Game Plan Adjustment Method, Device, Electronic Device and Storage Medium for Training Games

Abstract

An embodiment of the present invention relates to a method, device, electronic device and storage medium for adjusting a game plan of a training game. The method includes: obtaining an average focus value and a training performance score of a target user in the current game training stage; determining a game plan adjustment strategy for the next stage based on the average focus value and the training performance score; and adjusting the target training game based on the game plan adjustment strategy. Thus, according to the recent training results of the user, more positive feedback is given to the user in the next training cycle, enabling the user to have more motivation to persist in the game training and enhancing the user experience and training effect.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of digital game training, and in particular, to a method, apparatus, electronic device, and storage medium for adjusting a game plan of a training game. Background Art

[0002] Currently, some training games are widely used in people's lives, such as improving users' concentration and relaxing users' brain nerves. Some existing physical trainings require users to perform a large number of repetitive movements, which is usually a boring process and users do not like to participate. Users' training attitudes are directly related to their compliance and success rate during physical training. Research shows that by providing users with a sense of control to motivate and empower them, the training effect of users can be improved.

[0003] Games are things that can make people happy. Because games bring fun, people will improve their own abilities in order to continue playing games. Different game goals can bring different effects. When users use games for physical therapy, the compliance of users with the games can be improved. Therefore, games are increasingly being used to strengthen and verify training programs aimed at improving movement outcomes. In the existing game training systems today, the games used are mainly traditional games. However, the existing games do not adjust the game difficulty according to the training feedback of users. Therefore, it is very likely that there will be a sense of frustration due to too high game difficulty or a sense of boredom due to too low game difficulty, affecting the training process. As an important part of the game training system, the game part not being able to well reflect its role is a major defect of the existing game training systems. Summary of the Invention

[0004] In view of this, to solve the above technical problems or some of the technical problems, the embodiments of the present invention provide a method, apparatus, electronic device, and storage medium for adjusting a game plan of a training game.

[0005] In a first aspect, the embodiments of the present invention provide a method for adjusting a game plan of a training game, including:

[0006] Obtaining the average concentration value and training performance score of the target user in the current game training stage;

[0007] Determining the game plan adjustment strategy for the next stage based on the average concentration value and training performance score;

[0008] Adjusting the target training game based on the game plan adjustment strategy.

[0009] In a possible implementation manner, the method further includes:

[0010] Initial classification of the game difficulty level of the target training game is performed based on preset rules to obtain multiple initial game levels and the corresponding focus value intervals and level coefficients for each initial game level.

[0011] In a possible implementation manner, the method further includes:

[0012] Collect historical game training data obtained by multiple users through the target training game, where the historical game training data at least includes the average focus value and training performance score of the users;

[0013] Based on the average focus value of the users in the historical game training data, initial classification of the game difficulty level of the target training game is performed to obtain multiple initial game levels and the corresponding focus value intervals for each initial game level;

[0014] Set the corresponding level coefficients for each initial game level based on game experience and training performance scores.

[0015] In a possible implementation manner, the method further includes:

[0016] Collect game training data of a target user for multiple effective training days in the current game training phase, where an effective training day is a single-day game training duration reaching a preset time threshold;

[0017] Analyze the game training data of the multiple effective training days to obtain the average focus value and training performance score corresponding to each effective training day.

[0018] In a possible implementation manner, the method further includes:

[0019] Calculate the median of the average focus values of multiple effective training days;

[0020] Determine the target initial game level corresponding to the median of the average focus values, and the target level coefficient corresponding to the target initial game level;

[0021] Use the target level coefficient as the game training personalization coefficient of the target user;

[0022] Determine the game plan adjustment strategy for the next phase based on the game training personalization coefficient and the training performance score.

[0023] In a possible implementation manner, the method further includes:

[0024] Determine the personalized focus value interval of the target user based on the game training personalization coefficient and the focus value interval corresponding to the target initial game level;

[0025] Redetermine the game difficulty level based on the training performance score.

