle results corresponding to each battle lineup in the first adjustment lineup and the second battle lineup.

[0239] Step S1034, based on the lineup strengths corresponding to the Q lineups to be optimized, the lineup strengths corresponding to the Q adjusted lineups, the lineup strength corresponding to the first newly added lineup, the Q adjusted lineups and the first newly added lineup, the first lineup population to be optimized is updated to obtain the optimized lineup population corresponding to the first lineup population to be optimized.

[0240] Specifically, Q lineups to be optimized, Q adjusted lineups, and the first newly added lineup are determined as the population to be deduplicated, and in the population to be deduplicated, multiple lineups with the same non-core roles are obtained; among the lineup strengths corresponding to the Q lineups to be optimized, the lineup strengths corresponding to the Q adjusted lineups, and the lineup strengths corresponding to the first newly added lineup, the lineup strengths corresponding to the multiple lineups are obtained; the first lineup with the largest lineup strength is determined among the multiple lineups, and the second lineup is deleted from the population to be deduplicated to obtain a deduplicated lineup population; the second lineup includes the lineups in the multiple lineups except the first lineup; based on the first number of lineups in the deduplicated lineup population and the second number of lineups to be optimized in the first population to be optimized, the updated lineup population generated by the first iterative operation of the first population to be optimized is determined; based on the updated lineup population, the optimized lineup population corresponding to the first population to be optimized is determined.

[0241] Among them, based on the first number of lineups in the deduplicated lineup population and the second number of lineups to be optimized in the first lineup population to be optimized, the specific process of determining the updated lineup population generated by the first iterative operation of the first lineup population to be optimized may include: if the first number of lineups in the deduplicated lineup population is greater than the second number of lineups to be optimized in the first lineup population to be optimized, then obtaining the second lineup composition basic conditions associated with the first lineup population to be optimized; in the deduplicated lineup population, obtaining a third lineup that meets the second lineup composition basic conditions; the lineup strength of the third lineup is greater than or equal to the lineup strength of the fourth lineup; the fourth lineup includes the lineups in the deduplicated lineup population that meet the second lineup composition basic conditions except the third lineup. The lineup of this condition; determine the quantity difference between the second quantity and the fourth quantity of the third lineup, and obtain the fifth lineup that does not meet the basic conditions for the second lineup composition in the deduplicated lineup population; the quantity of the fifth lineup is the quantity difference; the lineup strength of the fifth lineup is greater than or equal to the lineup strength of the sixth lineup; the sixth lineup includes the lineups in the deduplicated lineup population that do not meet the basic conditions for the second lineup composition except the fifth lineup; the third lineup and the fifth lineup are determined as the updated lineup population generated by the first iterative operation of the first lineup population to be optimized; if the first quantity is less than or equal to the second quantity, the deduplicated lineup population is determined to be the updated lineup population generated by the first iterative operation of the first lineup population to be optimized.

[0242] Among them, based on the updated lineup population, the specific process of determining the optimized lineup population corresponding to the first lineup population to be optimized can include: obtaining an iterative operation number threshold corresponding to the iterative operation; the iterative operation includes a lineup addition operation, a lineup adjustment operation, a lineup evaluation operation, and a population update operation; if the iterative operation number threshold is equal to 1, then the updated lineup population is determined as the optimized lineup population corresponding to the first lineup population to be optimized; if the iterative operation number threshold is greater than 1, then a second iterative operation is performed by updating the lineup population, and when the iterative operation number is equal to the iterative operation number threshold, the lineup population generated by the iterative operation is determined as the optimized lineup population corresponding to the first lineup population to be optimized.

[0243] Step S1035: Determine the optimized lineup populations corresponding to the N lineup populations to be optimized as N optimized lineup populations.

[0244] The embodiment of the present application proposes an automated analysis method based on the strength assessment of the operation process. The main advantage is that during the game development and testing stages, without the need for game player data, a unified cost and star configuration can be used to specify target characteristics of multiple lineup compositions. By utilizing the game configuration file, a large number of lineups under different populations can be reasonably and randomly explored. After multiple rounds of non-repetitive random traversal combinations of chess pieces, equipment adjustments, and position adjustments, a rapid strength assessment is performed to find a stronger lineup composition method that meets the lineup composition target. At the same time, by correlating data under different populations, the strength changes of the lineup can be evaluated, providing effective reference data for game planners to adjust game values.

[0245] This application proposes a lineup balance assessment method that meets lineup composition goals in strategy games such as Auto Chess. It uses a method that conforms to the game settings to explore the optimal lineup under different populations. After multiple rounds of transformation, strength assessment, and equipment adjustment, it determines the strongest lineup composition method that meets the lineup composition goal. Then, it correlates multiple targeted results to evaluate the rationality of lineup strength changes under different populations for the same lineup composition goal, providing effective reference data for game planners to adjust game values. Please also refer to Figure 8 , Figure 8 This is a flow chart of a data processing method provided by the embodiment of the present application. Figure 3 The data processing method can be performed by a business server (for example, Figure 1 The service server 100 shown in FIG. 1 may also be executed by a terminal device (for example, the above Figure 1 The terminal device 200a shown in the figure can also be executed by the business server and the terminal device interactively. For ease of understanding, the embodiment of the present application takes the method executed by the business server as an example for explanation, that is, the business server executes the data processing method as a computer device. Figure 8 As shown, the data processing method may at least include the following steps S201 to S208.

[0246] Step S201, read the configuration file.

[0247] Specifically, this application also refers to the configuration file as role configuration information.

[0248] The business server reads the chess piece bond structure from the configuration file. Because different strategy games have different talent play styles, the configuration file is needed to understand the bond structure of different chess pieces. For example, a chess piece may have three bonds, but when assembling a lineup, you need to consider which bond to activate. A chess piece can have three active bonds in total.

[0249] The business server reads the relationship between chess piece types, virtual equipment, and positions from the configuration file. Because chess pieces have different roles, for example, some may act as damage-taking characters while others may provide damage, you need to confirm each piece's role type, select the appropriate virtual equipment from the virtual equipment pool, and choose the appropriate position from the position list.

[0250] Step S202: preset conditions.

[0251] Specifically, a lineup is a queue of chess pieces. When considering the lineup composition goal, the conditions of the lineup composition goal must be met. The combination of chess pieces to achieve the lineup composition goal under different populations can be called an operation process.

[0252] The business server first reads the target configuration of the lineup composition. The planner has an expected operation process for different lineup types, which is usually expressed as the basic form of a specific combination under different populations. This form usually means that the lineup contains certain pieces, or uses certain pieces as the main output or injury-bearing person, etc.

[0253] Furthermore, the business server confirms a unified strength evaluation standard, also referred to as resource allocation standard information in this application, and uses a unified cost and star allocation strategy under the same population of different lineup composition targets to ensure that the randomness is within a reasonable range.

[0254] Step S203: Generate a lineup.

[0255] Specifically, the content that the business server needs to complete includes non-preset lineup pieces (ie, non-core characters), virtual equipment that the pieces need to match, and position information of the pieces, and mark the lineup.

