Game pet training program recommendation method, device, electronic device and storage medium

Abstract

The present application relates to the field of artificial intelligence technology, and specifically discloses a method, device, electronic device and storage medium for recommending a training plan for a game pet, wherein the method comprises: obtaining game account information and video account information of a player; determining a target game style of the player according to the game account information and video account information; obtaining at least one average attribute threshold of the game pet corresponding to the target game style, wherein each average attribute value is the maximum value or minimum value of the corresponding attribute; comparing at least one average attribute threshold with at least one attribute value of the game pet to be trained to determine at least one attribute difference; querying a training database of the game pet to be trained according to at least one attribute difference to obtain at least one candidate training plan; generating a target training plan according to at least one candidate training plan, and recommending the target training plan to the player.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method, device, electronic device and storage medium for recommending a game pet training plan. Background Art

[0002] At present, with the development of the diversity of battle-type virtual pet game play, pet battles are no longer limited to simple numerical competitions, but will affect the final battle results from aspects such as development, move matching, point addition, and team matching. In order to avoid the loss of players due to a single gameplay, games usually set up a large number of game pets, and these game pets have different training methods for different gameplays. It is difficult for ordinary players to obtain a feasible training program with a certain training intensity through their own exploration. At present, ordinary players usually retrieve the game pet training program of game masters through retrieval to learn and train, but players do not necessarily meet the training requirements required by the retrieved training program, and the training program cannot be implemented normally, so that there is a certain gap between the trained game pets and the results in the training program, resulting in training failure and poor game experience for players. Summary of the invention

[0003] In order to solve the above-mentioned problems existing in the prior art, the embodiments of the present application provide a method, device, electronic device and storage medium for recommending a game pet training plan, which can recommend to players a game pet training plan that is suitable for their game style and feasible, thereby enhancing the player's gaming experience.

[0004] In a first aspect, an embodiment of the present application provides a method for recommending a game pet training plan, the method comprising:

[0005] Obtain the player's game account information and video account information, where the game account information is used to record the game data corresponding to the player's game account, and the video account information is used to record the viewing information of the video account associated with the game account;

[0006] Determine the player's target gaming style based on the game account information and video account information;

[0007] Obtaining at least one average attribute threshold of the game pet corresponding to the target game style, wherein each of the at least one average attribute threshold corresponds to an attribute of the game pet, and each average attribute value is a maximum value or a minimum value of a corresponding attribute;

[0008] Determine at least one attribute difference by comparing at least one average attribute threshold with at least one attribute value of the game pet to be cultivated;

[0009] According to at least one attribute difference, query the training database of the game pet to be trained to obtain at least one candidate training plan, wherein at least one candidate training plan corresponds to at least one attribute difference;

[0010] A target training plan is generated according to at least one candidate training plan, and the target training plan is recommended to the player.

[0011] In a second aspect, an embodiment of the present application provides a game pet training program recommendation device, comprising:

[0012] The acquisition module is used to obtain the game account information and video account information of the player, wherein the game account information is used to record the game data corresponding to the game account of the player, and the video account information is used to record the viewing information of the video account associated with the game account;

[0013] An analysis module, used to determine the target game style of the player according to the game account information and the video account information, and obtain at least one average attribute threshold of the game pet corresponding to the target game style, wherein each of the at least one average attribute threshold corresponds to an attribute of the game pet, and each average attribute value is the maximum value or the minimum value of the corresponding attribute;

[0014] A processing module, for comparing at least one average attribute threshold with at least one attribute value of the game pet to be cultivated, determining at least one attribute difference, and querying a cultivation database of the game pet to be cultivated according to the at least one attribute difference to obtain at least one candidate cultivation plan, wherein the at least one candidate cultivation plan corresponds to the at least one attribute difference in a one-to-one manner;

[0015] The recommendation module is used to generate a target training plan based on at least one candidate training plan and recommend the target training plan to the player.

[0016] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, the processor is connected to a memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device performs the method of the first aspect.

[0017] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program enables a computer to execute the method of the first aspect.

[0018] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and is computer-operable to cause the computer to execute the method of the first aspect.