[0026] In a possible implementation, the method further includes:

[0027] Redividing the game levels of the target training game based on the personalized focus value range of the target user;

[0028] Adjusting the game content of the target training game based on the newly determined game difficulty.

[0029] In a second aspect, an embodiment of the present invention provides a game plan adjustment device for a training game, including:

[0030] An acquisition module, configured to acquire the average focus value and training performance score of a target user in the current game training stage;

[0031] A determination module, configured to determine a game plan adjustment strategy for the next stage based on the average focus value and training performance score;

[0032] An adjustment module, configured to adjust the target training game based on the game plan adjustment strategy.

[0033] In a third aspect, an embodiment of the present invention provides an electronic device, including: a processor and a memory, where the processor is configured to execute a game plan adjustment program for a training game stored in the memory to implement the game plan adjustment method for a training game described in the first aspect above.

[0034] In a fourth aspect, an embodiment of the present invention provides a storage medium, including: the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the game plan adjustment method for a training game described in the first aspect above.

[0035] The game plan adjustment solution for a training game provided by the embodiment of the present invention obtains the average focus value and training performance score of a target user in the current game training stage; determines a game plan adjustment strategy for the next stage based on the average focus value and training performance score; and adjusts the target training game based on the game plan adjustment strategy. Compared with existing games that do not adjust the game difficulty according to the user's training feedback, there are problems such as a sense of frustration caused by too high game difficulty or boredom caused by too low game difficulty. With this solution, according to the user's recent training results, more positive feedback is given to the user in the next training cycle, making the user more motivated to persist in game training and improving the user experience and training effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flowchart of a game plan adjustment method for a training game provided by an embodiment of the present invention;

[0037] Figure 2 Schematic flowchart of another method for adjusting the game plan of the training game provided by an embodiment of the present invention;

[0038] Figure 3 Schematic structural diagram of a device for adjusting the game plan of a training game provided by an embodiment of the present invention;

[0039] Figure 4 Schematic structural diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0041] For ease of understanding of the embodiments of the present invention, the following will further explain with specific embodiments with reference to the accompanying drawings. The embodiments do not constitute a limitation to the embodiments of the present invention.

[0042] Figure 1 Schematic flowchart of a method for adjusting the game plan of a training game provided by an embodiment of the present invention, as Figure 1 shown, the method specifically includes:

[0043] S11. Obtain the average concentration value and training performance score of the target user in the current game training stage.

[0044] In the embodiments of the present invention, games, audio, and video content are used to test and train the user's concentration, reaction ability, and cognitive ability, etc. In order to balance the challenge and frustration of training, while ensuring the basic training effect, a game training difficulty that is more matched to the user's ability and recent training status is provided, balancing the challenge and frustration of training, and improving the user experience and training effect. The applicable training scope includes: games such as neurofeedback and brain relaxation training, excluding cognitive ability training and evaluation.

[0045] This method is preferably applicable to the scenario of adjusting the game plan of the concentration training game. First, the average concentration value and training performance score are explained:

[0046] Average concentration value: The average value of the concentration values obtained by the user through multiple game trainings; among them, the concentration value is obtained through electroencephalogram data analysis.

[0047] Training performance score: The performance score obtained by the user through in-game operations and clearing the level corresponding to each game training.

[0048] Further, collect the game training data of the target user on multiple effective training days in the current game training stage, where an effective training day is a single-day game training duration reaching a preset time threshold (for example, 3 hours); analyze the game training data of multiple effective training days to obtain the average focus value and training performance score corresponding to each effective training day.

[0049] S12. Determine the game plan adjustment strategy for the next stage based on the average focus value and training performance score.

[0050] S13. Adjust the target training game based on the game plan adjustment strategy.