[0256] For specific methods, please refer to Figure 9 , Figure 9 : This is a flow chart of a lineup generation method that meets the lineup composition goal provided by an embodiment of the present application. The method includes the following:

[0257] Step S2011, confirm the lineup composition.

[0258] The business server obtains the lineup composition target, which includes multiple lineup composition methods, that is, the basic conditions for lineup composition. For example, in a 6-population lineup composition method, chess piece A must exist and serve as the main output. This chess piece has bonds X and Y, and bond X is used as the main bond. Activating bond X requires 4 teammates. Therefore, this lineup composition method requires a total of 5 teammate chess pieces, and 3 of them must carry bond X.

[0259] Step S2012: confirm the core role.

[0260] According to the example of step S1021, the business server confirms that chess piece A is a core role, that is, a core chess piece.

[0261] Step S2013: Fill in non-core roles.

[0262] In the candidate character set, select a non-core character. Since 3 more people are required to carry bond X, the business server will first obtain the linkage character combination in the candidate character set. Assuming that in addition to piece A, there are 6 pieces in the candidate character set that carry bond X, the number of linkage character combinations is

[0263] Furthermore, the business server also needs to select 2 roles. At this time, there are 2 selection goals. The first goal is to activate more bonds that have not yet been activated, such as bond Y carried by the chess piece; the second goal is to improve the level of the already activated bond. The selection of teammate chess pieces can be completed based on the above two goals.

[0264] Step S2014, calculate the lineup bond.

[0265] Step S2015: Check whether the character meets the bond.

[0266] If the selected character meets the bond, execute step S2017; if the selected character does not meet the bond, execute step S2016.

[0267] Step S2016, complete the characters that meet the bond.

[0268] Step S2017, complete the role.

[0269] Among them, the difference between step S2016 and step S2017 is that the former gives priority to selecting chess pieces that meet the main constraints, while the latter can randomly select chess pieces.

[0270] Step S2018: Check whether the role meets the requirements.

[0271] The business server obtains the resource allocation standard information based on the number of characters in the basic conditions of the lineup composition, and determines whether the obtained characters meet the resource allocation standard information. If so, step 2019 is executed. If the characters do not meet the resource allocation standard information, the process returns to step S2014. This step is mainly used to determine whether the quality of the characters (i.e., chess pieces) meets the preset quality.

[0272] Step S2019, add star rating.

[0273] According to the value of the chess pieces, the business server randomly matches the star levels of the chess pieces.

[0274] Step S201a: adding virtual equipment.

[0275] According to the value and category of the chess pieces, the business server randomly divides the virtual equipment of the chess pieces. For example, high-value chess pieces have a higher probability of obtaining the equipment type that matches them.

[0276] Step S201b, adding a station.

[0277] According to the type of chess pieces, the business server randomly divides the chess pieces' standing areas. For example, the main output standing area is usually at the back, and the damaged chess pieces are usually at the front.

[0278] Step S201c: output the lineup and mark the lineup.

[0279] The business server outputs and marks the complete lineup to form the target lineup. After the generation is complete, it also marks the conditions under which the lineup survives, which is used for subsequent deduplication and association. The above process is repeated until a sufficient number of lineups or all lineups are randomly generated.

[0280] The above process is the process of generating the lineup to be optimized.

[0281] Step S204, evolving the lineup.

[0282] Step S205: Evaluate the strength.

[0283] For the specific implementation process of step S204-step S205, please refer to the above Figure 2 Step S103 in the corresponding embodiment, and the above Figure 4 The corresponding embodiments are not described in detail here.

[0284] Step S206: reporting lineup data.

[0285] Specifically, the first step is to construct a lineup ladder for optimization. For the same population, the business server conducts virtual battles with lineups selected from all populations. For example, five virtual battles are conducted. The lineups are ranked from high to low based on their average win rate, forming a lineup ladder for the population.

[0286] Step 2: Select a target lineup that meets the lineup composition target. The business server sorts the lineups in the population by strength from high to low, and selects lineups from strong to weak, prioritizing the five lineups with the highest win rates that meet each lineup composition target. For example, if there are 10 lineup composition targets, 50 evolved lineups that match the lineup composition targets will be selected, meaning that for each lineup composition target, the five strongest lineups will be selected.

[0287] Step 3: Select a comparison lineup. The business server sorts the lineups in the population by strength from high to low, and selects lineups from strong to weak. Except for the preset marks, U lineups are selected for the target level of each main bond. The target level includes all levels between the level threshold and the maximum level (including the level threshold and the maximum level) under the condition that the maximum level is equal to or greater than the level threshold. For example, a lineup that does not meet the lineup composition target has a maximum level of 4 for the main bond and a level threshold of 2. Then the target levels of the lineup include levels 2, 3, and 4. If U is equal to 5, the business server can select 15 lineups based on this lineup.

[0288] Step 4: Place the selected target lineup in a melee with the comparison lineup, for example, conduct 50 virtual battles between each two lineups to obtain the lineup ladder under the population size.

[0289] Step S207: Associating different population level lineups.

[0290] Specifically, the business server generates lineup ladders under different populations. In the ladders under different populations, the lineup with the highest win rate of the target lineup is taken to represent its optimal strength, and the top 5 lineup strengths are taken to calculate the win rate variance of the target lineup.

[0291] Step S208: Comprehensively analyze the strength of the operation process lineup.

[0292] Specifically, according to the target lineup mark, the same lineup with different populations can be associated to obtain the highest winning rate trend and winning rate variance trend to evaluate whether the strength change of the lineup during the operation is reasonable. The planner can use this to determine the adjustment direction of the lineup strength.

[0293] The main contributions of this embodiment are as follows:

[0294] 1. Use a random method that matches the game settings to find a stronger lineup that meets the lineup composition goals and evaluate the strength trend under different populations.

[0295] 2. Provide effective reference data for game planners to adjust game values ​​before the game goes online.

[0296] As can be seen from the above, the embodiments of the present application propose a lineup evaluation method that meets lineup composition goals. This method can evaluate the changes in lineup strength under different numbers of characters for a lineup composition goal, and can also evaluate the differences in lineup strength under the same number of characters for different lineup composition goals. Therefore, it can provide accurate data for game planners to adjust game values, thereby improving the balance of lineup strength between different lineups in the game. In addition, this method can be applied before the game goes online, so the embodiments of the present application can improve the efficiency of adjusting game values.

