CN114733195A - Game resource allocation method, device, equipment and medium based on bilateral adaptation - Google Patents
Game resource allocation method, device, equipment and medium based on bilateral adaptation Download PDFInfo
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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
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a game resource allocation method and device based on bilateral adaptation, electronic equipment and a computer readable storage medium.
Background
Along with the development of information society, the knowledge field that people contacted is more and more extensive, consequently, in order to understand more fast and master a large amount of knowledge, people prefer to accept online training teaching, simultaneously, in order to promote the interest of online teaching, the provider of online teaching often can combine together teaching content and teaching game to the realization is to the promotion of teaching effect.
The existing teaching game development process is usually to fixedly develop a set of adaptive game contents based on the teaching contents, the developed game contents are solidified, and when the teaching contents are slightly changed, the teaching development needs to be carried out again to meet the teaching contents, so that the development process is complicated; meanwhile, the developed game only realizes the matching of the teaching contents, and when the student is a practitioner in different fields, the matching degree of the contents of the teaching game and the student is low.
Disclosure of Invention
The invention provides a game resource allocation method and device based on bilateral adaptation and a computer readable storage medium, and mainly aims to solve the problems that teaching game development is complex and the matching degree of a developed game and a game object is low.
In order to achieve the above object, the present invention provides a game resource allocation method based on bilateral adaptation, which includes:
acquiring game configuration requirements of a user, extracting game release objects from the game configuration requirements, and constructing a semantic matrix of the game configuration requirements;
screening a game level matched with the semantic matrix from a predetermined game level pool;
acquiring the game content of each game level, identifying the association relationship between each game level according to the game content, and combining the game levels according to the association relationship to obtain a first game frame;
acquiring user data of the game object, constructing a data abstract of the user data, and screening a game scene matched with the data abstract from a preset scene pool;
and scene data of the game scene is acquired, and scene configuration is carried out on the first game frame according to the scene data to obtain a second game frame.
Optionally, the constructing a semantic matrix of the game configuration requirement includes:
performing word segmentation processing on the game configuration requirement to obtain a requirement word segmentation;
counting the word segmentation frequency of each word in the required word segmentation, selecting the required word segmentation with the word segmentation frequency larger than a preset frequency threshold value as a keyword, and converting each word in the keyword into a word vector;
and splicing the word vectors into a vector matrix, and determining the vector matrix as a semantic matrix of the game configuration requirement.
Optionally, the screening the game level matched with the semantic matrix from a predetermined game level pool includes:
obtaining a level tag of each game level in the game level pool;
calculating the direct distance value between the semantic matrix and each level tag one by one;
and selecting the game level corresponding to the level label with the distance value smaller than the preset distance threshold value, and determining the selected game level as the game level matched with the semantic matrix.
Optionally, the identifying, according to the game content, an association relationship between each of the game levels includes:
selecting one of the game stages one by one as a target stage;
screening out the game conditions of the target level from the game contents of the target level by using a regular expression;
constructing a condition matrix corresponding to the game condition, and assigning a preset decision function by using the condition matrix to obtain a decision tree of the target level;
collecting decision trees corresponding to all game levels to obtain a decision tree model;
inputting the game contents corresponding to all game levels into the decision tree model to obtain the association relationship between each game level output by the decision tree model.
Optionally, the combining the game levels according to the association relationship to obtain a first game frame includes:
screening out game stages with correlation relationship among the game stages;
combining the screened game level cards according to the sequence of the incidence relation to obtain a middle frame;
and randomly inserting the game stages which do not have the association relationship into the middle frame to obtain the first game frame.
Optionally, the constructing a data summary of the user data includes:
splitting the user number data into a plurality of data participles, and converting each data participle into a participle vector;
adding position coding information into the word segmentation vectors according to the sequence of the positions of each data word segmentation in the user data to obtain coding vectors;
performing convolution and pooling processing on each coding vector for preset times by using a preset abstract extraction model to obtain vector characteristics;
calculating word probability values of the word segmentation vectors corresponding to the coding vectors as abstract words according to the vector characteristics;
and collecting the word segmentation vectors with the word probability value larger than a preset probability threshold value as the data abstract of the user data.
