CN112221154A

CN112221154A - Game data processing method based on artificial intelligence and cloud computing and game cloud center

Info

Publication number: CN112221154A
Application number: CN202011080104.XA
Authority: CN
Inventors: 陈夏焱
Original assignee: Individual
Current assignee: Shenzhen Youxianqi Technology Co ltd
Priority date: 2020-10-10
Filing date: 2020-10-10
Publication date: 2021-01-15
Anticipated expiration: 2040-10-10
Also published as: CN112883043A; CN112860723A; CN112221154B

Abstract

The embodiment of the application provides a game data processing method based on artificial intelligence and cloud computing and a game cloud center, wherein operation behaviors are represented according to dynamic and static operation data of the operation behaviors, key element matching is carried out on the operation behaviors according to the operation relation between the dynamic and static operation data and operation behavior labels according to the types of the operation behavior labels, the types of the operation behavior labels after the key element matching are divided into a plurality of different operation behavior objects, so that the operation behavior objects are automatically divided, corresponding data statistics is carried out after target operation statistical elements of each target operation behavior object are determined, invalid operation behavior data are avoided being processed, in addition, the division standards of the operation behavior objects are unified, the operation behavior objects can be updated in time, the calculated amount is small, and if new operation behaviors are added, the operation behaviors can be directly added into the operation behavior objects, therefore, the partition efficiency of the operation behavior object is improved.

Description

Game data processing method based on artificial intelligence and cloud computing and game cloud center

Technical Field

The application relates to the technical field of cloud games, in particular to a game data processing method based on artificial intelligence and cloud computing and a game cloud center.

Background

In order to facilitate data statistics for game operation developers to facilitate game optimization, operation behavior objects of players are generally divided, however, the existing dividing mode has the problem of untimely updating, the threshold is high, the calculation amount is large, and if new operation behaviors are added, the problems of reclassification and the like are caused, so that the dividing efficiency is low, and therefore, it is difficult to avoid that a part of invalid operation behavior data is processed in the subsequent data statistics due to the influence of the dividing efficiency, so that cloud computing resources are excessively occupied, the operation processing speed of a game cloud center is also influenced, and meanwhile, the fairness of the game cannot be effectively ensured.

Disclosure of Invention

In order to overcome at least the above-mentioned deficiencies in the prior art, the present application aims to provide a game data processing method and a game cloud center based on artificial intelligence and cloud computing, wherein an operation behavior is represented according to dynamic and static operation data of the operation behavior, the operation behavior is subjected to key element matching according to the operation relationship between the dynamic and static operation data and an operation behavior tag according to the operation behavior tag category, the operation behavior tag category after the key element matching is divided into a plurality of different operation behavior objects, so that the operation behavior objects are automatically divided, corresponding data statistics is performed after a target operation statistical element of each target operation behavior object is determined, invalid operation behavior data is prevented from being processed, in addition, the division standards of the operation behavior objects are uniform, the operation behavior objects can be updated in time, the amount of computation is small, and if a new operation behavior is added, the operation behavior object can be directly added to the operation behavior object, so that the partition efficiency of the operation behavior object is improved.

In a first aspect, the present application provides a game data processing method based on artificial intelligence and cloud computing, which is applied to a game cloud center, where the game cloud center is in communication connection with a plurality of game client terminals, and the method includes:

classifying static operation data and dynamic operation data in a first game data packet of a game user of the game client terminal based on an artificial intelligence model to obtain a second game data packet, wherein a first number of static operation data and dynamic operation data with corresponding relations are recorded in the first game data packet, a first number of operation behavior tags are recorded in the second game data packet, and each operation behavior tag is used for representing one operation behavior data;

acquiring a third game data packet according to the second game data packet, wherein a second number of groups of operation behavior tags with corresponding relations, key element static operation data in the operation behavior data represented by the operation behavior tags, and key element dynamic operation data in the operation behavior data represented by the operation behavior tags are recorded in the third game data packet;

dividing operation behavior data represented by a second number of operation behavior tags into a third number of operation behavior objects according to the third game data packet, wherein each operation behavior object comprises operation behavior data represented by at least one operation behavior tag;

obtaining operation behavior logs included in a target operation behavior object in the third number of operation behavior objects to obtain an operation behavior log set, performing key element matching on each operation behavior log in the operation behavior log set to obtain a key element matching result, and determining the operation statistical element as the target operation statistical element of the target operation behavior object under the condition that the operation statistical element with the frequency greater than the influence value appears in the key element matching result, so as to perform corresponding cloud computing data statistics on the basis of the statistical operation behavior object of each target operation statistical element.

In one possible implementation manner of the first aspect, the first game data packet is obtained by:

acquiring a first number of operation behavior logs to be processed;

obtaining static operation data and dynamic operation data of the operation behavior represented by each operation behavior log in the first number of operation behavior logs by calling an API (application programming interface) interface to obtain a first number of operation behavior logs, static operation data and dynamic operation data with corresponding relations;

and forming the first number of operation behavior logs, static operation data and dynamic operation data with corresponding relations into the first game data packet.

In a possible implementation manner of the first aspect, the step of classifying the static operation data and the dynamic operation data in the first game data packet of the game user of the game client terminal based on the artificial intelligence model to obtain the second game data packet includes:

and classifying the static operation data and the dynamic operation data in the first game data packet based on an artificial intelligence model to obtain a second game data packet, wherein a first number of operation behavior logs and operation element codes which have corresponding relations are recorded in the second game data packet, and the operation behavior tag is the operation element code.

In a possible implementation manner of the first aspect, the step of obtaining a third game data packet according to the second game data packet includes:

performing key element matching on the operation element codes recorded by the second game data packet to obtain a second number of mutually different operation element codes;

determining key element static operation data in the operation behavior data represented by each operation element code in the second number of mutually different operation element codes and key element dynamic operation data in the operation behavior data represented by each operation element code;

and recording operation element codes with corresponding relations in a second quantity group, the static operation data of the key elements in the operation behavior data represented by the operation element codes, and the dynamic operation data of the key elements in the operation behavior data represented by the operation element codes to obtain the third game data packet.