[0297] Further, see Figure 10 , Figure 10 : is a structural diagram of a data processing device provided in an embodiment of the present application. The above-mentioned data processing device 1 can be a computer program (including program code) running on a computer device, for example, the data processing device 1 is an application software; the data processing device 1 can be used to execute the corresponding steps of the method provided in the embodiment of the present application. Figure 10 As shown, the data processing device 1 may include:

[0298] An acquisition module 11 is configured to acquire A lineup composition targets; A is a positive integer; each of the A lineup composition targets includes B lineup composition basic conditions, and the B lineup composition basic conditions belonging to one lineup composition target each include a different number of characters; each of the A lineup composition targets includes the same number of characters; B is a positive integer greater than 1; a lineup composition basic condition is used to indicate a basic condition for generating a type of lineup;

[0299] A generating module 12 is configured to generate a plurality of initial lineup sets based on the B basic lineup composition conditions respectively included in the A lineup composition targets; wherein one initial lineup set is generated based on one basic lineup composition condition in one lineup composition target;

[0300] An optimization module 13 is configured to merge initial lineup sets containing the same number of characters from a plurality of initial lineup sets to obtain B lineup sets to be optimized, and optimize each of the B lineup sets to be optimized to obtain B optimized lineup sets;

[0301] The evaluation module 14 is used to determine the lineup ladder corresponding to each optimized lineup set according to the lineup strength of the optimized lineup included in each optimized lineup set, and evaluate the A lineup composition targets respectively according to the B lineup ladders.

[0302] In a possible implementation, A lineup composition goals include C lineup composition basic conditions, where C equals A*B; the C lineup composition basic conditions include a first lineup composition basic condition; the first lineup composition basic condition is one of the C lineup composition basic conditions;

[0303] The generation module 12 generates a plurality of initial lineup sets according to the B lineup composition basic conditions respectively included in the A lineup composition targets, and is used to perform the following operations:

[0304] According to the basic conditions for forming the first lineup, a core role is obtained from the candidate role set; the candidate role set includes all roles in the role configuration information; the role configuration information includes detailed configuration information corresponding to all roles; A lineup formation target meets the role configuration information;

[0305] Subtract the number of characters corresponding to the basic conditions of the first lineup from the number of core characters to get the filling number;

[0306] According to the filling quantity, F filling role combinations are obtained from the candidate role set; F is a positive integer; wherein the number of roles included in a filling role combination is the filling quantity;

[0307] Based on the F filler role combinations and the core roles, generate an initial lineup set corresponding to the basic conditions for the first lineup;

[0308] The initial lineup sets corresponding to the C basic lineup composition conditions are determined as multiple initial lineup sets.

[0309] In a possible implementation, the generation module 12 obtains F filling role combinations from the candidate role set according to the filling quantity, and performs the following operations:

[0310] If the basic conditions for the first lineup include the linkage relationship of the core characters, then the linkage filling quantity corresponding to the linkage relationship is determined; the linkage relationship has the function of additional virtual combat power;

[0311] According to the linkage filling number, I linkage role combinations are obtained from the candidate role set; I is a positive integer; the number of roles included in a linkage role combination is the linkage filling number; I linkage role combination includes linkage role combination J k , k is a positive integer, and k is less than or equal to 1;

[0312] Subtract the filling quantity and the linkage filling quantity to obtain the remaining filling quantity;

[0313] According to the remaining number of fills, select the linkage role combination J from the candidate role set k Get one or more remaining role combinations, and combine the obtained one or more remaining role combinations with the linkage role combination J k Combine them separately to get the linkage role combination J k a corresponding combination of one or more filler roles;

[0314] The filling role combinations corresponding to the I linkage role combinations are determined to be F filling role combinations; F is equal to or greater than I.

[0315] In a possible implementation, the generation module 12 generates an initial lineup set corresponding to the basic conditions for forming the first lineup based on the F filler role combinations and the core role, and performs the following operations:

[0316] Combine F filler role combinations with the core roles respectively to obtain F lineups to be allocated; wherein one lineup to be allocated includes one filler role combination;

[0317] Obtain resource allocation standard information based on the number of characters corresponding to the basic conditions for the first lineup;

[0318] According to the resource allocation standard information, resource allocation processing is performed on F lineups to be allocated, and F initial lineups are obtained; wherein an initial lineup is obtained by performing resource allocation processing on one lineup to be allocated;

[0319] The F initial lineups each carrying a condition identifier indicating the basic condition for forming the first lineup are determined as the initial lineup set corresponding to the basic condition for forming the first lineup.

[0320] In a possible implementation, the B lineup sets to be optimized include the lineup set L to be optimized. m , m is a positive integer, and m is less than or equal to B;

[0321] The optimization module 13 optimizes each of the B lineup sets to be optimized to obtain B optimized lineup sets, which are used to perform the following operations:

[0322] The lineup to be optimized is set to L m The lineups to be optimized with the same lineup characteristics are divided into the same lineup population to be optimized, and N lineup populations to be optimized are obtained; N is a positive integer;

[0323] Optimize N lineup populations to be optimized respectively to obtain N optimized lineup populations; wherein, one optimized lineup population is obtained by optimizing one lineup population to be optimized;

[0324] Determine the N optimized lineup populations as the lineup set to be optimized L m The corresponding optimized lineup set.

[0325] In a possible implementation, the N lineup populations to be optimized include a first lineup population to be optimized; the first lineup population to be optimized is one of the N lineup populations to be optimized; the first lineup population to be optimized includes Q lineups to be optimized; Q is a positive integer;

[0326] The optimization module 13 optimizes the N to-be-optimized lineup populations respectively to obtain N optimized lineup populations, which are used to perform the following operations:

[0327] Perform strength evaluation processing on each of the Q lineups to be optimized to obtain the lineup strengths corresponding to each of the Q lineups to be optimized; wherein the lineup strength of a lineup to be optimized is obtained by performing strength evaluation processing on the lineup to be optimized;

[0328] Adjust the Q lineups to be optimized respectively to obtain Q adjusted lineups, and generate a first new lineup with the same lineup characteristics as the lineup characteristics corresponding to the Q lineups to be optimized respectively;

[0329] Perform a strength evaluation process on the first newly added lineup to obtain the lineup strength corresponding to the first newly added lineup, and perform a strength evaluation process on each of the Q adjusted lineups to obtain the lineup strength corresponding to each of the Q adjusted lineups;

[0330] According to the lineup strengths corresponding to the Q lineups to be optimized, the lineup strengths corresponding to the Q adjusted lineups, the lineup strength corresponding to the first newly added lineup, the Q adjusted lineups, and the first newly added lineup, a lineup update process is performed on the first lineup population to be optimized to obtain an optimized lineup population corresponding to the first lineup population to be optimized;

[0331] The optimized lineup populations corresponding to the N lineup populations to be optimized are determined as N optimized lineup populations.

[0332] In a possible implementation, the acquisition module 11 acquires A lineup composition targets, and is used to perform the following operations:

[0333] Get role configuration information; role configuration information includes detailed configuration information corresponding to all roles;

[0334] Generate A lineup composition targets that all meet the character configuration information;

[0335] Generate a reference lineup that meets the role configuration information; the number of roles included in the reference lineup is the same as the lineup set L to be optimized m same;

[0336] Then the Q lineups to be optimized include the first lineup to be optimized; the first lineup to be optimized is one of the Q lineups to be optimized;

[0337] The optimization module 13 performs strength evaluation processing on each of the Q lineups to be optimized, and obtains the lineup strength corresponding to each of the Q lineups to be optimized, which is used to perform the following operations:

[0338] Determine the second lineup to be optimized, excluding the first lineup to be optimized, and the reference lineup from the N lineup populations to be optimized as the first lineup to be optimized;

[0339] Performing virtual battles on the first lineup to be optimized and each of the first battle lineups, respectively, to obtain virtual battle results corresponding to the first lineup to be optimized and each of the first battle lineups;

[0340] The lineup strength of the first lineup to be optimized is determined according to the virtual battle results corresponding to the first lineup to be optimized and each competing lineup in the first competing lineup.

