Game anti-addiction processing method, device and server for preventing addiction

Abstract

The application relates to a game anti-addiction processing method and device, a server, a computer readable storage medium and a computer program product, which comprises the following steps: for a level x, the level x has a preset difficulty Ax; when a user passes the level x, the average passing difficulty Mx of all levels passed by the user is calculated, wherein the passing difficulty value of each level is proportional to the number of rounds; the preset difficulty Ax and the passing difficulty Mx are compared; when the preset difficulty Ax is greater than the passing difficulty Mx, the difference ax-P between the probability of the optimal condition of the current level and the difficulty coefficient P is taken as the new probability of the optimal condition, and the sum cx+P of the probability of the worst condition of the current level and the difficulty coefficient P is taken as the new probability of the worst condition; when the preset difficulty Ax is less than the passing difficulty Mx, the probability of the optimal condition and the probability of the worst condition of the current level are adjusted. The application can dynamically adjust the level difficulty and prevent the user from being addicted to the game for a long time.

Description

Technical Field

[0001] This application relates to the field of software technology, specifically to a method, apparatus, server, computer-readable storage medium, and computer program product for preventing game addiction. Background Technology

[0002] Puzzle and competitive games have become a popular leisure activity. Playing these games in moderation can help relieve stress and improve thinking skills, but excessive time spent playing can lead to addiction. Existing anti-addiction measures mostly involve setting up monitoring mechanisms in the system's backend to track user playtime. When a threshold is reached, the system reminds the user to stop playing voluntarily or directly suspends game services. While this method can limit a user's playtime for a given period, the effect is short-lived, and the restriction is superficial and easily circumvented.

[0003] There are many reasons for game addiction. One of them is that users gain a sense of satisfaction from completing levels of a certain difficulty. The satisfaction gained from completing a level stimulates the brain to release dopamine, producing a feeling of pleasure. This leads to users being immersed in the game for a long time, which in turn keeps the dopamine release mechanism activated for a long time. This further interferes with the brain's degradation and reabsorption of neurotransmitters, resulting in game addiction.

[0004] Due to the aforementioned objective reasons, there is currently a lack of anti-addiction methods based on game mechanics. How to prevent people from becoming addicted to games for extended periods by addressing the game's operating mechanisms is an urgent problem to be solved. Summary of the Invention

[0005] In view of this, embodiments of this application provide a method, apparatus, electronic device, and computer-readable storage medium for preventing game addiction, in order to solve at least one technical problem.

[0006] This application provides a method for preventing game addiction. The method prevents user addiction by adjusting the game difficulty. Each game level includes multiple obstacles. In a single level, the user performs multiple rounds of actions. If the current action matches the current obstacle, the obstacle is eliminated. If the current action does not match the current obstacle, the current action is converted into an obstacle, and the user proceeds to the next round. After all obstacles are cleared, the user proceeds to the next level. For each level... x It has a preset difficulty level A x The probability of the optimal condition occurring in each round is a. x The probability of the worst-case scenario occurring is c. x The probability of the ordinary condition occurring is b. x After a user passes level x, the difficulty M of passing that level is calculated based on the number of rounds taken to complete it. xThe difficulty of each level is proportional to the number of rounds; after passing n levels, the average difficulty M of completing all passed levels is calculated. xa ; Calculate the average preset difficulty A of all levels already passed. xa Compare the average preset difficulty A xa Compared to the average difficulty level M xa Size; when the average preset difficulty A xa >Average difficulty of completion M xa Then, select level y from the remaining levels, where the preset difficulty A of that level is... y = Average difficulty of completing the game M xa Then calculate the difficulty coefficient P, where P = |(A xa -M xa )*20%|, which is the difference a between the probability of the optimal condition for the current level and the difficulty coefficient P. x -P is the probability of the new optimal condition, which is the sum of the probability of the worst condition of the current level and the difficulty coefficient P, c. x +P represents the probability of the new worst-case scenario, increasing the difficulty of the current level; when the average preset difficulty A... xa Average difficulty level M xa At that time, in the remaining levels, there is a level y, where the preset difficulty A of the level is... y = Average difficulty of completing the game M xa Then calculate the difficulty coefficient P, where P = |(A xa -M xa )*20%| value, which is the sum of the probability of the optimal condition for the current level and the difficulty coefficient P. x +P is the probability of the new optimal condition, which is the difference c between the probability of the worst condition of the current level and the difficulty coefficient P. x -P represents the probability of the new worst-case scenario, used to reduce the difficulty of the current level; when the average preset difficulty A... xa = Average difficulty of completing the game M xa At that time, the difficulty of the remaining levels will not be adjusted.

[0007] Optionally, all levels are divided into segments of k levels. When calculating the average difficulty, only the levels that have been completed within the segment are considered. When adjusting the game difficulty within the segment, only the levels that have not been completed within the segment are selected and adjusted.