In a possible implementation manner of the first aspect, the third game data packet records a second number of groups of operation behavior tags having a corresponding relationship, the static operation data of the key element in the operation behavior data represented by the operation behavior tags, the dynamic operation data of the key element in the operation behavior data represented by the operation behavior tags, and the number of operation behaviors in the operation behavior data represented by the operation behavior tags;

wherein the step of dividing the operation behavior data represented by the second number of operation behavior tags into a third number of operation behavior objects according to the third game data packet includes:

determining the mean value of the operation behavior quantity in the operation behavior data represented by all the operation behavior labels and the variance value of the operation behavior quantity in the operation behavior data represented by all the operation behavior labels;

determining a difference value between the number of operation behaviors in the operation behavior data represented by each operation behavior label and the mean value, and determining a ratio between the difference value and the variance value as an influence value corresponding to a first key element in the operation behavior data represented by each operation behavior label;

in the case that the impact value is greater than a first impact value, marking the first key element as a mark target;

under the condition that the influence value is smaller than the first influence value and larger than a second influence value, marking the first key element as a marked target, a non-marked target or a noise target according to the number of second key elements except the first key element in the range with the first key element as a reference element and a preset expansion parameter value as an expansion parameter;

under the condition that the influence value is smaller than the second influence value, marking the first key element as a non-mark target or a noise target according to the number of second key elements except the first key element in a range which takes the first key element as a reference element and takes a preset expansion parameter value as an expansion parameter;

recording operation behavior data of the first key element and the second key element in the range as being located in a target operation behavior object under the condition that the first key element is marked as a marking target and the operation behavior data of one key element in the first key element and the second key element in the range is recorded as being located in the target operation behavior object;

and under the condition that the first key element is marked as a mark target and the operation behavior data of each key element in the first key element and the second key element in the range is not recorded as being in the operation behavior object, recording the operation behavior data of the first key element and the second key element in the range as being in the same operation behavior object, so as to divide the operation behavior data represented by the second number of operation behavior tags into a third number of operation behavior objects.

In a possible implementation manner of the first aspect, the step of performing key element matching on each operation behavior log in the operation behavior log set to obtain a key element matching result, and determining an operation behavior object as a statistical operation behavior object of the target operation behavior object when an operation behavior object whose occurrence frequency is greater than a third influence value occurs in the key element matching result includes:

extracting chart customized record information of each operation behavior log in the operation behavior log set;

according to the chart customized record information, obtaining an event attribute value of each operation drawing event in each operation behavior log, wherein the event attribute value refers to an event attribute value of a multi-terminal drawing calling event in any drawing control state of each operation behavior log under a monitored state, and the operation drawing event is an event record with the same effective state identification of a user terminal as the multi-terminal drawing calling event;

acquiring at least two operation drawing events according to the state monitoring priority of each operation drawing event to obtain at least two operation chain sets;

for any operation chain set, acquiring the most advanced event attribute value of each operation drawing event according to the event attribute value of each operation drawing event in the operation chain set in the monitored state;

acquiring a time sequence weighting result of the most advanced event attribute value of each operation drawing event included in the operation chain set to obtain an index reference value of the operation chain set;

when the index reference values of at least two operation chain sets meet set conditions, extracting first key element matching information of each operation behavior log in the multi-end drawing and calling event to obtain a key element matching result;

and under the condition that the occurrence frequency of the operation behavior object in the key element matching result is greater than a third influence value, determining the operation behavior object as a statistical operation behavior object of the target operation behavior object.

In a possible implementation manner of the first aspect, the step of extracting chart customized record information of each operation behavior log in the operation behavior log set includes:

dividing each operation behavior log into at least two first multi-dimensional hierarchical storage structures, wherein each first multi-dimensional hierarchical storage structure has the same hierarchical storage service;

identifying layered source layer information from each first multi-dimensional layered storage structure by adopting a preset layered source layer identification model;

and extracting chart customized record node information from the hierarchical source layer information of the at least two first multi-dimensional hierarchical storage structures, and acquiring the chart customized record information according to the extracted chart customized record node information.

In a possible implementation manner of the first aspect, the obtaining, according to the chart customized record information, an event attribute value of each operation drawing event in each operation behavior log includes:

inputting the chart customized record information into an operation chain identification program, and outputting event attribute values of each operation drawing event in each operation behavior log as a multi-terminal drawing call event;

the operation chain recognition program is used for detecting event records of the effective state identifications of the user terminals which are the same as the multi-terminal drawing calling events from each operation behavior log based on chart customized record information of the multi-terminal drawing calling events, and acquiring event attribute values of the multi-terminal drawing calling events when the event records of the effective state identifications of the user terminals which are the same as the multi-terminal drawing calling events are in the monitored state.

In a possible implementation manner of the first aspect, the key element matching result further includes second key element matching information, and the method further includes:

taking a key element matching extraction record of the game cloud center as a reference record when the index reference values of the at least two operation chain sets meet set conditions, and acquiring a second multi-dimensional hierarchical storage structure corresponding to a preset hierarchical storage service from each operation behavior log;

acquiring the updating information of the storage content of the second multi-dimensional hierarchical storage structure;

when the updating information of the storage content of the second multi-dimensional hierarchical storage structure meets a preset updating index, extracting second key element matching information of each operation behavior log in the multi-terminal drawing and calling event;

the time sequence description value of the second key element matching information is smaller than that of the first key element matching information, and the larger the time sequence description value is, the more forward the generation time of the corresponding key element matching information is represented;

wherein the obtaining of the storage content update information of the second multidimensional hierarchical storage structure includes:

dividing the second multi-dimensional hierarchical storage structure into at least two hierarchical decision-making behavior node sets, wherein each hierarchical decision-making behavior node set has the same hierarchical storage service;

obtaining a decision attribute value of a decision behavior characteristic corresponding to each hierarchical decision behavior node set;

obtaining a maximum decision attribute value and a minimum decision attribute value from the decision attribute values corresponding to the at least two hierarchical decision behavior node sets;

obtaining the storage content distribution of the middle decision attribute value between the maximum decision attribute value and the minimum decision attribute value to obtain the storage content updating information of the second multi-dimensional hierarchical storage structure;

the second multi-dimensional hierarchical storage structure comprises at least one of a third multi-dimensional hierarchical storage structure and a fourth multi-dimensional hierarchical storage structure, the third multi-dimensional hierarchical storage structure is a multi-dimensional hierarchical storage structure which takes the key element matching extraction record as a reference record and corresponds to a preset hierarchical storage service after the key element matching extraction record in each operation behavior log, and the fourth multi-dimensional hierarchical storage structure is a multi-dimensional hierarchical storage structure which takes the key element matching extraction record as a reference record and corresponds to a preset hierarchical storage service before the key element matching extraction record in each operation behavior log.

