Game advertisement data processing method and system based on player operation habits

Abstract

The present invention discloses a method and system for processing game advertisement data based on player operation habits, the method comprising: obtaining multiple historical game operations of the current user in the current game scene; based on a prediction algorithm, predicting the next game operation and next game perspective of the current user according to the multiple historical game operations; based on a portrait matching algorithm, determining the advertisement preference parameters corresponding to the current user according to the multiple historical game operations; calculating advertisement display data and advertisement display parameters according to the advertisement preference parameters and the next game perspective; the advertisement display data is used to display the advertisement display parameters in the next game perspective. It can be seen that the present invention can improve the rationality and effectiveness of advertisement display in the game, reduce the user's aversion to advertisements, and improve the effect of advertisement promotion.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for processing game advertisement data based on player operation habits. Background Art

[0002] Electronic games (also known as video games, video games, or video games for short, in English: Electronic games or Videogames) refer to all interactive games that rely on electronic media platforms to run. Electronic games can be divided into arcade games, handheld games, TV games (or home console games, video games, and video games in some areas), computer games, and mobile games (or mobile games) according to the game carrier. They refer to games played by people through electronic devices (such as computers, game consoles, and mobile phones). In existing electronic games, in order to make profits, advertising elements are often added to attract players' attention to specific products or services. However, when processing advertising data in the game, the existing technology does not fully consider analyzing the player's operating habits and determining the display parameters and positions of the advertisements. Therefore, the advertising data often appears abrupt or ignored in the game, affecting the player's gaming experience and failing to achieve a promotion effect. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for processing game advertisement data based on player operation habits, which can improve the rationality and effectiveness of advertisement display in the game, reduce the user's aversion to advertisements, and improve the effect of advertisement promotion.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a method for processing game advertisement data based on player operation habits, the method comprising:

[0005] Get multiple historical game operations of the current user in the current game scenario;

[0006] Based on the prediction algorithm, predicting the next game operation and next game perspective of the current user according to the multiple historical game operations;

[0007] Based on the portrait matching algorithm, determining the advertisement preference parameters corresponding to the current user according to the multiple historical game operations;

[0008] Advertisement display data and advertisement display parameters are calculated according to the advertisement preference parameters and the next game viewing angle; the advertisement display data is used for displaying the advertisement display parameters in the next game viewing angle.

[0009] As an optional implementation, in the first aspect of the present invention, the predicting the next game operation and the next game perspective of the current user based on the prediction algorithm according to the multiple historical game operations includes:

[0010] Determine the perspective change parameters corresponding to each of the historical game operations; the perspective change parameters include perspective movement direction, perspective movement angle and perspective movement speed;

[0011] According to each of the historical game operations and the corresponding operation time, based on a neural network algorithm, predict the next game operation of the current user;

[0012] According to each of the perspective change parameters and the corresponding operation time, based on a preset perspective change model, the next game perspective of the current user is predicted.

[0013] As an optional implementation, in the first aspect of the present invention, determining the perspective change parameter corresponding to each of the historical game operations includes:

[0014] Each of the historical game operations and the game character parameters of the current user are input into a trained perspective impact prediction neural network to obtain output perspective change parameters corresponding to each of the historical game operations; the perspective impact prediction neural network is trained by a training data set including multiple training game operations and corresponding game character annotations and perspective change parameter annotations; the game character parameters or the game character annotations include character level, character plot, character category, character current skills and character historical action route.

[0015] As an optional implementation, in the first aspect of the present invention, predicting the next game operation of the current user based on each of the historical game operations and the corresponding operation time based on a neural network algorithm includes:

[0016] Sorting all the historical game operations from early to late according to the corresponding operation time to obtain a game operation sequence;

[0017] Screening more than a preset number of operations belonging to the same operation type in the game operation sequence to obtain multiple sets of continuous operations;

[0018] Determine the operation type with the largest number of operations belonging to the same operation type among all the historical game operations, and obtain the highest frequency operation type;

[0019] The game operation sequence, the continuous operation set and the most frequent operation type are input into a trained next operation prediction neural network to obtain the output of the next game operation of the current user; the next operation prediction neural network is trained by a training data set including multiple training game operation sequences and corresponding continuous operation annotations, most frequent operation type annotations and next game operation annotations.

[0020] As an optional implementation, in the first aspect of the present invention, predicting the next game perspective of the current user based on each perspective change parameter and the corresponding operation time and based on a preset perspective change model includes:

[0021] Sorting all the perspective change parameters from early to late according to the operation time of the corresponding historical game operations to obtain a perspective change parameter sequence;

[0022] Inputting the perspective change parameter sequence into the perspective change simulation three-dimensional model corresponding to the current game scene to obtain a scene change video obtained by continuously changing the perspective according to the perspective change parameter sequence;

[0023] Based on a spatial search algorithm, a plurality of random next game perspectives corresponding to the video ending perspectives of the scene change video are randomly generated;

[0024] Inputting the scene change video and any of the random next game perspectives into a trained picture continuity prediction neural network to obtain a picture continuity parameter corresponding to any of the random next game perspectives; the picture continuity prediction neural network is trained using a training data set including a plurality of continuous pictures and corresponding continuity annotations;

[0025] The random next game perspective with the highest screen coherence parameter is determined as the next game perspective of the current user.