[0008] Optionally, P a =W e +W f +(W ne *50%)+(W nf *70%), of which P a W is the probability of condition. eW is the number of eliminated weights f W is the eliminated drop weight ne W is the number of eliminated weights after the new condition nf W is the eliminated drop weight after the new condition

[0009] The highest value calculated is the probability a of the optimal condition x The lowest value is the probability c of the worst condition x The probability b of the highest value and the lowest value is excluded x

[0010] Optionally, each level is preset with a pass round number r, and if the user does not eliminate all the obstacles within the pass round number r, it is determined that the user fails to pass the level.

[0011] Optionally, the game includes a reward prop that eliminates part of the obstacles; wherein, in the level with the reward prop, the probability d of the optimal condition, the probability e of the worst condition, and the probability f of the ordinary condition are x x x For the level with the reward prop, when the preset difficulty A x is greater than the pass difficulty M x , and |A x -M x | is greater than 2, the difference d x -P between the optimal condition probability of the current level and the difficulty coefficient P is taken as the new optimal condition probability, and the sum f x +P of the worst condition probability of the current level and the difficulty coefficient P is taken as the new worst condition probability; when |A x -M x | is less than 2, the optimal condition probability of the current level is adjusted to d x -2*(P / 20%), to increase the level difficulty; when the preset difficulty A x is less than the pass difficulty M x , and |A x -M x | is greater than 2, the sum d x +P of the optimal condition probability of the current level and the difficulty coefficient P is taken as the new optimal condition probability, and the difference f x -P of the worst condition probability of the current level and the difficulty coefficient P is taken as the new worst condition probability; when |A x -M x | is less than 2, the optimal condition probability of the current level is adjusted to d x +2*(P / 20%), to reduce the difficulty of the current level.

[0012] ​​​Optionally, if the difficulty of a certain level has been adjusted, the difficulty of the level is not adjusted subsequently.

[0013] The application further provides a game anti-addiction processing device, which prevents user addiction by adjusting game difficulty, wherein each game level includes a plurality of obstacles, a user experiences multiple rounds of operation in a single level, if a current operation result matches a current obstacle, the obstacle is eliminated, if the current operation result does not match the current obstacle, the current operation result is converted into an obstacle and a next round of operation is entered, and after all obstacles are eliminated, a next level is entered; wherein for a level x with a preset difficulty A x , a probability of occurrence of an optimal condition is a x , a probability of occurrence of a worst condition is c x , and a probability of occurrence of an ordinary condition is b x , the device comprises: a difficulty calculation module, which calculates a pass difficulty M of a user through a level x based on a number of rounds of passing through the level when the user passes through the level x x , wherein the pass difficulty value of each level is proportional to the number of rounds; a first difficulty average module, which calculates an average pass difficulty M of the user through all levels after passing through n levels xa ; a second difficulty average module, which calculates an average preset difficulty A of the user through all levels xa ; a difficulty comparison module, which compares the average preset difficulty A xa and the average pass difficulty M xa ; a difficulty increasing module, which selects a yth level from remaining levels when the average preset difficulty A xa > the average pass difficulty M xa , wherein the preset difficulty A y of the level is equal to the average pass difficulty M xa , then calculates a difficulty coefficient P, wherein P = |(A xa -M xa )*20%|, and a difference a x -P between the probability of occurrence of the optimal condition of the current level and the difficulty coefficient P is used as a new probability of occurrence of the optimal condition, and a sum c x +P between the probability of occurrence of the worst condition of the current level and the difficulty coefficient P is used as a new probability of occurrence of the worst condition, so as to increase the difficulty of the current level; and a difficulty decreasing module, which selects a yth level from remaining levels when the average pass difficulty A xa < the average pass difficulty M xa , wherein the preset difficulty Ay of the level is equal to the average pass difficulty M xa , then calculates a difficulty coefficient P, wherein P = |(A xa -M xa* 20% value, the sum of the probability of the optimal condition of the current level and the difficulty coefficient P a x + P as the probability of the new optimal condition, the difference between the probability of the worst condition of the current level and the difficulty coefficient P c x - P as the probability of the new worst condition, to reduce the difficulty of the current level; difficulty maintenance module, when the average passing difficulty A xa = average passing difficulty M xa , the remaining level difficulty is not adjusted.

[0014] The application further provides a server, comprising a processor and a memory storing computer program instructions; the server implements the method of any one of the preceding embodiments when executing the computer program instructions.

[0015] The application further provides a computer readable storage medium, storing computer program instructions, which are executed by a processor to implement the method of any one of the preceding embodiments.

[0016] The application further provides a computer program product, comprising computer program instructions, which are executed by a processor to implement the method of any one of the preceding embodiments.