For example, in a possible implementation manner of the first aspect, the step of extracting first key element matching information of each operation behavior log in the multi-end draw call event includes:

extracting a scene interaction rendering sequence of each operation behavior log in the multi-terminal drawing and calling event, wherein the scene interaction rendering sequence comprises a set of scene interaction rendering sequences to be identified in the same interaction time period of each operation behavior log;

performing state transition identification on the scene interaction rendering sequence through a tracking function in a dynamic state transition tracking node of a preset script, and determining a first state transition queue matched with the scene interaction rendering sequence;

determining a second state transition queue matched with the scene interaction rendering sequence through a non-tracking function in a dynamic state transition tracking node in the preset script based on the first state transition queue;

based on a second state transition queue matched with the scene interaction rendering sequence, continuous feature extraction is carried out on the scene interaction rendering sequence through a static state transition tracking node of the preset script, so that first key element matching information of the scene interaction rendering sequence subjected to time sequence continuity check is output;

the method for recognizing the state transition of the scene interaction rendering sequence through the tracking function in the dynamic state transition tracking node of the preset script and determining the first state transition queue matched with the scene interaction rendering sequence comprises the following steps:

determining a static description vector corresponding to the scene interaction rendering sequence through a first static state transition tracking node in the preset script;

determining description vector updating information corresponding to the static description vector through a tracking record in the tracking function;

responding to the description vector updating information, performing remote calling processing on the description feature distribution of any description information set in the static description vector through a multi-path simulator identifier in the tracking function, and determining a first game description loading result;

performing local calling processing and remote calling processing on the first game description loading result through a multi-path behavior parameter variable in the tracking function, and determining a second game description loading result; importing a second game description loading result in a cache region into a model sample set through a state transition identification model, and determining a first state transition queue matched with the scene interaction rendering sequence through a message tag identification network of the state transition identification model;

wherein, the determining a static description vector corresponding to the scene interaction rendering sequence through a first static state transition tracking node in the preset script includes:

carrying out multi-dimensional feature key element matching on the scene interaction rendering sequence through the first static state transition tracking node;

processing a list key element matching set subjected to multi-dimensional feature key element matching through the behavior parameter variable and the variable correlation coefficient of the first static state transition tracking node to obtain a target key element matching set of the scene interaction rendering sequence;

performing feature extraction on a target key element matching set of the scene interaction rendering sequence through a transmission node queue of the first static state transition tracking node, and determining a static description vector corresponding to the scene interaction rendering sequence;

wherein the performing multidimensional feature key element matching on the scene interaction rendering sequence through the first static state transition tracking node comprises:

determining script format parameters matched with the thread running script of the preset script according to the relative time sequence weight of the interactive time period corresponding to the scene interactive rendering sequence;

and carrying out multi-dimensional feature key element matching on the scene interaction rendering sequence through the first static state transition tracking node according to the script format parameters to form a scene interaction rendering sequence matched with the script format parameters.

For example, in a possible implementation manner of the first aspect, the determining, based on the first state transition queue and through a non-tracking function in a dynamic state transition tracking node in the preset script, a second state transition queue that matches the scene interaction rendering sequence includes:

segmenting the first state transition queue through a non-tracking function in a dynamic state transition tracking node in the preset script, and determining a state transition distribution node set of the scene interaction rendering sequence, wherein the non-tracking function comprises at least one variable tracking channel;

taking the state transition distribution node set as an input set of a current identification unit, and extracting the input state transition distribution node set through the current identification unit to obtain an output set of the current identification unit;

comparing the similarity of the output set of the current identification unit and the input set of the current identification unit to obtain a comparison result;

and performing state transition screening on the comparison result based on all identification units included by the non-tracking function, and determining a second state transition queue matched with the scene interaction rendering sequence.

For example, in one possible implementation manner of the first aspect, it is determined that the index reference values of at least two operation chain sets both satisfy the setting condition by:

generating first operation chain feature dimension data corresponding to one operation chain set and second operation chain feature dimension data corresponding to the other operation chain set, and determining feature dimension data sets of a plurality of different consecutive parameters respectively included in the first operation chain feature dimension data and the second operation chain feature dimension data;

drawing call event running data of one operation chain set in any one feature dimension data set of the first operation chain feature dimension data is extracted, and a feature dimension data set with the smallest consistency parameter in the second operation chain feature dimension data is determined as a target feature dimension data set;

copying the operation data of the drawing and calling event to the target feature dimension data set according to a grading numerical value interval in which the difference of index reference numerical values between the index reference numerical values of at least two operation chain sets is positioned, so as to obtain mirror image information in the target feature dimension data set;

generating an event association list between the one operation chain set and the other operation chain set based on scene difference characteristics between the drawing call event running data and the mirror image information;

acquiring to-be-processed operation data in the target characteristic dimension data set by taking the mirror image information as reference information, copying the to-be-processed operation data to the characteristic dimension data set where the drawing and calling event operation data is located according to the sequence from large to small of the event association priority corresponding to the event association list, acquiring target operation data corresponding to the to-be-processed operation data in the characteristic dimension data set where the drawing and calling event operation data is located, and determining the consistency parameters of one operation chain set and the other operation chain set based on the target operation data;

respectively weighting a first index reference value corresponding to one of the operation chain sets by using the coherence parameters to obtain a first target index reference value and weighting a second index reference value corresponding to the other operation chain set to obtain a second target index reference value;

and if the first target index reference value and the second target index reference value are both greater than a set index reference value, determining that the index reference values of at least two operation chain sets both meet a set condition.