[0051] In the embodiment of the present invention, the game difficulty levels of the training games are pre-classified to obtain multiple game levels and the corresponding focus value intervals for each game level. Calculate the median of the average focus values of the multiple effective training days of the target user collected above; then determine the target game level corresponding to the median of the average focus values. Then determine the game plan adjustment strategy for the next stage based on the target game level and training performance score.

[0052] For example, the game levels and corresponding focus value intervals shown in Table 1:

[0053] Table 1

[0054] Game gear Focus value range 5 [75,100] 4 [65,75) 3 [50,65) 2 [40,50) 1 [0,40)

[0055] The five levels correspond to five groups of focus value intervals. The second level is initially [40, 50), and the third level is initially [50, 65). When the average focus value of the user's recent effective training days reaches 49, according to the initial rule, it can only fall into the second level. If the user continuously trains hard but repeatedly falls into a lower level and cannot break through, it is easy to generate a sense of frustration and affect the enthusiasm for subsequent training. Therefore, in the embodiment of the present invention, when the average focus values for N consecutive times are all close to (reach a certain range, such as within the third level) the upper limit of the initial level, the range values of all five levels can be dynamically adjusted downward. The third level is adjusted to [48, 62), so that the user can smoothly upgrade and obtain a good performance score, and the user can see their good performance score and record on the screen. The closer the average focus value is to the upper limit, the greater the adjustment range. At the same time, the hidden score representing the real performance under the initial level rule can be retained, and subsequent game levels and difficulties can be configured according to the hidden score.

[0056] The game plan adjustment method for a training game provided by an embodiment of the present invention obtains the average focus value and training performance score of a target user in the current game training stage; determines the game plan adjustment strategy for the next stage based on the average focus value and training performance score; and adjusts the target training game based on the game plan adjustment strategy. Compared with existing games that do not adjust the game difficulty according to the user's training feedback, there are problems such as a sense of frustration caused by too high game difficulty or boredom caused by too low difficulty. With this method, according to the user's recent training results, more positive feedback is given to the user in the next training cycle, making the user more motivated to persist in the game training and improving the user experience and training effect.

[0057] Figure 2 As shown in the flowchart of another game plan adjustment method for a training game provided by an embodiment of the present invention, Figure 2 as shown, the method specifically includes:

[0058] S21. Collect historical game training data obtained by multiple users through the target training game.

[0059] Collect historical game training data obtained by multiple (e.g., 600 high-frequency users) past users through the target training game. Among them, the historical game training data at least includes the average focus value and training performance score of each user. When the amount of newly entered data reaches the standard (e.g., the amount of newly entered data reaches 600), the base number can be periodically refreshed according to the online data of the target game to ensure that the base number conforms to the overall situation of the user group.

[0060] S22. Based on the average focus value of the users in the historical game training data, initially classify the game difficulty of the target training game to obtain multiple initial game levels and the focus value intervals corresponding to each initial game level.

[0061] S23. Set the corresponding level coefficient for each initial game level based on game experience and training performance score.

[0062] Based on the average focus value of the users in the historical game training data and combined with empiricism, roughly simulate a normal distribution to initially classify the game difficulty of the target training game to obtain multiple initial game levels and the focus value intervals corresponding to each initial game level; set the corresponding level coefficient for each initial game level according to the training performance score. As shown in Table 2:

[0063] Table 2

[0064] Game gear Focus value range Gear coefficient 5 [75,100] 1.10 4 [65,75) 1.05 3 [50,65) 1.00 2 [40,50) 0.95 1 [0,40) 0.90

[0065] S24. Collect the game training data of the target user on multiple effective training days in the current game training stage.

[0066] Collect the game training data of the target user on multiple effective training days during the current game training phase. Here, an effective training day is a day when the single-day game training duration reaches a preset time threshold (for example, 3 hours); analyze the game training data of multiple effective training days to obtain the average focus value and training performance score corresponding to each effective training day.

[0067] S25. Analyze the game training data of the multiple effective training days to obtain the average focus value and training performance score corresponding to each effective training day.

