CN114733195A - Game resource allocation method, device, equipment and medium based on bilateral adaptation - Google Patents

Abstract

The invention relates to an artificial intelligence technology, and discloses a game resource allocation method based on bilateral adaptation, which comprises the following steps: extracting a game release object from the game configuration requirement, and constructing a semantic matrix of the game configuration requirement; screening the game level cards matched with the semantic matrix from the game level card pool, and combining the game level cards into a first game frame according to the incidence relation among the game level cards; acquiring user data of a game object, and screening a game scene matched with the user data from a preset scene pool; and configuring the first game frame scene according to the scene data of the game scene to obtain a second game frame. In addition, the invention also relates to a blockchain technology, and game configuration requirements can be stored in the nodes of the blockchain. The invention also provides a game resource allocation device, equipment and medium based on bilateral adaptation. The invention can solve the problems of complexity in teaching game development and low matching degree between the developed game and the game object.

Description

Game resource allocation method, device, device and medium based on bilateral adaptation

technical field

The present invention relates to the technical field of artificial intelligence, and in particular, to a method, device, electronic device and computer-readable storage medium for configuring game resources based on bilateral adaptation.

Background technique

With the development of the information society, people are exposed to more and more fields of knowledge. Therefore, in order to understand and master a large amount of knowledge more quickly, people are more and more inclined to accept online training and teaching. At the same time, in order to improve online teaching In order to improve the teaching effect, online teaching providers often combine teaching content with teaching games.

In the existing teaching game development process, a set of suitable game content is often developed based on the teaching content. As a result, the development process is cumbersome; at the same time, the games developed only match the teaching content. When the students are practitioners in different fields, the content of the teaching games and the students have a low degree of matching.

SUMMARY OF THE INVENTION

The present invention provides a game resource configuration method, device and computer-readable storage medium based on bilateral adaptation, the main purpose of which is to solve the problem that the development of teaching games is complicated and the degree of matching between the developed game and the game object is low. .

In order to achieve the above object, the present invention provides a method for configuring game resources based on bilateral adaptation, including:

Obtaining the user's game configuration requirements, extracting a game release object from the game configuration requirements, and constructing a semantic matrix of the game configuration requirements;

Filter out the game level matching the semantic matrix from the predetermined game level pool;

Obtain the game content of each of the game levels, identify the association relationship between each of the game levels according to the game content, and combine the game levels according to the association relationship to obtain a first game framework;

Obtaining the user data of the game object, constructing a data abstract of the user data, and filtering out a game scene that matches the data abstract from a preset scene pool;

Acquire scene data of the game scene, and perform scene configuration on the first game frame according to the scene data to obtain a second game frame.

Optionally, the constructing the semantic matrix of the game configuration requirements includes:

Perform word segmentation processing on the game configuration requirements to obtain demand word segmentation;

Counting the word segmentation frequency of each word segment in the demand word segmentation, selecting a demand word segment whose word segmentation frequency is greater than a preset frequency threshold as a keyword, and converting each word in the keyword into a word vector;

The word vectors are spliced into a vector matrix, and the vector matrix is determined to be a semantic matrix required by the game configuration.

Optionally, the filtering to obtain game levels matching the semantic matrix from a predetermined game level pool, including:

Obtain the level label of each game level in the game level pool;

Calculate the direct distance value between the semantic matrix and each of the level labels one by one;

The game level corresponding to the level label whose distance value is smaller than the preset distance threshold is selected, and the selected game level is determined to be the game level matching the semantic matrix.

Optionally, identifying the association relationship between each of the game levels according to the game content includes:

Selecting one of the game levels as the target level from the game levels one by one;

Using regular expressions to filter out the game conditions of the target level from the game content of the target level;

Constructing a condition matrix corresponding to the game condition, and using the condition matrix to assign a value to a preset decision function to obtain a decision tree of the target level;

Collect decision trees corresponding to all game levels to get a decision tree model;

The game content corresponding to all game levels is input into the decision tree model, and the association relationship between each game level output by the decision tree model is obtained.