In a second aspect, an embodiment of the present application further provides a game data processing apparatus based on artificial intelligence and cloud computing, which is applied to a game cloud center, where the game cloud center is in communication connection with a plurality of game client terminals, and the apparatus includes:

the game client terminal comprises a classification module, a game client terminal and a game client terminal, wherein the classification module is used for classifying static operation data and dynamic operation data in a first game data packet of a game user of the game client terminal based on an artificial intelligence model to obtain a second game data packet, a first number of static operation data and dynamic operation data which have corresponding relations are recorded in the first game data packet, a first number of operation behavior labels are recorded in the second game data packet, and each operation behavior label is used for representing one operation behavior data;

an obtaining module, configured to obtain a third game data packet according to the second game data packet, where a second quantity group of operation behavior tags having a corresponding relationship, key element static operation data in the operation behavior data represented by the operation behavior tags, and key element dynamic operation data in the operation behavior data represented by the operation behavior tags are recorded in the third game data packet;

a dividing module, configured to divide, according to the third game data packet, operation behavior data represented by a second number of the operation behavior tags into a third number of operation behavior objects, where each of the operation behavior objects includes operation behavior data represented by at least one of the operation behavior tags;

and the determining module is used for acquiring operation behavior logs included in a target operation behavior object in the third number of operation behavior objects to obtain an operation behavior log set, performing key element matching on each operation behavior log in the operation behavior log set to obtain a key element matching result, and determining the operation statistical elements as the target operation statistical elements of the target operation behavior object under the condition that the operation statistical elements with the occurrence frequency larger than the influence value exist in the key element matching result, so as to perform corresponding cloud computing data statistics on the basis of the statistical operation behavior object of each target operation statistical element.

In a third aspect, an embodiment of the present application further provides a game data processing system based on artificial intelligence and cloud computing, where the game data processing system based on artificial intelligence and cloud computing includes a game cloud center and a plurality of game client terminals communicatively connected to the game cloud center;

the game cloud center is used for:

In a fourth aspect, an embodiment of the present application further provides a game cloud center, where the game cloud center includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is used for being communicatively connected with at least one game client terminal, the machine-readable storage medium is used for storing a program, an instruction, or a code, and the processor is used for executing the program, the instruction, or the code in the machine-readable storage medium to perform the method for processing game data based on artificial intelligence and cloud computing in the first aspect or any one of possible implementation manners in the first aspect.

In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed, the computer executes the artificial intelligence and cloud computing-based game data processing method in the first aspect or any one of the possible implementation manners of the first aspect.

Based on any one of the aspects, the method includes classifying static operation data and dynamic operation data in a first game data packet to obtain a second game data packet, obtaining a third game data packet according to the second game data packet, dividing operation behavior data represented by a plurality of operation behavior labels into a plurality of operation behavior objects according to the third game data packet, representing operation behaviors according to the dynamic and static operation data of the operation behaviors, performing key element matching on the operation behaviors according to the operation behavior label types of the operation behaviors according to the operation relation between the dynamic and static operation data and the operation behavior labels, dividing the operation behavior label types after the key element matching into a plurality of different operation behavior objects, automatically dividing the operation behavior objects, determining target operation statistical elements of each target operation behavior object, and performing corresponding data statistics, the invalid operation behavior data are prevented from being processed, cloud computing resources are saved, the operation processing speed of the game cloud center is increased, and the fairness of the game is effectively guaranteed. In addition, the division standards of the operation behavior objects are uniform, the operation behavior objects can be updated in time, the calculation amount is small, and if new operation behaviors are added, the new operation behaviors can be directly added to the operation behavior objects, so that the division efficiency of the operation behavior objects is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that need to be called in the embodiments are briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is a schematic application scenario diagram of a game data processing system based on artificial intelligence and cloud computing according to an embodiment of the present application;

fig. 2 is a schematic flowchart of a game data processing method based on artificial intelligence and cloud computing according to an embodiment of the present application;

fig. 3 is a functional module schematic diagram of a game data processing device based on artificial intelligence and cloud computing according to an embodiment of the present application;

fig. 4 is a schematic block diagram of structural components of a game cloud center for implementing the above-described artificial intelligence and cloud computing-based game data processing method according to an embodiment of the present application.

Detailed Description

The present application will now be described in detail with reference to the drawings, and the specific operations in the method embodiments may also be applied to the apparatus embodiments or the system embodiments.

FIG. 1 is an interaction diagram of an artificial intelligence and cloud computing based game data processing system 10 provided by an embodiment of the present application. The artificial intelligence and cloud computing based game data processing system 10 may include a game cloud center 100 and a game client terminal 200 communicatively connected to the game cloud center 100. The artificial intelligence and cloud computing based gaming data processing system 10 shown in FIG. 1 is but one possible example, and in other possible embodiments, the artificial intelligence and cloud computing based gaming data processing system 10 may also include only some of the components shown in FIG. 1 or may also include other components.

In this embodiment, the game client terminal 200 may comprise a mobile device, a tablet computer, a laptop computer, etc., or any combination thereof. In some embodiments, the mobile device may include an internet of things device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the internet of things device may include a control device of a smart appliance device, a smart monitoring device, a smart television, a smart camera, and the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart lace, smart glass, a smart helmet, a smart watch, a smart garment, a smart backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a personal digital assistant, a gaming device, and the like, or any combination thereof. In some embodiments, the virtual reality device and the augmented reality device may include a virtual reality helmet, virtual reality glass, a virtual reality patch, an augmented reality helmet, augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, virtual reality devices and augmented reality devices may include various virtual reality products and the like.