Optionally, the performing scene configuration on the first game frame according to the scene data to obtain a second game frame includes:
carrying out parameter coding on the scene data to obtain scene parameters;
and performing parameter assignment on the first game frame by using the scene parameters to obtain a second game frame.
In order to solve the above problem, the present invention further provides a game resource allocation device based on bilateral adaptation, where the device includes:
the demand analysis module is used for acquiring game configuration demands of users, extracting game publishing objects from the game configuration demands and constructing a semantic matrix of the game configuration demands;
the level screening module is used for screening a predetermined game level pool to obtain game levels matched with the semantic matrix;
the first configuration module is used for acquiring the game content of each game level, identifying the association relationship between each game level according to the game content, and combining the game levels according to the association relationship to obtain a first game frame;
the scene screening module is used for acquiring the user data of the game object, constructing a data abstract of the user data, and screening the game scene matched with the data abstract from a preset scene pool;
and the second configuration module is used for acquiring scene data of the game scene, and performing scene configuration on the first game frame according to the scene data to obtain a second game frame.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the bilateral adaptation-based game resource allocation method described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, where the at least one computer program is executed by a processor in an electronic device to implement the bilateral adaptation-based game resource allocation method described above.
The embodiment of the invention analyzes the game configuration requirements of the user to screen the game level meeting the user requirements from the level pool, and combines the levels into the first game frame according to the incidence relation among the levels, thereby realizing the adaptation to the requirements of the configuration party; meanwhile, the user data of the game object is analyzed to screen out the game scenes which accord with the game object, and the first game frame is assigned according to the game scenes to obtain a second game frame with the game scenes corresponding to the game object, so that the game object is adapted; namely, the bilateral adaptation of the demands of the configurator and the user data is completed at the same time, and the matching degree of the game and each party is improved. Therefore, the game resource allocation method, the game resource allocation device, the electronic equipment and the computer readable storage medium based on bilateral adaptation provided by the invention can solve the problems that the development of the teaching game is complex and the matching degree of the developed game and the game object is low.
Drawings
FIG. 1 is a flowchart illustrating a method for allocating game resources based on bilateral adaptation according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a process for constructing a semantic matrix according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart illustrating a process of screening a game level from a game level pool to obtain a game level matching a semantic matrix according to an embodiment of the present invention;
fig. 4 is a functional block diagram of a game resource allocation apparatus based on bilateral adaptation according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device for implementing the bilateral adaptation-based game resource allocation method according to an embodiment of the present invention.
The implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a game resource allocation method based on bilateral adaptation. The execution subject of the game resource allocation method based on bilateral adaptation includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the bilateral adaptation-based game resource allocation method may be executed by software or hardware installed in the terminal device or the server device, and the software may 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 be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Fig. 1 is a schematic flowchart of a game resource allocation method based on bilateral adaptation according to an embodiment of the present invention. In this embodiment, the bilateral adaptation-based game resource allocation method includes:
s1, obtaining game configuration requirements of users, extracting game publishing objects from the game configuration requirements, and constructing a semantic matrix of the game configuration requirements.
In the embodiment of the invention, the user is a person for configuring game content; the game configuration requirements comprise data such as level number, difficulty level, game type and the like determined by a user for the finally configurable game content; the game object refers to the audience group of the finally configured game.
For example, teachers have the following game configuration requirements: the method comprises the steps of configuring a time-limited breakthrough game containing a plurality of math selection questions for students according to math subject requirements, wherein a teacher is the user, and a student is a game issuing object.
In detail, a computer sentence with data crawling function (such as java sentence, python sentence, etc.) may be used to crawl pre-stored game configuration requirements from a predetermined data store, wherein the data store includes but is not limited to a database, a blockchain node, a network cache, etc.
