Game resource configuration method, device, equipment and medium based on bilateral adaptation

Abstract

The present invention relates to artificial intelligence technology, and discloses a game resource configuration method based on bilateral adaptation, including: extracting a game publishing object from a game configuration requirement, and constructing a semantic matrix of the game configuration requirement; screening a game level matching the semantic matrix from a game level pool, and combining the game levels into a first game framework according to the association between each game level; obtaining user data of a game object, and screening a game scene matching the user data from a preset scene pool; and configuring the first game framework scene according to the scene data of the game scene to obtain a second game framework. In addition, the present invention also relates to blockchain technology, and the game configuration requirement can be stored in a node of a blockchain. The present invention also proposes a game resource configuration device, equipment, and medium based on bilateral adaptation. The present invention can solve the problem that the development of teaching games is relatively complicated and the matching degree between the developed games and game objects is low.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a game resource configuration method, device, electronic device and computer-readable storage medium based on bilateral adaptation. Background Art

[0002] With the development of the information society, people are exposed to more and more extensive fields of knowledge. Therefore, in order to understand and master a large amount of knowledge more quickly, people are increasingly inclined to receive online training and teaching. At the same time, in order to enhance the fun of online teaching, providers of online teaching often combine teaching content with teaching games to improve teaching effectiveness.

[0003] The existing teaching game development process often involves developing a set of adaptive game content based on the teaching content. The developed game content is relatively rigid. When there are minor changes in the teaching content, the game needs to be redeveloped to match the teaching content, which results in a cumbersome development process. At the same time, the developed games only match the teaching content. When the students are practitioners in different fields, the matching degree between the teaching game content and the students is low. Summary of the invention

[0004] 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 relatively complicated and the matching degree between the developed games and game objects is low.

[0005] To achieve the above object, the present invention provides a game resource configuration method based on bilateral adaptation, comprising:

[0006] Obtaining the user's game configuration requirements, extracting the game publishing object from the game configuration requirements, and constructing a semantic matrix of the game configuration requirements;

[0007] Screening a game level that matches the semantic matrix from a predetermined game level pool;

[0008] Acquire game content of each of the game levels, identify associations between each of the game levels according to the game content, and combine the game levels according to the associations to obtain a first game framework;

[0009] Acquire user data of the game object, construct a data summary of the user data, and filter out a game scene that matches the data summary from a preset scene pool;

[0010] The scene data of the game scene is obtained, and the scene configuration of the first game framework is performed according to the scene data to obtain a second game framework.

[0011] Optionally, constructing the semantic matrix of the game configuration requirements includes:

[0012] Performing word segmentation processing on the game configuration requirements to obtain requirement word segmentation;

[0013] Counting the segmentation frequency of each segmentation in the demand segmentation, selecting the demand segmentation whose segmentation frequency is greater than a preset frequency threshold as a keyword, and converting each word in the keyword into a word vector;

[0014] The word vectors are concatenated into a vector matrix, and the vector matrix is ​​determined to be a semantic matrix of the game configuration requirement.

[0015] Optionally, the step of selecting a game level that matches the semantic matrix from a predetermined game level pool includes:

[0016] Obtaining a level tag for each game level in the game level pool;

[0017] Calculating the distance value between the semantic matrix and each of the level labels one by one;

[0018] The game level corresponding to the level tag whose distance value is less than the preset distance threshold is selected, and the selected game level is determined to be a game level matching the semantic matrix.

[0019] Optionally, identifying the association relationship between each of the game levels according to the game content includes:

[0020] Selecting one of the game levels as the target level one by one from the game levels;

[0021] Filtering the game conditions of the target level from the game content of the target level by using a regular expression;

[0022] Constructing a condition matrix corresponding to the game conditions, and using the condition matrix to assign values ​​to a preset decision function to obtain a decision tree for the target level;

[0023] Gather the decision trees corresponding to all game levels to obtain a decision tree model;

[0024] The game contents corresponding to all game levels are input into the decision tree model to obtain the association relationship between each of the game levels output by the decision tree model.

[0025] Optionally, combining the game levels according to the association relationship to obtain a first game framework includes:

[0026] Filter out game levels that are related to each other;

[0027] Combining the selected game levels according to the order of the association relationships to obtain an intermediate framework;

[0028] Game levels that have no association relationship with each other are randomly inserted into the intermediate frame to obtain the first game frame.

[0029] Optionally, constructing a data summary of the user data includes:

[0030] Splitting the user data into multiple data tokens, and converting each of the data tokens into a token vector;

[0031] Add position coding information to the word segmentation vector according to the order of the position of each data word in the user data to obtain a coding vector;

[0032] Using a preset summary extraction model, each of the encoding vectors is subjected to a preset number of convolution and pooling processes to obtain vector features;

[0033] Calculate the word probability value of the word segmentation vector corresponding to each of the encoding vectors as a summary word according to the vector features;

[0034] The word segmentation vectors whose word probability values ​​are greater than a preset probability threshold are aggregated as a data summary of the user data.

