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

Abstract

The invention relates to a game anti-addiction processing method and device, a server, a computer readable storage medium and a computer program product, and the method comprises the steps: for a level x, the level x has a preset difficulty Ax; after the user passes through the checkpoint x, the average clearance difficulty Mx of all checkpoints passed by the user is calculated, and the clearance difficulty value of each checkpoint is in direct proportion to the round number; comparing the preset difficulty Ax with the customs clearance difficulty Mx; when the preset difficulty Ax is larger than the customs clearance difficulty Mx, the difference value ax-P of the probability of the optimal condition of the current level and the difficulty coefficient P is used as the probability of a new optimal condition, and the sum value cx + P of the probability of the worst condition of the current level and the difficulty coefficient P is used as the probability of a new worst condition; when the customs clearance difficulty Ax is smaller than the customs clearance difficulty Mx, the probability of the optimal condition and the probability of the worst condition of the current checkpoint are adjusted. The level difficulty can be dynamically adjusted, and the user is prevented from being addicted to the game for a long time.

Description

Technical Field

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

[0002] Games such as puzzles and competitive games have become a form of leisure that people can choose. Playing these games appropriately can help people relieve stress and exercise their thinking ability, but if they spend too much time playing, they may become addicted to the game. Most of the existing anti-addiction measures are to set up a monitoring mechanism in the system background, which mainly monitors the user's game time and reminds the user to stop the game on their own when the threshold is reached, or directly stops providing game services. This method can prevent users from playing games for a long time, but the effect is short-lived, and this restriction is superficial and easy to circumvent.

[0003] There are many reasons for game addiction. One of them is that users will get a certain sense of satisfaction when completing a level of a certain difficulty. The satisfaction gained from completing the level will stimulate the brain to release dopamine, producing a feeling of pleasure, which will then cause the user to immerse himself in the game for a long time, causing the dopamine release mechanism to be activated for a long time, which will further interfere with the brain's degradation and reabsorption of neurotransmitters, resulting in game addiction.

[0004] Due to the above objective reasons, there is currently a lack of anti-addiction methods based on game mechanisms. How to prevent people from being addicted to games for a long time through the game operation mechanism is an urgent problem to be solved. Summary of the invention

[0005] In view of this, the embodiments of the present application provide a game addiction prevention processing method, device, electronic device and computer-readable storage medium for solving at least one technical problem.

[0006] The embodiment of the present application provides a method for preventing users from becoming addicted to games by adjusting the difficulty of the game, wherein each game level includes multiple obstacles, and the user undergoes multiple rounds of operations 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 the next round of operations is entered. After all obstacles are cleared, the next level is entered; wherein, for the level, x , which has a preset difficulty of A x , the probability of the optimal condition appearing in each round is a x , the probability of the worst condition is c x , the probability of the normal condition occurring is b x ; When the user passes the level x, the difficulty M of passing the level is calculated based on the number of rounds passed. x, where the difficulty of each level is proportional to the number of rounds; after passing n levels, calculate the average difficulty M of all levels that have been passed xa ; Calculate the average preset difficulty A of all levels that have been passed xa ; Compare the average preset difficulty A xa Average clearance difficulty M xa The size of the average preset difficulty A xa >Average clearance difficulty M xa , select a yth level from the remaining levels, where the preset difficulty of the level is A y =Average difficulty level 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 is a x -P is used as the probability of the new optimal condition, and the sum of the probability of the worst condition of the current level and the difficulty coefficient P is c x +P is used as the probability of the new worst condition to increase the difficulty of the current level; when the average preset difficulty A xa <Average difficulty level M xa , there is a y-th level in the remaining levels, where the preset difficulty of the level is A y =Average difficulty level M xa , and then calculate the difficulty coefficient P, where 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 is a x +P is used as the probability of the new optimal condition, and the difference between the probability of the worst condition of the current level and the difficulty coefficient P is c x -P is the probability of the new worst condition to reduce the difficulty of the current level; when the average preset difficulty A xa =Average difficulty level M xa , the difficulty of the remaining levels will not be adjusted.

[0007] Optionally, every k levels among all the levels are divided into a segment, wherein when calculating the average clearance difficulty, only the cleared levels in the segment are calculated, and when adjusting the game difficulty in the segment, only the uncleared levels in the segment are selected and adjusted.