[0017] The embodiments of the application infer the game level of different users by analyzing the completed levels of the users, and then dynamically adjust the difficulty of subsequent levels to adapt to the needs of users with different game levels. In this way, whether the user has a high or low game level, after he or she completes some levels relatively smoothly, the application can increase the difficulty of subsequent levels to avoid the user's continuous playing and addiction due to too low difficulty, for example, after he or she completes some levels relatively hard, the application can reduce the difficulty of subsequent levels to avoid the user's game experience being damaged due to too high difficulty, and after reducing the difficulty, the user can pass some levels smoothly, and the difficulty can be increased to a certain extent, so that the user will not be addicted to the game for a long time. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following briefly introduces the drawings in the embodiments of the application.

[0019] Figure 1 is a schematic diagram of the system architecture of the embodiments of the application.

[0020] Figure 2 is a game anti-addiction processing method flowchart of the embodiments of the application.

[0021] Figures 3-4 is a game scene diagram of the embodiments of the application.

[0022] Figure 5 is a game anti-addiction processing device schematic diagram of an embodiment of the present application.

[0023] Figure 6 is a schematic diagram of an electronic device for implementing a game anti-addiction processing method for anti-addiction of an embodiment of the present application. DETAILED DESCRIPTION

[0024] The principles and spirits of the present application will be described below with reference to a number of exemplary embodiments. It should be understood that the purpose of providing these embodiments is to make the principles and spirits of the present application clearer and more thorough, so that those skilled in the art can better understand and implement the principles and spirits of the present application. The exemplary embodiments provided herein are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments herein, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0025] Embodiments of the present application relate to terminal devices and / or servers. Those skilled in the art know that embodiments of the present application can be implemented as a system, device, apparatus, method, computer readable storage medium or computer program product. Therefore, the present disclosure can be embodied in at least one of the following forms: complete hardware, complete software, or a combination of hardware and software. According to embodiments of the present application, a gift sending method, device, electronic device and storage medium in a live broadcast application are claimed. Figure 1 A schematic diagram of a system architecture of an embodiment of the present application is shown. As shown in Figure 1 The system includes a terminal device 102 and a server 104. The terminal device 102 can include at least one of the following: a smartphone, a tablet computer, a notebook computer, a desktop computer, a smart television, various wearable devices, an augmented reality (AR) device, a virtual reality (VR) device, and the like. A client can be installed on the terminal device 102, for example, the client can be a client (such as an application program (app)) specially designed to perform a specific function, or a client embedded with various application programs (different functions), or a client logged in through a browser. A user can operate on the terminal device 102, for example, the user can open the client installed on the terminal device 102 and input instructions through the client operation, or the user can open the browser installed on the terminal device 102 and input instructions through the browser operation. After the terminal device 102 receives the instructions input by the user, the request information containing the instructions is sent to the server 104. After the server 104 receives the request information, it performs corresponding processing, and then returns the processing result information to the terminal device 102. The user's instructions are completed through a series of data processing and information interaction.

[0026] In this document, terms such as first, second, third, and the like, are used merely to distinguish one entity (or action) from another, without necessarily requiring or implying any order or sequence between such entities (or actions).

[0027] The following briefly describes concepts and technical terms that can be involved in embodiments of the present application.

[0028] In embodiments of the present application, a game includes multiple game levels, each game level including multiple obstacles, and a user obtains a condition in each round in a single game level, and can choose how to use the condition. If the current operation result and the current obstacle match each other, the obstacle is eliminated. If the current operation result and the current obstacle do not match each other, the current operation result is converted into an obstacle and the next round of operation is entered. For example, in a game (such as Bubble Bobble, Tetris, etc.) in which the pass goal is achieved by eliminating obstacles, if the user reasonably places balls or blocks (such as balls of the same color together), the corresponding balls or blocks can be eliminated. After multiple rounds of operation, if all obstacles in the current game level are cleared, the next level can be entered.

[0029] In embodiments of the present application, for a certain level of a certain game x (or the first x level), it has a preset difficulty A x . The preset difficulty can be calculated or rated by game testers. The probability of the optimal condition appearing in each round is a x , the probability of the worst condition appearing is c x , and the probability of the ordinary condition appearing is b x . Among them, the condition that can pass through the least number of rounds is the optimal condition, the condition that can pass through the most number of rounds is the worst condition, and the others are ordinary conditions.

[0030] Figure 2 is a game anti-addiction processing method flowchart of the present application, as shown in Figure 2 , the method comprises:

[0031] Step S201: When the user passes the level x, the pass difficulty M x of the user passing the level is calculated based on the number of rounds passing the level, wherein the pass difficulty value of each level is proportional to the number of rounds.

[0032] Step S202: After passing n levels, the average pass difficulty M xa of completing all levels passed by the user is calculated.

[0033] Step S203: Calculate the average preset difficulty A of all the levels which have been passed xa .

[0034] Step S204: Compare the preset difficulty A xa with the passing difficulty M xa .