[0068] In the embodiment of the present invention, assume that the user's training plan includes n trainings. Find the most recent training day that satisfies the completion of n trainings and the head-mounted device wearing duration of each game training reaches more than 90% of the preset time threshold, and count 1; until the most recent 3 effective training days are repeatedly found. Obtain the average focus value and training performance score of all game training results within the 3 effective training days. If there are not enough effective training days, take the default value as 1.

[0069] S26. Calculate the median of the average focus values of the multiple effective training days.

[0070] S27. Determine the target initial game gear corresponding to the median of the average focus values, and the target gear coefficient corresponding to the target initial game gear.

[0071] S28. Use the target gear coefficient as the game training personalization coefficient of the target user.

[0072] Calculate the median of the average focus values of the multiple effective training days. According to the above-mentioned classification result of the initial classification of the game difficulty of the target training game, determine the target initial game gear and the target gear coefficient corresponding to the median of the average focus values, and obtain the game training personalization coefficient of the target user.

[0073] S29. Based on the game training personalization coefficient and the focus value interval corresponding to the target initial game gear, determine the personalized focus value interval of the target user.

[0074] S210. Re-determine the game difficulty based on the training performance score.

[0075] Re-classify the game gears of the target training game based on the personalized focus value interval of the target user. Adjust the game content of the target training game based on the re-determined game difficulty.

[0076] For example, if the median of the average concentration values of multiple effective training days of the target user falls in the second gear in Table 2, the personalized coefficient of the game training for the target user is determined to be 0.95, and the personalized concentration value intervals for each gear corresponding to the target user are recalculated according to the concentration value intervals in the second column of the table, as shown in Table 3:

[0077] Table 3

[0078] Game gear Focus value range 5 [71,100] 4 [61,71) 3 [47,61) 2 [38,47) 1 [0,38)

[0079] The above is for the user to obtain an average concentration value and a performance score through game training. However, a dynamic performance score can also be obtained during global training. For example, a score is obtained according to the gear corresponding to the real-time (or average within the last three seconds) concentration value, and then the scores for all process times are summed to obtain the performance score for a single training. This score has stronger volatility and can better reflect the comprehensive performance of the entire process. The performance score can also be dynamically adjusted during a single training. For example, when the performance score corresponding to the first half of the training is far lower than the expected range, the gear threshold for the second half can be appropriately adjusted to make it easier for the user to obtain a high gear and the corresponding high performance score, or the performance score standard corresponding to each gear can be directly increased. The adjustment range can be adapted to the difference between the actual performance score in the first half and the expectation. At the same time, the hidden score representing the real performance under the initial gear rule can be retained, and subsequent game levels and difficulties can be configured according to the hidden score.

[0080] The game plan adjustment method for the training game provided by the embodiment of the present invention obtains the average concentration value and the training performance score of the target user in the current game training stage; determines the game plan adjustment strategy for the next stage based on the average concentration value and the training performance score; and adjusts the target training game based on the game plan adjustment strategy. Compared with the existing games that do not adjust the game difficulty according to the user's training feedback, there are problems such as frustration caused by too high game difficulty or boredom caused by too low difficulty. With this method, according to the user's recent training results, more positive feedback is given to the user in the next training cycle, such as increasing the performance score or having a more lenient scoring rule for encouragement, providing a game training difficulty that better matches the user's ability and recent training status, balancing the challenge and frustration of training, making the user more motivated to persist in game training, and improving the user experience and training effect.

[0081] Figure 3 It is a structural schematic diagram of a game plan adjustment device for a training game provided by an embodiment of the present invention, specifically including:

[0082] An acquisition module 301, configured to acquire the average concentration value and the training performance score of the target user in the current game training stage. For detailed description, refer to the relevant description corresponding to the above method embodiment, which will not be elaborated here.

[0083] A determination module 302, configured to determine an adjustment strategy for the game plan in the next stage based on the average concentration value and the training performance score. For the detailed description, refer to the relevant description corresponding to the above method embodiment, which will not be elaborated here.