Optionally, combining the game levels according to the association relationship to obtain a first game framework, including:

Screen out game levels that are related to each other;

Combine the screened game levels according to the sequence of the association relationship to obtain an intermediate frame;

The first game frame is obtained by randomly inserting game levels that are not associated with each other into the intermediate frame.

Optionally, the constructing the data digest of the user data includes:

Splitting the user number data into a plurality of data segmentations, and converting each of the data segmentations into a segmentation vector;

Add position encoding information to the word segmentation vector according to the sequence of the position of each data segment in the user data to obtain an encoding vector;

Using a preset abstract extraction model to perform a preset number of convolution and pooling processes on each of the encoding vectors to obtain vector features;

Calculate the word probability value that the word segmentation vector corresponding to each described encoding vector is the abstract word according to the vector feature;

The word segmentation vectors whose word probability value is greater than the preset probability threshold are collected into a data abstract of the user data.

Optionally, performing scene configuration on the first game framework according to the scene data to obtain a second game framework, including:

performing parameter encoding on the scene data to obtain scene parameters;

Parameter assignment is performed on the first game frame by using the scene parameters to obtain a second game frame.

In order to solve the above problems, the present invention also provides a game resource configuration device based on bilateral adaptation, the device includes:

A demand analysis module, used for acquiring the game configuration requirements of the user, extracting the game release object from the game configuration requirements, and constructing a semantic matrix of the game configuration requirements;

a level screening module, used for filtering out game levels matching the semantic matrix from a predetermined pool of game levels;

a first configuration module, configured to acquire the game content of each of the game levels, identify the association relationship between each of the game levels according to the game content, and combine the game levels according to the association relationship, Get the first game frame;

a scene screening module, configured to obtain the user data of the game object, construct a data abstract of the user data, and filter out a game scene matching the data abstract from a preset scene pool;

The second configuration module is configured to acquire scene data of the game scene, and perform scene configuration on the first game frame according to the scene data to obtain a second game frame.

In order to solve the above problems, the present invention also provides an electronic device, the electronic device includes:

at least one processor; and,

a memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the bilateral adaptation-based algorithm described above. Game resource allocation method.

In order to solve the above problems, the present invention also provides a computer-readable storage medium, where at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is executed by a processor in an electronic device to realize the above-mentioned The described game resource allocation method based on bilateral adaptation.

The embodiment of the present invention analyzes the user's game configuration requirements, selects game levels that meet the user's requirements from the level pool, and combines the levels into a first game framework according to the relationship between the levels, thereby realizing the configuration method. The game is divided into multiple levels for combination, which reduces the overall coupling of the game and improves the flexibility of the game configuration; at the same time, analyzes the user data of game objects to filter out games The game scene of the object, and the first game frame is assigned according to the game scene, and the second game frame with the game scene corresponding to the game object is obtained, which realizes the adaptation of the game object; Bilateral adaptation of user data improves the match between the game and all parties. Therefore, the game resource allocation method, device, electronic device and computer-readable storage medium based on bilateral adaptation proposed by the present invention can solve the problems that the development of teaching games is complicated and the matching degree between the developed game and the game object is low. .

Description of drawings

1 is a schematic flowchart of a method for configuring game resources based on bilateral adaptation provided by an embodiment of the present invention;

2 is a schematic flowchart of constructing a semantic matrix according to an embodiment of the present invention;

FIG. 3 is a schematic flowchart of screening a game level matching a semantic matrix from a game level pool according to an embodiment of the present invention;

4 is a functional block diagram of a device for configuring game resources based on bilateral adaptation provided by an embodiment of the present invention;

FIG. 5 is a schematic structural diagram of an electronic device implementing the method for configuring game resources based on bilateral adaptation according to an embodiment of the present invention.

The realization, functional characteristics and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments.

Detailed ways

It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

The embodiment of the present application provides a game resource configuration method based on bilateral adaptation. The execution subject of the method for configuring game resources based on bilateral adaptation includes, but is not limited to, at least one of electronic devices that can be configured to execute the method provided by the embodiments of the present application, such as a server and a terminal. In other words, the method for configuring game resources based on bilateral adaptation can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), as well as cloud servers for basic cloud computing services such as big data and artificial intelligence platforms.