In this embodiment, the game cloud center 100 and the game client terminal 200 in the game data processing system 10 based on artificial intelligence and cloud computing may cooperatively perform the game data processing method based on artificial intelligence and cloud computing described in the following method embodiments, and for the specific steps performed by the game cloud center 100 and the game client terminal 200, reference may be made to the detailed description of the following method embodiments.

In order to solve the technical problem in the foregoing background art, fig. 2 is a schematic flowchart of a game data processing method based on artificial intelligence and cloud computing according to an embodiment of the present application, where the game data processing method based on artificial intelligence and cloud computing according to the present embodiment may be executed by the game cloud center 100 shown in fig. 1, and the game data processing method based on artificial intelligence and cloud computing is described in detail below.

Step S110, classifying the static operation data and the dynamic operation data in the first game data packet of the game user of the game client terminal 200 based on the artificial intelligence model to obtain a second game data packet.

And step S120, acquiring a third game data packet according to the second game data packet.

In step S130, the operation behavior data represented by the second number of operation behavior tags is divided into a third number of operation behavior objects according to the third game data packet.

Step S140, obtaining operation behavior logs included in a target operation behavior object located in the third number of operation behavior objects, obtaining an operation behavior log set, performing key element matching on each operation behavior log in the operation behavior log set, obtaining a key element matching result, determining an operation statistical element as a target operation statistical element of the target operation behavior object under the condition that an operation statistical element whose occurrence frequency is greater than an influence value appears in the key element matching result, and performing corresponding cloud computing data statistics on the operation behavior object based on statistics of each target operation statistical element.

In this embodiment, a first number of static operation data and dynamic operation data having a corresponding relationship are recorded in the first game data packet, and a first number of operation behavior tags are recorded in the second game data packet, where each operation behavior tag is used to represent one operation behavior data. For example, the static operation data may refer to operation behavior data in which a game character is not dynamically angularly moved during operation, and the dynamic operation data may refer to operation behavior data in which a game character is dynamically angularly moved during operation. The operation behavior tag may be a classification tag indicating each operation behavior data, and may be, for example, a certain upper level tag, or a refined lower level tag below a certain upper level tag.

In this embodiment, the third game data packet records the second number of groups of operation behavior tags having correspondence, the static operation data of the key element in the operation behavior data represented by the operation behavior tags, and the dynamic operation data of the key element in the operation behavior data represented by the operation behavior tags. The key elements may be flexibly determined according to the priorities of the statistical elements in the actual game scene, and are not limited in detail herein.

In this embodiment, each operation behavior object may include operation behavior data represented by at least one operation behavior tag.

Based on the above steps, the present embodiment represents the operation behavior according to the dynamic and static operation data of the operation behavior, performing key element matching on the operation behavior according to the operation relationship between the dynamic and static operation data and the operation behavior tags according to the class of the operation behavior tags where the operation behavior is located, dividing the class of the operation behavior tags after the key element matching into a plurality of different operation behavior objects, therefore, by automatically dividing the operation behavior objects, and then determining the target operation statistical elements of each target operation behavior object and then performing corresponding data statistics, invalid operation behavior data is prevented from being processed, and in addition, the division standards of the operation behavior objects are unified, the operation behavior objects can be updated in time, the calculation amount is small, and if new operation behaviors are added, the operation behavior object can be directly added to the operation behavior object, so that the partition efficiency of the operation behavior object is improved.

In a possible implementation manner, the first game data packet may be obtained by:

(1) a first number of operation behavior logs to be processed are obtained.

(2) And obtaining static operation data and dynamic operation data of the operation behavior represented by each operation behavior log in the first number of operation behavior logs by calling an API (application programming interface) interface so as to obtain the first number of operation behavior logs, the static operation data and the dynamic operation data with corresponding relations.

(3) A first number of operation behavior logs, static operation data and dynamic operation data having a correspondence relationship are formed as a first game data packet.

Thus, in one possible implementation manner, in step S110, in the process of classifying the static operation data and the dynamic operation data in the first game data packet of the game user of the game client terminal 200 based on the artificial intelligence model to obtain the second game data packet, the static operation data and the dynamic operation data in the first game data packet may be classified by the artificial intelligence model to obtain the second game data packet.

It should be noted that, a first number of operation behavior logs and operation element codes having a corresponding relationship are recorded in the second game data packet, and the operation behavior tag is an operation element code.

It is worth explaining that the artificial intelligence model can be realized by adopting a conventional deep learning network, and the artificial intelligence model can have the recognition capability of the operation elements by combining a large number of training samples, so that the second game data packet can be obtained by classifying the operation elements.

Thus, with respect to step S120, in acquiring the third game data package from the second game data package, the following exemplary sub-steps may be implemented.

And a substep S121 of performing key element matching on the operation element codes recorded in the second game data packet to obtain a second number of mutually different operation element codes.

And a substep S122, determining the static operation data of the key element in the operation behavior data represented by each operation element code in the second number of different operation element codes and the dynamic operation data of the key element in the operation behavior data represented by each operation element code.

And a substep S123 of recording the second quantity group of operation element codes with corresponding relations, the static operation data of the key elements in the operation behavior data represented by the operation element codes, and the dynamic operation data of the key elements in the operation behavior data represented by the operation element codes to obtain a third game data packet.

In a possible implementation manner, the third game data packet records a second number of operation behavior tags having a corresponding relationship, the static operation data of the key element in the operation behavior data represented by the operation behavior tags, the dynamic operation data of the key element in the operation behavior data represented by the operation behavior tags, and the number of operation behaviors in the operation behavior data represented by the operation behavior tags.

Thus, with respect to step S130, in dividing the operation behavior data represented by the second number of operation behavior tags into the third number of operation behavior objects according to the third game data package, it can be realized by the following exemplary sub-steps.

The sub-step S131 determines a mean value of the number of operation behaviors in the operation behavior data represented by all the operation behavior tags and a variance value of the number of operation behaviors in the operation behavior data represented by all the operation behavior tags.