[0026] As an optional implementation, in the first aspect of the present invention, the determining the advertisement preference parameters corresponding to the current user based on the portrait matching algorithm according to the multiple historical game operations includes:

[0027] For each candidate user portrait in a preset user portrait database, calculating the operation similarity between the historical game operation data set corresponding to the candidate user portrait and the plurality of historical game operations;

[0028] Determine all the candidate user portraits whose operation similarity is greater than a preset similarity threshold as multiple user portraits of the current user;

[0029] Determine the portrait advertising preference parameters of each user portrait according to the preset correspondence between the user portrait and the advertising preference parameters;

[0030] The intersection of the portrait advertising preference parameters of all the user portraits is determined as the advertising preference parameters corresponding to the current user; the advertising preference parameters include preferred advertising size, preferred advertising position, preferred advertising type and preferred advertising duration.

[0031] As an optional implementation, in the first aspect of the present invention, the calculating of the advertisement display data and the advertisement display parameters according to the advertisement preference parameters and the next game viewing angle includes:

[0032] For each candidate advertisement data, calculating the type similarity between the advertisement type of the candidate advertisement data and the preferred advertisement type;

[0033] Calculate the duration difference between the advertisement duration of the candidate advertisement data and the preferred advertisement duration;

[0034] Calculate the product of the type similarity and the duration difference to obtain the priority of the candidate advertisement data;

[0035] Determine the candidate advertisement data with the highest priority as advertisement display data;

[0036] Based on the next game viewing angle, the preferred advertisement size and the preferred advertisement position are modified to obtain advertisement display parameters.

[0037] As an optional implementation, in the first aspect of the present invention, the preferred advertisement size and the preferred advertisement position are modified based on the next game viewing angle to obtain advertisement display parameters, including:

[0038] Determine other screen areas in the perspective screen of the next game perspective, except for the prompt image, the task image and the interactive image, as non-important image areas;

[0039] Calculate the intersection of the preferred advertisement position and the non-important image area to obtain an advertisement display position;

[0040] Calculating the proportion of prompt images, the proportion of task images, and the proportion of interactive images in the perspective screen of the next game perspective;

[0041] Calculate the weighted average of the percentage of prompt images, the percentage of task images, and the percentage of interactive images to obtain the percentage of important images;

[0042] Calculate the difference between 1 and the proportion of important images to obtain the proportion of non-important images;

[0043] Calculate the product of the preferred ad size and the weight of the proportion to obtain the ad display size; the weight of the proportion is proportional to the proportion of the non-important images

[0044] The advertisement display position and the advertisement display size are determined as advertisement display parameters of the advertisement display data.

[0045] A second aspect of an embodiment of the present invention discloses a game advertisement data processing system based on player operation habits, the system comprising:

[0046] The acquisition module is used to obtain multiple historical game operations of the current user in the current game scene;

[0047] A prediction module, configured to predict the next game operation and the next game perspective of the current user based on the prediction algorithm and the plurality of historical game operations;

[0048] A determination module, configured to determine the advertisement preference parameters corresponding to the current user based on the multiple historical game operations based on a portrait matching algorithm;

[0049] The calculation module is used to calculate the advertisement display data and the advertisement display parameters according to the advertisement preference parameters and the next game viewing angle; the advertisement display data is used to display the advertisement display parameters in the next game viewing angle.

[0050] As an optional implementation, in the second aspect of the present invention, the prediction module predicts the next game operation and the next game perspective of the current user based on the prediction algorithm and the multiple historical game operations, including:

[0051] Determine the perspective change parameters corresponding to each of the historical game operations; the perspective change parameters include perspective movement direction, perspective movement angle and perspective movement speed;

[0052] According to each of the historical game operations and the corresponding operation time, based on a neural network algorithm, predict the next game operation of the current user;

[0053] According to each of the perspective change parameters and the corresponding operation time, based on a preset perspective change model, the next game perspective of the current user is predicted.

[0054] As an optional implementation, in the second aspect of the present invention, the prediction module determines the specific manner of the viewing angle change parameter corresponding to each of the historical game operations, including:

[0055] Each of the historical game operations and the game character parameters of the current user are input into a trained perspective impact prediction neural network to obtain output perspective change parameters corresponding to each of the historical game operations; the perspective impact prediction neural network is trained by a training data set including multiple training game operations and corresponding game character annotations and perspective change parameter annotations; the game character parameters or the game character annotations include character level, character plot, character category, character current skills and character historical action route.

[0056] As an optional implementation, in the second aspect of the present invention, the prediction module predicts the specific manner of the next game operation of the current user based on each of the historical game operations and the corresponding operation time based on a neural network algorithm, including:

[0057] Sorting all the historical game operations from early to late according to the corresponding operation time to obtain a game operation sequence;

[0058] Screening more than a preset number of operations belonging to the same operation type in the game operation sequence to obtain multiple sets of continuous operations;

[0059] Determine the operation type with the largest number of operations belonging to the same operation type among all the historical game operations, and obtain the highest frequency operation type;

[0060] The game operation sequence, the continuous operation set and the most frequent operation type are input into a trained next operation prediction neural network to obtain the output of the next game operation of the current user; the next operation prediction neural network is trained by a training data set including multiple training game operation sequences and corresponding continuous operation annotations, most frequent operation type annotations and next game operation annotations.