Referring to FIG. 1 , it is a schematic flowchart of a method for configuring game resources based on bilateral adaptation provided by an embodiment of the present invention. In this embodiment, the method for configuring game resources based on bilateral adaptation includes:

S1. Acquire a user's game configuration requirement, extract a game release object from the game configuration requirement, and construct a semantic matrix of the game configuration requirement.

In this embodiment of the present invention, the user is a person who configures game content; the game configuration requirements include data such as the number of levels, difficulty level, game type, etc. determined by the user for the final configurable game content; the game The object refers to the audience of the final configured game.

For example, a teacher has the following game configuration requirements: "configure a time-limited game that includes multiple multiple-choice math questions for students according to the subject requirements of mathematics", where "teacher" is the user and "student" is the game release object .

In detail, a computer statement (such as a java statement, a python statement, etc.) with a data grabbing function can be used to grab the pre-stored game configuration requirements from a predetermined data store, wherein the data store includes but is not limited to a database, Blockchain nodes, network caches, etc.

In this embodiment of the present invention, a pre-trained named entity recognition model may be used to extract the game release object from the game configuration requirements, wherein the named entity recognition model includes a pre-trained CRF (conditional random field algorithm, conditional random field algorithm) Random field) entity recognition model, SVM (supportvector machines, support vector machine)-based entity recognition model, etc.

Further, in order to achieve accurate analysis of the game configuration requirements, the game configuration requirements may be analyzed to construct a semantic matrix of the game configuration requirements.

In the embodiment of the present invention, as shown in FIG. 2 , the construction of the semantic matrix of the game configuration requirements includes:

S21, performing word segmentation processing on the game configuration requirement to obtain a requirement word segmentation;

S22, count the word segmentation frequency of each word segment in the demand word segmentation, select the demand word segmentation whose word segmentation frequency is greater than a preset frequency threshold as a keyword, and convert each word in the keyword into a word vector;

S23, splicing the word vectors into a vector matrix, and determining that the vector matrix is a semantic matrix required by the game configuration.

In detail, a pre-trained artificial intelligence model with a word segmentation function can be used to perform word segmentation processing on the game configuration requirements to obtain the demand word segmentation, wherein the artificial intelligence model includes but is not limited to NLP (Natural Language Processing, natural language processing). ) model, HMM (Hidden Markov Model, Hidden Markov Model).

Specifically, the word segmentation frequency refers to the number of times a certain word appears in the required word segmentation of the game configuration requirements. When the word segmentation frequency of the word segmentation is higher, the importance of the word segmentation is greater. Therefore, the Demand word segmentation whose word segmentation frequency is greater than the preset frequency threshold is regarded as a keyword.

Further, in order to improve the processing efficiency of the keyword, a pre-trained word vector model can be used to convert the keyword into a numerical word vector, wherein the word vector model includes but is not limited to the word2vec model, the bert Model.

In this embodiment of the present invention, splicing the word vectors into a vector matrix includes: counting the vector lengths of each word vector in the word vectors, and determining the maximum value of the vector lengths as the target length; using a preset The parameter extends the vector length of each of the word vectors to the target length; and splices each of the extended word vectors as a row vector to obtain a vector matrix.

In detail, since the word vectors are converted from different keywords, the vector lengths of different vectors in the word vectors may be different. Extend to uniform length.

For example, the word vector contains vector A: (1, 4, 6), vector B: (2, 3), and vector C: (3, 7, 8, 9). Statistics show that the vector length of vector A is is 3, the vector length of vector B is 2, and the vector length of vector C is 4, then determine 4 as the target length, and use preset parameters (such as x) to extend the vector length of vector A to 4, and after the extension The vector A: (1,4,6,x), extend the vector length of the vector B to 4, and get the extended vector B: (2,3,x,x).

Further, each extended word vector can be used as a row vector, and spliced into the following vector matrix:

In the embodiment of the present invention, since the vector matrix is formed by splicing vectors corresponding to the keywords of the game configuration requirement, the vector matrix can be used as the semantic matrix of the game configuration requirement.