[0341] In a possible implementation, the Q lineups to be optimized include a first lineup to be optimized; the first lineup to be optimized is one of the Q lineups to be optimized;

[0342] The optimization module 13 adjusts the Q lineups to be optimized respectively to obtain Q adjusted lineups, which are used to perform the following operations:

[0343] Performing an adjustment process on the first lineup to be optimized to obtain a lineup to be confirmed, and obtaining an adjustment type of the adjustment process;

[0344] If the adjustment type is a target adjustment type, then the condition identifier carried by the first lineup to be optimized is obtained; the condition identifier carried by the first lineup to be optimized is used to indicate the basic conditions of the lineup composition corresponding to the first lineup to be optimized;

[0345] If the lineup to be confirmed meets the basic lineup composition conditions corresponding to the first lineup to be optimized, the lineup to be confirmed will be determined as the adjusted lineup corresponding to the first lineup to be optimized;

[0346] If the lineup to be confirmed does not meet the basic lineup composition conditions corresponding to the first lineup to be optimized, the lineup to be confirmed is adjusted. If the lineup after the adjustment meets the basic lineup composition conditions corresponding to the first lineup to be optimized, the lineup after the adjustment is determined as the adjusted lineup corresponding to the first lineup to be optimized;

[0347] If the adjustment type is not the target adjustment type, the lineup to be confirmed will be determined as the adjustment lineup corresponding to the first lineup to be optimized.

[0348] In a possible implementation, the Q adjusted lineups include a first adjusted lineup; the first adjusted lineup is one of the Q adjusted lineups;

[0349] The optimization module 13 performs strength evaluation processing on the Q adjusted lineups respectively to obtain the lineup strengths corresponding to the Q adjusted lineups, which is used to perform the following operations:

[0350] Obtaining a second lineup population to be optimized other than the first lineup population to be optimized from the N lineup populations to be optimized;

[0351] Obtaining a second adjusted lineup obtained by adjusting the lineup to be optimized in the second population of lineups to be optimized;

[0352] The third adjusted lineup, the second adjusted lineup, the reference lineup, the first newly added lineup, and the second newly added lineup among the Q adjusted lineups, excluding the first adjusted lineup, are determined as the second match lineup of the first adjusted lineup; the second newly added lineup refers to the lineup generated for the second lineup population to be optimized, and the lineup characteristics corresponding to the second newly added lineup are the same as the lineup characteristics corresponding to the second lineup population to be optimized;

[0353] Performing virtual battles on each of the first adjusted lineup and the second battle lineup, respectively, to obtain virtual battle results corresponding to each of the first adjusted lineup and the second battle lineup;

[0354] The lineup strength of the first adjusted lineup is determined according to the virtual battle results corresponding to each of the first adjusted lineup and the second battle lineup.

[0355] In one possible implementation, the optimization module 13 performs lineup update processing on the first lineup population to be optimized based on the lineup strengths corresponding to the Q lineups to be optimized, the lineup strengths corresponding to the Q adjusted lineups, the lineup strength corresponding to the first newly added lineup, the Q adjusted lineups, and the first newly added lineup, to obtain an optimized lineup population corresponding to the first lineup population to be optimized, and performs the following operations:

[0356] The Q lineups to be optimized, the Q adjusted lineups, and the first newly added lineup are determined as the population to be deduplicated, and multiple lineups with the same non-core characters are obtained from the population to be deduplicated;

[0357] Obtain the lineup strengths corresponding to the multiple lineups from the lineup strengths corresponding to the Q lineups to be optimized, the lineup strengths corresponding to the Q adjusted lineups, and the lineup strength corresponding to the first newly added lineup;

[0358] Determine a first lineup with the greatest lineup strength among multiple lineups, delete the second lineup from the population to be deduplicated, and obtain a deduplicated lineup population; the second lineup includes the lineups among the multiple lineups except the first lineup;

[0359] Determining an updated lineup population generated by a first iterative operation of the first lineup population to be optimized based on the first number of lineups in the deduplicated lineup population and the second number of lineups to be optimized in the first lineup population to be optimized;

[0360] According to the updated lineup population, the optimized lineup population corresponding to the first lineup population to be optimized is determined.

[0361] In one possible implementation, the optimization module 13 determines, based on the first number of lineups in the deduplicated lineup population and the second number of lineups to be optimized in the first lineup population to be optimized, an updated lineup population generated by performing a first iteration operation on the first lineup population to be optimized, and performs the following operations:

[0362] If the first number of lineups in the deduplicated lineup population is greater than the second number of lineups to be optimized in the first lineup population to be optimized, obtaining a second lineup composition basic condition associated with the first lineup population to be optimized;

[0363] In the deduplicated lineup population, obtain a third lineup that meets the basic conditions for forming the second lineup; the lineup strength of the third lineup is greater than or equal to the lineup strength of the fourth lineup; the fourth lineup includes lineups in the deduplicated lineup population that meet the basic conditions for forming the second lineup except the third lineup;

[0364] Determine the difference between the second number and the fourth number of the third lineup, and obtain a fifth lineup that does not meet the basic conditions for forming the second lineup from the deduplicated lineup population; the number of the fifth lineup is the difference; the lineup strength of the fifth lineup is greater than or equal to the lineup strength of the sixth lineup; the sixth lineup includes lineups in the deduplicated lineup population that do not meet the basic conditions for forming the second lineup except the fifth lineup;

[0365] The third lineup and the fifth lineup are determined as the updated lineup population generated by the first iteration operation of the first lineup population to be optimized;

[0366] If the first number is less than or equal to the second number, the deduplicated lineup population is determined to be the updated lineup population generated by the first iterative operation on the first lineup population to be optimized.

[0367] In a possible implementation, the optimization module 13 determines the optimized lineup population corresponding to the first lineup population to be optimized based on the updated lineup population, and performs the following operations:

[0368] Get the iterative operation count threshold corresponding to the iterative operation; the iterative operation includes lineup addition operation, lineup adjustment operation, lineup evaluation operation and population update operation;

[0369] If the iterative operation number threshold is equal to 1, the updated lineup population is determined to be the optimized lineup population corresponding to the first lineup population to be optimized;

[0370] If the iterative operation number threshold is greater than 1, a second iterative operation is performed by updating the lineup population. When the iterative operation number is equal to the iterative operation number threshold, the lineup population generated by the iterative operation is determined as the optimized lineup population corresponding to the first lineup population to be optimized.