In the embodiment of the present invention, a pre-trained named entity recognition model may be used to extract a game publishing object from the game configuration requirement, where the named entity recognition model includes a pre-trained CRF (conditional random field) based entity recognition model, an SVM (support vector machines) based entity recognition model, and the like.
Further, in order to realize accurate analysis of the game configuration requirements, the game configuration requirements can be analyzed to construct a semantic matrix of the game configuration requirements.
In the embodiment of the present invention, referring to fig. 2, the constructing the semantic matrix of the game configuration requirement includes:
s21, performing word segmentation processing on the game configuration requirement to obtain a requirement word segmentation;
s22, counting the word segmentation frequency of each word in the demand word segmentation, selecting the demand word with the word segmentation frequency larger than a preset frequency threshold as a keyword, and converting each word in the keyword into a word vector;
s23, splicing the word vectors into a vector matrix, and determining the vector matrix as a semantic matrix of the game configuration requirement.
In detail, the game configuration requirement may be segmented by using a pre-trained artificial intelligence Model with a segmentation function, so as to obtain the requirement segmentation, where the artificial intelligence Model includes, but is not limited to, an NLP (Natural Language Processing) Model, and an HMM (Hidden Markov Model).
Specifically, the word segmentation frequency refers to the frequency of occurrence of a word in the required word segmentation required by the game configuration, and when the word segmentation frequency of the word segmentation is higher, the importance of the word segmentation is higher, so that the required word segmentation with the word segmentation frequency larger than a preset frequency threshold value can be selected as the keyword.
Further, in order to improve the processing efficiency of the keywords, the keywords may be converted into word vectors in numerical form using a pre-trained word vector model, wherein the word vector model includes, but is not limited to, word2vec model, bert model.
In the embodiment of the present invention, the splicing the word vectors into a vector matrix includes: counting the vector length of each word vector in the word vectors, and determining the maximum value in the vector lengths as a target length; extending the vector length of each of the word vectors to the target length using preset parameters; and splicing each prolonged word vector as a row vector to obtain a vector matrix.
In detail, since the word vectors are obtained by converting different keywords, there may be differences in vector lengths of different vectors in the word vectors, and for convenience of subsequent concatenation, lengths of all word vectors may be extended to a uniform length by using preset parameters.
For example, the word vector includes vector a: (1,4,6), vector B: (2,3), vector C: (3,7,8,9), it can be known through statistics that the vector length of the vector a is 3, the vector length of the vector B is 2, and the vector length of the vector C is 4, then it is determined that 4 is the target length, and the vector length of the vector a is extended to 4 by using a preset parameter (e.g. x), so as to obtain an extended vector a: (1,4,6, x), extending the vector length of vector B to 4, resulting in an extended vector B: (2,3, x, x).
Further, each word vector after extension can be used as a row vector and is spliced into a vector matrix as follows:
in the embodiment of the invention, the vector matrix is formed by splicing the vectors corresponding to the keywords of the game configuration requirement, so that the vector matrix can be used as the semantic matrix of the game configuration requirement.
And S2, screening the predetermined game level pool to obtain the game level matched with the semantic matrix.
In one practical application scenario, the game level pool comprises multiple preset game levels, and in order to realize accurate configuration of a game, a corresponding game level can be screened from the game level pool according to the semantic matrix so as to ensure the coincidence degree of a subsequently configured game and game configuration requirements.
In the embodiment of the present invention, referring to fig. 3, the step of screening a predetermined game level pool to obtain a game level matched with the semantic matrix includes:
s31, acquiring the level label of each game level in the game level pool;
s32, calculating the distance value between the semantic matrix and each level label;
s33, selecting the game level corresponding to the level label with the distance value smaller than the preset distance threshold value, and determining the selected game level as the game level matched with the semantic matrix.
In detail, the level tags are tags for marking the contents of different game levels.