S2. Filter out a game level matching the semantic matrix from a predetermined game level pool.

In one of the practical application scenarios of the present invention, the game level pool contains a variety of preset game levels. In order to achieve precise configuration of the game, the corresponding game can be obtained by filtering the game level pool according to the semantic matrix. level to ensure that the subsequently configured game matches the game configuration requirements.

In the embodiment of the present invention, as shown in FIG. 3 , the game level that matches the semantic matrix is obtained by screening the predetermined game level pool, including:

S31. Obtain the level label of each game level in the game level pool;

S32, calculate the direct distance value between the semantic matrix and each of the level labels one by one;

S33. Select the game level corresponding to the level label whose distance value is smaller than the preset distance threshold, and determine that the selected game level is the game level matching the semantic matrix.

In detail, the level tag is a tag used to mark the content of different game levels.

Specifically, an algorithm with a distance value calculation function (eg, Euclidean distance algorithm, cosine distance algorithm, etc.) can be used to calculate the direct distance value between the semantic matrix and each of the level labels one by one.

S3. Obtain the game content of each of the game levels, identify the association relationship between each of the game levels according to the game content, and combine the game levels according to the association relationship to obtain a first game framework .

In the embodiment of the present invention, the game content refers to data content such as the game noun, the game suitable group, the game rules, etc. of the game included in each game level.

In one of the practical application scenarios of the present invention, there may be many game levels for compounding the game configuration requirements, and there may be an association relationship between each different level.

For example, there are level A and level B, where the clearance condition of level B is to use the item a obtained after level A is cleared, then level B must be arranged after level A.

Therefore, it is possible to analyze the game content of each game level to identify the relationship between each game level, and then combine multiple game levels according to the relationship to improve the coherence of the configured game. Achieve precise configuration of the game.

In this embodiment of the present invention, identifying the association relationship between each of the game levels according to the game content includes: selecting one game level from the game levels as a target level one by one; Screen out the game conditions of the target level from the game content of the target level; construct a condition matrix corresponding to the game conditions, and use the condition matrix to assign a value to a preset decision function to obtain a decision tree of the target level ; Bring together the decision trees corresponding to all game levels to obtain a decision tree model; Input the game content corresponding to all game levels into the decision tree model, and obtain the output of the decision tree model between each of the game levels. connection relation.

In detail, the game conditions include entry conditions and pass conditions of each level.

Specifically, the step of constructing the condition matrix corresponding to the game condition is the same as the step of constructing the semantic matrix of the game configuration requirement in S1, and will not be repeated here.

Further, in order to identify the association relationship between each of the game levels, a predetermined decision function can be used to construct a decision tree for decision-making and identification of the game content of the game level.

In detail, the decision function can be:

Wherein, f(x) is the output value of the decision function, x is the parameter of the decision function, and g(y) is the input value of the decision function.

Specifically, the preset decision function can be assigned values by using the condition matrix corresponding to the game conditions to obtain the decision tree of the target level, and then the game contents corresponding to all game levels are input into the decision tree model, Further, the decision tree model is used to identify the relationship between each of the game levels.

In the embodiment of the present invention, combining the game levels according to the association relationship to obtain the first game framework includes: screening out game levels that are associated with each other; The screened game levels are combined to obtain an intermediate frame; game levels that do not have an associated relationship with each other are randomly inserted into the intermediate frame to obtain the first game frame.

In detail, when there are more than two game levels, there may be an association relationship between some game levels, but there is no association relationship between some of the game levels and any other levels.

For example, there are level A, level B, and level C. Among them, there is "the entry condition of level B is to obtain the clearance reward items of level A" between level A and level B, and there is no level between level C and level A and level B. any affiliation.

Therefore, in order to achieve efficient combination of game levels, the levels with associated relationships in the game levels can be filtered out, and then the filtered game levels can be combined according to the sequence of the association relationships between the levels to obtain an intermediate frame. .

Specifically, since the correlation between game levels that do not have an associated relationship and other levels is relatively low, after the game levels that have an associated relationship are combined, the game levels that do not have an associated relationship with each other can be randomly inserted into all levels. In the middle frame, the first game frame is obtained.