[0371] In a possible implementation, the B optimized lineup sets include the optimized lineup set X y ; y is a positive integer, and y is less than or equal to B;

[0372] The evaluation module 14 determines the lineup ladder corresponding to each optimized lineup set based on the lineup strength of the optimized lineups included in each optimized lineup set, and is used to perform the following operations:

[0373] In the optimized lineup set X y Get the first optimized lineup; the first optimized lineup is the optimized lineup set X y An optimized lineup in

[0374] The first optimized lineup and the second optimized lineup are virtual battled to obtain the lineup strength of the first optimized lineup; the second optimized lineup includes the optimized lineup set X y The optimized lineup except the first optimized lineup;

[0375] According to the optimized lineup set X y The optimized lineups in the corresponding lineup strengths are the same as the optimized lineup set X. y Sorting the optimized lineup in the , and getting the optimized lineup set X y The corresponding lineup ladder to be optimized;

[0376] According to the optimized lineup set X y The corresponding lineup ladder to be optimized generates the optimized lineup set X y Corresponding lineup ladder.

[0377] In a possible implementation, the evaluation module 14 optimizes the lineup set X y The corresponding lineup ladder to be optimized generates the optimized lineup set X y The corresponding lineup ladder is used to perform the following operations:

[0378] In the B basic conditions of lineup composition included in the A lineup composition goals, obtain the optimized lineup set X y The corresponding A basic conditions for the formation of a lineup; the basic conditions for the formation of a lineup include the basic conditions for the formation of a lineup Z w , w is a positive integer, and w is less than or equal to A;

[0379] In the optimized lineup set X y In the corresponding lineup ladder to be optimized, obtain the basic conditions Z of the lineup composition w The third optimized lineup; the lineup strength of the third optimized lineup is greater than or equal to the lineup strength of the fourth optimized lineup; the fourth optimized lineup includes the optimized lineup set X y The corresponding lineup to be optimized in the ladder meets the basic conditions of lineup composition except the third optimized lineup Zw Optimized lineup;

[0380] In the optimized lineup set X y In the corresponding lineup ladder to be optimized, obtain a fifth optimized lineup that does not meet the basic conditions for A lineups, and determine V linkage relationships included in the fifth optimized lineup; V is a positive integer;

[0381] Determine the maximum levels corresponding to the V linkage relationships, and among the V maximum levels, obtain the maximum level that is greater than or equal to the level threshold as the effective level;

[0382] In the fifth optimized lineup, obtain the sixth optimized lineup for the effective level; the lineup strength of the sixth optimized lineup is greater than or equal to the lineup strength of the seventh optimized lineup; the seventh optimized lineup includes the optimized lineups in the fifth optimized lineup except the sixth optimized lineup;

[0383] Based on the third optimized lineup and the sixth optimized lineup, generate the optimized lineup set X y Corresponding lineup ladder.

[0384] In a possible implementation, the evaluation module 14 generates an optimized lineup set X according to the third optimized lineup and the sixth optimized lineup. y The corresponding lineup ladder is used to perform the following operations:

[0385] Performing a virtual battle between the third optimized lineup and the sixth optimized lineup to obtain a result of the virtual battle between the third optimized lineup and the sixth optimized lineup;

[0386] According to the virtual battle results between the third optimized lineup and the sixth optimized lineup, the third optimized lineup and the sixth optimized lineup are sorted to obtain the optimized lineup set X. y Corresponding lineup ladder.

[0387] In a possible implementation, the A lineup composition targets include a first lineup composition target, and the first lineup composition target is one of the A lineup composition targets;

[0388] The evaluation module 14 evaluates the A lineup composition targets according to the B lineup ladders, and performs the following operations:

[0389] In each of the B lineup ladders, obtain a lineup that meets the first lineup composition goal and has the greatest lineup strength;

[0390] Based on the lineup strengths corresponding to each of the B lineups, determine the strength trend of the first lineup composition target with respect to the number of B characters;

[0391] According to the intensity trend, the first lineup composition target is evaluated and processed to obtain a first evaluation result; the first evaluation result is used to adjust the first lineup composition target.

[0392] In a possible implementation, the A lineup composition targets include the second lineup composition target, and the second lineup composition target is one of the A lineup composition targets; the B lineup ladder includes the lineup ladder O p , p is a positive integer, and p is less than or equal to B; the number of B characters includes the lineup ladder O p The corresponding number of roles G p ;

[0393] The evaluation module 14 evaluates the A lineup composition targets according to the B lineup ladders, and performs the following operations:

[0394] In the lineup ladder O p In the second lineup formation goal, obtain U lineups; U is a positive integer greater than 1; the lineup strength corresponding to each of the U lineups is greater than or equal to the lineup strength of the remaining lineups; the remaining lineups include the lineup ladder O p The lineup that meets the goal of forming the second lineup except for the U lineup;

[0395] Perform variance processing on the lineup strength corresponding to each of the U lineups to obtain the second lineup composition target for the number of characters G p The variance of lineup strength;

[0396] Determine the lineup strength variance corresponding to the number of B characters in the second lineup composition target;

[0397] Based on the strength variance of B lineups, determine the strength variance trend of the second lineup composition target for the number of B characters;

[0398] According to the strength variance trend, the second lineup composition target is evaluated and processed to obtain a second evaluation result; the second evaluation result is used to adjust the second lineup composition target.

[0399] In the embodiments of the present application, the term "module" refers to a computer program or a part of a computer program that has a predetermined function and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0400] As can be seen from the above, the embodiments of the present application propose a lineup evaluation method that meets lineup composition goals. This method can evaluate the changes in lineup strength under different numbers of characters for a lineup composition goal, and can also evaluate the differences in lineup strength under the same number of characters for different lineup composition goals. Therefore, it can provide accurate data for game planners to adjust game values, thereby improving the balance of lineup strength between different lineups in the game. In addition, this method can be applied before the game goes online, so the embodiments of the present application can improve the efficiency of adjusting game values.

[0401] Further, see Figure 11 , Figure 11 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. Figure 11 As shown, the computer device 1000 may include: at least one processor 1001, such as a CPU, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002. The communication bus 1002 is used to realize the connection and communication between these components. In some embodiments, the user interface 1003 may include a display screen (Display), a keyboard (Keyboard), and the network interface 1004 may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk storage. The memory 1005 may optionally also be at least one storage device located away from the aforementioned processor 1001. As Figure 11 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a device control application.

[0402] exist Figure 11 In the computer device 1000 shown, the network interface 1004 can provide network communication functions; the user interface 1003 is mainly used to provide an interface for user input; and the processor 1001 can be used to call the device control application stored in the memory 1005 to achieve:

[0403] Get A lineup composition targets; A is a positive integer; A lineup composition targets each include B lineup composition basic conditions, and the number of characters included in the B lineup composition basic conditions belonging to a lineup composition target is different from each other; A lineup composition targets each include the same number of characters; B is a positive integer greater than 1; a lineup composition basic condition is used to indicate the basic conditions for generating a type of lineup;

[0404] Generate multiple initial lineup sets based on the B basic lineup composition conditions included in the A lineup composition targets; wherein one initial lineup set is generated based on one basic lineup composition condition in one lineup composition target;

[0405] Merge multiple initial lineup sets containing the same number of characters to obtain B lineup sets to be optimized, and optimize each of the B lineup sets to be optimized to obtain B optimized lineup sets;

[0406] According to the lineup strength of the optimized lineups included in each optimized lineup set, the lineup ladder corresponding to each optimized lineup set is determined, and according to the B lineup ladders, the A lineup composition targets are evaluated and processed respectively.