[0019] The implementation of the present application has the following beneficial effects:

[0020] In the embodiment of the present application, by analyzing the game account information and video account information of the player, the target game style that the player likes can be accurately determined. Then, by performing feature analysis on the game pet corresponding to the target game style, at least one average attribute threshold of the game pet of the game style is determined as the feature of the game pet of the game style. Then, at least one attribute difference is determined based on at least one average attribute threshold and at least one attribute value of the game pet to be cultivated, and then the cultivation database of the game pet to be cultivated is queried according to at least one attribute difference to obtain at least one candidate cultivation plan. Finally, a target cultivation plan is generated according to at least one candidate cultivation plan, and the target cultivation plan is recommended to the player. Thus, based on the target game style that the player likes, the gap between the feature of the game pet under the style and the current state of the game pet to be cultivated (i.e., at least one attribute difference) is determined, and then the cultivation database is consulted for the gap and combined with the game account information of the player, and the cultivation plan that the player can implement is obtained, and the recommendation is displayed to the player. Then, the player can quickly obtain a cultivation plan for the game pet that can be implemented immediately, has a certain strength, and is compounded with his own game style, thereby improving the player's gaming experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the implementation modes of the present application, the drawings required for use in the description of the implementation modes will be briefly introduced below. Obviously, the drawings described below are some implementation modes of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 A schematic diagram of the hardware structure of a gaming pet training program recommendation device provided in an embodiment of the present application;

[0023] Figure 2 A system framework diagram of a method for recommending a game pet training program provided in an embodiment of the present application;

[0024] Figure 3 A flowchart of a method for recommending a game pet training program provided in an embodiment of the present application;

[0025] Figure 4 A schematic diagram of a property list of a game pet provided in an embodiment of the present application;

[0026] Figure 5 A flowchart of a method for obtaining at least one average attribute threshold of a game pet corresponding to a target game style provided in an embodiment of the present application;

[0027] Figure 6 A schematic diagram of determining a cultivation direction of an attribute corresponding to each attribute difference value according to each attribute difference value in at least one attribute difference value provided by an embodiment of the present application;

[0028] Figure 7 A functional module composition block diagram of a gaming pet training program recommendation device provided in an embodiment of the present application;

[0029] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0031] The terms "first", "second", "third" and "fourth" etc. in the specification and claims of the present application and the drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0032] Reference to "embodiment" herein means that a particular feature, result, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0033] First, see Figure 1 , Figure 1 The hardware structure diagram of a game pet training program recommendation device provided in the embodiment of the present application is as follows. The game pet training program recommendation device 100 includes at least one processor 101, a communication line 102, a memory 103 and at least one communication interface 104.

[0034] In this embodiment, the processor 101 can be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present application.

[0035] The communication link 102 may include a path to transmit information between the above components.

[0036] The communication interface 104 may be any transceiver-like device (eg, antenna, etc.) for communicating with other devices or communication networks, such as Ethernet, RAN, wireless local area networks (WLAN), etc.

[0037] The memory 103 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0038] In this embodiment, the memory 103 can exist independently and be connected to the processor 101 via the communication line 102. The memory 103 can also be integrated with the processor 101. The memory 103 provided in the embodiment of the present application can generally be non-volatile. Among them, the memory 103 is used to store computer-executable instructions for executing the scheme of the present application, and the execution is controlled by the processor 101. The processor 101 is used to execute the computer-executable instructions stored in the memory 103, so as to implement the method provided in the following embodiment of the present application.

[0039] In an optional implementation, the computer-executable instructions may also be referred to as application code, which is not specifically limited in this application.

[0040] In an optional embodiment, the processor 101 may include one or more CPUs, such as Figure 1 CPU0 and CPU1 in.

[0041] In an optional embodiment, the game pet training program recommendation device 100 may include multiple processors, such as Figure 1 1 and 107. Each of these processors may be a single-CPU processor or a multi-CPU processor. A processor herein may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0042] In an optional embodiment, if the game pet training program recommendation device 100 is a server, for example, it can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms. Then the game pet training program recommendation device 100 can also include an output device 105 and an input device 106. The output device 105 communicates with the processor 101 and can display information in a variety of ways. For example, the output device 105 can be a liquid crystal display (LCD), a light emitting diode (LED) display device, a cathode ray tube (CRT) display device, or a projector. The input device 106 communicates with the processor 101 and can receive user input in a variety of ways. For example, the input device 106 can be a mouse, a keyboard, a touch screen device, or a sensor device.

[0043] The above-mentioned game pet training program recommendation device 100 can be a general device or a special device. The embodiment of the present application does not limit the type of the game pet training program recommendation device 100.