Specifically, the distance value between the semantic matrix and each of the level labels can be calculated one by using an algorithm (such as a euclidean distance algorithm, a cosine distance algorithm, etc.) having a distance value calculation function.
S3, obtaining the game content of each game level, identifying the association relation between each game level according to the game content, and combining the game levels according to the association relation to obtain a first game frame.
In the embodiment of the present invention, the game content refers to data content such as a game noun, a game suitable group, and a game rule of a game included in each game level.
In one practical application scenario of the present invention, there may be a plurality of game levels 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 passing condition of level B is prop a obtained after passing using level a, level B must be aligned with level a.
Therefore, the analysis can be carried out according to the game content of each game stage so as to identify the association relationship among the game stages, and then the plurality of game stages are combined according to the association relationship so as to improve the continuity of the configured game and realize the accurate configuration of the game.
In an embodiment of the present invention, the identifying, according to the game content, an association relationship between each of the game levels includes: selecting one of the game stages one by one as a target stage; screening out the game conditions of the target level from the game contents of the target level by using a regular expression; constructing a condition matrix corresponding to the game condition, and assigning a preset decision function by using the condition matrix to obtain a decision tree of the target level; collecting decision trees corresponding to all game levels to obtain a decision tree model; inputting the game contents corresponding to all game levels into the decision tree model to obtain the association relationship between each game level output by the decision tree model.
In detail, the game conditions include entry conditions and pass conditions for 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 details are not repeated here.
Further, in order to identify the association relationship between each game level, a decision tree for performing decision identification on the game content of the game level can be constructed by using a preset decision function.
In detail, the decision function may 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, a preset decision function can be assigned by using a condition matrix corresponding to the game condition to obtain a decision tree of the target level, and then the game content corresponding to all game levels is input to the decision tree model, and then the incidence relation between each game level is identified by using the decision tree model.
In this embodiment of the present invention, the combining the game levels according to the association relationship to obtain a first game frame includes: screening out game stages with correlation relationship among the game stages; combining the screened game level cards according to the sequence of the incidence relation to obtain a middle frame; and randomly inserting the game stages which do not have the association relationship into the middle frame to obtain the first game frame.
In detail, when there are more than two game stages, there may be an association between part of the game stages, but there is no association between part of the stages and any other stage.
For example, there are a level a, a level B and a level C, where there is "the entry condition of the level B is to obtain a clearance reward item of the level a" between the level a and the level B, and there is no association between the level C and the levels a and B.
Therefore, in order to realize efficient combination of the game stages, the stages with the association relationship among the game stages can be screened out, and the screened game stages are combined according to the sequence of the association relationship among the stages to obtain the intermediate frame.
Specifically, because the association between the game level without the association relationship and the other level is low, when the combination of the game levels with the association relationship is completed, the game levels without the association relationship can be randomly inserted into the middle frame, so as to obtain the first game frame.
S4, obtaining the user data of the game object, constructing the data abstract of the user data, and screening the game scene matched with the data abstract from a preset scene pool.
In the embodiment of the invention, the user data comprises data of the age, the sex, the education level, the occupation and the like of the game object; the user data can be uploaded to a preset data form or page by the game object.
In one practical application scenario of the present invention, because the generated first game frame is only obtained by combining a plurality of game levels according to an association relationship, the generated first game frame has a low matching degree with a game object, and in order to implement accurate configuration of the game object, user data of the game object may be analyzed to screen a game scenario matching the game object from a preset scenario pool, thereby improving the adaptability of the finally generated game frame and the game object.
In detail, the scene pool comprises a plurality of different game scenes, and the game scenes are obtained by analyzing and creating user groups with different ages, sexes, professions, education degrees and the like in advance.