[0008] Optionally, P a =W e +W f +(W ne *50%)+(W nf *70%), of which P a is the conditional probability, W eis the weight of the number of eliminations, W f To eliminate the drop weight, W ne = the quantity weight of the new condition after elimination, W nf To eliminate the drop weight of 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 , except that the probability of the highest and lowest values ​​is b x .

[0010] Optionally, each level is preset with a number of clearance rounds r. If the user fails to eliminate all obstacles within the number of clearance rounds r, it is deemed that the user has failed to pass the level.

[0011] Optionally, the game includes a bonus prop, which eliminates some obstacles; wherein the probability of the optimal condition appearing in the level with the bonus prop is d x , the probability of the worst condition is e x , the probability of the normal condition occurring is f x For levels with bonus items, when the preset difficulty is A x >Clear level M x At the same time |A x -M x |>2, the difference d between the probability of the optimal condition of the current level and the difficulty coefficient P x -P is used as the probability of the new optimal condition, and the sum of the probability of the worst condition of the current level and the difficulty coefficient P is calculated. x +P is the probability of the new worst condition. If |A x -M x |<2, adjust the probability of the optimal condition of the current level to d x -2*(P / 20%) to increase the level difficulty; when the preset difficulty is A x <Clearance Difficulty M x At the same time |A x -M x |>2, the sum of the probability of the optimal condition of the current level and the difficulty coefficient P is d x +P is used as the probability of the new optimal condition, and the difference between the probability of the worst condition of the current level and the difficulty coefficient P is f x -P is the probability of the new worst condition; when |A x -M x |<2, adjust the probability of the optimal condition of the current level 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 will not be adjusted subsequently.

[0013] The present application further provides a game addiction prevention processing device, which prevents users from becoming addicted by adjusting the difficulty of the game, wherein each game level includes multiple obstacles, and the user undergoes multiple rounds of operations 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 the next round of operations is entered. After all obstacles are cleared, the next level is entered; wherein, for the level x , which has a preset difficulty of A x , the probability of the optimal condition appearing in each round is a x , the probability of the worst condition is c x , the probability of the normal condition occurring is b x The device includes: a difficulty calculation module, when the user passes the level x, based on the number of rounds passed through the level, calculates the level passing difficulty M of the user passing the level x , where the clearance difficulty value of each level is proportional to the number of rounds; the first difficulty average module is used to calculate the average clearance difficulty M of all levels after passing n levels. xa The second difficulty average module is used to calculate the average preset difficulty A of all levels that have been passed. xa ; Difficulty comparison module, used to compare the average preset difficulty A xa Average clearance difficulty M xa The size of the difficulty increase module is used when the average preset difficulty A xa >Average clearance difficulty M xa , select a yth level from the remaining levels, where the preset difficulty of the level is A y =Average difficulty level 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 is a x -P is used as the probability of the new optimal condition, and the sum of the probability of the worst condition of the current level and the difficulty coefficient P is c x +P is used as the probability of the new worst condition to increase the difficulty of the current level; the difficulty reduction module, when the average clearance difficulty A xa <Average difficulty level M xa When the remaining levels are a y-th level, the preset difficulty of the level Ay = the average clearance difficulty M xa , and then calculate the difficulty coefficient P, where 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 is a x +P is used as the probability of the new optimal condition, and the difference between the probability of the worst condition of the current level and the difficulty coefficient P is c x -P is used as the probability of the new worst condition to reduce the difficulty of the current level; the difficulty maintenance module, when the average clearance difficulty A xa =Average difficulty level M xa , the difficulty of the remaining levels will not be adjusted.

[0014] The present application further provides a server, comprising: a processor and a memory storing computer program instructions; when the server executes the computer program instructions, any of the aforementioned methods is implemented.

[0015] The present application further provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method as described in any one of the above is implemented.

[0016] The present application further provides a computer program product, which includes computer program instructions, and when the computer program instructions are executed by a processor, the method as described in any one of the above is implemented.