[0044] Secondly, Figure 2A system framework diagram of a method for recommending a game pet training program provided in an embodiment of the present application. Specifically, the system may include: a collection device 201, a recommendation device 202, and a database 203. Among them, the collection device 201 may be a smart phone (such as an Android phone, an iOS phone, a Windows Phone phone, etc.), a tablet computer, a PDA, a laptop computer, a mobile Internet device MID (Mobile Internet Devices, referred to as: MID) and other electronic devices that can run games. Specifically, the collection device 201 can collect the player's game account information and video account information during the game, and send the acquired information to the recommendation device 202. The recommendation device 202 may be a server for receiving the game account information and video account information sent by the collection device 201, and analyzing it, so as to call the data in the database 203 to generate a training program according to the analysis results.

[0045] In this embodiment, based on the target game style preferred by the player, the difference between the characteristics of the game pet under the style and the current state of the game pet to be cultivated (i.e., at least one attribute difference) is determined, and then the cultivation database is consulted for the difference and combined with the game account information of the player, and the cultivation plan that the player can implement is obtained, and the cultivation plan is recommended and displayed to the player. Then, the player can quickly obtain a cultivation plan for the game pet that can be implemented immediately, has a certain strength, and is compatible with his own game style, thereby improving the player's gaming experience.

[0046] The following is an explanation of the recommended method for the game pet training program disclosed in this application:

[0047] See also Figure 3 , Figure 3 A flowchart of a method for recommending a game pet training program provided in an embodiment of the present application. The method for recommending a game pet training program includes the following steps:

[0048] 301: Get the player's game account information and video account information.

[0049] In this embodiment, the game account information is used to record the game data corresponding to the player's game account, such as: account ID, character information, battle information, item list, pet list, pet information, etc. The video account information is used to record the viewing information of the player's video account, such as: favorite video list, followed video account list, viewing time, etc. Specifically, the identity corresponding to the video account and the game account is the player. When the player registers the game account, the player can fill in the corresponding video account information to associate the video account with the game account; or the identity information filled in by the player when registering the game account can be used to query the video account with the same identity information and display it to the player, and then determine the specific video account according to the player's choice, and then associate the video account with the game account.

[0050] Exemplarily, the information acquisition step can be an operation automatically triggered when the game software detects that the player performs operations related to cultivation in the game, such as opening the cultivation interface, checking the cultivation direction of the game pet, etc. Specifically, when the player performs the above operations, the game software can perform the information acquisition step through the device it runs, that is, the acquisition device 201, such as a smart phone, a personal computer, etc., to obtain the game account information and video account information of the player. Specifically, after the player installs the game software in the acquisition device 201, the game software will display the information acquisition request to the player while popping up the registration and login interface, requesting the player to grant the game software the right to call the personal information entered by the player when registering, the various information in the video account corresponding to the personal information, and the various data in the game. Based on this, during the player's game process, when the information acquisition operation is triggered, the game software can obtain the game account information and video account information of the player according to the information acquisition authority granted by the player when registering and logging in.

[0051] 302: Determine the target game style of the player according to the game account information and the video account information.

[0052] In this embodiment, the game account information is used to record the game data under the game account, such as account ID, character information, battle information, item list, pet list, pet information, etc. Based on this, the player's style tendency when playing games can be determined by analyzing these data. For example, is it simply to pursue victory or to have fun? For the former, it means that in order to win, the player does not have too many requirements for a specific play style, and can use any style as long as it can win. For the latter, it means that the player is fond of a certain style and plays for his own interest, and does not care whether he wins or not.

[0053] At the same time, in this embodiment, the video account information records the viewing information of the video account associated with the game account. According to the description of the method of associating the video account and the game account in step 301, the owner of the video account is also the player. Therefore, by analyzing the video account information, the game style of the game video that the player pays attention to when offline can be obtained. Now a major reason for people to watch videos is because the videos contain things that they are interested in. Based on this, by analyzing this information, the game style that the player likes or is interested in can be obtained.

[0054] Based on this, in this embodiment, the weights of the game style with a high winning rate in the current environment and the game style preferred by the player can be determined according to the style tendency of the player when playing the game. Specifically, when the style tendency is more inclined to seek victory, it means that the player does not have too many requirements for the specific playing style, and can use any game style to play the game in order to win. At this time, a higher weight can be given to the game style with a high winning rate in the current environment. When the style tendency is more inclined to seek happiness, a higher weight can be given to the game style preferred by the player, and the specific weight value can be determined by the degree of tendency. Thus, the game style with a high winning rate in the current environment and the game style preferred by the player are fused through the weights of the game style with a high winning rate in the current environment and the game style preferred by the player, and the target game style can be obtained.