And a substep S132, determining a difference value between the number of operation behaviors in the operation behavior data represented by each operation behavior label and the mean value, and determining a ratio between the difference value and the variance value as an influence value corresponding to the first key element in the operation behavior data represented by each operation behavior label.

For example, in the case where the impact value is greater than the first impact value, the first key element is marked as a mark target.

For another example, when the influence value is smaller than the first influence value and larger than the second influence value, the first key element is marked as a mark target, a non-mark target, or a noise target based on the number of second key elements other than the first key element existing within a range in which the first key element is used as the reference element and the predetermined extension parameter value is used as the extension parameter.

For another example, when the influence value is smaller than the second influence value, the first key element is marked as a non-mark target or a noise target according to the number of second key elements other than the first key element existing within a range in which the first key element is used as the reference element and the predetermined extension parameter value is used as the extension parameter.

And a substep S133, recording the operation behavior data of the first key element and the second key element in the range as being located in the target operation behavior object under the condition that the first key element is marked as the mark target and the operation behavior data of one key element in the first key element and the second key element in the range is recorded as being located in the target operation behavior object.

And a substep S134, recording the operation behavior data of the first key element and the second key element in the range as being located in the same operation behavior object under the condition that the first key element is marked as the mark target and the operation behavior data of each key element in the first key element and the second key element in the range is not recorded as being located in the operation behavior object, so as to divide the operation behavior data represented by the second number of operation behavior tags into a third number of operation behavior objects.

On this basis, for step S140, in the process of performing key element matching on each operation behavior log in the operation behavior log set to obtain a key element matching result, and determining the operation behavior object as the statistical operation behavior object of the target operation behavior object in the case of an operation behavior object whose occurrence frequency is greater than the third influence value in the key element matching result, the following exemplary sub-steps may be implemented.

In the substep S141, chart customized record information of each operation behavior log in the operation behavior log set is extracted.

And a substep S142, obtaining an event attribute value of each operation drawing event in each operation behavior log according to the chart customized record information, wherein the event attribute value refers to an event attribute value of a multi-terminal drawing call event in any drawing control state of each operation behavior log in a monitored state, and the operation drawing event is an event record of an effective state identifier of the user terminal which is the same as the multi-terminal drawing call event.

And a substep S143, acquiring at least two operation drawing events according to the state monitoring priority of each operation drawing event, and obtaining at least two operation chain sets.

And a substep S144, for any operation chain set, obtaining the most advanced event attribute value of each operation drawing event according to the event attribute value of each operation drawing event in the operation chain set in the monitored state.

And a substep S145, obtaining a time sequence weighting result of the most advanced event attribute value of each operation drawing event included in the operation chain set, and obtaining an index reference value of the operation chain set.

And a substep S146, when the index reference values of at least two operation chain sets meet the set conditions, extracting first key element matching information of each operation behavior log in the multi-end drawing and calling event to obtain a key element matching result.

In sub-step S147, in the case of an operation behavior object whose occurrence frequency is greater than the third influence value in the key element matching result, the operation behavior object is determined as a statistical operation behavior object of the target operation behavior object.

Exemplarily, in the sub-step S141, in the process of extracting the chart customized record information of each operation behavior log in the operation behavior log set, it may be implemented by the following exemplary embodiments.

(1) Each operation behavior log is divided into at least two first multi-dimensional hierarchical storage structures, and each first multi-dimensional hierarchical storage structure has the same hierarchical storage service.

(2) And identifying the layered source layer information from each first multi-dimensional layered storage structure by adopting a preset layered source layer identification model.

(3) And extracting chart customized record node information from the hierarchical source layer information of at least two first multi-dimensional hierarchical storage structures, and acquiring the chart customized record information according to the extracted chart customized record node information.

In the sub-step S142, in the process of obtaining the event attribute value of each operation drawing event in each operation behavior log according to the chart customized record information, the chart customized record information may be input into the operation chain identification program, and the event attribute value of each operation drawing event in each operation behavior log as a multi-end drawing call event may be output.

It should be noted that the operation chain recognition program is configured to detect, from each operation behavior log, an event record having the same valid state identifier of the user terminal as the multi-end draw call event based on the chart customized record information of the multi-end draw call event, and acquire an event attribute value of the multi-end draw call event when the event record having the same valid state identifier of the user terminal as the multi-end draw call event is in the monitored state.

Illustratively, on the basis of the above, the key element matching result may further include second key element matching information.

Therefore, when the index reference values of at least two operation chain sets are determined to meet the set conditions, the key element matching extraction record of the game cloud center is used as a reference record, and a second multi-dimensional hierarchical storage structure corresponding to the preset hierarchical storage service is obtained from each operation behavior log. Then, the storage content update information of the second multi-dimensional hierarchical storage structure is acquired.

For example, when the storage content update information of the second multidimensional hierarchical storage structure meets a preset update index, second key element matching information of each operation behavior log in a multi-terminal drawing call event is extracted.

And the time sequence description value of the second key element matching information is smaller than that of the first key element matching information, and the larger the time sequence description value is, the more forward the generation time of the corresponding key element matching information is represented.

In the process of obtaining the storage content update information of the second multidimensional hierarchical storage structure, the second multidimensional hierarchical storage structure may be divided into at least two hierarchical decision-making behavior node sets, and each hierarchical decision-making behavior node set has the same hierarchical storage service. And then, obtaining a decision attribute value of the decision behavior characteristic corresponding to each hierarchical decision behavior node set. And urgently, obtaining a maximum decision attribute value and a minimum decision attribute value from decision attribute values corresponding to at least two hierarchical decision behavior node sets. Therefore, the storage content distribution of the middle decision attribute value of the maximum decision attribute value and the minimum decision attribute value is obtained, and the storage content updating information of the second multi-dimensional hierarchical storage structure is obtained.