[0061] As an optional implementation, in the second aspect of the present invention, the prediction module predicts the specific manner of the next game perspective of the current user based on each perspective change parameter and the corresponding operation time and based on a preset perspective change model, including:

[0062] Sorting all the perspective change parameters from early to late according to the operation time of the corresponding historical game operations to obtain a perspective change parameter sequence;

[0063] Inputting the perspective change parameter sequence into the perspective change simulation three-dimensional model corresponding to the current game scene to obtain a scene change video obtained by continuously changing the perspective according to the perspective change parameter sequence;

[0064] Based on a spatial search algorithm, a plurality of random next game perspectives corresponding to the video ending perspectives of the scene change video are randomly generated;

[0065] Inputting the scene change video and any of the random next game perspectives into a trained picture continuity prediction neural network to obtain a picture continuity parameter corresponding to any of the random next game perspectives; the picture continuity prediction neural network is trained using a training data set including a plurality of continuous pictures and corresponding continuity annotations;

[0066] The random next game perspective with the highest screen coherence parameter is determined as the next game perspective of the current user.

[0067] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module determines the advertisement preference parameters corresponding to the current user based on the multiple historical game operations based on a portrait matching algorithm includes:

[0068] For each candidate user portrait in a preset user portrait database, calculating the operation similarity between the historical game operation data set corresponding to the candidate user portrait and the plurality of historical game operations;

[0069] Determine all the candidate user portraits whose operation similarity is greater than a preset similarity threshold as multiple user portraits of the current user;

[0070] Determine the portrait advertising preference parameters of each user portrait according to the preset correspondence between the user portrait and the advertising preference parameters;

[0071] The intersection of the portrait advertising preference parameters of all the user portraits is determined as the advertising preference parameters corresponding to the current user; the advertising preference parameters include preferred advertising size, preferred advertising position, preferred advertising type and preferred advertising duration.

[0072] As an optional implementation, in the second aspect of the present invention, the specific manner in which the calculation module calculates the advertisement display data and the advertisement display parameters according to the advertisement preference parameters and the next game viewing angle includes:

[0073] For each candidate advertisement data, calculating the type similarity between the advertisement type of the candidate advertisement data and the preferred advertisement type;

[0074] Calculate the duration difference between the advertisement duration of the candidate advertisement data and the preferred advertisement duration;

[0075] Calculate the product of the type similarity and the duration difference to obtain the priority of the candidate advertisement data;

[0076] Determine the candidate advertisement data with the highest priority as advertisement display data;

[0077] Based on the next game viewing angle, the preferred advertisement size and the preferred advertisement position are modified to obtain advertisement display parameters.

[0078] As an optional implementation, in the second aspect of the present invention, the calculation module modifies the preferred advertisement size and the preferred advertisement position based on the next game perspective to obtain a specific method of advertisement display parameters, including:

[0079] Determine other screen areas in the perspective screen of the next game perspective, except for the prompt image, the task image and the interactive image, as non-important image areas;

[0080] Calculate the intersection of the preferred advertisement position and the non-important image area to obtain an advertisement display position;

[0081] Calculating the proportion of prompt images, the proportion of task images, and the proportion of interactive images in the perspective screen of the next game perspective;

[0082] Calculate the weighted average of the percentage of prompt images, the percentage of task images, and the percentage of interactive images to obtain the percentage of important images;

[0083] Calculate the difference between 1 and the proportion of important images to obtain the proportion of non-important images;

[0084] Calculate the product of the preferred ad size and the weight of the proportion to obtain the ad display size; the weight of the proportion is proportional to the proportion of the non-important images

[0085] The advertisement display position and the advertisement display size are determined as advertisement display parameters of the advertisement display data.

[0086] The third aspect of the present invention discloses another game advertisement data processing system based on player operation habits, the system comprising:

[0087] A memory storing executable program code;

[0088] a processor coupled to the memory;

[0089] The processor calls the executable program code stored in the memory to execute part or all of the steps in the game advertisement data processing method based on player operation habits disclosed in the first aspect of the present invention.

[0090] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the game advertising data processing method based on player operating habits disclosed in the first aspect of the present invention.

[0091] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0092] The present invention can predict the user's next operation and next viewing angle based on multiple historical game operations, and predict the user's advertising preference based on historical operations, so as to comprehensively calculate advertising display data and advertising display parameters, thereby improving the rationality and effectiveness of advertising display in the game, reducing the user's aversion to advertising, and improving the advertising promotion effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0094] Figure 1 It is a flowchart of a method for processing game advertisement data based on player operation habits disclosed in an embodiment of the present invention.

[0095] Figure 2 It is a structural schematic diagram of a game advertisement data processing system based on player operation habits disclosed in an embodiment of the present invention.

[0096] Figure 3 It is a structural schematic diagram of another game advertisement data processing system based on player operation habits disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0097] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0098] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0099] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0100] The present invention discloses a method and system for processing game advertisement data based on player operation habits, which can predict the user's next operation and next viewing angle based on multiple historical game operations, and predict the user's advertisement preference based on historical operations, so as to comprehensively calculate advertisement display data and advertisement display parameters, thereby improving the rationality and effectiveness of advertisement display in the game, reducing the user's aversion to advertisements, and improving the effect of advertisement promotion. The following are detailed descriptions.

[0101] Embodiment 1

[0102] See also Figure 1 , Figure 1 1 is a flowchart of a method for processing game advertisement data based on player operation habits disclosed in an embodiment of the present invention. Figure 1 The game advertisement data processing method based on player operation habits described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the game advertisement data processing method based on player operation habits may include the following operations:

[0103] 101. Obtain multiple historical game operations of the current user in the current game scene.

[0104] 102. Based on a prediction algorithm, predict the next game operation and next game perspective of the current user according to multiple historical game operations.

[0105] 103. Based on the portrait matching algorithm, determine the advertising preference parameters corresponding to the current user according to multiple historical game operations.