[0084] An adjustment module 303, configured to adjust the target training game based on the adjustment strategy for the game plan. For the detailed description, refer to the relevant description corresponding to the above method embodiment, which will not be elaborated here.

[0085] The game plan adjustment device for the training game provided in this embodiment may be the game plan adjustment device for the training game as shown in Figure 3 and can execute all steps of the game plan adjustment method for the training game as shown in Figure 1-2 so as to achieve the technical effects of the game plan adjustment method for the training game as shown in Figure 1-2 For the specific details, please refer to Figure 1-2 the relevant description. For the sake of brevity, it will not be elaborated here.

[0086] Figure 4 FIG. is a schematic structural diagram of an electronic device provided in an embodiment of the present invention. Figure 4 The electronic device 400 shown includes at least one processor 401, a memory 402, at least one network interface 404, and other user interfaces 403. Each component in the electronic device 400 is coupled together through a bus system 405. It can be understood that the bus system 405 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 405 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clear description, in Figure 4 all kinds of buses are labeled as the bus system 405.

[0087] Among them, the user interface 403 may include a display, a keyboard, or a pointing device (such as a mouse, a trackball, a touchpad, or a touch screen, etc.).

[0088] It can be understood that the memory 402 in the embodiments of the present invention can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synch link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM). The memory 402 described herein is intended to include but not be limited to these and any other suitable types of memory.

[0089] In some embodiments, the memory 402 stores the following elements, executable units, or data structures, or subsets thereof, or extended sets thereof: the operating system 4021 and the application program 4022.

[0090] Among them, the operating system 4021 includes various system programs, such as the framework layer, the core library layer, the driver layer, etc., and is used to implement various basic services and process hardware-based tasks. The application program 4022 includes various application programs, such as a media player and a browser, etc., and is used to implement various application services. The program for implementing the method of the embodiments of the present invention can be included in the application program 4022.

[0091] In the embodiments of the present invention, by calling the program or instruction stored in the memory 402, specifically, the program or instruction stored in the application program 4022, the processor 401 is used to execute the method steps provided by each method embodiment, for example, including:

[0092] Obtain the average concentration value and training performance score of the target user in the current game training stage; determine the game plan adjustment strategy for the next stage based on the average concentration value and training performance score; adjust the target training game based on the game plan adjustment strategy.

[0093] In a possible implementation manner, initially classify the game difficulty level of the target training game based on preset rules to obtain multiple initial game levels and the corresponding concentration value intervals and level coefficients for each initial game level.

[0094] In a possible implementation manner, collect the historical game training data obtained by multiple users through the target training game, where the historical game training data at least includes the average concentration value and training performance score of the users; initially classify the game difficulty level of the target training game based on the average concentration value of the users in the historical game training data to obtain multiple initial game levels and the corresponding concentration value intervals for each initial game level; set the corresponding level coefficients for each initial game level based on game experience and training performance score.

[0095] In a possible implementation manner, collect the game training data of the target user on multiple effective training days in the current game training stage, where an effective training day is a single-day game training duration reaching a preset time threshold; analyze the game training data of the multiple effective training days to obtain the average concentration value and training performance score corresponding to each effective training day.

[0096] In a possible implementation manner, calculate the median of the average concentration values of multiple effective training days; determine the target initial game level corresponding to the median of the average concentration values and the target level coefficient corresponding to the target initial game level; use the target level coefficient as the game training personalization coefficient of the target user; determine the game plan adjustment strategy for the next stage based on the game training personalization coefficient and training performance score.

[0097] In a possible implementation manner, determine the personalized concentration value interval of the target user based on the game training personalization coefficient and the concentration value interval corresponding to the target initial game level; re-determine the game difficulty level based on the training performance score.

[0098] In a possible implementation manner, re-classify the game levels of the target training game based on the personalized concentration value interval of the target user; adjust the game content of the target training game based on the re-determined game difficulty level.