[0407] It should be understood that the computer device 1000 described in the embodiments of the present application can execute the description of the data processing method or device in the above embodiments, which will not be repeated here. In addition, the description of the beneficial effects of using the same method will not be repeated.

[0408] The present application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the data processing methods or apparatuses described in the preceding embodiments, which are not described in detail here. Furthermore, the description of the beneficial effects of the same methods is not described in detail here.

[0409] The computer-readable storage medium may be the data processing device provided in any of the aforementioned embodiments or the internal storage unit of the computer device, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device. Furthermore, the computer-readable storage medium may also include both the internal storage unit of the computer device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the computer device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0410] The present application also provides a computer program product, comprising a computer program stored in a computer-readable storage medium. A processor of a computer device reads the computer program from the computer-readable storage medium and executes the computer program, enabling the computer device to perform the data processing methods or apparatuses described in the preceding embodiments, which are not further detailed here. Furthermore, the beneficial effects of the same methods are not further detailed here.

[0411] The terms "first", "second", etc. in the description, claims, and drawings of the embodiments of the present application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or units is not limited to the listed steps or modules, but may optionally include steps or modules not listed, or may optionally include other step units inherent to these processes, methods, apparatuses, products, or devices.

[0412] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0413] The above disclosure is only a preferred embodiment of the present application, and certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A data processing method, characterized in that: include: Get A lineup composition targets; A is a positive integer; The A lineup composition targets each include B lineup composition basic conditions, and the B lineup composition basic conditions belonging to one lineup composition target each include different numbers of characters; the A lineup composition targets each include the same number of characters; B is a positive integer greater than 1; one lineup composition basic condition is used to indicate the basic conditions for generating a type of lineup; generating a plurality of initial lineup sets according to the B basic lineup composition conditions respectively included in the A lineup composition targets; wherein one initial lineup set is generated according to one basic lineup composition condition in one lineup composition target; Merging the initial lineup sets containing the same number of characters among the multiple initial lineup sets to obtain B lineup sets to be optimized, and optimizing the B lineup sets to be optimized to obtain B optimized lineup sets; According to the lineup strength of the optimized lineup included in each optimized lineup set, the lineup ladder corresponding to each optimized lineup set is determined, and according to the B lineup ladders, the A lineup composition targets are evaluated and processed respectively.

2. The method according to claim 1, characterized in that The A lineup composition targets include C lineup composition basic conditions, where C equals A*B; the C lineup composition basic conditions include a first lineup composition basic condition; the first lineup composition basic condition is one of the C lineup composition basic conditions; The generating of a plurality of initial lineup sets according to the B basic lineup composition conditions respectively included in the A lineup composition targets includes: According to the first basic conditions for forming a lineup, a core role is obtained from a candidate role set; the candidate role set includes all roles in the role configuration information; the role configuration information includes detailed configuration information corresponding to all the roles; Subtract the number of characters included in the first basic condition for forming a lineup from the number of core characters to obtain a fill number; According to the filling quantity, F filling role combinations are obtained from the candidate role set; F is a positive integer; wherein the number of roles included in a filling role combination is the filling quantity; Generating an initial lineup set corresponding to the first lineup composition basic condition based on the F filler character combinations and the core character; The initial lineup sets corresponding to the C basic lineup composition conditions are determined as multiple initial lineup sets.

3. The method according to claim 2, characterized in that The step of obtaining F filling role combinations from the candidate role set according to the filling quantity includes: If the first lineup composition basic condition includes the linkage relationship of the core characters, determining the linkage filling quantity corresponding to the linkage relationship; the linkage relationship has the function of additional virtual combat power; According to the linkage filling number, I linkage role combinations are obtained from the candidate role set; I is a positive integer; wherein the number of roles included in a linkage role combination is the linkage filling number; the I linkage role combination includes linkage role combination J k , k is a positive integer, and k is less than or equal to 1; performing a difference processing on the filling quantity and the linkage filling quantity to obtain a remaining filling quantity; According to the remaining filling quantity, select the linkage role combination J from the candidate role set. k Obtain one or more remaining role combinations, and combine the obtained one or more remaining role combinations with the linkage role combination J k Combine them respectively to obtain the linkage role combination J k a corresponding combination of one or more filler roles; The filling role combinations corresponding to the I linkage role combinations are determined to be F filling role combinations; F is equal to or greater than I.

4. The method according to claim 2, characterized in that Generating an initial lineup set corresponding to the first lineup composition basic condition based on the F filler character combinations and the core character includes: Combining the F filling role combinations with the core role respectively to obtain F lineups to be allocated; wherein one lineup to be allocated includes one filling role combination and the core role; Obtaining resource allocation standard information based on the number of characters included in the first lineup composition basic condition; According to the resource allocation standard information, resource allocation processing is performed on the F lineups to be allocated, respectively, to obtain F initial lineups; wherein an initial lineup is obtained by performing resource allocation processing on one lineup to be allocated; F initial lineups, each carrying a condition identifier indicating the first basic lineup composition condition, are determined as an initial lineup set corresponding to the first basic lineup composition condition.

5. The method according to claim 1, wherein The B lineup sets to be optimized include the lineup set L to be optimized m , m is a positive integer, and m is less than or equal to B; The B lineup sets to be optimized are optimized separately to obtain B optimized lineup sets, including: The lineup set to be optimized L m The lineups to be optimized with the same lineup characteristics are divided into the same lineup population to be optimized, and N lineup populations to be optimized are obtained; N is a positive integer; Optimizing the N lineup populations to be optimized respectively to obtain N optimized lineup populations; wherein one optimized lineup population is obtained by optimizing one lineup population to be optimized; The N optimized lineup populations are determined as the lineup set to be optimized L m The corresponding optimized lineup set.