S4. Acquire the user data of the game object, construct a data abstract of the user data, and filter out a game scene matching the data abstract from a preset scene pool.

In this embodiment of the present invention, the user data includes the age, gender, education level, occupation and other data of the game object; the user data can be uploaded by the game object to a preset data form or page.

In one of the practical application scenarios of the present invention, since the generated first game frame is only obtained by combining multiple game levels according to the association relationship, the matching degree between the generated first game frame and the game object is low, in order to Realize the precise configuration of the game object, and analyze the user data of the game object to filter out the game scene that matches the game object from the preset scene pool, and improve the final generated game frame and the game object. suitability.

In detail, the scene pool includes a variety of different game scenes, and the game scenes are obtained by analyzing and creating user groups of different ages, genders, occupations, education levels, etc. in advance.

In the embodiment of the present invention, the constructing the data summary of the user data includes: dividing the user number data into multiple data segments, and converting each data segment into a segment vector; according to each data segment The order of the position of the word segmentation in the user data is to add position coding information to the word segmentation vector to obtain a coding vector; use a preset abstract extraction model to perform a preset number of convolutions and pooling on each of the coding vectors processing to obtain a vector feature; calculating the word probability value of the word segmentation vector corresponding to each of the coding vectors as the abstract word according to the vector feature; collecting the word segmentation vectors with the word probability value greater than the preset probability threshold as the user data data summary.

In detail, the user data can be split into several data word segmentations by using preset word segmentation tools such as jieba, SnowNLP, PKUse, THULAC, etc.; or, the user data can be split by the word_tokenize() method in the NLTK library in Python Divide into several data segmentations.

Exemplarily, the following word_tokenize() method can be used to split the user data into several data tokens:

from nltk.tokenize import word_tokenize

data="y"

print(word_tokenize(data))

Wherein, y is the user data.

Specifically, an encoder with a word encoding function, such as a transformer encoder, a word2vec encoder, and an Autoencoder encoder, may be used to encode each word segment of the data into a word vector.

In detail, the BERT model can be used to encode the order of the position of each word segmentation vector in the user data according to the above encoding method, and the encoded position information can be added to the word segmentation vector to obtain a coding vector to be used in The encoding vector reflects the position information of different word segmentation vectors, thereby improving the accuracy of subsequent analysis of the user data.

Further, the abstract extraction model includes but is not limited to a Convolutional Neural Network (CNN) model and a Support Vector Machines (SVM) model with an abstract word recognition function; the keyword analysis model includes However, it is not limited to a conditional random field (Conditional Random Field, CRF) model and an Lstm (Long short-term memory, long short-term memory) model with a keyword recognition function.

In detail, the activation function preset in the abstract extraction model can be used to calculate the probability value that the word segmentation vector corresponding to each encoding vector is an abstract word according to the vector feature, and the word probability value is greater than the preset probability. The thresholded word segmentation vectors are aggregated into a data digest of the user data.

In the embodiment of the present invention, the step of filtering out the game scene matching the data abstract from the preset scene pool is the same as filtering out the predetermined game level pool in S2 and obtaining the game scene matching the semantic matrix. The steps of the game levels are the same, and will not be repeated here.

S5. Acquire scene data of the game scene, and perform scene configuration on the first game frame according to the scene data to obtain a second game frame.

In the embodiment of the present invention, the game scene data includes data such as game element style, interface tone, npc outfit style, game voice style, and the like.

In detail, the step of acquiring the scene data of the game scene is the same as the step of acquiring the user's game configuration requirement in S1, and will not be repeated here.

In one of the practical application scenarios of the present invention, since the first game framework is obtained by configuring multiple game levels according to the user's game configuration requirements, the first game framework meets the user's game configuration requirements, However, the game content is related to the game object. Furthermore, in order to achieve bilateral matching between the user and the game object, the first game frame A bilateral match with the user.

In this embodiment of the present invention, performing scene configuration on the first game framework according to the scene data to obtain the second game framework includes: performing parameter encoding on the scene data to obtain scene parameters; using the scene parameters Parameter assignment is performed on the first game frame to obtain a second game frame.