[0017] The embodiment of the present application calculates and analyzes the levels that users have completed, infers the game levels of different users, and then dynamically adjusts the difficulty of subsequent levels to meet the needs of users with different game levels. In this way, no matter whether the user's game level is high or low, after they have completed some levels relatively smoothly, the difficulty of subsequent levels can be increased through this application to avoid users continuing to play and becoming addicted due to too low difficulty. For example, after they have completed some levels relatively hard, the difficulty of subsequent levels can be reduced through this application to avoid destroying the user's game experience due to too high difficulty. After reducing the difficulty, the user successfully passes some levels, and the difficulty can be increased to a certain extent, so that the user will not become addicted due to the simplicity of the levels, thereby achieving the purpose of preventing users from becoming addicted to the game for a long time. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0019] Figure 1 It is a schematic diagram of the system architecture of an embodiment of the present application.

[0020] Figure 2 It is a flowchart of a game anti-addiction processing method according to an embodiment of the present application.

[0021] Figure 3 to Figure 4 It is a schematic diagram of a game scene of an embodiment of the present application.

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

[0023] Figure 6 It is a schematic diagram of an electronic device used to implement the game anti-addiction processing method for preventing addiction in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The principles and spirit of the present application will be described below with reference to several exemplary embodiments. It should be understood that the purpose of providing these embodiments is to make the principles and spirit of the present application clearer and more thorough, so that those skilled in the art can better understand and implement the principles and spirit of the present application. The exemplary embodiments provided herein are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments herein, all other embodiments obtained by ordinary technicians of the art without creative work are within the scope of protection of this application.

[0025] Embodiments of the present application relate to terminal devices and / or servers. Those skilled in the art will appreciate that the embodiments of the present application may be implemented as a system, apparatus, device, method, computer-readable storage medium, or computer program product. Therefore, the present disclosure may be specifically implemented in at least one of the following forms: complete hardware, complete software, or a combination of hardware and software. According to the embodiments of the present application, the present application requests protection for a gift giving method, apparatus, electronic device, and storage medium in a live broadcast application. Figure 1 A schematic diagram of a system architecture of an embodiment of the present application is shown. Figure 1 As shown, the system includes a terminal device 102 and a server 104. Among them, the terminal device 102 may include at least one of the following: a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart TV, various wearable devices, an augmented reality AR device, a virtual reality VR device, etc. A client can be installed on the terminal device 102. For example, the client can be a client that specifically performs a specific function (such as an application app), or a client with multiple application applets (different functions) embedded in it, or a client logged in through a browser. The 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 instruction input by the user, the request information containing the instruction is sent to the server 104. After receiving the request information, the server 104 performs corresponding processing and then returns the processing result information to the terminal device 102. The user instruction is completed through a series of data processing and information interaction.

[0026] In this document, terms such as first, second, third, etc. are only used to distinguish one entity (or operation) from another entity (or operation), but not to require or imply any order or relationship between these entities (or operations).

[0027] The following is a brief description of the concepts, technical terms and other related contents that may be involved in the embodiments of the present application.

[0028] In an embodiment of the present application, the game includes multiple game levels, each of which includes multiple obstacles. In a single game level, a user will obtain a condition in each round, and the user can choose how to use the condition. 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 the next round of operation is entered. For example, in a game that achieves the purpose of clearing a level by eliminating obstacles (such as Bubble Dragon, Tetris, etc.), if the user reasonably places the ball or block (such as putting the same color balls together), the corresponding ball or block can be eliminated. After multiple rounds of operation, if all obstacles are cleared in the current game level, the next level can be entered.

[0029] In the embodiment of the present application, for a certain level of a certain game x (or called the x Off), which has a preset difficulty of A x The preset difficulty can be calculated or evaluated by game testers. The probability of the optimal condition appearing in each round is a x , the probability of the worst condition is c x , the probability of the normal condition occurring is b x Among them, the condition that can be passed through the least rounds is the optimal condition, the condition that can be passed through the most rounds is the worst condition, and the others are ordinary conditions.

[0030] Figure 2 This is a flowchart of a method for preventing game addiction in this application. Figure 2 As shown, the method includes:

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

[0032] Step S202: After passing n levels, calculate the average level-clearing difficulty M of all levels that have been passed xa .

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

[0034] Step S204: Compare the preset difficulty A xa With clearance difficulty M xa size.