The second multi-dimensional hierarchical storage structure comprises at least one of a third multi-dimensional hierarchical storage structure and a fourth multi-dimensional hierarchical storage structure, the third multi-dimensional hierarchical storage structure is a multi-dimensional hierarchical storage structure which takes the key element matching extraction record as a reference record and is positioned behind the key element matching extraction record in each operation behavior log and corresponds to the preset hierarchical storage service, and the fourth multi-dimensional hierarchical storage structure is a multi-dimensional hierarchical storage structure which takes the key element matching extraction record as a reference record and is positioned in each operation behavior log and corresponds to the preset hierarchical storage service before the key element matching extraction record.

For example, in one possible implementation, in the sub-step S146, in the process of extracting the first key element matching information of each operation behavior log in the multi-end draw call event, the following exemplary embodiments may be implemented.

(1) And extracting a scene interaction rendering sequence of each operation behavior log in the multi-terminal drawing call event, wherein the scene interaction rendering sequence comprises a set of scene interaction rendering sequences to be identified in the same interaction time period of each operation behavior log.

(2) And performing state transition identification on the scene interaction rendering sequence through a tracking function in a dynamic state transition tracking node of a preset script, and determining a first state transition queue matched with the scene interaction rendering sequence.

(3) And determining a second state transition queue matched with the scene interaction rendering sequence through a non-tracking function in a dynamic state transition tracking node in a preset script based on the first state transition queue.

(4) And based on a second state transition queue matched with the scene interaction rendering sequence, performing continuity feature extraction on the scene interaction rendering sequence through a static state transition tracking node of a preset script to realize outputting of first key element matching information of the scene interaction rendering sequence subjected to time sequence continuity check.

For example, in (2), exemplarily, multidimensional feature key element matching may be performed on the scene interaction rendering sequence through a first static state transition tracking node, a list key element matching set subjected to multidimensional feature key element matching is processed through a behavior parameter variable and a variable correlation coefficient of the first static state transition tracking node to obtain a target key element matching set of the scene interaction rendering sequence, feature extraction is performed on the target key element matching set of the scene interaction rendering sequence through a transmission node queue of the first static state transition tracking node, and a static description vector corresponding to the scene interaction rendering sequence is determined.

For example, in the process of performing multi-dimensional feature key element matching on a scene interaction rendering sequence through a first static state transition tracking node, a script format parameter matched with a thread running script of a preset script can be determined according to a relative time sequence weight of an interaction time period corresponding to the scene interaction rendering sequence. And then, carrying out multi-dimensional feature key element matching on the scene interactive rendering sequence through the first static state transition tracking node according to the script format parameters to form the scene interactive rendering sequence matched with the script format parameters.

For example, in (3), the first state transition queue may be segmented by an untracked function in a dynamic state transition trace node in a preset script, and the state transition distribution node set of the scene interaction rendering sequence is determined, where the untracked function includes at least one variable trace channel.

Then, the state transition distribution node set is used as an input set of the current identification unit, the input state transition distribution node set is extracted through the current identification unit to obtain an output set of the current identification unit, and then the output set of the current identification unit and the input set of the current identification unit are subjected to similarity comparison to obtain a comparison result. Therefore, the result can be subjected to state transition screening based on comparison of all the identification units included in the non-tracking function, and a second state transition queue matched with the scene interaction rendering sequence is determined.

For example, in one possible implementation manner, it may be determined that the index reference values of at least two operation chain sets both satisfy the set condition by:

(1) generating first operation chain feature dimension data corresponding to one operation chain set and second operation chain feature dimension data corresponding to the other operation chain set, and determining feature dimension data sets of a plurality of different consecutive parameters respectively included in the first operation chain feature dimension data and the second operation chain feature dimension data.

(2) Drawing call event running data of one operation chain set in any one feature dimension data set of the first operation chain feature dimension data is extracted, and the feature dimension data set with the minimum consistency parameter in the second operation chain feature dimension data is determined as a target feature dimension data set.

(3) And copying the operation data of the drawing and calling event to a target feature dimension data set according to a hierarchical value interval in which the difference of the index reference values between the index reference values of at least two operation chain sets is positioned, so as to obtain mirror image information in the target feature dimension data set.

(4) And generating an event association list between one operation chain set and the other operation chain set based on the scene difference characteristics between the drawing call event running data and the mirror image information.

(5) The method comprises the steps of obtaining to-be-processed operation data in a target feature dimension data set by taking mirror image information as reference information, copying the to-be-processed operation data to a feature dimension data set where event operation data are drawn and called according to the sequence of event association priorities corresponding to an event association list from large to small, obtaining target operation data corresponding to the to-be-processed operation data in the feature dimension data set where the event operation data are drawn and called, and determining the consistency parameters of one operation chain set and the other operation chain set based on the target operation data.

(6) And weighting the first index reference value corresponding to one operation chain set by adopting the coherence parameters to obtain a first target index reference value and weighting the second index reference value corresponding to the other operation chain set to obtain a second target index reference value.

(7) And if the first target index reference value and the second target index reference value are both greater than the set index reference value, determining that the index reference values of at least two operation chain sets both meet the set condition.

Fig. 3 is a schematic diagram of functional modules of a game data processing apparatus 300 based on artificial intelligence and cloud computing according to an embodiment of the present disclosure, in this embodiment, the game data processing apparatus 300 based on artificial intelligence and cloud computing may be divided into the functional modules according to the method embodiment executed by the game cloud center 100, that is, the following functional modules corresponding to the game data processing apparatus 300 based on artificial intelligence and cloud computing may be used to execute each method embodiment executed by the game cloud center 100. The artificial intelligence and cloud computing based game data processing apparatus 300 may include a classification module 310, an acquisition module 320, a division module 330, and a determination module 340, and the functions of the functional modules of the artificial intelligence and cloud computing based game data processing apparatus 300 are described in detail below.

The classification module 310 is configured to classify, based on an artificial intelligence model, static operation data and dynamic operation data in a first game data packet of a game user of the game client terminal 200 to obtain a second game data packet, where a first number of static operation data and dynamic operation data having a corresponding relationship are recorded in the first game data packet, and a first number of operation behavior tags are recorded in the second game data packet, where each operation behavior tag is used to represent one operation behavior data. The classifying module 310 may be configured to perform the step S110, and the detailed implementation of the classifying module 310 may refer to the detailed description of the step S110.