[0106] 104. Calculate advertisement display data and advertisement display parameters according to advertisement preference parameters and the next game perspective.

[0107] Optionally, the advertisement display data is used to display the advertisement display parameters in the next game perspective.

[0108] It can be seen that the above-mentioned embodiments of the invention can predict the user's next operation and next perspective based on multiple historical game operations, and predict the user's advertising preferences based on historical operations, so as to comprehensively calculate the advertising display data and advertising display parameters, thereby improving the rationality and effectiveness of advertising display in the game, reducing the user's aversion to advertising, and improving the advertising promotion effect.

[0109] As an optional embodiment, in the above steps, based on a prediction algorithm, predicting the next game operation and the next game perspective of the current user according to multiple historical game operations includes:

[0110] Determine the perspective change parameters corresponding to each historical game operation; optionally, the perspective change parameters include perspective movement direction, perspective movement angle, and perspective movement speed;

[0111] According to each historical game operation and the corresponding operation time, based on the neural network algorithm, the next game operation of the current user is predicted;

[0112] According to each perspective change parameter and the corresponding operation time, based on the preset perspective change model, the next game perspective of the current user is predicted.

[0113] It can be seen that through the above-mentioned optional embodiments, the next game operation can be predicted through the game operation and operation time, and the next game perspective can be predicted through the perspective change parameters and operation time, so as to facilitate the subsequent accurate calculation of advertising display data and advertising display parameters, and assist in improving the rationality and effectiveness of advertising display in the game, reducing users' aversion to advertising, and improving the effectiveness of advertising promotion.

[0114] As an optional embodiment, in the above step, determining the perspective change parameter corresponding to each historical game operation includes:

[0115] Each historical game operation and the game character parameters of the current user are input into a trained perspective impact prediction neural network to obtain the output perspective change parameters corresponding to each historical game operation; optionally, the perspective impact prediction neural network is trained by a training data set including multiple training game operations and corresponding game character annotations and perspective change parameter annotations; the game character parameters or game character annotations include character level, character's plot, character category, character's current skills and character's historical action route.

[0116] It can be seen that through the above optional embodiments, the perspective change parameters can be predicted according to the historical game operations and game character parameters through the trained perspective impact prediction neural network, so as to more accurately predict the perspective change parameters that may be caused by the game operation. The purpose of this setting is that in some application scenarios, the background prediction program cannot obtain the perspective change parameters in real time or the obtained game operation does not have the accompanying perspective change information. Therefore, prediction is needed to facilitate the subsequent accurate calculation of the next game perspective and advertising display parameters, to assist in improving the rationality and effectiveness of advertising display in the game, reduce the user's aversion to advertising, and improve the advertising promotion effect.

[0117] As an optional embodiment, in the above steps, predicting the next game operation of the current user based on each historical game operation and the corresponding operation time based on a neural network algorithm includes:

[0118] Sort all historical game operations from early to late according to their corresponding operation time to obtain a game operation sequence;

[0119] Screening operations of the same operation type that are continuous and have a preset number or more in the game operation sequence to obtain multiple continuous operation sets;

[0120] Determine the operation type with the largest number of operations belonging to the same operation type among all historical game operations, and obtain the highest frequency operation type;

[0121] The game operation sequence, continuous operation set and most frequent operation type are input into the trained next operation prediction neural network to obtain the output of the next game operation of the current user; the next operation prediction neural network is trained by a training data set including multiple training game operation sequences and corresponding continuous operation annotations, most frequent operation type annotations and next game operation annotations.

[0122] It can be seen that through the above-mentioned optional embodiments, the next operation can be predicted according to the game operation sequence, continuous operation set and the most frequent operation type through the trained next operation prediction neural network, so as to more accurately predict the next game operation of the current user, so as to facilitate the subsequent accurate calculation of advertising display data and advertising display parameters, and assist in improving the rationality and effectiveness of advertising display in the game, reducing the user's aversion to advertising, and improving the advertising promotion effect.

[0123] As an optional embodiment, in the above steps, according to each perspective change parameter and the corresponding operation time, based on a preset perspective change model, predicting the next game perspective of the current user includes:

[0124] All perspective change parameters are sorted from early to late according to the operation time of the corresponding historical game operations to obtain a perspective change parameter sequence;

[0125] Inputting the perspective change parameter sequence into the perspective change simulation three-dimensional model corresponding to the current game scene to obtain a scene change video obtained by continuously changing the perspective according to the perspective change parameter sequence;

[0126] Based on the spatial search algorithm, multiple random next game perspectives corresponding to the video ending perspectives of the scene change video are randomly generated;

[0127] Inputting the scene change video and any random next game perspective into a trained picture continuity prediction neural network to obtain a picture continuity parameter corresponding to any random next game perspective; optionally, the picture continuity prediction neural network is trained by a training data set including a plurality of continuous pictures and corresponding continuity annotations;

[0128] The random next game perspective with the highest screen coherence parameter is determined as the next game perspective of the current user.

[0129] It can be seen that through the above optional embodiments, it is possible to determine the scene change video by simulating the three-dimensional model through the perspective change, and then predict and screen the next game perspective of the current user based on the picture continuity prediction neural network, so as to more accurately predict the next game perspective of the current user, so as to facilitate the subsequent accurate calculation of advertising display data and advertising display parameters, and assist in improving the rationality and effectiveness of advertising display in the game, reducing the user's aversion to advertising, and improving the advertising promotion effect.