In this embodiment of the present invention, the constructing the data summary of the user data includes: splitting the user number data into a plurality of data participles, and converting each data participle into a participle vector; adding position coding information into the word segmentation vectors according to the sequence of the positions of each data word segmentation in the user data to obtain coding vectors; performing convolution and pooling processing on each coding vector for preset times by using a preset abstract extraction model to obtain vector characteristics; calculating word probability values of the word segmentation vectors corresponding to the coding vectors as abstract words according to the vector characteristics; and collecting the word segmentation vectors with the word probability value larger than a preset probability threshold value as the data abstract of the user data.
In detail, word segmentation tools such as a jieba, a SnowNLP, a PKUse and a THULAC which are preset can be used for splitting the user data into a plurality of data word segments; alternatively, the user data may be split into several data participles by the word token () method in the NLTK library in Python.
Illustratively, the user data may be split into several data participles using the following word token () method:
from nltk.tokenize import word_tokenize
data＝"y"
print(word_tokenize(data))
wherein y is the user data.
Specifically, each of the data participles may be encoded into a word vector using a transform encoder, a word2vec encoder, an Autoencoder, or other encoder having a word encoding function.
In detail, the BERT model can be used for coding the position sequence of each word segmentation vector in the user data according to the coding method, and the coded position information is added into the word segmentation vectors to obtain the coding vectors, so that the position information of different word segmentation vectors is embodied in the coding vectors, and the accuracy of the subsequent analysis of the user data is improved.
Further, the abstract extraction model includes, but is not limited to, a Convolutional Neural Network (CNN) model with an abstract word recognition function, a Support Vector Machine (SVM) model; the keyword analysis model includes, but is not limited to, a Conditional Random Field (CRF) model with a keyword recognition function, and an Lstm (Long short-term memory) model.
In detail, the probability value of the word segmentation vector corresponding to each coding vector as the abstract word can be calculated according to the vector characteristics by using an activation function preset in the abstract extraction model, and the word segmentation vectors with the word probability value larger than a preset probability threshold are collected as the data abstract of the user data.
In the embodiment of the present invention, the step of screening the game scene matching the data abstract from the preset scene pool is consistent with the step of screening the game level matching the semantic matrix from the predetermined game level pool in S2, and details are not repeated herein.
S5, scene data of the game scene are obtained, scene configuration is carried out on the first game frame according to the scene data, and a second game frame is obtained.
In the embodiment of the invention, the game scene data comprises data such as element style, interface tone, npc lap style, game voice style and the like of the game.
In detail, the step of acquiring the scene data of the game scene is the same as the step of acquiring the game configuration requirement of the user in S1, and details are not repeated here.
In one practical application scenario of the present invention, since the first game frame is obtained by configuring the plurality of game levels according to the game configuration requirement of the user, the first game frame conforms to the game configuration requirement of the user, but the game content is a game object, and further, in order to implement bilateral matching between the user and the game object, the first game frame may be configured according to a game scene corresponding to the game object, so as to implement bilateral matching between the game object and the user.
In this embodiment of the present invention, the performing scene configuration on the first game frame according to the scene data to obtain a second game frame includes: carrying out parameter coding on the scene data to obtain scene parameters; and performing parameter assignment on the first game frame by using the scene parameters to obtain a second game frame.
In detail, the scene data may be parameter-encoded by using a preset Encoder to obtain scene parameters, where the Encoder includes, but is not limited to, VAE (variant Auto-Encoder) and DAE (noise automatic Encoder).
Specifically, the scene parameters obtained after encoding may be assigned to the first game frame to obtain a second game frame having bilateral matching with the game object for the user.
The embodiment of the invention analyzes the game configuration requirements of the user to screen the game level which meets the requirements of the user from the level pool, and combines the level into the first game frame according to the incidence relation among the levels, thereby realizing the adaptation to the requirements of the configuration party, and disassembling the game into a plurality of levels for combination, thereby reducing the coupling of the whole game and improving the flexibility of the game configuration; meanwhile, the user data of the game object is analyzed to screen out the game scenes which accord with the game object, and the first game frame is assigned according to the game scenes to obtain a second game frame with the game scenes which correspond to the game object, so that the game object is adapted; namely, the bilateral adaptation of the demands of the configurator and the user data is completed at the same time, and the matching degree of the game and each party is improved. Therefore, the game resource allocation method, the game resource allocation device, the electronic equipment and the computer readable storage medium based on bilateral adaptation provided by the invention can solve the problems that the development of the teaching game is complex and the matching degree of the developed game and the game object is low.