[0055] 303: Obtain at least one average attribute threshold of the game pet corresponding to the target game style.

[0056] In this embodiment, each of the at least one average attribute threshold corresponds to an attribute of the game pet, and each average attribute value is the maximum or minimum value of the corresponding attribute. Figure 4 As shown, Figure 4 A property list of a game pet is shown, and it can be seen that each game pet has a series of properties, such as health, attack power, defense power, speed, etc. Based on this, the at least one average property threshold is a set of average thresholds corresponding to each of these properties.

[0057] In this embodiment, a method for obtaining at least one average attribute threshold of a game pet corresponding to a target game style is provided, such as Figure 5 As shown, the method includes:

[0058] 501: Query the battle database according to the target game style and obtain at least one game team.

[0059] In this embodiment, the battle library is used to store all game teams and game styles of all game teams that appear in the game, and the game style of each game team in at least one game team is the same as the target game style. In short, the battle library stores multiple game teams, and each game team corresponds to a game style tag. These game teams can be uploaded by players themselves or set by the game official. Based on this, the battle library is queried through the target game style, and the game teams with the same game style tags as the target game style are selected as the at least one game team.

[0060] 502: Obtain the main game pet of each game team respectively, and obtain at least one main game pet.

[0061] In this embodiment, each game style corresponds to a game skill in a game. In simple terms, different game styles are styles formed by the gameplay derived from the mechanisms of different game skills. Therefore, the game skills corresponding to each game style are the core skills of the game style. Based on this, a game pet with the core skills of the game style can be determined in each game team as the main game pet of the game team.

[0062] 503: Extract features of at least one main game pet to obtain game pet features.

[0063] In this embodiment, the game pet feature is used to identify the tendency information of the attribute value of at least one attribute of the game pet corresponding to the target game style. Specifically, feature extraction can be performed on at least one main game pet to obtain at least one candidate game pet feature corresponding to at least one main game pet, and then statistical analysis can be performed on at least one candidate game pet feature to obtain the game pet feature. Exemplarily, after feature extraction of at least one main game pet, it is found that the speed values ​​of all main game pets are lower than the average speed value in the current environment, and all have high attack power, then the game pet feature corresponding to the game style is: low speed, high attack.

[0064] 504: Determine at least one average attribute threshold value according to the game pet characteristics and the current game environment information.

[0065] In this embodiment, the tendency information of each attribute in at least one attribute can be determined according to the game pet feature, and then the maximum or minimum value of each attribute in the current game environment can be determined according to the tendency information of each attribute, and then the set of the maximum or minimum value of each attribute can be used as at least one average attribute threshold. Specifically, the example of the game pet feature "low speed, high attack" in step 503 above is used, where the tendency of speed is low, which means that in this game style, the lower the speed value, the better. Therefore, the minimum value of the speed value of the game pet under other game styles in the current game environment can be obtained as the average attribute threshold of the game pet speed value under this game style, that is, the maximum value of the game pet speed value under this game style is the minimum value of the speed value of the game pet under other game styles in the current game environment. Similarly, the tendency of attack power is high, which means that in this game style, the higher the attack power, the better. Therefore, the maximum value of the attack power of the game pet under other game styles in the current game environment can be obtained as the average attribute threshold of the game pet attack power under this game style, that is, the minimum value of the game pet attack power under this game style is the maximum value of the game pet attack power under other game styles in the current game environment.

[0066] 304: Compare at least one average attribute threshold with at least one attribute value of the game pet to be cultivated to determine at least one attribute difference.

[0067] In this embodiment, the difference between each average attribute threshold value in at least one average attribute threshold value and the corresponding attribute value in at least one attribute value is used as the attribute difference value of the attribute. Specifically, the difference between the average attribute threshold value of attack power and the actual attack power value is used as the attribute difference value of attack power; the difference between the average attribute threshold value of defense power and the actual defense power value is used as the attribute difference value of defense power; and so on.

[0068] 305: Query the training database of the game pet to be trained according to at least one attribute difference to obtain at least one candidate training plan.