[0035] Step S205: When the preset difficulty A xa >Clear level M xa , select a yth level from the remaining levels, where the preset difficulty of the level is A y = Difficulty level 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, that is, a x -P is used as the probability of the new optimal condition, and the sum of the probability of the worst condition of the current level and the difficulty coefficient P, that is, c x +P is the probability of the new worst condition to increase the difficulty of the current level.

[0036] Step S206: When the preset difficulty A xa <Clearance Difficulty M xa , there is a y-th level in the remaining levels, where the preset difficulty of the level is A y = Difficulty level M xa , and then calculate the difficulty coefficient P, where 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, that is, a x +P is used as the probability of the new optimal condition, and the difference between the probability of the worst condition of the current level and the difficulty coefficient P, that is, c x -P is the probability of the new worst condition to reduce the difficulty of the current level.

[0037] Step S207: When the clearance difficulty is A xa = Level clearance difficulty M xa , the difficulty of the remaining levels will not be adjusted.

[0038] The greater the difficulty value, the more difficult it is for the user to pass the level. Generally, after calculating the difficulty value and the average difficulty value, the integer value is selected as the result. y =Average difficulty level M xaThe purpose of setting levels is to first adapt the difficulty of the level itself to the user's level, and then adjust the difficulty on this basis, so as to form a certain degree of psychological gap for the user, so that the user can clearly feel the change in difficulty, so as to achieve the effect of preventing addiction.

[0039] Generally, when a user has played several levels, the difficulty of each level is first obtained based on the performance of each level completed, and then the average difficulty of multiple levels is taken to calculate the average difficulty M of all levels completed. xa Here we assume 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 Average clearance difficulty M xa The size of A x With M xa The size of the game is compared to determine the player's level. After judgment, if the player's clearance difficulty is lower than A xa , the player is considered to have a high level, so the difficulty of a subsequent level is increased through the P value. xa , it is considered that the player's level is low, so the difficulty of a subsequent level is reduced through the P value.

[0040] When the user's level is compatible with the current level difficulty, the level difficulty does not need to be adjusted. This is because, in general, a certain proportion of difficult levels will be set in the game at the beginning. When the game difficulty is compatible with the user's level, when the user encounters such a level during the game, it will consume the user's energy to a certain extent. Therefore, in this case, the user will not be addicted to the game because it is too easy.

[0041] This application solution calculates and analyzes the levels that users have completed, infers the game levels of different users, and then dynamically adjusts the difficulty of subsequent levels to meet the needs of users with different game levels. In this way, no matter whether the user's game level is high or low, after they have completed some levels relatively smoothly, the difficulty of subsequent levels can be increased through this application to avoid users continuing to play and becoming addicted due to too low difficulty. After they have completed some levels with great effort, the difficulty of subsequent levels can be reduced through this application to avoid ruining the user's gaming experience due to too high difficulty.

[0042] In some embodiments, every k levels among all levels are divided into a segment, wherein when calculating the average clearance difficulty, only the cleared levels in the segment are calculated, and when adjusting the game difficulty in the segment, only the uncleared levels in the segment are selected and adjusted.

[0043] For example, you can select 10 consecutive levels as a segment, so that users can quickly feel the difficulty adjustment without lagging behind.

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

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

[0046] Among them, P a is the conditional probability, W e is the weight of the number of eliminations, W f To eliminate the drop weight, W ne = the quantity weight of the new condition after elimination, W nf is the drop weight of the new condition after elimination. 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 , except that the probability of the highest and lowest values ​​is b x .

[0047] The number of eliminations can be the number of obstacles that can be directly eliminated by this round of operations. The number of eliminations can be the number of obstacles that can be indirectly eliminated by this round of operations. For example, Figure 3 If the five blue balls (i.e., obstacles) on the right side of the middle disappear, the number of eliminated balls is 5, and the four green balls below them will also fall, so the number of eliminated balls is 4. In some embodiments, the number of new conditions after elimination is the number of obstacles that can be directly eliminated by the newly emerged conditions after each round of operation. The number of new conditions after elimination can be the number of obstacles that can be indirectly eliminated by the newly emerged conditions.

[0048] In some embodiments, each level is preset with a number of clearance rounds r. If the user fails to eliminate all obstacles within the number of clearance rounds, it is deemed that the user has failed the level.