[0099] The method disclosed in the embodiments of the present invention above can be applied to or implemented by the processor 401. The processor 401 may be an integrated circuit chip with signal processing capabilities. During implementation, the steps of the above method can be completed by the integrated logic circuit in hardware or instructions in software form in the processor 401. The above-mentioned processor 401 may be a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention can be directly embodied as being executed and completed by a hardware decoding processor, or by a combination of hardware and software units in the decoding processor. The software unit may be located in a mature storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers, etc. This storage medium is located in the memory 402, and the processor 401 reads the information in the memory 402 and combines its hardware to complete the steps of the above method.

[0100] It can be understood that these embodiments described herein can be implemented using hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in this application, or a combination thereof.

[0101] For a software implementation, the techniques described herein can be implemented by units that execute the functions described herein. The software code can be stored in a memory and executed by a processor. The memory can be implemented inside or outside the processor.

[0102] The electronic device provided in this embodiment may be the electronic device shown in Figure 4 and can execute all steps of the game plan adjustment method for the training game shown in Figure 1-2 , thereby achieving the technical effect of the game plan adjustment method for the training game shown in Figure 1-2 . For specific reference, please refer to Figure 1-2 for the relevant description. For the sake of concise description, it will not be elaborated here.

[0103] The embodiment of the present invention also provides a storage medium (computer-readable storage medium). One or more programs are stored in the storage medium here. Among them, the storage medium may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk or solid-state drive; the memory may also include a combination of the above types of memory.

[0104] When one or more programs in the storage medium can be executed by one or more processors to implement the above-mentioned game plan adjustment method for the training game executed on the electronic device side.

[0105] The processor is used to execute the game plan adjustment program stored in the memory to implement the following steps of the game plan adjustment method for the training game executed on the electronic device side:

[0106] Obtain the average focus value and training performance score of the target user in the current game training stage; determine the game plan adjustment strategy for the next stage based on the average focus value and training performance score; adjust the target training game based on the game plan adjustment strategy.

[0107] In a possible implementation manner, the game difficulty level of the target training game is initially graded based on preset rules to obtain a plurality of initial game levels and the focus value interval and level coefficient corresponding to each initial game level.

[0108] In a possible implementation manner, collect historical game training data obtained by multiple users through the target training game, where the historical game training data at least includes the average focus value and training performance score of the users; initially grade the game difficulty level of the target training game based on the average focus value of the users in the historical game training data to obtain a plurality of initial game levels and the focus value interval corresponding to each initial game level; set the corresponding level coefficient for each initial game level based on game experience and training performance score.

[0109] In a possible implementation, game training data of a target user for multiple valid training days in the current game training phase is collected, where a valid training day is a day when the single-day game training duration reaches a preset time threshold; the game training data of the multiple valid training days is analyzed to obtain the average focus value and training performance score corresponding to each valid training day.

[0110] In a possible implementation, the median of the average focus values of multiple valid training days is calculated; the target initial game gear corresponding to the median of the average focus values and the target gear coefficient corresponding to the target initial game gear are determined; the target gear coefficient is used as the game training personalization coefficient of the target user; a game plan adjustment strategy for the next phase is determined based on the game training personalization coefficient and the training performance score.

[0111] In a possible implementation, based on the game training personalization coefficient and the focus value interval corresponding to the target initial game gear, the personalized focus value interval of the target user is determined; the game difficulty level is re-determined based on the training performance score.

[0112] In a possible implementation, the game gears of the target training game are re-divided based on the personalized focus value interval of the target user; the game content of the target training game is adjusted based on the re-determined game difficulty level.

[0113] Professional personnel should also be able to further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0114] The steps of the method or algorithm described in combination with the embodiments disclosed in this article can be implemented by hardware, software modules executed by a processor, or a combination of both. The software modules can be placed in a random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the technical field.