[0069] In this embodiment, at least one candidate training program corresponds to at least one attribute difference. Specifically, the training direction of the attribute corresponding to each attribute difference can be determined according to each attribute difference in the at least one attribute difference. For example, Figure 6As shown, when the attribute difference is a negative number, it means that the average attribute threshold is less than the actual value. Combined with the tendency information of each attribute, when the tendency is low, it means that the smaller the actual value, the better, and the maximum value that can be achieved is the average attribute threshold. In the above case, the cultivation direction of the attribute is to decrease; when the tendency is high, it means that the larger the actual value, the better, and the minimum value that can be achieved is the average attribute threshold. The above situation has met the condition, and the cultivation direction of the attribute is empty, that is, no cultivation is required.

[0070] Then, according to each attribute difference, the cultivation direction of the attribute corresponding to each attribute difference, and the game account information, the cultivation database can be queried to obtain the candidate cultivation plans of the attribute corresponding to each attribute difference, and then the set of candidate cultivation plans of the attribute corresponding to each attribute difference is used as at least one candidate cultivation plan.

[0071] 306: Generate a target training plan according to at least one candidate training plan, and recommend the target training plan to the player.

[0072] In this embodiment, the influence of each candidate training scheme in at least one candidate training scheme on other attributes can be determined to obtain at least one influence information, wherein the other attributes are all attributes of the pet to be trained, except the attributes corresponding to each candidate training scheme, and the at least one influence information corresponds to the at least one candidate training scheme. Then, the at least one candidate training scheme is integrated and adjusted according to the at least one influence information to obtain the target training scheme.

[0073] In addition, in this embodiment, the training program generated in the end may be arranged as one, and the resources and time spent on each training program are determined respectively, and then each training program is scored in combination with the player's item list. Then, the training programs are sorted from large to small according to the score of each training program, and after the corresponding display list is generated, the display list is displayed to the player through the display device in the acquisition device 201 for the convenience of the player to browse.

[0074] In summary, in the game pet training program recommendation method provided by the present invention, by analyzing the game account information and video account information of the player, the target game style preferred by the player can be accurately determined. Then, by performing feature analysis on the game pet corresponding to the target game style, at least one average attribute threshold of the game pet of the game style is determined as the feature of the game pet of the game style. Then, at least one attribute difference is determined based on at least one average attribute threshold and at least one attribute value of the game pet to be trained, and then the training database of the game pet to be trained is queried according to at least one attribute difference to obtain at least one candidate training program. Finally, a target training program is generated according to at least one candidate training program, and the target training program is recommended to the player. Thus, based on the target game style preferred by the player, the gap between the feature of the game pet under the style and the current state of the game pet to be trained (i.e., at least one attribute difference) is determined, and then the training database is consulted for the gap and combined with the game account information of the player, and the training program that the player can implement is obtained, and the training program is recommended to the player. Then, the player can quickly obtain a training program for the game pet that can be implemented immediately, has a certain strength, and is compounded with his own game style, thereby improving the player's gaming experience.

[0075] See also Figure 7 , Figure 7 This is a functional module block diagram of a game pet training program recommendation device provided in the embodiment of the present application. Figure 7 As shown, the game pet training program recommendation device 700 includes:

[0076] The acquisition module 701 is used to obtain the game account information and video account information of the player, wherein the game account information is used to record the game data corresponding to the game account of the player, and the video account information is used to record the viewing information of the video account associated with the game account;

[0077] The analysis module 702 is used to determine the target game style of the player according to the game account information and the video account information, and obtain at least one average attribute threshold of the game pet corresponding to the target game style, wherein each average attribute threshold of the at least one average attribute threshold corresponds to an attribute of the game pet, and each average attribute value is the maximum value or the minimum value of the corresponding attribute;

[0078] The processing module 703 is used to compare at least one average attribute threshold with at least one attribute value of the game pet to be cultivated, determine at least one attribute difference, and query the cultivation database of the game pet to be cultivated according to the at least one attribute difference to obtain at least one candidate cultivation plan, wherein the at least one candidate cultivation plan corresponds to the at least one attribute difference one by one;

[0079] The recommendation module 704 is used to generate a target training program according to at least one candidate training program, and recommend the target training program to the player.