[0049] Setting the number of clearance rounds can increase the user's sense of frustration to a certain extent and enhance the game's anti-addiction effect. In addition, the number of clearance rounds of the user can be compared with the preset number of clearance rounds to calculate the user's clearance difficulty M for the level. x For example, if the number of rounds that the user has completed is greater than the preset number of rounds, then it is considered that the user has completed the level with a difficulty level M. x Greater than the preset difficulty A x .

[0050] In some embodiments, the game includes a bonus prop, which eliminates some obstacles; wherein, in a level with a bonus prop, the probability of the optimal condition appearing is d x , the probability of the worst condition is e x , the probability of the normal condition occurring is f x For levels with bonus items, when the average preset difficulty A xa >Average clearance difficulty M xa At the same time |A xa -M xa |>2, the difference between the probability of the optimal condition of the current level and the difficulty coefficient P, that is, d x -P is used as the probability of the new optimal condition, and the sum of the probability of the worst condition of the current level and the difficulty coefficient P, that is, f x +P is the probability of the new worst condition. xa -M xa |<2, adjust the probability of the optimal condition of the current level to d x -2*(P / 20%) to increase the level difficulty. xa <Average difficulty level M xa At the same time |A xa -M xa |>2, the sum of the probability of the optimal condition of the current level and the difficulty coefficient P, that is, d x +P is used as the probability of the new optimal condition, and the difference between the probability of the worst condition of the current level and the difficulty coefficient P, that is, f x -P is the probability of the new worst condition. xa -M xa |<2, adjust the probability of the optimal condition of the current level to d x +2*(P / 20%) to reduce the difficulty of the current level.

[0051] Bonus props can be a special gameplay. When the bonus is triggered, a set of obstacles can be removed. If the bonus prop is triggered, the relevant algorithm also needs to be adjusted to adapt to the adjusted situation. The specific method is to compare A xa and M xa Whether the difference between the two is too great due to the reward items, so as to keep the difficulty within a reasonable range.

[0052] The following is a specific explanation using a bubble dragon game as an example. Figure 3-Figure 5 The conditions here can be balls of different colors given below, and the obstacles can be balls above that need to be eliminated.

[0053] According to the calculation formula given above, Figure 3 For example. Figure 3 It shows that some balls are eliminated, that is, the purple circles in the figure are obstacles that are eliminated. Before these balls are eliminated, the probability of each group of color balls is calculated according to the conditional probability:

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

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

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

[0057] Red ball probability = 0+0+0+0 = 0% (theoretically, it cannot be hit)

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

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

[0060] Obviously, the best condition here is the probability of the green ball in the upper left corner, which is 15%, and the worst condition is the probability of the red ball, which is 0%. x =15%, c x =0%. The value here is the initial value. As the game progresses, the probability of different conditions will change, but the calculation method remains unchanged.

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

[0062] User 1:

[0063] When clearing level 18, the average difficulty is 6, the initial difficulty is 3, and the unfinished levels 19-30 are randomly selected to level 26, P = |(3-6)*20%| = 60%. Modify level 26, the worst condition probability = 70%-60%, the best condition probability = 10%+60%.

[0064] Since the average difficulty of User 1 to pass the level is relatively high, it indicates that his gaming level is relatively low, so the difficulty needs to be lowered, and a dynamic adjustment is performed through the method of this application.

[0065] User 2:

[0066] When clearing level 18, the average difficulty is 1 and the initial difficulty is 3. In the unfinished levels, 19-30 are randomly changed to 23, P = |(3-1)*20%| = 40% to modify level 26, so the worst condition probability = 70% + 40%, the best condition probability = 10% - 40%, where the worst condition probability exceeds 100% means that the worst condition will definitely be refreshed in the next round, and the best condition probability is less than 0 means that the best condition will definitely not be refreshed in the next round.

[0067] Since the average difficulty of user 2 to pass the level is relatively low, it means that his gaming level is relatively high, so the difficulty needs to be increased, which is dynamically adjusted through the method of this application.

[0068] In some embodiments, for the reward props gameplay, it is also possible not to adjust f x and d x The value of . The new value calculated can be used as the preset number of rounds for the level by means of r-2*(P / 20%) or r+2*(P / 20%). In this way, the level difficulty can be adjusted, and the user's psychological expectations can also be adjusted, which can also achieve the purpose of preventing users from being addicted to the game.