[0130] As an optional embodiment, in the above steps, based on the portrait matching algorithm, determining the advertisement preference parameters corresponding to the current user according to multiple historical game operations includes:

[0131] For each candidate user portrait in a preset user portrait database, calculating the operation similarity between the historical game operation data set corresponding to the candidate user portrait and multiple historical game operations;

[0132] Determine all candidate user portraits whose operation similarity is greater than a preset similarity threshold as multiple user portraits of the current user;

[0133] Determine the profile advertising preference parameters for each user profile based on the correspondence between the preset user profiles and the advertising preference parameters;

[0134] The intersection of the portrait advertising preference parameters of all user portraits is determined as the advertising preference parameters corresponding to the current user; the advertising preference parameters include preferred advertising size, preferred advertising position, preferred advertising type and preferred advertising duration.

[0135] It can be seen that through the above-mentioned optional embodiments, multiple user portraits can be screened out through similarity calculations on the historical game operation data sets corresponding to the candidate user portraits, and then the advertising preference parameters corresponding to the current user can be determined based on the preset correspondence and intersection calculations, so as to facilitate the subsequent accurate calculation of the advertising display data and advertising display parameters, and assist in improving the rationality and effectiveness of the advertising display in the game, reducing the user's aversion to advertising, and improving the advertising promotion effect.

[0136] As an optional embodiment, in the above steps, calculating the advertisement display data and advertisement display parameters according to the advertisement preference parameters and the next game viewing angle includes:

[0137] For each candidate advertisement data, calculating the type similarity between the advertisement type of the candidate advertisement data and the preferred advertisement type;

[0138] Calculate the duration difference between the advertisement duration of the candidate advertisement data and the preferred advertisement duration;

[0139] Calculate the product of type similarity and duration difference to obtain the priority of the candidate advertisement data;

[0140] Determine the candidate advertisement data with the highest priority as advertisement display data;

[0141] Based on the next game perspective, the preferred advertisement size and the preferred advertisement position are modified to obtain advertisement display parameters.

[0142] It can be seen that through the above-mentioned optional embodiments, it is possible to screen out advertising display data by determining the degree of proximity between the type and duration of candidate advertising data and user preferences, and then obtain advertising display parameters based on the next game perspective correction, thereby improving the rationality and effectiveness of advertising display in the game, reducing users' aversion to advertising, and improving the effectiveness of advertising promotion.

[0143] As an optional embodiment, in the above steps, based on the next game perspective, the preferred advertisement size and preferred advertisement position are modified to obtain advertisement display parameters, including:

[0144] Determine other screen areas in the perspective screen of the next game perspective, except for the prompt image, the task image and the interactive image, as non-important image areas;

[0145] Calculate the intersection of the preferred advertising position and the non-important image area to obtain the advertising display position;

[0146] Calculate the percentage of prompt images, the percentage of task images, and the percentage of interactive images in the perspective screen of the next game perspective;

[0147] Calculate the weighted average of the percentage of prompt images, the percentage of task images, and the percentage of interactive images to obtain the percentage of important images;

[0148] Calculate the difference between 1 and the proportion of important images to get the proportion of non-important images;

[0149] Calculate the product of the preferred ad size and the weight of the proportion to get the ad display size; the weight of the proportion is proportional to the proportion of non-important images

[0150] An advertisement display position and an advertisement display size are determined as advertisement display parameters of the advertisement display data.

[0151] It can be seen that through the above-mentioned optional embodiments, it is possible to obtain the area and proportion of non-important images by calculating the area and proportion of important images such as prompt images, task images and interactive images in the perspective screen of the next game perspective, and then determine a more reasonable advertising display position and advertising display size based on intersection calculation and weight calculation, thereby improving the rationality and effectiveness of advertising display in the game, reducing users' aversion to advertising, and improving the effectiveness of advertising promotion.

[0152] Embodiment 2

[0153] See also Figure 2 , Figure 2 : is a schematic diagram of a game advertisement data processing system based on player operation habits disclosed in an embodiment of the present invention. Figure 2 The game advertisement data processing system based on player operation habits described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the game advertisement data processing system based on player operation habits may include:

[0154] The acquisition module 201 is used to acquire multiple historical game operations of the current user in the current game scene.

[0155] The prediction module 202 is used to predict the next game operation and the next game perspective of the current user based on a prediction algorithm and multiple historical game operations.

[0156] The determination module 203 is used to determine the advertisement preference parameters corresponding to the current user based on a portrait matching algorithm and according to a plurality of historical game operations.

[0157] The calculation module 204 is used to calculate the advertisement display data and advertisement display parameters according to the advertisement preference parameters and the next game viewing angle.

[0158] Optionally, the advertisement display data is used to display the advertisement display parameters in the next game perspective.

[0159] It can be seen that the above-mentioned embodiments of the invention can predict the user's next operation and next perspective based on multiple historical game operations, and predict the user's advertising preferences based on historical operations, so as to comprehensively calculate the advertising display data and advertising display parameters, thereby improving the rationality and effectiveness of advertising display in the game, reducing the user's aversion to advertising, and improving the advertising promotion effect.

[0160] As an optional embodiment, the prediction module predicts the next game operation and the next game perspective of the current user based on a prediction algorithm and multiple historical game operations, including:

[0161] Determine the perspective change parameters corresponding to each historical game operation; optionally, the perspective change parameters include perspective movement direction, perspective movement angle, and perspective movement speed;

[0162] According to each historical game operation and the corresponding operation time, based on the neural network algorithm, the next game operation of the current user is predicted;

[0163] According to each perspective change parameter and the corresponding operation time, based on the preset perspective change model, the next game perspective of the current user is predicted.