In detail, a preset encoder can be used to perform parameter encoding on the scene data to obtain scene parameters, wherein the encoder includes but is not limited to VAE (Variational Auto-Encoder, variational auto-encoder), DAE (denoising autoencoder, denoising autoencoder).

Specifically, the scene parameters obtained after encoding can be assigned to the first game frame, so as to obtain a second game frame with bilateral matching between the user and the game object.

The embodiment of the present invention analyzes the user's game configuration requirements, selects game levels that meet the user's requirements from the level pool, and combines the levels into a first game framework according to the relationship between the levels, thereby realizing the configuration method. The game is divided into multiple levels for combination, which reduces the overall coupling of the game and improves the flexibility of the game configuration; at the same time, analyzes the user data of game objects to filter out games The game scene of the object, and the first game frame is assigned according to the game scene, and the second game frame with the game scene corresponding to the game object is obtained, which realizes the adaptation of the game object; Bilateral adaptation of user data improves the match between the game and all parties. Therefore, the game resource allocation method, device, electronic device and computer-readable storage medium based on bilateral adaptation proposed by the present invention can solve the problems that the development of teaching games is complicated and the matching degree between the developed game and the game object is low. .

As shown in FIG. 4 , it is a functional block diagram of an apparatus for configuring game resources based on bilateral adaptation provided by an embodiment of the present invention.

The apparatus 100 for configuring game resources based on bilateral adaptation of the present invention can be installed in an electronic device. According to the implemented functions, the apparatus 100 for configuring game resources based on bilateral adaptation may include a demand analysis module 101 , a level screening module 102 , a first configuration module 103 , a scene screening module 104 and a second configuration module 105 . The modules in the present invention can also be called units, which refer to a series of computer program segments that can be executed by the electronic device processor and can perform fixed functions, and are stored in the memory of the electronic device.

In this embodiment, the functions of each module/unit are as follows:

The requirement analysis module 101 is used to obtain the game configuration requirement of the user, extract the game release object from the game configuration requirement, and construct a semantic matrix of the game configuration requirement;

The level screening module 102 is configured to filter out game levels matching the semantic matrix from a predetermined pool of game levels;

The first configuration module 103 is configured to acquire the game content of each of the game levels, identify the association relationship between each of the game levels according to the game content, and assign the game levels according to the association relationship. Combining to get the first game frame;

The scene screening module 104 is configured to obtain the user data of the game object, construct a data digest of the user data, and filter out a game scene matching the data digest from a preset scene pool;

The second configuration module 105 is configured to acquire scene data of the game scene, and perform scene configuration on the first game frame according to the scene data to obtain a second game frame.

In detail, each module described in the apparatus 100 for configuring game resources based on bilateral adaptation in the embodiment of the present invention adopts the same method for configuring game resources based on bilateral adaptation described in FIG. 1 to FIG. 3 when in use. The same technical means can produce the same technical effect, which will not be repeated here.

As shown in FIG. 5 , it is a schematic structural diagram of an electronic device for implementing a method for configuring game resources based on bilateral adaptation provided by an embodiment of the present invention.

The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as based on bilateral adaptation. Matched game resource configuration program.

The processor 10 may be composed of integrated circuits in some embodiments, for example, may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits packaged with the same function or different functions, including one or A combination of multiple central processing units (Central Processing Units, CPUs), microprocessors, digital processing chips, graphics processors, and various control chips, etc. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and by running or executing the program or module stored in the memory 11 (for example, executing a A game resource configuration program for bilateral adaptation, etc.), and calling the data stored in the memory 11 to execute various functions of the electronic device and process data.

The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. . In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of the electronic device, such as a pluggable mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) equipped on the electronic device. ) card, flash card (Flash Card) and so on. Further, the memory 11 may also include both an internal storage unit of an electronic device and an external storage device. The memory 11 can not only be used to store application software and various data installed in the electronic device, such as the code of the game resource configuration program based on bilateral adaptation, but also can be used to temporarily store data that has been output or will be output.

The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus or the like. The bus can be divided into address bus, data bus, control bus and so on. The bus is configured to implement connection communication between the memory 11 and at least one processor 10 and the like.