Fig. 4 is a functional block diagram of a game resource allocation apparatus based on bilateral adaptation according to an embodiment of the present invention.
The bilateral adaptation-based game resource allocation device 100 of the present invention may be installed in an electronic device. According to the implemented functions, the bilateral adaptation-based game resource configuration device 100 may include a requirement 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 module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the requirement analysis module 101 is configured to obtain a game configuration requirement of a user, extract a 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 screen a predetermined game level pool to obtain a game level matched with the semantic matrix;
the first configuration module 103 is configured to obtain game content of each game level, identify an association relationship between each game level according to the game content, and combine the game levels according to the association relationship to obtain a first game frame;
the scene screening module 104 is configured to acquire user data of the game object, construct a data summary of the user data, and screen a game scene matched with the data summary from a preset scene pool;
the second configuration module 105 is configured to obtain 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, in the embodiment of the present invention, when the modules in the game resource allocation apparatus 100 based on bilateral adaptation are used, the same technical means as the game resource allocation method based on bilateral adaptation described in fig. 1 to 3 are adopted, and the same technical effects can be produced, which is not described herein again.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a game resource allocation method based on bilateral adaptation according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a game resource configuration program based on bilateral adaptation, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing a game resource configuration program based on bilateral adaptation, etc.) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used to store not only application software installed in the electronic device and various types of data, such as codes of game resource allocation programs based on bilateral adaptation, but also 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. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically 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 a standard wired interface, a wireless interface. Alternatively, 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) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Only electronic devices having components are shown, and those skilled in the art will appreciate that the structures shown in the figures do not constitute limitations on the electronic devices, and may include fewer or more components than shown, or some components in combination, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The game resource allocation program based on bilateral adaptation stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, and when running in the processor 10, can realize:
acquiring game configuration requirements of a user, extracting game release objects from the game configuration requirements, and constructing a semantic matrix of the game configuration requirements;
screening a predetermined game level pool to obtain a game level matched with the semantic matrix;
acquiring the game content of each game level, identifying the association relationship between each game level according to the game content, and combining the game levels according to the association relationship to obtain a first game frame;
acquiring user data of the game object, constructing a data abstract of the user data, and screening a game scene matched with the data abstract from a preset scene pool;
and scene data of the game scene is acquired, and scene configuration is carried out on the first game frame according to the scene data to obtain a second game frame.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, 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 said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring game configuration requirements of a user, extracting game release objects from the game configuration requirements, and constructing a semantic matrix of the game configuration requirements;
screening a predetermined game level pool to obtain a game level matched with the semantic matrix;
acquiring the game content of each game level, identifying the association relationship between each game level according to the game content, and combining the game levels according to the association relationship to obtain a first game frame;
acquiring user data of the game object, constructing a data abstract of the user data, and screening a game scene matched with the data abstract from a preset scene pool;
and scene data of the game scene is acquired, and scene configuration is carried out on the first game frame according to the scene data to obtain a second game frame.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a string of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, which is used for verifying the validity (anti-counterfeiting) of the information and generating a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention 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 by comprising the following steps:
acquiring game configuration requirements of a user, extracting game release objects from the game configuration requirements, and constructing a semantic matrix of the game configuration requirements;
screening a game level matched with the semantic matrix from a predetermined game level pool;
acquiring the game content of each game level, identifying the association relationship between each game level according to the game content, and combining the game levels according to the association relationship to obtain a first game frame;
acquiring user data of the game object, constructing a data abstract of the user data, and screening a game scene matched with the data abstract from a preset scene pool;
and scene data of the game scene is obtained, scene configuration is carried out on the first game frame according to the scene data, and a second game frame is obtained.