[0164] It can be seen that through the above-mentioned optional embodiments, the next game operation can be predicted through the game operation and operation time, and the next game perspective can be predicted through the perspective change parameters and operation time, so as to facilitate the subsequent accurate calculation of advertising display data and advertising display parameters, and assist in improving the rationality and effectiveness of advertising display in the game, reducing users' aversion to advertising, and improving the effectiveness of advertising promotion.

[0165] As an optional embodiment, the prediction module determines the specific manner of the perspective change parameter corresponding to each historical game operation, including:

[0166] Each historical game operation and the game character parameters of the current user are input into a trained perspective impact prediction neural network to obtain the output perspective change parameters corresponding to each historical game operation; optionally, the perspective impact prediction neural network is trained by a training data set including multiple training game operations and corresponding game character annotations and perspective change parameter annotations; the game character parameters or game character annotations include character level, character's plot, character category, character's current skills and character's historical action route.

[0167] It can be seen that through the above optional embodiments, the perspective change parameters can be predicted according to the historical game operations and game character parameters through the trained perspective impact prediction neural network, so as to more accurately predict the perspective change parameters that may be caused by the game operation. The purpose of this setting is that in some application scenarios, the background prediction program cannot obtain the perspective change parameters in real time or the obtained game operation does not have the accompanying perspective change information. Therefore, prediction is needed to facilitate the subsequent accurate calculation of the next game perspective and advertising display parameters, to assist in improving the rationality and effectiveness of advertising display in the game, reduce the user's aversion to advertising, and improve the advertising promotion effect.

[0168] As an optional embodiment, the prediction module predicts the specific mode of the next game operation of the current user based on each historical game operation and the corresponding operation time based on a neural network algorithm, including:

[0169] Sort all historical game operations from early to late according to their corresponding operation time to obtain a game operation sequence;

[0170] Screening operations of the same operation type that are continuous and have a preset number or more in the game operation sequence to obtain multiple continuous operation sets;

[0171] Determine the operation type with the largest number of operations belonging to the same operation type among all historical game operations, and obtain the highest frequency operation type;

[0172] The game operation sequence, continuous operation set and most frequent operation type are input into the trained next operation prediction neural network to obtain the output of the next game operation of the current user; the next operation prediction neural network is trained by a training data set including multiple training game operation sequences and corresponding continuous operation annotations, most frequent operation type annotations and next game operation annotations.

[0173] It can be seen that through the above-mentioned optional embodiments, the next operation can be predicted according to the game operation sequence, continuous operation set and the most frequent operation type through the trained next operation prediction neural network, so as to more accurately predict the next game operation of the current user, so as to facilitate the subsequent accurate calculation of advertising display data and advertising display parameters, and assist in improving the rationality and effectiveness of advertising display in the game, reducing the user's aversion to advertising, and improving the advertising promotion effect.

[0174] As an optional embodiment, the prediction module predicts the specific manner of the next game perspective of the current user according to each perspective change parameter and the corresponding operation time based on a preset perspective change model, including:

[0175] All perspective change parameters are sorted from early to late according to the operation time of the corresponding historical game operations to obtain a perspective change parameter sequence;

[0176] Inputting the perspective change parameter sequence into the perspective change simulation three-dimensional model corresponding to the current game scene to obtain a scene change video obtained by continuously changing the perspective according to the perspective change parameter sequence;

[0177] Based on the spatial search algorithm, multiple random next game perspectives corresponding to the video ending perspectives of the scene change video are randomly generated;

[0178] Inputting the scene change video and any random next game perspective into a trained picture continuity prediction neural network to obtain a picture continuity parameter corresponding to any random next game perspective; optionally, the picture continuity prediction neural network is trained by a training data set including a plurality of continuous pictures and corresponding continuity annotations;

[0179] The random next game perspective with the highest screen coherence parameter is determined as the next game perspective of the current user.

[0180] It can be seen that through the above optional embodiments, it is possible to determine the scene change video by simulating the three-dimensional model through the perspective change, and then predict and screen the next game perspective of the current user based on the picture continuity prediction neural network, so as to more accurately predict the next game perspective of the current user, so as to facilitate the subsequent accurate calculation of advertising display data and advertising display parameters, and assist in improving the rationality and effectiveness of advertising display in the game, reducing the user's aversion to advertising, and improving the advertising promotion effect.

[0181] As an optional embodiment, the specific manner in which the determination module determines the advertisement preference parameters corresponding to the current user based on a portrait matching algorithm and according to a plurality of historical game operations includes:

[0182] For each candidate user portrait in a preset user portrait database, calculating the operation similarity between the historical game operation data set corresponding to the candidate user portrait and multiple historical game operations;

[0183] Determine all candidate user portraits whose operation similarity is greater than a preset similarity threshold as multiple user portraits of the current user;

[0184] Determine the profile advertising preference parameters for each user profile based on the correspondence between the preset user profiles and the advertising preference parameters;

[0185] The intersection of the portrait advertising preference parameters of all user portraits is determined as the advertising preference parameters corresponding to the current user; the advertising preference parameters include preferred advertising size, preferred advertising position, preferred advertising type and preferred advertising duration.

[0186] It can be seen that through the above-mentioned optional embodiments, multiple user portraits can be screened out through similarity calculations on the historical game operation data sets corresponding to the candidate user portraits, and then the advertising preference parameters corresponding to the current user can be determined based on the preset correspondence and intersection calculations, so as to facilitate the subsequent accurate calculation of the advertising display data and advertising display parameters, and assist in improving the rationality and effectiveness of the advertising display in the game, reducing the user's aversion to advertising, and improving the advertising promotion effect.