[0035] Optionally, performing scene configuration on the first game framework according to the scene data to obtain a second game framework includes:

[0036] Performing parameter encoding on the scene data to obtain scene parameters;

[0037] The first game frame is assigned parameters using the scene parameters to obtain a second game frame.

[0038] In order to solve the above problems, the present invention further provides a game resource configuration device based on bilateral adaptation, the device comprising:

[0039] A demand analysis module is used to obtain the game configuration requirements of the user, extract the game publishing object from the game configuration requirements, and construct a semantic matrix of the game configuration requirements;

[0040] A level screening module, used to screen game levels matching the semantic matrix from a predetermined game level pool;

[0041] A first configuration module is used to obtain game content of each of the game levels, identify associations between each of the game levels according to the game content, and combine the game levels according to the associations to obtain a first game framework;

[0042] A scene screening module, used to obtain user data of the game object, construct a data summary of the user data, and screen out game scenes matching the data summary from a preset scene pool;

[0043] The second configuration module is used to obtain scene data of the game scene, and perform scene configuration on the first game framework according to the scene data to obtain a second game framework.

[0044] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0045] at least one processor; and,

[0046] a memory communicatively connected to the at least one processor; wherein,

[0047] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned game resource configuration method based on bilateral adaptation.

[0048] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned game resource configuration method based on bilateral adaptation.

[0049] The embodiment of the present invention analyzes the game configuration requirements of the user to select the game levels that meet the user's requirements from the level pool, and combines the levels into a first game framework according to the association relationship between the levels, thereby achieving adaptation to the configuration party's requirements, and disassembles the game into multiple levels for combination, thereby reducing the overall coupling of the game and improving the flexibility of the game configuration; at the same time, the user data of the game object is analyzed to select the game scene that meets the game object, and the first game framework is assigned according to the game scene to obtain a second game framework with a game scene corresponding to the game object, thereby achieving adaptation to the game object; that is, the bilateral adaptation of the configuration party's requirements and the user data is completed at the same time, thereby improving the matching degree of the game with all parties. Therefore, the game resource configuration method, device, electronic device and computer-readable storage medium based on bilateral adaptation proposed by the present invention can solve the problem that the development of teaching games is relatively complicated and the matching degree between the developed games and the game objects is low. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of a flow chart of a game resource configuration method based on bilateral adaptation provided by an embodiment of the present invention;

[0051] Figure 2 A schematic diagram of a process for constructing a semantic matrix according to an embodiment of the present invention;

[0052] Figure 3 A schematic diagram of a process of selecting a game level that matches a semantic matrix from a game level pool provided by an embodiment of the present invention;

[0053] Figure 4 A functional module diagram of a game resource configuration device based on bilateral adaptation provided by an embodiment of the present invention;

[0054] Figure 5 A schematic diagram of the structure of an electronic device for implementing the game resource configuration method based on bilateral adaptation provided by an embodiment of the present invention.

[0055] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0056] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0057] The embodiment of the present application provides a game resource configuration method based on bilateral adaptation. The execution subject of the game resource configuration method based on bilateral adaptation includes but is not limited to at least one of the electronic devices such as the server, the terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the game resource configuration method based on bilateral adaptation can be executed by software or hardware installed on the terminal device or the 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, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0058] Reference Figure 1 FIG. 1 is a flow chart of a method for configuring game resources based on bilateral adaptation according to an embodiment of the present invention. In this embodiment, the method for configuring game resources based on bilateral adaptation includes:

[0059] S1. Obtain the game configuration requirements of the user, extract the game publishing object from the game configuration requirements, and construct a semantic matrix of the game configuration requirements.

[0060] In an embodiment of the present invention, the user is a person who configures the 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 game content that can be finally configured; and the game object refers to the audience group of the finally configured game.

[0061] For example, a teacher has the following game configuration requirement: "Configure a time-limited level-breaking game containing multiple math multiple-choice questions for students based on the subject requirements of math", where "teacher" is the user and "student" is the game publishing object.

[0062] In detail, computer statements with data capture functions (such as Java statements, Python statements, etc.) can be used to capture pre-stored game configuration requirements from predetermined data storage, where the data storage includes but is not limited to databases, blockchain nodes, network caches, etc.

[0063] In an embodiment of the present invention, a pre-trained named entity recognition model can be used to extract the game publishing object from the game configuration requirements, wherein the named entity recognition model includes a pre-trained entity recognition model based on CRF (conditional random field algorithm), an entity recognition model based on SVM (support vector machines), etc.

[0064] Furthermore, 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.

[0065] In the embodiment of the present invention, Figure 2 As shown, the semantic matrix of constructing the game configuration requirements includes:

[0066] S21, performing word segmentation processing on the game configuration requirements to obtain requirement word segmentation;

[0067] S22, counting the segmentation frequency of each segmentation in the demand segmentation, selecting the demand segmentation whose segmentation frequency is greater than a preset frequency threshold as a keyword, and converting each word in the keyword into a word vector;

[0068] S23, concatenating the word vectors into a vector matrix, and determining that the vector matrix is ​​a semantic matrix of the game configuration requirement.