2. The bilateral adaptation-based game resource allocation method according to claim 1, wherein the constructing the semantic matrix of the game allocation requirement includes:
performing word segmentation processing on the game configuration requirement to obtain a requirement word segmentation;
counting the word segmentation frequency of each word in the required word segmentation, selecting the required word with the word segmentation frequency larger than a preset frequency threshold as a keyword, and converting each word in the keyword into a word vector;
and splicing the word vectors into a vector matrix, and determining the vector matrix as a semantic matrix of the game configuration requirement.
3. The bilateral adaptation-based game resource allocation method according to claim 1, wherein the screening of the game level matching the semantic matrix from a predetermined game level pool comprises:
acquiring a level tag of each game level in the game level pool;
calculating the direct distance value between the semantic matrix and each level tag one by one;
and selecting the game level corresponding to the level label with the distance value smaller than the preset distance threshold value, and determining that the selected game level is the game level matched with the semantic matrix.
4. The bilateral adaptation-based game resource allocation method of claim 1, wherein the identifying the association relationship between each of the game stages according to the game content comprises:
selecting one of the game levels from the game levels one by one as a target level;
screening out the game conditions of the target level from the game contents of the target level by using a regular expression;
constructing a condition matrix corresponding to the game condition, and assigning a preset decision function by using the condition matrix to obtain a decision tree of the target level;
collecting decision trees corresponding to all game levels to obtain a decision tree model;
inputting the game contents corresponding to all game levels into the decision tree model to obtain the association relationship between each game level output by the decision tree model.
5. The bilateral adaptation-based game resource allocation method according to claim 1, wherein the combining the game levels according to the association relationship to obtain a first game frame comprises:
screening out game stages with correlation relationship among the game stages;
combining the screened game level cards according to the sequence of the incidence relation to obtain a middle frame;
and randomly inserting the game stages which do not have the association relationship into the middle frame to obtain the first game frame.
6. The bilateral adaptation-based game resource allocation method according to claim 1, wherein the constructing the data summary of the user data comprises:
dividing the user number data into a plurality of data participles, and converting each data participle into a participle vector;
adding position coding information into the word segmentation vectors according to the sequence of the positions of each data word segmentation in the user data to obtain coding vectors;
performing convolution and pooling processing on each coding vector for preset times by using a preset abstract extraction model to obtain vector characteristics;
calculating word probability values of the word segmentation vectors corresponding to the coding vectors as abstract words according to the vector characteristics;
and collecting the word segmentation vectors with the word probability value larger than a preset probability threshold value as the data abstract of the user data.
7. The bilateral adaptation-based game resource allocation method according to any one of claims 1 to 6, wherein the performing scene allocation on the first game frame according to the scene data to obtain a second game frame comprises:
carrying out parameter coding on the scene data to obtain scene parameters;
and performing parameter assignment on the first game frame by using the scene parameters to obtain a second game frame.