[0035] Step S205: When the preset difficulty A xa > the passing difficulty M xa , select a yth level among the remaining levels, where the preset difficulty A y = the passing difficulty M xa , and then calculate the difficulty coefficient P, where P = |(A xa - M xa ) * 20% |, the difference between the probability of the optimal condition of the current level and the difficulty coefficient P, i.e. a x - P, is taken as the new probability of the optimal condition, and the sum of the probability of the worst condition of the current level and the difficulty coefficient P, i.e. c x + P, is taken as the new probability of the worst condition, so as to increase the difficulty of the current level.

[0036] Step S206: When the preset difficulty A xa < the passing difficulty M xa , select a yth level among the remaining levels, where the preset difficulty A y = the passing difficulty M xa , and then calculate the difficulty coefficient P, where P = |(A xa - M xa ) * 20% |, the sum of the probability of the optimal condition of the current level and the difficulty coefficient P, i.e. a x + P, is taken as the new probability of the optimal condition, and the difference between the probability of the worst condition of the current level and the difficulty coefficient P, i.e. c x - P, is taken as the new probability of the worst condition, so as to decrease the difficulty of the current level.

[0037] Step S207: When the preset difficulty A xa = the passing difficulty M xa , no adjustment is made to the difficulty of the remaining levels.

[0038] where the greater the difficulty value is, the more difficult it is for the user to pass the level. Generally, after the difficulty value and the average difficulty value are calculated, the integer value thereof is taken as the result. The preset difficulty A y = the average passing difficulty M xaThe level is to make the level difficulty first adapt to the user level, and then adjust the difficulty on this basis, form a certain degree of psychological gap in the psychology of the user, so that the user can clearly feel the change of difficulty, so as to prevent the effect of indulging.

[0039] Generally, when a user plays several levels, according to the performance of each level he completes, the average pass difficulty M of all levels he has passed is calculated. xa Here, it is assumed that the preset difficulty of each level is the same, that is, A x , then the average preset difficulty A xa = A x . Compare the average preset difficulty A xa and the average pass difficulty M xa , that is, compare A x and M xa to determine the level of the player. After judging, if the player's pass difficulty is lower than A xa , it is considered that the player's level is higher, so the difficulty of the subsequent level is increased by P value. If the player's pass difficulty is higher than A xa , it is considered that the player's level is lower, so the difficulty of the subsequent level is reduced by P value.

[0040] When the level of the user is adapted to the difficulty of the current level, the level difficulty can not be adjusted. Because the game difficulty is adapted to the user level, the user will consume the user's energy to a certain extent when encountering such a level in the game, so in this case, the user will not be addicted to the game because the game is too easy.

[0041] The present application scheme analyzes the levels completed by the user, infers the game level of different users, and then dynamically adjusts the difficulty of the subsequent levels to adapt to the needs of users with different game levels. In this way, whether the user's game level is high or low, after completing some levels relatively smoothly, the difficulty of the subsequent levels can be increased by the present application to avoid the user's continuous play due to the difficulty being too low. After completing some levels relatively hard, the difficulty of the subsequent levels can be reduced by the present application to avoid the difficulty being too high and damaging the user's game experience.

[0042] In some embodiments, every k levels in all levels are divided into a section, wherein when calculating the average pass difficulty, only the levels passed in the section are calculated, and when adjusting the game difficulty in the section, only the levels not passed in the section are selected and adjusted.

[0043] For example, 10 consecutive levels can be selected as a section. In this way, the user can quickly feel the adjustment of the difficulty, and the adjustment will not be too lagging.

[0044] In some embodiments, the calculation of the conditional probability comprises:

[0045] P a = W e + W f + (W ne * 50%) + (W nf * 70%)

[0046] wherein P a is the probability of the condition, W e is the number of eliminations, W f is the drop of eliminations, W ne is the number of new conditions after eliminations, and W nf is the drop of new conditions after eliminations. The highest value of the calculation is the probability of the optimal condition a x , and the lowest value is the probability of the worst condition c x , and the probabilities of the highest value and the lowest value are b x .

[0047] wherein the number of eliminations can be the number of obstacles that can be directly eliminated in the current operation, and the drop of eliminations can be the number of obstacles that can be indirectly eliminated in the current operation. For example, Figure 3 if the five blue balls (i.e., obstacles) on the right side of the middle disappear, the number of eliminations is 5, and the four green balls below them will drop, so the number of drop of eliminations is 4. In some embodiments, the number of new conditions after eliminations is the number of conditions that can directly eliminate obstacles after each operation. The drop of new conditions after eliminations can be the number of conditions that can indirectly eliminate obstacles.

[0048] In some embodiments, each level is preset with a pass round r, and if the user does not eliminate all obstacles within the pass round, it is determined that the user fails to pass the level.

[0049] Setting the pass round can increase the frustration of the user to some extent, and enhance the effect of preventing the user from being addicted to the game. Moreover, the number of rounds passed by the user and the preset pass round can be compared to calculate the pass difficulty M x of the user through the level. For example, if the number of rounds passed by the user is greater than the preset pass round, it is considered that the pass difficulty M x of the user through the level is greater than the preset difficulty A x .