[0069] This application calculates and analyzes the levels that users have completed, infers the game levels of different users, and then dynamically adjusts the difficulty of subsequent levels to meet the needs of users with different game levels. In this way, no matter whether the user's game level is high or low, after they have completed some levels relatively smoothly, the difficulty of subsequent levels can be increased through this application to avoid users continuing to play and becoming addicted due to too low difficulty. After they have completed some levels with great effort, the difficulty of subsequent levels can be reduced through this application to avoid destroying the user's gaming experience due to too high difficulty. After reducing the difficulty, users can successfully pass some levels, and the difficulty can be increased to a certain extent, so that they will not become addicted due to simple levels. For users of different levels, this application can dynamically adjust the difficulty of subsequent levels so that users will no longer be addicted to the game for a long time.

[0070] Corresponding to the method embodiment of the present application, the present application also provides a game addiction prevention processing device, such as Figure 5 As shown, the game addiction prevention processing device 100 includes:

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

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

[0073] The second difficulty average module 130 is used to calculate the average preset difficulty A of all the levels that have been passed. xa .

[0074] The difficulty comparison module 140 is used to compare the average preset difficulty A xa Average clearance difficulty M xa size.

[0075] The difficulty increasing module 150 is used to increase the average preset difficulty A xa >Average clearance difficulty M xa , select a yth level from the remaining levels, where the preset difficulty of the level is A y =Average difficulty level 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 is a x -P is used as the probability of the new optimal condition, and the sum of the probability of the worst condition of the current level and the difficulty coefficient P is c x +P is the probability of the new worst condition to increase the difficulty of the current level.

[0076] Difficulty reduction module 160, when the average clearance difficulty A xa <Average difficulty level M xa When the remaining levels are a y-th level, the preset difficulty of the level Ay = the average clearance difficulty M xa , and then calculate the difficulty coefficient P, where 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 is a x +P is used as the probability of the new optimal condition, and the difference between the probability of the worst condition of the current level and the difficulty coefficient P is c x -P is the probability of the new worst condition to reduce the difficulty of the current level.

[0077] Difficulty maintenance module 170, when the average clearance difficulty A xa =Average difficulty level M xa , the difficulty of the remaining levels will not be adjusted.

[0078] This application solution calculates and analyzes the levels that users have completed, infers the game levels of different users, and then dynamically adjusts the difficulty of subsequent levels to meet the needs of users with different game levels. In this way, no matter whether the user's game level is high or low, after they have completed some levels relatively smoothly, the difficulty of subsequent levels can be increased through this application to avoid users continuing to play and becoming addicted due to too low difficulty. After they have completed some levels with great effort, the difficulty of subsequent levels can be reduced through this application to avoid ruining the user's gaming experience due to too high difficulty.

[0079] The electronic device in the embodiment of the present application may be a user terminal device, a server, other computing devices, or a cloud server. Figure 6 A schematic diagram of the hardware structure of an electronic device according to an embodiment of the present application is shown. The electronic device may include a processor 601 and a memory 602 storing computer program instructions. When the processor 601 executes the computer program instructions, the process or function of any of the above-mentioned embodiments of the method is implemented.

[0080] Specifically, the processor 601 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application. The memory 602 may include a large-capacity memory for data or instructions. For example, the memory 602 may be at least one of the following: a hard disk drive (HDD), a read-only memory (ROM), a random access memory (RAM), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, a universal serial bus (USB) drive or other physical / tangible memory storage device. For another example, the memory 602 may include a removable or non-removable (or fixed) medium. For another example, the memory 602 may be inside or outside the integrated gateway disaster recovery device. The memory 602 may be a non-volatile solid-state memory. In other words, the memory 602 usually 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 executed by one or more processors), the operation described in the method of the embodiment of the present application can be performed. The processor 601 implements the process or function of any method in the above embodiments by reading and executing the computer program instructions stored in the memory 602.