The obtaining module 320 is configured to obtain a third game data packet according to the second game data packet, where the third game data packet records operation behavior tags having a corresponding relationship in the second quantity group, key element static operation data in the operation behavior data represented by the operation behavior tags, and key element dynamic operation data in the operation behavior data represented by the operation behavior tags. The obtaining module 320 may be configured to perform the step S120, and the detailed implementation of the obtaining module 320 may refer to the detailed description of the step S120.

The dividing module 330 is configured to divide, according to the third game data packet, the operation behavior data represented by the second number of operation behavior tags into a third number of operation behavior objects, where each operation behavior object includes operation behavior data represented by at least one operation behavior tag. The dividing module 330 may be configured to perform the step S130, and the detailed implementation of the dividing module 330 may refer to the detailed description of the step S130.

The determining module 340 is configured to obtain operation behavior logs included in a target operation behavior object located in the third number of operation behavior objects, obtain an operation behavior log set, perform key element matching on each operation behavior log in the operation behavior log set, obtain a key element matching result, determine an operation statistical element as a target operation statistical element of the target operation behavior object when an operation statistical element whose occurrence frequency is greater than an influence value occurs in the key element matching result, and perform corresponding cloud computing data statistics on the operation behavior object based on statistics of each target operation statistical element. The determining module 340 may be configured to perform the step S140, and the detailed implementation of the determining module 340 may refer to the detailed description of the step S140.

It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules may all be implemented in software invoked by a processing element. Or may be implemented entirely in hardware. And part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the classification module 310 may be a separate processing element, or may be integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a processing element of the apparatus calls and executes the functions of the classification module 310. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).

Fig. 4 shows a hardware structure diagram of the game cloud center 100 for implementing the control device according to the embodiment of the present disclosure, and as shown in fig. 4, the game cloud center 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the classification module 310, the acquisition module 320, the division module 330, and the determination module 340 included in the game data processing apparatus 300 based on artificial intelligence and cloud computing shown in fig. 3), so that the processor 110 may execute the game data processing method based on artificial intelligence and cloud computing according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected through the bus 130, and the processor 110 may be configured to control the transceiving action of the transceiver 140, so as to perform data transceiving with the aforementioned game client terminal 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned method embodiments executed by the game cloud center 100, which implement principles and technical effects are similar, and this embodiment is not described herein again.

In the embodiment shown in fig. 4, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The machine-readable storage medium 120 may comprise high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.

The bus 130 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus 130 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

In addition, the embodiment of the application also provides a readable storage medium, wherein the readable storage medium stores computer execution instructions, and when a processor executes the computer execution instructions, the game data identification method based on artificial intelligence and big data is realized.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, the description uses specific words to describe embodiments of the description. Such as "one possible implementation," "one possible example," and/or "exemplary" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be noted that two or more references to "one possible implementation," "one possible example," and/or "exemplary" in various words or phrases in this specification are not necessarily referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or contexts, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including a computer-readable program code, embodied in one or more computer-readable media.

The computer storage medium may comprise a propagated data signal with the computer program embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The program classes located on computer storage media may be propagated over any suitable media, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

The computer program classes required for operation of the various portions of this specification can be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages. The program classification may run entirely on the user's computer, as a stand-alone software package, partly on the user's computer, partly on a remote computer, or entirely on the remote computer or user terminal. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which the elements and lists are processed, the use of alphanumeric characters, or other designations in this specification is not intended to limit the order in which the processes and methods of this specification are performed, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by interactive services, they may also be implemented by software-only solutions, such as installing the described system on an existing user terminal or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A game data processing method based on artificial intelligence and cloud computing is applied to a game cloud center, the game cloud center is in communication connection with a plurality of game client terminals, and the method comprises the following steps:

2. The artificial intelligence and cloud computing based game data processing method according to claim 1, wherein the first game data packet is obtained by:

acquiring a first number of operation behavior logs to be processed;

3. The artificial intelligence and cloud computing based game data processing method according to claim 2, wherein the step of classifying the static operation data and the dynamic operation data in the first game data packet of the game user of the game client terminal based on the artificial intelligence model to obtain the second game data packet comprises:

4. The artificial intelligence and cloud computing based game data processing method according to claim 3, wherein the step of obtaining a third game data packet from the second game data packet includes:

5. The game data processing method based on artificial intelligence and cloud computing according to claim 1, wherein a second number of groups of operation behavior tags having a correspondence relationship, the static operation data of key elements in the operation behavior data represented by the operation behavior tags, the dynamic operation data of key elements in the operation behavior data represented by the operation behavior tags, and the number of operation behaviors in the operation behavior data represented by the operation behavior tags are recorded in the third game data packet;

6. The game data processing method based on artificial intelligence and cloud computing according to any one of claims 1 to 5, wherein the step of performing key element matching on each operation behavior log in the operation behavior log set to obtain a key element matching result, and determining an operation behavior object as a statistical operation behavior object of the target operation behavior object when an operation behavior object whose occurrence frequency is greater than a third influence value occurs in the key element matching result includes:

7. The artificial intelligence and cloud computing based game data processing method according to claim 1, wherein the step of extracting chart customized record information of each operation behavior log in the operation behavior log set includes:

8. The artificial intelligence and cloud computing based game data processing method according to claim 6, wherein the obtaining event attribute values of the operation drawing events in each operation behavior log according to the chart customized record information includes:

9. The artificial intelligence and cloud computing based game data processing method of claim 6, wherein the key element matching result further includes second key element matching information, the method further comprising:

10. A game cloud center, characterized in that the game cloud center comprises a processor, a machine-readable storage medium, and a network interface, the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is used for being connected with at least one game client terminal in a communication manner, the machine-readable storage medium is used for storing programs, instructions, or codes, and the processor is used for executing the programs, instructions, or codes in the machine-readable storage medium to execute the artificial intelligence and cloud computing based game data processing method of any one of claims 1 to 9.