[0050] In some embodiments, the game includes a reward prop, which eliminates part of the obstacles; wherein the probability of the optimal condition in the level with the reward prop is d x , the probability of the worst condition is e x , and the probability of the ordinary condition is f x For the level with the reward prop, when the average preset difficulty A xa is greater than the average passing difficulty M xa , and |A xa -M xa |>2, the difference between the probability of the optimal condition of the current level and the difficulty coefficient P, i.e. d x -P, is taken as the new probability of the optimal condition, and the sum of the probability of the worst condition of the current level and the difficulty coefficient P, i.e. f x +P, is taken as the new probability of the worst condition. If |A xa -M xa |<2, the probability of the optimal condition of the current level is adjusted to d x -2*(P / 20%), to increase the difficulty of the level. When the average preset difficulty A xa is less than the average passing difficulty M xa , and |A xa -M xa |>2, the sum of the probability of the optimal condition of the current level and the difficulty coefficient P, i.e. d x +P, is taken as the new probability of the optimal condition, and the difference between the probability of the worst condition of the current level and the difficulty coefficient P, i.e. f x -P, is taken as the new probability of the worst condition. If |A xa -M xa |<2, the probability of the optimal condition of the current level is adjusted to d x +2*(P / 20%), to reduce the difficulty of the current level.

[0051] The reward prop can be a special game play. When the reward is triggered, a group of obstacles can be eliminated. If the reward prop is triggered, the related algorithm also needs to be adjusted to adapt to the adjusted situation. The specific method is to compare whether the difference between A xa and M xa causes the difficulty to be too large due to the reward prop, so as to keep the difficulty within a reasonable range.

[0052] The following takes a bubble dragon game as an example for specific description, as shown in Figures 3-5 . The conditions here can be different colored balls given below, and the obstacles can be balls that need to be eliminated above.

[0053] According to the calculation formula given above, take Figure 3 as an example. WhereinFigure 3 Part of the balls are eliminated, i.e. the purple circles in the figure are the eliminated obstacles. Before the balls are eliminated, the probability for each group of color balls is calculated according to the conditional probability:

[0054] The probability of the blue ball at the lower left = 5 + 0 + 0 + 0 = 5%

[0055] The probability of the green ball at the lower right = 4 + 4 + (4 * 50%) + (5 * 70%) = 13.5%

[0056] The probability of the blue ball above the green ball at the lower right = 0 + 0 + 0 + 0 = 0% (theoretically cannot be hit)

[0057] The probability of the red ball = 0 + 0 + 0 + 0 = 0% (theoretically cannot be hit)

[0058] The probability of the green ball at the upper left = 5 + 10 + 0 + 0 = 15%

[0059] The probability of the blue ball at the upper left = 3 + 0 + 0 + 0 = 3%

[0060] Obviously, the probability of the optimal condition here is the probability of the green ball at the upper left 15%, and the probability of the worst condition is the probability of the red ball 0%. That is, a x = 15%, c x = 0%. The values here are initial values, and the probabilities of different conditions will change as the game progresses, but the calculation method remains unchanged.

[0061] Further, for different levels, the probabilities can be adjusted after considering the preset difficulty and average difficulty. For example, Figure 4 The level ID of Figure 4 is 18 (the initial difficulty is 3, a = 10%, and c = 70%).

[0062] User 1:

[0063] When passing level 18, the average difficulty is 6, the initial difficulty is 3, and the random number of the uncompleted levels 19-30 is 26, P = |(3-6) * 20%| = 60%. Modify level 26, the worst condition probability = 70% - 60%, and the optimal condition probability = 10% + 60%.

[0064] Since user 1 passes the level with a high average difficulty, it means that his game level is low, so the difficulty needs to be reduced, and it is dynamically adjusted by the method of the present application.

[0065] User 2:

[0066] When the user 2 passes the 18th level, the average difficulty is 1, and the initial difficulty is 3. In the uncompleted levels 19-30, the random is 23, P = |(3-1)*20%| = 40% modifies the 26th level, so the worst condition probability = 70% + 40%, the best condition probability = 10% - 40%, where the worst condition probability is more than 100% indicating that the next round must refresh the worst condition, and the best condition probability is less than 0 indicating that the next round must not refresh the best condition.

[0067] Since the user 2 passes the level with a low average difficulty, it indicates that the user has a high game level, so the difficulty needs to be increased, which is dynamically adjusted through the method of the application.

[0068] In some embodiments, for the reward prop game, the value of f x and d x may also not be adjusted. The new value calculated can be used as the preset pass round number of the level in the form of r-2*(P / 20%) or r+2*(P / 20%). In this way, the level difficulty can be adjusted, the user's psychological expectation can also be adjusted, and the purpose of preventing the user from being addicted to the game can also be achieved.