[0080] In the embodiment of the present invention, in terms of obtaining at least one average attribute threshold of the game pet corresponding to the target game style, the analysis module 702 is specifically used to:

[0081] Querying a battle library according to the target game style to obtain at least one game team, wherein the battle library is used to store all game teams and game styles of all game teams that appear in the game, and the game style of each game team in the at least one game team is the same as the target game style;

[0082] Obtain the main game pet of each game team respectively, and obtain at least one main game pet;

[0083] Extracting features of at least one main game pet to obtain game pet features, wherein the game pet features are used to identify tendency information of attribute values ​​of at least one attribute of the game pet corresponding to the target game style;

[0084] At least one average attribute threshold is determined based on the game pet characteristics and current game environment information.

[0085] In the embodiment of the present invention, in extracting features of at least one main game pet to obtain game pet features, the analysis module 702 is specifically used to:

[0086] Extracting features of at least one main game pet to obtain at least one candidate game pet feature, wherein the at least one candidate game pet feature corresponds one-to-one to the at least one main game pet;

[0087] Perform statistical analysis on at least one candidate game pet feature to obtain the game pet feature.

[0088] In an embodiment of the present invention, in determining at least one average attribute threshold according to the game pet characteristics and the current game environment information, the analysis module 702 is further used to:

[0089] Determine tendency information of each attribute of at least one attribute according to the game pet characteristics;

[0090] Determine the maximum or minimum value of each attribute in the current game environment based on the tendency information of each attribute;

[0091] The set of maximum or minimum values ​​of each attribute is used as at least one average attribute threshold.

[0092] In the embodiment of the present invention, in terms of querying the cultivation database of the game pet to be cultivated according to at least one attribute difference to obtain at least one candidate cultivation plan, the processing module 703 is specifically used to:

[0093] According to each attribute difference of the at least one attribute difference, determining a cultivation direction of the attribute corresponding to each attribute difference;

[0094] According to each attribute difference, the cultivation direction of the attribute corresponding to each attribute difference, and the game account information, query the cultivation database to obtain a candidate cultivation plan for the attribute corresponding to each attribute difference;

[0095] A set of candidate training schemes for the attribute corresponding to each attribute difference is used as at least one candidate training scheme.

[0096] In an embodiment of the present invention, in terms of generating a target training program according to at least one candidate training program, the recommendation module 704 is specifically configured to:

[0097] Determine the influence of each candidate training scheme in at least one candidate training scheme on other attributes to obtain at least one influence information, wherein the other attributes are all attributes of the pet to be trained, excluding the attributes corresponding to each candidate training scheme, and the at least one influence information corresponds to the at least one candidate training scheme one by one;

[0098] At least one candidate training program is integrated and adjusted according to at least one influencing information to obtain a target training program.

[0099] See also Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 8 As shown, the electronic device 800 includes a transceiver 801, a processor 802 and a memory 803. They are connected via a bus 804. The memory 803 is used to store computer programs and data, and can transmit the data stored in the memory 803 to the processor 802.

[0100] The processor 802 is used to read the computer program in the memory 803 and perform the following operations:

[0101] Obtain the player's game account information and video account information, where the game account information is used to record the game data corresponding to the player's game account, and the video account information is used to record the viewing information of the video account associated with the game account;

[0102] Determine the player's target gaming style based on the game account information and video account information;

[0103] Obtaining at least one average attribute threshold of the game pet corresponding to the target game style, wherein each of the at least one average attribute threshold corresponds to an attribute of the game pet, and each average attribute value is a maximum value or a minimum value of a corresponding attribute;

[0104] Determine at least one attribute difference by comparing at least one average attribute threshold with at least one attribute value of the game pet to be cultivated;

[0105] According to at least one attribute difference, query the training database of the game pet to be trained to obtain at least one candidate training plan, wherein at least one candidate training plan corresponds to at least one attribute difference;

[0106] A target training plan is generated according to at least one candidate training plan, and the target training plan is recommended to the player.

[0107] In the embodiment of the present invention, in terms of obtaining at least one average attribute threshold of the game pet corresponding to the target game style, the processor 802 is specifically configured to perform the following operations:

[0108] Querying a battle library according to the target game style to obtain at least one game team, wherein the battle library is used to store all game teams and game styles of all game teams that appear in the game, and the game style of each game team in the at least one game team is the same as the target game style;

[0109] Obtain the main game pet of each game team respectively, and obtain at least one main game pet;

[0110] Extracting features of at least one main game pet to obtain game pet features, wherein the game pet features are used to identify tendency information of attribute values ​​of at least one attribute of the game pet corresponding to the target game style;

[0111] At least one average attribute threshold is determined based on the game pet characteristics and current game environment information.