The communication interface 13 is used for communication between the above electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (eg, a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, and the like. The display may also be appropriately called a display screen or a display unit, which is used for displaying information processed in the electronic device and for displaying a visual user interface.

The figure only shows an electronic device with components, and those skilled in the art can understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than those shown in the figure. , or a combination of certain components, or a different arrangement of components.

For example, although not shown, the electronic device may also include a power source (such as a battery) for powering the various components, preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power source can be logically connected through the power management device. Implement functions such as charge management, discharge management, and power management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and any other components. The electronic device may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

It should be understood that the embodiments are only used for illustration, and are not limited by this structure in the scope of the patent application.

The game resource configuration program based on bilateral adaptation stored in the memory 11 in the electronic device 1 is a combination of multiple instructions, and when running in the processor 10, it can achieve:

Obtaining the user's game configuration requirements, extracting a game release object from the game configuration requirements, and constructing a semantic matrix of the game configuration requirements;

Filter out the game level matching the semantic matrix from the predetermined game level pool;

Obtain the game content of each of the game levels, identify the association relationship between each of the game levels according to the game content, and combine the game levels according to the association relationship to obtain a first game framework;

Obtaining the user data of the game object, constructing a data abstract of the user data, and filtering out a game scene that matches the data abstract from a preset scene pool;

Acquire scene data of the game scene, and perform scene configuration on the first game frame according to the scene data to obtain a second game frame.

Specifically, for the specific implementation method of the above-mentioned instruction by the processor 10, reference may be made to the description of the relevant steps in the embodiment corresponding to the accompanying drawings, which will not be repeated here.

Further, if the modules/units integrated in the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. The computer-readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM, Read-Only). Memory).

The present invention also provides a computer-readable storage medium, where the readable storage medium stores a computer program, and when executed by a processor of an electronic device, the computer program can realize:

Obtaining the user's game configuration requirements, extracting a game release object from the game configuration requirements, and constructing a semantic matrix of the game configuration requirements;

Filter out the game level matching the semantic matrix from the predetermined game level pool;

Obtain the game content of each of the game levels, identify the association relationship between each of the game levels according to the game content, and combine the game levels according to the association relationship to obtain a first game framework;

Obtaining the user data of the game object, constructing a data abstract of the user data, and filtering out a game scene that matches the data abstract from a preset scene pool;

Acquire scene data of the game scene, and perform scene configuration on the first game frame according to the scene data to obtain a second game frame.

In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and there may be other division manners in actual implementation.

The modules described as separate components may or may not be physically separated, and components shown as modules may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware, or can be implemented in the form of hardware plus software function modules.

It will be apparent to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, but that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics of the invention.

Therefore, the embodiments are to be regarded in all respects as illustrative and not restrictive, and the scope of the invention is to be defined by the appended claims rather than the foregoing description, which are therefore intended to fall within the scope of the claims. All changes within the meaning and range of the equivalents of , are included in the present invention. Any reference signs in the claims shall not be construed as limiting the involved claim.

The blockchain referred to in the present invention is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information to verify its Validity of information (anti-counterfeiting) and generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

The embodiments of the present application may acquire and process related data based on artificial intelligence technology. Among them, artificial intelligence (AI) is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. .

Furthermore, it is clear that the word "comprising" does not exclude other units or steps and the singular does not exclude the plural. Several units or means recited in the system claims can also be realized by one unit or means by means of software or hardware. The words first, second, etc. are used to denote names and do not denote any particular order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent substitutions can be made without departing from the spirit and scope of the technical solutions of the present invention.