[0187] As an optional embodiment, the calculation module calculates the advertisement display data and the advertisement display parameters according to the advertisement preference parameters and the next game viewing angle in a specific manner, including:

[0188] For each candidate advertisement data, calculating the type similarity between the advertisement type of the candidate advertisement data and the preferred advertisement type;

[0189] Calculate the duration difference between the advertisement duration of the candidate advertisement data and the preferred advertisement duration;

[0190] Calculate the product of type similarity and duration difference to obtain the priority of the candidate advertisement data;

[0191] Determine the candidate advertisement data with the highest priority as advertisement display data;

[0192] Based on the next game perspective, the preferred advertisement size and the preferred advertisement position are modified to obtain advertisement display parameters.

[0193] It can be seen that through the above-mentioned optional embodiments, it is possible to screen out advertising display data by determining the degree of proximity between the type and duration of candidate advertising data and user preferences, and then obtain advertising display parameters based on the next game perspective correction, thereby improving the rationality and effectiveness of advertising display in the game, reducing users' aversion to advertising, and improving the effectiveness of advertising promotion.

[0194] As an optional embodiment, the calculation module modifies the preferred advertisement size and preferred advertisement position based on the next game perspective to obtain the specific method of advertisement display parameters, including:

[0195] Determine other screen areas in the perspective screen of the next game perspective, except for the prompt image, the task image and the interactive image, as non-important image areas;

[0196] Calculate the intersection of the preferred advertising position and the non-important image area to obtain the advertising display position;

[0197] Calculate the percentage of prompt images, the percentage of task images, and the percentage of interactive images in the perspective screen of the next game perspective;

[0198] Calculate the weighted average of the percentage of prompt images, the percentage of task images, and the percentage of interactive images to obtain the percentage of important images;

[0199] Calculate the difference between 1 and the proportion of important images to get the proportion of non-important images;

[0200] Calculate the product of the preferred ad size and the weight of the proportion to get the ad display size; the weight of the proportion is proportional to the proportion of non-important images

[0201] An advertisement display position and an advertisement display size are determined as advertisement display parameters of the advertisement display data.

[0202] It can be seen that through the above-mentioned optional embodiments, it is possible to obtain the area and proportion of non-important images by calculating the area and proportion of important images such as prompt images, task images and interactive images in the perspective screen of the next game perspective, and then determine a more reasonable advertising display position and advertising display size based on intersection calculation and weight calculation, thereby improving the rationality and effectiveness of advertising display in the game, reducing users' aversion to advertising, and improving the effectiveness of advertising promotion.

[0203] Embodiment 3

[0204] See also Figure 3 , Figure 3 This is another game advertisement data processing system based on player operation habits disclosed in an embodiment of the present invention. Figure 3 The game advertisement data processing system based on player operation habits is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the game advertisement data processing system based on player operation habits may include:

[0205] A memory 301 storing executable program codes;

[0206] a processor 302 coupled to the memory 301;

[0207] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the game advertisement data processing method based on the player's operation habits described in the first embodiment.

[0208] Embodiment 4

[0209] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the method for processing game advertisement data based on player operation habits described in the first embodiment.

[0210] Embodiment 5

[0211] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the game advertisement data processing method based on player operation habits described in the first embodiment.

[0212] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0213] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0214] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0215] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0216] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0217] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0218] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0219] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0220] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0221] Computer readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory media such as modulated data signals and carrier waves.

[0222] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0223] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0224] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0225] Finally, it should be noted that the game advertisement data processing method and system based on player operation habits disclosed in the embodiment of the present invention only discloses the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for processing game advertisement data based on player operation habits, characterized in that: The method comprises: Get multiple historical game operations of the current user in the current game scenario; Based on the prediction algorithm, predicting the next game operation and next game perspective of the current user according to the multiple historical game operations; Based on the portrait matching algorithm, according to the multiple historical game operations, determine the advertising preference parameters corresponding to the current user; the advertising preference parameters include preferred advertising size, preferred advertising position, preferred advertising type and preferred advertising duration; Calculating advertisement display data and advertisement display parameters according to the advertisement preference parameter and the next game viewing angle includes: Determine other screen areas in the perspective screen of the next game perspective, except for the prompt image, the task image and the interactive image, as non-important image areas; Calculate the intersection of the preferred advertisement position and the non-important image area to obtain an advertisement display position; Calculating the proportion of prompt images, the proportion of task images, and the proportion of interactive images in the perspective screen of the next game perspective; Calculate the weighted average of the percentage of prompt images, the percentage of task images, and the percentage of interactive images to obtain the percentage of important images; Calculate the difference between 1 and the proportion of important images to obtain the proportion of non-important images; Calculate the product of the preferred advertisement size and the proportion weight to obtain the advertisement display size; the proportion weight is proportional to the proportion of the non-important images; The advertisement display position and the advertisement display size are determined as advertisement display parameters of the advertisement display data; the advertisement display data is used to display the advertisement display parameters in the next game viewing angle.

2. The method for processing game advertisement data based on player operation habits according to claim 1, characterized in that: The predicting, based on the prediction algorithm and according to the plurality of historical game operations, the next game operation and the next game perspective of the current user comprises: Determine the perspective change parameters corresponding to each of the historical game operations; the perspective change parameters include perspective movement direction, perspective movement angle and perspective movement speed; According to each of the historical game operations and the corresponding operation time, based on a neural network algorithm, predict the next game operation of the current user; According to each of the perspective change parameters and the corresponding operation time, based on a preset perspective change model, the next game perspective of the current user is predicted.