8. An apparatus for game resource allocation based on bilateral adaptation, the apparatus comprising:
the demand analysis module is used for acquiring game configuration demands of users, extracting game release objects from the game configuration demands and constructing a semantic matrix of the game configuration demands;
the level screening module is used for screening a predetermined game level pool to obtain game levels matched with the semantic matrix;
the first configuration module is used for acquiring the game content of each game level, identifying the association relationship between each game level according to the game content, and combining the game levels according to the association relationship to obtain a first game frame;
the scene screening module is used for acquiring the user data of the game object, constructing a data abstract of the user data, and screening the game scene matched with the data abstract from a preset scene pool;
and the second configuration module is used for acquiring scene data of the game scene, and performing 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 the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a bilateral adaptation-based game resource allocation method as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the bilateral adaptation-based game resource allocation method as claimed in any one of claims 1 to 7.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060063575A1 (en) * | 2003-03-10 | 2006-03-23 | Cyberscan Technology, Inc. | Dynamic theming of a gaming system |
| CN103106340A (en) * | 2013-01-21 | 2013-05-15 | 天津大学 | Game level automatic generation system and method based on data mining and data fusion |
| US8753200B1 (en) * | 2013-02-01 | 2014-06-17 | Pearson Education, Inc. | Evaluation and rectification system |
| CN108066987A (en) * | 2016-11-18 | 2018-05-25 | 腾讯科技（深圳）有限公司 | Distribution method, device and the server of application scenarios outpost of the tax office combination |
| CN109529354A (en) * | 2018-12-21 | 2019-03-29 | 咪咕互动娱乐有限公司 | The determination method, apparatus and storage medium of game background music |
| CN111104513A (en) * | 2019-12-13 | 2020-05-05 | 中山大学 | Short text classification method for game platform user question-answer service |
| CN111564064A (en) * | 2020-05-27 | 2020-08-21 | 上海乂学教育科技有限公司 | Intelligent education system and method based on game interaction |
| CN112619132A (en) * | 2020-12-18 | 2021-04-09 | 腾讯科技（深圳）有限公司 | Interface display method and device, storage medium and electronic equipment |
| CN112632277A (en) * | 2020-12-15 | 2021-04-09 | 五八同城信息技术有限公司 | Resource processing method and device for target content object |
| CN113041623A (en) * | 2019-12-26 | 2021-06-29 | 波克科技股份有限公司 | Game parameter configuration method and device and computer readable storage medium |
| CN113723114A (en) * | 2021-08-31 | 2021-11-30 | 平安普惠企业管理有限公司 | Semantic analysis method, device and equipment based on multi-intent recognition and storage medium |
| CN117273820A (en) * | 2023-10-13 | 2023-12-22 | 上海愉游网络科技有限公司 | Game online lottery drawing system and method based on artificial intelligence |
-
2022
- 2022-04-07 CN CN202210361136.XA patent/CN114733195A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060063575A1 (en) * | 2003-03-10 | 2006-03-23 | Cyberscan Technology, Inc. | Dynamic theming of a gaming system |
| CN103106340A (en) * | 2013-01-21 | 2013-05-15 | 天津大学 | Game level automatic generation system and method based on data mining and data fusion |
| US8753200B1 (en) * | 2013-02-01 | 2014-06-17 | Pearson Education, Inc. | Evaluation and rectification system |
| CN108066987A (en) * | 2016-11-18 | 2018-05-25 | 腾讯科技（深圳）有限公司 | Distribution method, device and the server of application scenarios outpost of the tax office combination |
| CN109529354A (en) * | 2018-12-21 | 2019-03-29 | 咪咕互动娱乐有限公司 | The determination method, apparatus and storage medium of game background music |
| CN111104513A (en) * | 2019-12-13 | 2020-05-05 | 中山大学 | Short text classification method for game platform user question-answer service |
| CN113041623A (en) * | 2019-12-26 | 2021-06-29 | 波克科技股份有限公司 | Game parameter configuration method and device and computer readable storage medium |
| CN111564064A (en) * | 2020-05-27 | 2020-08-21 | 上海乂学教育科技有限公司 | Intelligent education system and method based on game interaction |
| CN112632277A (en) * | 2020-12-15 | 2021-04-09 | 五八同城信息技术有限公司 | Resource processing method and device for target content object |
| CN112619132A (en) * | 2020-12-18 | 2021-04-09 | 腾讯科技（深圳）有限公司 | Interface display method and device, storage medium and electronic equipment |
| CN113723114A (en) * | 2021-08-31 | 2021-11-30 | 平安普惠企业管理有限公司 | Semantic analysis method, device and equipment based on multi-intent recognition and storage medium |
| CN117273820A (en) * | 2023-10-13 | 2023-12-22 | 上海愉游网络科技有限公司 | Game online lottery drawing system and method based on artificial intelligence |
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