6. The method according to claim 5, characterized in that The N lineup populations to be optimized include a first lineup population to be optimized; the first lineup population to be optimized is one lineup population to be optimized among the N lineup populations to be optimized; the first lineup population to be optimized includes Q lineups to be optimized; Q is a positive integer; The N to-be-optimized lineup populations are optimized separately to obtain N optimized lineup populations, including: Performing strength evaluation processing on each of the Q lineups to be optimized to obtain lineup strengths corresponding to each of the Q lineups to be optimized; wherein the lineup strength of a lineup to be optimized is obtained by performing strength evaluation processing on the lineup to be optimized; Performing adjustment processing on each of the Q lineups to be optimized to obtain Q adjusted lineups, and generating a first new lineup with the same lineup characteristics as the lineup characteristics corresponding to each of the Q lineups to be optimized; Performing a strength evaluation process on the first newly added lineup to obtain a lineup strength corresponding to the first newly added lineup, and performing a strength evaluation process on each of the Q adjusted lineups to obtain a lineup strength corresponding to each of the Q adjusted lineups; Performing lineup updating processing on the first lineup population to be optimized based on the lineup strengths corresponding to the Q lineups to be optimized, the lineup strengths corresponding to the Q adjusted lineups, the lineup strength corresponding to the first newly added lineup, the Q adjusted lineups, and the first newly added lineup to obtain an optimized lineup population corresponding to the first lineup population to be optimized; The optimized lineup populations corresponding to the N lineup populations to be optimized are determined as N optimized lineup populations.

7. The method according to claim 6, characterized in that The obtaining of A lineup composition goals includes: Obtain role configuration information; the role configuration information includes detailed configuration information corresponding to all roles; Generate A lineup composition targets that all meet the character configuration information; Generate a reference lineup that meets the role configuration information; the number of roles included in the reference lineup is the same as the number of the lineup set L to be optimized m same; Then the Q lineups to be optimized include a first lineup to be optimized; the first lineup to be optimized is one of the Q lineups to be optimized; The performing strength evaluation processing on each of the Q lineups to be optimized to obtain the lineup strengths corresponding to each of the Q lineups to be optimized includes: Determine a second lineup to be optimized, excluding the first lineup to be optimized, and the reference lineup from the N lineup populations to be optimized as a first battle lineup for the first lineup to be optimized; Performing virtual battles on the first lineup to be optimized and each of the first battle lineups, respectively, to obtain virtual battle results corresponding to the first lineup to be optimized and each of the first battle lineups; The lineup strength of the first lineup to be optimized is determined according to the virtual battle results corresponding to the first lineup to be optimized and each of the first battle lineups.

8. The method according to claim 6, characterized in that The Q lineups to be optimized include a first lineup to be optimized; the first lineup to be optimized is one of the Q lineups to be optimized; The Q lineups to be optimized are adjusted respectively to obtain Q adjusted lineups, including: Performing an adjustment process on the first lineup to be optimized to obtain a lineup to be confirmed, and obtaining an adjustment type of the adjustment process; If the adjustment type is a target adjustment type, obtaining a condition identifier carried by the first lineup to be optimized; the condition identifier carried by the first lineup to be optimized is used to indicate a basic lineup composition condition corresponding to the first lineup to be optimized; If the lineup to be confirmed meets the basic lineup composition conditions corresponding to the first lineup to be optimized, then the lineup to be confirmed is determined as the adjusted lineup corresponding to the first lineup to be optimized; If the lineup to be confirmed does not meet the basic lineup composition conditions corresponding to the first lineup to be optimized, then adjusting the lineup to be confirmed; if the lineup after the adjustment meets the basic lineup composition conditions corresponding to the first lineup to be optimized, then determining the lineup after the adjustment as the adjusted lineup corresponding to the first lineup to be optimized; If the adjustment type is not the target adjustment type, the lineup to be confirmed is determined as the adjustment lineup corresponding to the first lineup to be optimized.

9. The method according to claim 7, characterized in that The Q adjusted lineups include a first adjusted lineup; the first adjusted lineup is one of the Q adjusted lineups; The performing strength evaluation processing on the Q adjusted lineups to obtain the lineup strengths corresponding to the Q adjusted lineups respectively includes: Obtaining a second lineup population to be optimized other than the first lineup population to be optimized from the N lineup populations to be optimized; Obtaining a second adjusted lineup obtained by adjusting the lineup to be optimized in the second population of lineups to be optimized; Determine the third adjusted lineup, excluding the first adjusted lineup, the second adjusted lineup, the reference lineup, the first newly added lineup, and the second newly added lineup among the Q adjusted lineups as the second playing lineup for the first adjusted lineup; the second newly added lineup is a lineup generated for the second population of lineups to be optimized, and the lineup characteristics corresponding to the second newly added lineup are the same as the lineup characteristics corresponding to the second population of lineups to be optimized; Performing a virtual battle on each of the first adjusted lineup and the second battle lineup, respectively, to obtain a virtual battle result corresponding to each of the first adjusted lineup and the second battle lineup; The lineup strength of the first adjusted lineup is determined according to the virtual battle results corresponding to each of the first adjusted lineup and the second battle lineup.

10. The method according to claim 6, characterized in that The performing lineup updating processing on the first lineup population to be optimized based on the lineup strengths corresponding to the Q lineups to be optimized, the lineup strengths corresponding to the Q adjusted lineups, the lineup strength corresponding to the first newly added lineup, the Q adjusted lineups, and the first newly added lineup to obtain an optimized lineup population corresponding to the first lineup population to be optimized, including: Determine the Q lineups to be optimized, the Q adjusted lineups, and the first newly added lineup as a population to be deduplicated, and obtain multiple lineups with the same non-core characters from the population to be deduplicated; Obtaining the lineup strengths corresponding to the multiple lineups from the lineup strengths corresponding to the Q lineups to be optimized, the lineup strengths corresponding to the Q adjusted lineups, and the lineup strength corresponding to the first newly added lineup; Determining a first lineup with the greatest lineup strength among the multiple lineups, deleting a second lineup from the population to be deduplicated to obtain a deduplicated lineup population; the second lineup includes lineups among the multiple lineups except the first lineup; Determining, based on the first number of lineups in the deduplicated lineup population and the second number of lineups to be optimized in the first lineup population to be optimized, an updated lineup population generated by performing a first iterative operation on the first lineup population to be optimized; According to the updated lineup population, an optimized lineup population corresponding to the first lineup population to be optimized is determined.

11. The method according to claim 10, characterized in that The step of determining, based on the first number of lineups in the deduplicated lineup population and the second number of lineups to be optimized in the first lineup population to be optimized, an updated lineup population generated by performing a first iterative operation on the first lineup population to be optimized includes: If the first number of lineups in the deduplicated lineup population is greater than the second number of lineups to be optimized in the first lineup population to be optimized, obtaining a second lineup composition basic condition associated with the first lineup population to be optimized; Obtaining a third lineup from the deduplicated lineup population that meets the second lineup composition basic conditions; the lineup strength of the third lineup is greater than or equal to the lineup strength of a fourth lineup; the fourth lineup includes lineups from the deduplicated lineup population that meet the second lineup composition basic conditions except for the third lineup; Determine a difference between the second number and the fourth number of the third lineup, and obtain a fifth lineup from the deduplicated lineup population that does not meet the second lineup composition basic conditions; the number of the fifth lineup is the difference; the lineup strength of the fifth lineup is greater than or equal to the lineup strength of the sixth lineup; the sixth lineup includes lineups in the deduplicated lineup population that do not meet the second lineup composition basic conditions except the fifth lineup; Determine the third lineup and the fifth lineup as updated lineup populations generated by performing a first iteration operation on the first lineup population to be optimized; If the first number is less than or equal to the second number, the deduplicated lineup population is determined to be the updated lineup population generated by performing the first iterative operation on the first lineup population to be optimized.