[0069] The application analyzes the completed levels of the user, infers the game level of different users, and then dynamically adjusts the difficulty of subsequent levels to adapt to the needs of users with different game levels. In this way, whether the user has a high or low game level, after completing some levels relatively smoothly, the difficulty of subsequent levels can be increased through the application to avoid the user being addicted to the game due to the low difficulty. After completing some levels relatively hard, the difficulty of subsequent levels can be reduced through the application to avoid the user's game experience being damaged due to the high difficulty, and after the user smoothly passes some levels, the difficulty can be increased to a certain extent, and the user will not be addicted to the game due to the simple levels. The application can dynamically adjust the difficulty of subsequent levels for users with different levels, so that the user will no longer be addicted to the game for a long time.

[0070] Corresponding to the method embodiment of the application, the application also provides a game anti-addiction processing device, as shown in Figure 5 The game anti-addiction processing device 100 includes:

[0071] The difficulty calculation module 110 calculates the pass difficulty M x of the user through the level x based on the number of rounds through the level, where the pass difficulty value of each level is proportional to the number of rounds;

[0072] The first difficulty average module 120 is used to calculate the average pass difficulty M xa of the user through all the levels after passing n levels;

[0073] The second difficulty average module 130 is used to calculate the average preset difficulty A after completing all levels. xa .

[0074] Difficulty comparison module 140 is used to compare the average preset difficulty A. xa Compared to the average difficulty level M xa Size.

[0075] Difficulty Increase Module 150, used when the average preset difficulty A xa >Average difficulty of completion M xa Then, select level y from the remaining levels, where the preset difficulty A of that level is... y = Average difficulty of completing the game M xa Then calculate the difficulty coefficient P, where P = |(A xa -M xa )*20%|, which is the difference a between the probability of the optimal condition for the current level and the difficulty coefficient P. x -P is the probability of the new optimal condition, which is the sum of the probability of the worst condition of the current level and the difficulty coefficient P, c. x +P represents the probability of the new worst-case scenario, thus increasing the difficulty of the current level.

[0076] Difficulty reduction module 160, when the average completion difficulty is A xa Average difficulty level M xa At that time, in the remaining levels, there is a level y, where the preset difficulty Ay of the level is equal to the average difficulty M. xa Then calculate the difficulty coefficient P, where P = |(A xa -M xa )*20%| value, which is the sum of the probability of the optimal condition for the current level and the difficulty coefficient P. x +P is the probability of the new optimal condition, which is the difference c between the probability of the worst condition of the current level and the difficulty coefficient P. x -P represents the probability of the new worst-case scenario, thus reducing the difficulty of the current level.

[0077] Difficulty maintenance module 170, when the average completion difficulty is A xa = Average difficulty of completing the game M xa At that time, the difficulty of the remaining levels will not be adjusted.

[0078] The application scheme infers the game levels of different users by calculating and analyzing the completed levels of the users, and then dynamically adjusts the difficulty of subsequent levels to adapt to the needs of users with different game levels. In this way, whether the user has a high or low game level, after he or she completes some levels relatively smoothly, the application can increase the difficulty of subsequent levels to avoid the user's continuous playing and addiction due to too low difficulty. After he or she completes some levels relatively hard, the application can reduce the difficulty of subsequent levels to avoid damaging the user's game experience due to too high difficulty.

[0079] The electronic device in the embodiments of the application can be a user terminal device, can be a server, can also be other computing devices, and can also be a cloud server. Figure 6 A hardware structure schematic diagram of an electronic device of an embodiment of the application is shown, which can include a processor 601 and a memory 602 storing computer program instructions, and the processor 601 implements the flow or function of the method of any of the above embodiments when executing the computer program instructions.

[0080] Specifically, the processor 601 can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or can be configured to implement one or more integrated circuits of the embodiments of the application. The memory 602 can include a mass storage device for data or instructions. For example, the memory 602 can be at least one of a hard disk drive (HDD), a read-only memory (ROM), a random access memory (RAM), a floppy disk drive, a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, a universal serial bus (USB) drive, or other physical / tangible memory storage devices. For another example, the memory 602 can include removable or non-removable (or fixed) media. For another example, the memory 602 can be inside or outside the integrated gateway disaster recovery device. The memory 602 can be a non-volatile solid-state memory. In other words, the memory 602 generally includes a tangible (non-transitory) computer-readable storage medium (such as a memory device) encoded with computer-executable instructions, and when the software is executed (such as by one or more processors), the operations described in the method of the embodiments of the application can be performed. The processor 601 implements the flow or function of any of the above embodiments by reading and executing the computer program instructions stored in the memory 602.