[0115] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for adjusting a game plan of a training game, characterized in that, Including: Obtain the average concentration value and training performance score of the target user in the current game training stage; Determine the game plan adjustment strategy for the next stage based on the average concentration value and training performance score, wherein the game plan adjustment strategy matches the training state and game training difficulty of the target user's game ability within a preset time period; The determining the game plan adjustment strategy for the next stage based on the average concentration value and training performance score includes: Calculate the median of the average concentration values of multiple effective training days; Judge the target initial game gear corresponding to the median of the average concentration value, and the target gear coefficient corresponding to the target initial game gear; Use the target gear coefficient as the game training personalization coefficient of the target user; Determine the game plan adjustment strategy for the next stage based on the game training personalization coefficient and training performance score; The determining the game plan adjustment strategy for the next stage based on the game training personalization coefficient and training performance score includes: Determine the personalized concentration value range of the target user based on the game training personalization coefficient and the concentration value range corresponding to the target initial game gear; Redetermine the game difficulty based on the training performance score; Adjust the target training game based on the game plan adjustment strategy; The adjusting the target training game based on the game plan adjustment strategy includes: Redivide the game gears of the target training game based on the personalized concentration value range of the target user; Adjust the game content of the target training game based on the redetermined game difficulty.

2. The method according to claim 1, characterized in that, The method further includes: Based on preset rules, initially classify the game difficulty of the target training game to obtain multiple initial game gears and the corresponding concentration value range and gear coefficient for each initial game gear.

3. The method according to claim 2, wherein The initially classifying the game difficulty of the target training game based on preset rules to obtain multiple initial game gears and the corresponding concentration value range and gear coefficient for each initial game gear includes: Collect historical game training data obtained by multiple users through the target training game, where the historical game training data at least includes the average concentration value and training performance score of the users; Based on the average concentration value of the users in the historical game training data, initially classify the game difficulty of the target training game to obtain multiple initial game gears and the corresponding concentration value range for each initial game gear; Set the corresponding gear coefficient for each initial game gear based on game experience and training performance score.

4. The method according to claim 3, characterized in that, The obtaining the average concentration value and training performance score of the target user in the current game training stage includes: Collect the game training data of the target user on multiple effective training days in the current game training stage, where an effective training day is a single-day game training duration reaching a preset time threshold; Analyze the game training data of the multiple effective training days to obtain the average concentration value and training performance score corresponding to each effective training day.

5. A game plan adjustment device for a training game, characterized in that Including: An obtaining module, configured to obtain the average concentration value and training performance score of the target user in the current game training stage; A determination module, configured to determine an adjustment strategy for the game plan in the next stage based on the average concentration value and the training performance score, wherein the game plan adjustment strategy matches the game training difficulty of the training status and game ability of the target user within a preset time period; the determining the adjustment strategy for the game plan in the next stage based on the average concentration value and the training performance score includes: Calculating the median of the average concentration values of multiple valid training days; Judging the target initial game gear corresponding to the median of the average concentration value, and the target gear coefficient corresponding to the target initial game gear; Taking the target gear coefficient as the game training personalization coefficient of the target user; Determining an adjustment strategy for the game plan in the next stage based on the game training personalization coefficient and the training performance score; The determining the adjustment strategy for the game plan in the next stage based on the game training personalization coefficient and the training performance score includes: Determining the personalized concentration value range of the target user based on the game training personalization coefficient and the concentration value range corresponding to the target initial game gear; Redetermining the game difficulty level based on the training performance score; An adjustment module, configured to adjust the target training game based on the game plan adjustment strategy; The adjusting the target training game based on the game plan adjustment strategy includes: Redividing the game gears of the target training game based on the personalized concentration value range of the target user; Adjusting the game content of the target training game based on the redetermined game difficulty level.

6. An electronic device, characterized in that, Including: A processor and a memory, the processor is configured to execute the game plan adjustment program of the training game stored in the memory to implement the game plan adjustment method of the training game according to any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the game plan adjustment method of the training game according to any one of claims 1 to 4.

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