[0112] In the embodiment of the present invention, in extracting features of at least one main game pet to obtain game pet features, the processor 802 is specifically configured to perform the following operations:

[0113] Extracting features of at least one main game pet to obtain at least one candidate game pet feature, wherein the at least one candidate game pet feature corresponds one-to-one to the at least one main game pet;

[0114] Perform statistical analysis on at least one candidate game pet feature to obtain the game pet feature.

[0115] In an embodiment of the present invention, in determining at least one average attribute threshold according to the game pet characteristics and the current game environment information, the processor 802 is specifically configured to perform the following operations:

[0116] Determine tendency information of each attribute of at least one attribute according to the game pet characteristics;

[0117] Determine the maximum or minimum value of each attribute in the current game environment based on the tendency information of each attribute;

[0118] The set of maximum or minimum values ​​of each attribute is used as at least one average attribute threshold.

[0119] In an embodiment of the present invention, in terms of querying a cultivation database of a game pet to be cultivated according to at least one attribute difference to obtain at least one candidate cultivation plan, the processor 802 is specifically configured to perform the following operations:

[0120] According to each attribute difference of the at least one attribute difference, determining a cultivation direction of the attribute corresponding to each attribute difference;

[0121] According to each attribute difference, the cultivation direction of the attribute corresponding to each attribute difference, and the game account information, query the cultivation database to obtain a candidate cultivation plan for the attribute corresponding to each attribute difference;

[0122] A set of candidate training schemes for the attribute corresponding to each attribute difference is used as at least one candidate training scheme.

[0123] In an embodiment of the present invention, in terms of generating a target training program according to at least one candidate training program, the processor 802 is specifically configured to perform the following operations:

[0124] Determine the influence of each candidate training scheme in at least one candidate training scheme on other attributes to obtain at least one influence information, wherein the other attributes are all attributes of the pet to be trained, excluding the attributes corresponding to each candidate training scheme, and the at least one influence information corresponds to the at least one candidate training scheme one by one;

[0125] At least one candidate training program is integrated and adjusted according to at least one influencing information to obtain a target training program.

[0126] It should be understood that the game pet training program recommendation device in the present application may include a smart phone (such as an Android phone, an iOS phone, a Windows Phone phone, etc.), a tablet computer, a PDA, a laptop computer, a mobile Internet device MID (Mobile Internet Devices, referred to as: MID), a robot or a wearable device, etc. The above-mentioned game pet training program recommendation device is only an example, not an exhaustive list, including but not limited to the above-mentioned game pet training program recommendation device. In actual applications, the above-mentioned game pet training program recommendation device may also include: a smart vehicle terminal, a computer device, etc.

[0127] Through the description of the above implementation modes, those skilled in the art can clearly understand that the present invention can be implemented by combining software with a hardware platform. Based on such an understanding, all or part of the contribution of the technical solution of the present invention to the background technology can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various implementation modes of the present invention or some parts of the implementation modes.

[0128] Therefore, the present application also provides a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement some or all steps of any of the game pet training program recommendation methods described in the above method implementation. For example, the storage medium may include a hard disk, a floppy disk, an optical disk, a tape, a disk, a USB flash drive, a flash memory, etc.

[0129] The present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any one of the game pet training program recommendation methods described in the above method implementation.

[0130] It should be noted that, for the sake of simplicity, the aforementioned method implementations are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the implementations described in the specification are all optional implementations, and the actions and modules involved are not necessarily required by the present application.

[0131] In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device implementation described above is only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0133] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0134] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software program module.