3. The method for processing game advertisement data based on player operation habits according to claim 2, characterized in that: The determining of the perspective change parameter corresponding to each of the historical game operations includes: Each of the historical game operations and the game character parameters of the current user are input into a trained perspective impact prediction neural network to obtain output perspective change parameters corresponding to each of the historical game operations; the perspective impact prediction neural network is trained by a training data set including multiple training game operations and corresponding game character annotations and perspective change parameter annotations; the game character parameters or the game character annotations include character level, character plot, character category, character current skills and character historical action route.

4. The method for processing game advertisement data based on player operation habits according to claim 2, characterized in that: The predicting the next game operation of the current user based on each of the historical game operations and the corresponding operation time based on a neural network algorithm includes: Sorting all the historical game operations from early to late according to the corresponding operation time to obtain a game operation sequence; Screening more than a preset number of operations belonging to the same operation type in the game operation sequence to obtain multiple sets of continuous operations; Determine the operation type with the largest number of operations belonging to the same operation type among all the historical game operations, and obtain the highest frequency operation type; The game operation sequence, the continuous operation set and the most frequent operation type are input into a trained next operation prediction neural network to obtain the output of the next game operation of the current user; the next operation prediction neural network is trained by a training data set including multiple training game operation sequences and corresponding continuous operation annotations, most frequent operation type annotations and next game operation annotations.

5. The method for processing game advertisement data based on player operation habits according to claim 2, characterized in that: The predicting the next game perspective of the current user according to each perspective change parameter and the corresponding operation time based on a preset perspective change model includes: Sorting all the perspective change parameters from early to late according to the operation time of the corresponding historical game operations to obtain a perspective change parameter sequence; Inputting the perspective change parameter sequence into the perspective change simulation three-dimensional model corresponding to the current game scene to obtain a scene change video obtained by continuously changing the perspective according to the perspective change parameter sequence; Based on a spatial search algorithm, a plurality of random next game perspectives corresponding to the video ending perspectives of the scene change video are randomly generated; Inputting the scene change video and any of the random next game perspectives into a trained picture continuity prediction neural network to obtain a picture continuity parameter corresponding to any of the random next game perspectives; the picture continuity prediction neural network is trained using a training data set including a plurality of continuous pictures and corresponding continuity annotations; The random next game perspective with the highest screen coherence parameter is determined as the next game perspective of the current user.

6. The method for processing game advertisement data based on player operation habits according to claim 1, characterized in that: The determining, based on the portrait matching algorithm and according to the plurality of historical game operations, the advertisement preference parameters corresponding to the current user includes: For each candidate user portrait in a preset user portrait database, calculating the operation similarity between the historical game operation data set corresponding to the candidate user portrait and the plurality of historical game operations; Determine all the candidate user portraits whose operation similarity is greater than a preset similarity threshold as multiple user portraits of the current user; Determine the portrait advertising preference parameters of each user portrait according to the preset correspondence between the user portrait and the advertising preference parameters; The intersection of the portrait advertising preference parameters of all the user portraits is determined as the advertising preference parameters corresponding to the current user.

7. The method for processing game advertisement data based on player operation habits according to claim 6, characterized in that: The calculating the advertisement display data and the advertisement display parameters according to the advertisement preference parameters and the next game viewing angle includes: For each candidate advertisement data, calculating the type similarity between the advertisement type of the candidate advertisement data and the preferred advertisement type; Calculate the duration difference between the advertisement duration of the candidate advertisement data and the preferred advertisement duration; Calculate the product of the type similarity and the duration difference to obtain the priority of the candidate advertisement data; The candidate advertisement data with the highest priority is determined as advertisement display data.

8. A game advertisement data processing system based on player operation habits, characterized in that: The system comprises: The acquisition module is used to obtain multiple historical game operations of the current user in the current game scene; A prediction module, configured to predict the next game operation and the next game perspective of the current user based on the prediction algorithm and the plurality of historical game operations; A determination module, configured to determine the advertisement preference parameters corresponding to the current user based on the portrait matching algorithm and the plurality of historical game operations; the advertisement preference parameters include a preferred advertisement size, a preferred advertisement position, a preferred advertisement type, and a preferred advertisement duration; A calculation module, used to calculate advertisement display data and advertisement display parameters according to the advertisement preference parameters and the next game viewing angle, including: Determine other screen areas in the perspective screen of the next game perspective, except for the prompt image, the task image and the interactive image, as non-important image areas; Calculate the intersection of the preferred advertisement position and the non-important image area to obtain an advertisement display position; Calculating the proportion of prompt images, the proportion of task images, and the proportion of interactive images in the perspective screen of the next game perspective; Calculate the weighted average of the percentage of prompt images, the percentage of task images, and the percentage of interactive images to obtain the percentage of important images; Calculate the difference between 1 and the proportion of important images to obtain the proportion of non-important images; Calculate the product of the preferred advertisement size and the proportion weight to obtain the advertisement display size; the proportion weight is proportional to the proportion of the non-important images; The advertisement display position and the advertisement display size are determined as advertisement display parameters of the advertisement display data; the advertisement display data is used to display the advertisement display parameters in the next game viewing angle.

9. A game advertisement data processing system based on player operation habits, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the game advertisement data processing method based on player operation habits as described in any one of claims 1-7.

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