Claims (10)

1. a game resource allocation method based on bilateral adaptation, is characterized in that, described method comprises: Obtaining the user's game configuration requirements, extracting a game release object from the game configuration requirements, and constructing a semantic matrix of the game configuration requirements; Filter out the game level matching the semantic matrix from the predetermined game level pool; Obtain the game content of each of the game levels, identify the association relationship between each of the game levels according to the game content, and combine the game levels according to the association relationship to obtain a first game framework; Obtaining the user data of the game object, constructing a data abstract of the user data, and filtering out a game scene that matches the data abstract from a preset scene pool; Acquire scene data of the game scene, and perform scene configuration on the first game frame according to the scene data to obtain a second game frame. 2. The game resource configuration method based on bilateral adaptation as claimed in claim 1, wherein the construction of the semantic matrix of the game configuration requirements comprises: Perform word segmentation processing on the game configuration requirements to obtain demand word segmentation; Counting the word segmentation frequency of each word segment in the demand word segmentation, selecting a demand word segment whose word segmentation frequency is greater than a preset frequency threshold as a keyword, and converting each word in the keyword into a word vector; The word vectors are spliced into a vector matrix, and the vector matrix is determined to be a semantic matrix required by the game configuration. 3. The game resource allocation method based on bilateral adaptation as claimed in claim 1, wherein the filtering to obtain a game level matching the semantic matrix from a predetermined game level pool, comprising: Obtain the level label of each game level in the game level pool; Calculate the direct distance value between the semantic matrix and each of the level labels one by one; The game level corresponding to the level label whose distance value is smaller than the preset distance threshold is selected, and the selected game level is determined to be the game level matching the semantic matrix. 4. The method for configuring game resources based on bilateral adaptation according to claim 1, wherein the identifying the association relationship between each of the game levels according to the game content comprises: Selecting one of the game levels as the target level from the game levels one by one; Using regular expressions to filter out the game conditions of the target level from the game content of the target level; Constructing a condition matrix corresponding to the game condition, and using the condition matrix to assign a value to a preset decision function to obtain a decision tree of the target level; Collect decision trees corresponding to all game levels to get a decision tree model; The game content corresponding to all game levels is input into the decision tree model, and the association relationship between each game level output by the decision tree model is obtained. 5. The method for configuring game resources based on bilateral adaptation according to claim 1, wherein the combining the game levels according to the association relationship to obtain a first game framework, comprising: Screen out game levels that are related to each other; Combine the screened game levels according to the sequence of the association relationship to obtain an intermediate frame; The first game frame is obtained by randomly inserting game levels that are not associated with each other into the intermediate frame. 6. The game resource allocation method based on bilateral adaptation as claimed in claim 1, wherein, the construction of the data digest of the user data comprises: Splitting the user number data into a plurality of data segmentations, and converting each of the data segmentations into a segmentation vector; Add position encoding information to the word segmentation vector according to the sequence of the position of each data segment in the user data to obtain an encoding vector; Using a preset abstract extraction model to perform a preset number of convolution and pooling processes on each of the encoding vectors to obtain vector features; Calculate the word probability value that the word segmentation vector corresponding to each described encoding vector is the abstract word according to the vector feature; The word segmentation vectors whose word probability value is greater than the preset probability threshold are collected into a data abstract of the user data. 7. The method for configuring game resources based on bilateral adaptation according to any one of claims 1 to 6, wherein the scene configuration is performed on the first game framework according to the scene data to obtain a second game resource. Game framework, including: performing parameter encoding on the scene data to obtain scene parameters; Parameter assignment is performed on the first game frame by using the scene parameters to obtain a second game frame. 8. A device for configuring game resources based on bilateral adaptation, wherein the device comprises: A demand analysis module, used for acquiring the game configuration requirements of the user, extracting the game release object from the game configuration requirements, and constructing a semantic matrix of the game configuration requirements; a level screening module, used for filtering out game levels matching the semantic matrix from a predetermined pool of game levels; a first configuration module, configured to acquire the game content of each of the game levels, identify the association relationship between each of the game levels according to the game content, and combine the game levels according to the association relationship, Get the first game frame; a scene screening module, configured to obtain the user data of the game object, construct a data abstract of the user data, and filter out a game scene matching the data abstract from a preset scene pool; The second configuration module is configured to acquire scene data of the game scene, and perform scene configuration on the first game frame according to the scene data to obtain a second game frame. 9. An electronic device, characterized in that the electronic device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform any one of claims 1 to 7 The game resource configuration method based on bilateral adaptation described in item. 10. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the game resource configuration based on bilateral adaptation as described in any one of claims 1 to 7 is realized method.

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