[0135] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0136] A person skilled in the art may understand that all or part of the steps in the various methods of the above-mentioned embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable memory, and the memory may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0137] The above is a detailed introduction to the implementation methods of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above implementation methods is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for recommending a game pet training plan, characterized in that: The method comprises: Obtaining the game account information and video account information of the player, wherein the game account information is used to record the game data corresponding to the game account of the player, and the video account information is used to record the viewing information of the video account associated with the game account; Determining a target gaming style of the player according to the gaming account information and the video account information; Obtaining at least one average attribute threshold of the game pet corresponding to the target game style, wherein each of the at least one average attribute threshold corresponds to an attribute of the game pet, and each average attribute threshold is a maximum value or a minimum value of a corresponding attribute; Determine at least one attribute difference by comparing the at least one average attribute threshold with at least one attribute value of the game pet to be cultivated; According to the at least one attribute difference, query the training database of the game pet to be trained to obtain at least one candidate training plan, wherein the at least one candidate training plan corresponds to the at least one attribute difference in one-to-one manner; generating a target training program according to the at least one candidate training program, and recommending the target training program to the player; The step of obtaining at least one average attribute threshold of the game pet corresponding to the target game style includes: querying a battle library according to the target game style to obtain at least one game team, wherein the battle library is used to store all game teams appearing in the game and the game styles of all game teams, and the game style of each game team in the at least one game team is the same as the target game style; Obtaining the main game pet of each game team respectively, and obtaining at least one main game pet; Extracting features of the at least one main game pet to obtain game pet features, wherein the game pet features are used to identify tendency information of attribute values ​​of at least one attribute of the game pet corresponding to the target game style; The at least one average attribute threshold is determined according to the game pet characteristics and current game environment information.

2. The method according to claim 1, characterized in that The step of extracting features from the at least one main game pet to obtain game pet features includes: Extracting features of the at least one main game pet to obtain at least one candidate game pet feature, wherein the at least one candidate game pet feature corresponds one-to-one to the at least one main game pet; Perform statistical analysis on the at least one candidate game pet feature to obtain the game pet feature.

3. The method according to claim 2, characterized in that The step of determining the at least one average attribute threshold according to the game pet characteristics and the current game environment information includes: Determine the tendency information of each attribute of the at least one attribute according to the game pet feature; Determine the maximum value or minimum value of each attribute in the current game environment according to the tendency information of each attribute; A set of maximum values ​​or minimum values ​​of each attribute is used as the at least one average attribute threshold.

4. The method according to claim 1, characterized in that: The step of querying the training database of the game pet to be trained according to the at least one attribute difference to obtain at least one candidate training plan includes: According to each attribute difference of the at least one attribute difference, determining a cultivation direction of the attribute corresponding to each attribute difference; According to each attribute difference and the cultivation direction of the attribute corresponding to each attribute difference, query the cultivation database to obtain a candidate cultivation plan for the attribute corresponding to each attribute difference; A set of candidate training schemes for the attributes corresponding to each attribute difference is used as the at least one candidate training scheme.

5. The method according to claim 1, characterized in that The generating a target training program according to the at least one candidate training program comprises: Determine the influence of each candidate training scheme in the at least one candidate training scheme on other attributes to obtain at least one influence information, wherein the other attributes are all attributes of the game pet to be trained, excluding the attributes corresponding to each candidate training scheme, and the at least one influence information corresponds to the at least one candidate training scheme one by one; The at least one candidate training program is integrated and adjusted according to the at least one influence information to obtain the target training program.

6. A device for recommending a game pet training plan, characterized in that: The device comprises: A collection module, used to obtain the game account information and video account information of the player, wherein the game account information is used to record the game data corresponding to the game account of the player, and the video account information is used to record the viewing information of the video account associated with the game account; An analysis module, used to determine the target game style of the player according to the game account information and the video account information, and obtain at least one average attribute threshold of the game pet corresponding to the target game style, wherein each of the at least one average attribute threshold corresponds to an attribute of the game pet, and each average attribute threshold is a maximum value or a minimum value of a corresponding attribute; A processing module, used for comparing the at least one average attribute threshold with at least one attribute value of the game pet to be cultivated, determining at least one attribute difference, and querying the cultivation database of the game pet to be cultivated according to the at least one attribute difference to obtain at least one candidate cultivation plan, wherein the at least one candidate cultivation plan corresponds to the at least one attribute difference one by one; A recommendation module, configured to generate a target training program according to the at least one candidate training program, and recommend the target training program to the player; Wherein, in the aspect of obtaining at least one average attribute threshold of the game pet corresponding to the target game style, the analysis module is specifically used to: querying a battle library according to the target game style to obtain at least one game team, wherein the battle library is used to store all game teams appearing in the game and the game styles of all game teams, and the game style of each game team in the at least one game team is the same as the target game style; Obtaining the main game pet of each game team respectively, and obtaining at least one main game pet; Extracting features of the at least one main game pet to obtain game pet features, wherein the game pet features are used to identify tendency information of attribute values ​​of at least one attribute of the game pet corresponding to the target game style; The at least one average attribute threshold is determined according to the game pet characteristics and current game environment information.

7. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the one or more programs include instructions for executing the steps in the method described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 5.

Images