[0081] In one example, Figure 6The illustrated electronic device may also include a communication interface 603 and a bus 610. The processor 601, memory 602, and communication interface 603 are connected via the bus 610 and communicate with each other. The communication interface 603 is mainly used to realize communication between the various modules, devices, units, and / or equipment in the embodiments of this application. The bus 610 includes hardware, software, or both, and can couple the components of the online data traffic metering device together. For example, the bus may include at least one of the following: Accelerated Graphics Port (AGP) or other graphics bus, Enhanced Industry Standard Architecture (EISA) bus, Front Side Bus (FSB), HyperTransport (HT) Interconnect, Industry Standard Architecture (ISA) bus, Infinite Bandwidth Interconnect, Low Pin Count (LPC) bus, memory bus, Microchannel Architecture (MCA) bus, Peripheral Component Interconnect (PCI) bus, PCI-E. x press(PCI- X The bus may be a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable bus. Bus 610 may include one or more buses. Although specific buses are described or illustrated in embodiments of this application, any suitable bus or interconnection may be considered in embodiments of this application.

[0082] In conjunction with the methods in the above embodiments, this application also provides a computer storage medium storing computer program instructions, which, when executed by a processor, implement the process or function of any of the methods in the above embodiments.

[0083] In addition, this application also provides a computer program product that stores computer program instructions, which, when executed by a processor, implement the process or function of any of the methods described above.

[0084] The flowcharts and / or block diagrams of methods, apparatuses, systems, and computer program products according to embodiments of this application have been described above, and related aspects have been described. It should be understood that each block or combination thereof in the flowcharts and / or block diagrams may be implemented by computer program instructions, by dedicated hardware performing a target function or action, or by a combination of dedicated hardware and computer instructions. For example, these computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to form a machine that enables the implementation of the target function / action in each block or combination thereof in the flowcharts and / or block diagrams, as executed via such processor. Such a processor may be a general-purpose processor, a dedicated processor, a special application processor, or a field-programmable logic circuit.

[0085] The functional blocks shown in the structural block diagram of the embodiments of the present application can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, and the like; when implemented in software, it is a program or a code segment used to perform the required tasks. The program or code segment can be stored in a memory or transmitted through a data signal carried in a carrier wave over a transmission medium or a communication link. The code segment can be downloaded via a computer network, such as the Internet, an intranet, and the like.

[0086] It should be noted that the present application is not limited to the specific configurations and processes described above or shown in the drawings. The above is merely a specific implementation of the present application, and those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the described systems, devices, modules or units can refer to the corresponding processes in the method embodiments, which need not be described again. It should be understood that the protection scope of the present application is not limited thereto, and any skilled person in the art can think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A game anti-addiction processing method, characterized in that, The method prevents user addiction by adjusting the game difficulty, wherein each game level includes a plurality of obstacles, the user experiences multiple rounds of operation in a single level, if the current operation result matches the current obstacle, the obstacle is eliminated, if the current operation result does not match the current obstacle, the current operation result is converted into an obstacle and enters the next round of operation, and after all obstacles are cleared, the next level is entered; wherein for a level x, it has a preset difficulty A x The probability of the optimal condition appearing in each round is a x The probability of the worst condition appearing is c x The probability of the ordinary condition appearing is b x The method comprises: When the user passes the level x, the passing difficulty M of the user passing the level x is calculated based on the number of rounds passing the level x wherein the passing difficulty value of each level is proportional to the number of rounds. After passing through n levels, calculate the average difficulty M of passing through all levels that it has passed xa ; Computes the average preset difficulty A of which it has completed all the stages xa ; Comparing average preset difficulty A xa to the size of average passing difficulty M xa ; When the average preset difficulty A xa > average passing difficulty M xa , a yth stage is selected from the remaining stages, wherein the preset difficulty A y = average passing difficulty M xa of the stage, and a difficulty coefficient P is calculated, wherein P = |(A xa - M xa ) * 20% |, the difference a x - P between the probability of the optimal condition of the current stage and the difficulty coefficient P is taken as the new probability of the optimal condition, and the sum c x + P between the probability of the worst condition of the current stage and the difficulty coefficient P is taken as the new probability of the worst condition, so as to increase the difficulty of the current stage. When the average preset difficulty A xa <average passing difficulty M xa , in a yth level of the remaining levels, wherein the preset difficulty A y = average passing difficulty M xa of the level, then calculate the difficulty coefficient P, wherein P = |(A xa -M xa )*20%| value, the probability of the optimal condition of the current level and the sum value a x +P of the difficulty coefficient P as the new probability of the optimal condition, the probability of the worst condition of the current level and the difference value c x -P of the difficulty coefficient P as the new probability of the worst condition, to reduce the difficulty of the current level; When the average preset difficulty A xa = average pass difficulty M xa , the remaining stage difficulty is not adjusted.

2. The method of claim 1, wherein, Also included are: Divide each k levels in all levels into a section, wherein when calculating the average difficulty and average preset difficulty, only consider the levels in the section have been passed, and when adjusting the game difficulty in the section, only select and adjust the levels in the section have not been passed.