12. The method according to claim 10, characterized in that The step of determining, based on the updated lineup population, an optimized lineup population corresponding to the first lineup population to be optimized includes: Obtaining an iterative operation number threshold corresponding to the iterative operation; the iterative operation includes a lineup addition operation, a lineup adjustment operation, a lineup evaluation operation, and a population update operation; If the iterative operation number threshold is equal to 1, determining the updated lineup population as the optimized lineup population corresponding to the first lineup population to be optimized; If the iterative operation number threshold is greater than 1, a second iterative operation is performed through the updated lineup population. When the iterative operation number is equal to the iterative operation number threshold, the lineup population generated by the iterative operation is determined as the optimized lineup population corresponding to the first lineup population to be optimized.

13. The method according to claim 1, wherein The B optimized lineup sets include the optimized lineup set X y ; y is a positive integer, and y is less than or equal to B; Determining the lineup ladder corresponding to each optimized lineup set according to the lineup strength of the optimized lineups included in each optimized lineup set includes: In the optimized lineup set X y The first optimized lineup is obtained from the optimized lineup set X. y An optimized lineup in The first optimized lineup and the second optimized lineup are virtual battled to obtain the lineup strength of the first optimized lineup; the second optimized lineup includes the optimized lineup set X y The optimized lineup other than the first optimized lineup; According to the optimized lineup set X y The optimized lineups in the above table correspond to the lineup strengths, and the optimized lineup set X y The optimized lineup in is sorted to obtain the optimized lineup set X y The corresponding lineup ladder to be optimized; According to the optimized lineup set X y The corresponding lineup ladder to be optimized generates the optimized lineup set X y Corresponding lineup ladder.

14. The method according to claim 13, characterized in that The optimized lineup set X y The corresponding lineup ladder to be optimized generates the optimized lineup set X y The corresponding lineup ladder includes: In the B basic conditions of lineup composition respectively included in the A lineup composition targets, the optimized lineup set X is obtained. y The corresponding A basic conditions for the formation of a lineup; the A basic conditions for the formation of a lineup include the basic conditions for the formation of a lineup Z w , w is a positive integer, and w is less than or equal to A; In the optimized lineup set X y In the corresponding lineup ladder to be optimized, obtain the basic conditions Z of the lineup composition. w The third optimized lineup; the lineup strength of the third optimized lineup is greater than or equal to the lineup strength of the fourth optimized lineup; the fourth optimized lineup includes the optimized lineup set X y The corresponding lineup to be optimized in the ladder meets the basic conditions of the lineup composition Z except for the third optimized lineup w Optimized lineup; In the optimized lineup set X y In the corresponding lineup ladder to be optimized, a fifth optimized lineup that does not meet the basic conditions for the A lineups is obtained, and V linkage relationships included in the fifth optimized lineup are determined; V is a positive integer; Determine the maximum levels corresponding to the V linkage relationships respectively, and obtain, among the V maximum levels, a maximum level that is greater than or equal to a level threshold as a valid level; In the fifth optimized lineup, a sixth optimized lineup is obtained for the effective level; the lineup strength of the sixth optimized lineup is greater than or equal to the lineup strength of the seventh optimized lineup; the seventh optimized lineup includes the optimized lineups in the fifth optimized lineup except the sixth optimized lineup; The optimized lineup set X is generated based on the third optimized lineup and the sixth optimized lineup. y Corresponding lineup ladder.

15. The method according to claim 1, wherein The A lineup composition targets include a first lineup composition target, and the first lineup composition target is one of the A lineup composition targets; The evaluation process of the A lineup composition targets is performed based on the B lineup ladders, including: In the B lineup ladders, respectively obtain a lineup that meets the first lineup composition goal and has the maximum lineup strength; Determine the strength trend of the first lineup composition target with respect to the number of B characters based on the lineup strengths corresponding to the B lineups; The first lineup composition target is evaluated based on the intensity trend to obtain a first evaluation result; the first evaluation result is used to adjust the first lineup composition target.

16. The method according to claim 1, wherein The A lineup composition targets include a second lineup composition target, and the second lineup composition target is a lineup composition target among the A lineup composition targets; the B lineup ladder includes a lineup ladder O p , p is a positive integer, and p is less than or equal to B; the number of B characters includes the lineup ladder O p The corresponding number of roles G p ; The evaluation process of the A lineup composition targets is performed based on the B lineup ladders, including: In the lineup ladder O p , for the second lineup composition target, U lineups are obtained; U is a positive integer greater than 1; the lineup strength corresponding to each of the U lineups is greater than or equal to the lineup strength of the remaining lineups; the remaining lineups include the lineup ladder O p A lineup that meets the second lineup composition goal except the U lineups; Perform variance processing on the lineup strength corresponding to the U lineups to obtain the second lineup composition target for the number of characters G p The variance of lineup strength; Determine the lineup strength variance corresponding to the second lineup composition target for the B number of characters; Determining, based on the B lineup strength variances, a strength variance trend of the second lineup composition target for the B number of characters; The second lineup composition target is evaluated based on the strength variance trend to obtain a second evaluation result; the second evaluation result is used to adjust the second lineup composition target.

17. A data processing device, characterized in that: include: The acquisition module is used to obtain A lineup composition targets; A is a positive integer; The A lineup composition targets each include B lineup composition basic conditions, and the B lineup composition basic conditions belonging to one lineup composition target each include different numbers of characters; the A lineup composition targets each include the same number of characters; B is a positive integer greater than 1; one lineup composition basic condition is used to indicate the basic conditions for generating a type of lineup; a generation module, configured to generate a plurality of initial lineup sets based on the B basic lineup composition conditions respectively included in the A lineup composition targets; wherein one initial lineup set is generated based on one basic lineup composition condition in one lineup composition target; An optimization module is configured to merge the initial lineup sets containing the same number of characters from the multiple initial lineup sets to obtain B lineup sets to be optimized, and optimize the B lineup sets to be optimized to obtain B optimized lineup sets; An evaluation module is used to determine the lineup ladder corresponding to each optimized lineup set according to the lineup strength of the optimized lineup included in each optimized lineup set, and to evaluate the A lineup composition targets respectively according to the B lineup ladders.

18. A computer device, characterized in that: include: processor, memory, and network interface; The processor is connected to the memory and the network interface, wherein the network interface is used to provide a data communication function, the memory is used to store a computer program, and the processor is used to call the computer program so that the computer device executes the method according to any one of claims 1 to 16.

19. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which is suitable for being loaded and executed by a processor, so that a computer device having the processor executes the method according to any one of claims 1 to 16.

20. A computer program product, characterized in that The computer program product comprises a computer program stored in a computer-readable storage medium. The computer program is suitable for being read and executed by a processor, so as to enable a computer device having the processor to perform the method according to any one of claims 1 to 16.

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