[0081] In one example, Figure 6The electronic device shown may also include a communication interface 603 and a bus 610. Among them, the processor 601, the memory 602, and the communication interface 603 are connected through the bus 610 and complete communication with each other. The communication interface 603 is mainly used to realize the communication between the modules, devices, units and / or devices in the embodiment of the present application. The bus 610 includes hardware, software or both, and can couple the components of the online data traffic billing device to each other. For example, the bus may include at least one of the following: an accelerated graphics port (AGP) or other graphics bus, an enhanced industrial standard architecture (EISA) bus, a front-end bus (FSB), a hypertransport (HT) interconnect, an industrial standard architecture (ISA) bus, an infinite bandwidth interconnect, a low pin count (LPC) bus, a memory bus, a micro channel architecture (MCA) bus, a peripheral component interconnect (PCI) bus, a PCI-E x press(PCI- X ) bus, a serial advanced technology attachment (SATA) bus, a video electronics standard association local (VLB) bus, or other suitable bus. Bus 610 may include one or more buses. Although the embodiments of the present application describe or illustrate a specific bus, the embodiments of the present application may consider any suitable bus or interconnection method.

[0082] In combination with the method in the above embodiments, an embodiment of the present application also provides a computer storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the process or function of any method in the above embodiments is implemented.

[0083] In addition, an embodiment of the present application further provides a computer program product, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the process or function of any one of the methods in the above embodiments is implemented.

[0084] The above exemplarily describes the flowchart and / or block diagram of the method, device, system and computer program product of the embodiment of the present application, and describes the relevant various aspects. It should be understood that each box or combination thereof in the flowchart and / or block diagram can be implemented by computer program instructions, or can be implemented by special hardware that performs the target function or action, or can be implemented by a combination of special hardware and computer instructions. For example, these computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to form a machine that enables these instructions executed by such a processor to enable the realization of the function / action of the target in each box or combination thereof in the flowchart and / or block diagram. Such a processor can be a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit.

[0085] The functional blocks shown in the structured block diagram of the embodiment 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 function card, etc.; when implemented in software, it is a program or code segment used to perform the required task. The program or code segment can be stored in a memory, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0086] It should be noted that the present application is not limited to the specific configurations and processes described above or shown in the figures. The above is only a specific implementation mode of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the described system, device, module or unit can refer to the corresponding process in the method embodiment without further description. It should be understood that the scope of protection of the present application is not limited to this. Any technician familiar with the technical field can think of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and these modifications or substitutions should be included in the scope of protection of the present application.

Claims

1. A method for preventing game addiction, characterized in that: The method prevents users from getting addicted by adjusting the difficulty of the game, wherein each game level includes multiple obstacles, and the user undergoes multiple rounds of operations in a single level. If the current operation result matches the current obstacle, the obstacle is removed; if the current operation result does not match the current obstacle, the current operation result is converted into an obstacle and the next round of operations is entered. After all obstacles are cleared, the next level is entered; wherein, for a certain 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 is c x , the probability of the normal condition occurring is b x , the method comprising: When the user passes the level x, the difficulty M of passing the level is calculated based on the number of rounds passed. x , where the difficulty of each level is proportional to the number of rounds; After passing n levels, calculate the average difficulty M of all levels that have been passed. xa ; Calculate the average preset difficulty A of all levels that have been completed xa ; Comparison of average preset difficulty A xa Average clearance difficulty M xa size; When the average preset difficulty A xa >Average clearance difficulty M xa , select a yth level from the remaining levels, where the preset difficulty of the level is A y =Average difficulty level 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 is a x -P is used as the probability of the new optimal condition, and the sum of the probability of the worst condition of the current level and the difficulty coefficient P is c x +P is the probability of the new worst condition to increase the difficulty of the current level; When the average preset difficulty A xa <Average difficulty level M xa , there is a y-th level in the remaining levels, where the preset difficulty of the level is A y =Average difficulty level M xa , and then calculate the difficulty coefficient P, where 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 is a x +P is used as the probability of the new optimal condition, and the difference between the probability of the worst condition of the current level and the difficulty coefficient P is c x -P is the probability of the new worst condition to reduce the difficulty of the current level; When the average preset difficulty A xa =Average difficulty level M xa , the difficulty of the remaining levels will not be adjusted.

2. The method according to claim 1, characterized in that Also includes: Every k levels among all levels are divided into a segment. When calculating the average clearance difficulty and the average preset difficulty, only the cleared levels in the segment are considered. When adjusting the game difficulty in the segment, only the uncleared levels in the segment are selected and adjusted.