3. The method of claim 1, wherein, The calculation of conditional probability includes: P a = W e + W f + (W ne * 50%) + (W nf * 70%) where P a is the conditional probability, W e is the quantity weight to eliminate, W f is the drop weight to eliminate, W ne is the quantity weight of the new condition after elimination, and W nf is the drop weight of the new condition after elimination. where P a The highest value of the calculation is the probability a of optimal conditions x The lowest value is the probability c of the occurrence of the worst conditions x The probability b of the occurrence of conditions between the highest and the lowest value x .

4. The method of claim 1, wherein, Each level is preset with a pass round r, and the method further includes: if the user does not eliminate all obstacles within the pass round, it is determined that the user fails to pass.

5. The method of claim 1, wherein, The game includes a reward prop, the reward prop eliminates part of the obstacles, wherein, in the level with the reward prop, the probability of the optimal condition is d x , the probability of the worst condition is e x , the probability of the ordinary condition is f x For the level with the reward prop, the method further comprises: When average preset difficulty A xa > average pass difficulty M xa , at the same time |A xa -M xa |>2, the difference d x -P between the probability of the optimal condition of the current level and the difficulty coefficient P is taken as the new probability of the optimal condition, and the sum f x +P between the probability of the worst condition of the current level and the difficulty coefficient P is taken as the new probability of the worst condition, if |A xa -M xa |<2, the probability of the optimal condition of the current level is adjusted to d x -2*(P / 20%), to increase the level difficulty. When preset difficulty A x <Passing difficulty M x |A x -M x |>2, the sum value d xa +P of the probability of the optimal condition of the current stage and the difficulty coefficient P is taken as the probability of the new optimal condition, and the difference value f xa -P of the probability of the worst condition of the current stage and the difficulty coefficient P is taken as the probability of the new worst condition; when |A x -M x |<2, the probability of the optimal condition of the current stage is adjusted to d xa +2*(P / 20%), so as to reduce the difficulty of the current stage.

6. The method of claim 1, wherein, Also included are: If the difficulty of a level has been adjusted, the difficulty of the level will not be adjusted subsequently. 7.A game anti-addiction processing apparatus, characterized by comprising: The device prevents user from being addicted by adjusting game difficulty, wherein each game level includes a plurality of obstacles, the user experiences multiple rounds of operation in a single level, if the current operation result matches the current obstacle, the obstacle is eliminated, if the current operation result does not match the current obstacle, the current operation result is converted into an obstacle and enters the next round of operation, and after all obstacles are cleared, the next level is entered; wherein, for the level x with a preset difficulty A x , the probability of the optimal condition appearing in each round is a x , the probability of the worst condition appearing is c x , the probability of the ordinary condition appearing is b x , the device comprises: A difficulty calculation module calculates the pass difficulty M of the user through the level x based on the number of rounds the user has passed through the level x wherein the pass difficulty value of each level is proportional to the number of rounds. a first difficulty average module for calculating an average difficulty M of passing all the levels after passing n levels xa ; a second difficulty average module for calculating an average preset difficulty A of which it has passed all the stages xa ; a difficulty comparison module for comparing the average preset difficulty A xa with the average passing difficulty M xa in size; a difficulty increasing module for selecting a yth level among the remaining levels when the average preset difficulty A xa > average passing difficulty M xa , wherein preset difficulty A y = average passing difficulty M xa of the yth level, and then calculating a difficulty coefficient P, wherein P = |(A xa - M xa ) * 20% |, taking the difference a x - P between the probability of the optimal condition of the current level and the difficulty coefficient P as the new probability of the optimal condition, and taking the sum c x + P between the probability of the worst condition of the current level and the difficulty coefficient P as the new probability of the worst condition, so as to increase the difficulty of the current level. a difficulty reduction module, when the average passing difficulty A xa < average passing difficulty M xa , in a remaining level, a yth level, wherein a preset difficulty Ay of the level = average passing difficulty M xa , then calculate a difficulty coefficient P, wherein P = |(A xa - M xa ) * 20% | value, the sum value a of the probability of the optimal condition of the current level and the difficulty coefficient P x + P as the new probability of the optimal condition, the difference value c of the probability of the worst condition of the current level and the difficulty coefficient P x - P as the new probability of the worst condition, to reduce the difficulty of the current level; A difficulty maintenance module does not adjust the remaining level difficulty when the average difficulty A xa = average difficulty M xa of passing is M.

8. A server, characterized by The server includes: a processor and a memory storing computer program instructions; the server executes the computer program instructions to realize the method as claimed in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer storage medium stores computer program instructions, and the computer program instructions are executed by the processor to realize the method as claimed in any one of claims 1-6.

10. A computer program product, characterised in that, It includes computer program instructions, and the computer program instructions are executed by the processor to realize the method as claimed in any one of claims 1-6.

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