3. The method according to claim 1, characterized in that The calculation of conditional probability includes: P a =W e +W f +(W ne *50%)+(W nf *70%) Among them, P a is the conditional probability, W e is the weight of the number of eliminations, W f To eliminate the drop weight, W ne = the quantity weight of the new condition after elimination, W nf To eliminate the drop weight of the new condition, Where P a The probability that the highest value calculated is the optimal condition is a x , the lowest value is the probability c of the worst condition x , except that the probability of the highest and lowest values ​​is b x .

4. The method according to claim 1, characterized in that: Each level is preset with a clearance number r, and the method further includes: if the user fails to eliminate all obstacles within the clearance number r, the clearance is deemed to have failed.

5. The method according to claim 1, characterized in that: The game includes a bonus prop, which eliminates some obstacles; wherein, in a level with a bonus prop, the probability of the optimal condition appearing is d x , the probability of the worst condition is e x , the probability of the normal condition occurring is f x For a level with a reward item, the method further includes: When the average preset difficulty A xa >Average clearance difficulty M xa At the same time |A xa -M xa |>2, the difference d between the probability of the optimal condition of the current level and the difficulty coefficient P is x -P is used as the probability of the new optimal condition, and the probability of the worst condition of the current level and the sum of the difficulty coefficient P are calculated. x +P is the probability of the new worst condition. If |A xa -M xa |<2, adjust the probability of the optimal condition of the current level to d x -2*(P / 20%) to increase the level difficulty; When the preset difficulty is A x <Difficulty level M x At the same time |A x -M x |>2, the sum of the probability of the optimal condition of the current level and the difficulty coefficient P is d xa +P is used as the probability of the new optimal condition, and the difference between the probability of the worst condition of the current level and the difficulty coefficient P is f xa -P is the probability of the new worst condition; when |A x -M x |<2, adjust the probability of the optimal condition of the current level to d xa +2*(P / 20%) to reduce the difficulty of the current level.

6. The method according to claim 1, characterized in that Also includes: If the difficulty of a level has been adjusted, it will not be adjusted again in the future.

7. A game addiction prevention device, characterized in that: The device prevents users from getting addicted by adjusting the difficulty of the game, wherein each game level includes multiple obstacles, and the user undergoes multiple rounds of operations 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 the next round of operations is entered. After all obstacles are cleared, the next level is entered; wherein, for the level x , which has a preset difficulty of A x , the probability of the optimal condition appearing in each round is a x , the probability of the worst condition is c x , the probability of the normal condition occurring is b x , the device comprises: Difficulty calculation module: when the user passes the level x, the difficulty M of passing the level is calculated based on the number of rounds passed. x , where the difficulty of each level is proportional to the number of rounds; The first difficulty average module is used to calculate the average difficulty M of all the levels after passing n levels. xa ; The second difficulty average module is used to calculate the average preset difficulty A of all levels that have been passed. xa ; Difficulty comparison module, used to compare the average preset difficulty A xa Average clearance difficulty M xa size; Difficulty increase module, used when the average preset difficulty A xa >Average clearance difficulty M xa , select a yth level from the remaining levels, where the preset difficulty of the level is A y =Average difficulty level 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 is a x -P is used as the probability of the new optimal condition, and the sum of the probability of the worst condition of the current level and the difficulty coefficient P is c x +P is the probability of the new worst condition to increase the difficulty of the current level; Difficulty reduction module, when the average clearance difficulty is A xa <Average difficulty level M xa When the remaining levels are a y-th level, the preset difficulty of the level Ay = the average clearance difficulty M xa , and then calculate the difficulty coefficient P, where 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 is a x +P is used as the probability of the new optimal condition, and the difference between the probability of the worst condition of the current level and the difficulty coefficient P is c x -P is the probability of the new worst condition to reduce the difficulty of the current level; Difficulty maintenance module, when the average clearance difficulty is A xa =Average difficulty level M xa , the difficulty of the remaining levels will not be adjusted.

8. A server, characterized in that: The server comprises: a processor and a memory storing computer program instructions; when the server executes the computer program instructions, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer storage medium stores computer program instructions, which, when executed by a processor, implement the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that It comprises computer program instructions, which, when executed by a processor, implement the method according to any one of claims 1 to 6.

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