[0069] In detail, a pre-trained artificial intelligence model with a word segmentation function can be used to perform word segmentation on the game configuration requirements to obtain the requirement segmentation, wherein the artificial intelligence model includes but is not limited to an NLP (Natural Language Processing) model and an HMM (Hidden Markov Model).

[0070] Specifically, the word segmentation frequency refers to the number of times a certain word segmentation appears in the demand word segmentation of the game configuration requirement. The higher the word segmentation frequency of a word segmentation, the greater the importance of the word segmentation. Therefore, the demand word segmentation whose word segmentation frequency is greater than the preset frequency threshold can be selected as a keyword.

[0071] Furthermore, in order to improve the processing efficiency of the keywords, a pre-trained word vector model may be used to convert the keywords into word vectors in numerical form, wherein the word vector model includes but is not limited to a word2vec model and a bert model.

[0072] In an embodiment of the present invention, the word vectors are spliced ​​into a vector matrix, including: counting the vector length of each word vector in the word vector, and determining the maximum value of the vector lengths as the target length; using preset parameters to extend the vector length of each vector in the word vector to the target length; and splicing each vector in the extended word vector as a row vector to obtain a vector matrix.

[0073] In detail, since the word vectors are converted from different keywords, the vector lengths of different vectors in the word vectors may be different. In order to facilitate subsequent splicing, the lengths of all word vectors can be extended to a uniform length using preset parameters.

[0074] For example, the word vector includes vector A: (1, 4, 6), vector B: (2, 3), and vector C: (3, 7, 8, 9). According to statistics, the vector length of vector A is 3, the vector length of vector B is 2, and the vector length of vector C is 4. Then 4 is determined as the target length, and the vector length of vector A is extended to 4 using preset parameters (such as x), and the extended vector A is obtained: (1, 4, 6, x), and the vector length of vector B is extended to 4, and the extended vector B is obtained: (2, 3, x, x).

[0075] Furthermore, each extended word vector can be used as a row vector and concatenated into the following vector matrix:

[0076]

[0077] In the embodiment of the present invention, since the vector matrix is ​​formed by concatenating the vectors corresponding to the keywords of the game configuration requirements, the vector matrix can be used as the semantic matrix of the game configuration requirements.

[0078] S2. Filtering a predetermined pool of game levels to obtain a game level that matches the semantic matrix.

[0079] In one of the practical application scenarios of the present invention, the game level pool contains a plurality of preset game levels. In order to achieve accurate configuration of the game, the corresponding game levels can be screened from the game level pool according to the semantic matrix to ensure that the subsequently configured game is consistent with the game configuration requirements.

[0080] In the embodiment of the present invention, Figure 3 As shown, the step of selecting a game level matching the semantic matrix from a predetermined game level pool includes:

[0081] S31, obtaining a level tag of each game level in the game level pool;

[0082] S32, calculating the distance value between the semantic matrix and each of the level labels one by one;

[0083] S33: Selecting a game level corresponding to a level tag whose distance value is less than a preset distance threshold, and determining that the selected game level is a game level that matches the semantic matrix.

[0084] In detail, the level tags are tags used to mark the contents of different game levels.

[0085] Specifically, an algorithm with a distance value calculation function (such as a Euclidean distance algorithm, a cosine distance algorithm, etc.) may be used to calculate the distance value between the semantic matrix and each of the level labels one by one.

[0086] S3. Obtain game content of each of the game levels, identify associations between each of the game levels according to the game content, and combine the game levels according to the associations to obtain a first game framework.

[0087] In the embodiment of the present invention, the game content refers to the data content such as the game name, the game suitable group, and the game rules contained in each game level.

[0088] In one of the practical application scenarios of the present invention, there may be many game levels that meet the game configuration requirements, and there may be associations between different levels.

[0089] For example, there are level A and level B, where the condition for passing level B is to use item a obtained after passing level A, then level B must be arranged after level A.

[0090] Therefore, the game content of each game level can be analyzed to identify the association between each game level, and then multiple game levels can be combined according to the association to improve the coherence of the configured game and achieve accurate configuration of the game.

[0091] In an embodiment of the present invention, the identifying the association relationship between each of the game levels according to the game content includes: selecting one of the game levels as a target level from the game levels one by one; filtering out the game conditions of the target level from the game content of the target level using a regular expression; constructing a condition matrix corresponding to the game conditions, and using the condition matrix to assign values ​​to a preset decision function to obtain a decision tree of the target level; collecting the decision trees corresponding to all game levels to obtain a decision tree model; and inputting the game contents corresponding to all game levels into the decision tree model to obtain the association relationship between each of the game levels output by the decision tree model.

[0092] In detail, the game conditions include entry conditions and passing conditions for each level.

[0093] Specifically, the step of constructing the condition matrix corresponding to the game conditions is consistent with the step of constructing the semantic matrix of the game configuration requirements in S1, and will not be repeated here.

[0094] Furthermore, in order to identify the association relationship between each of the game levels, a preset decision function may be used to construct a decision tree for making decisions on the game contents of the game levels.

[0095] In detail, the decision function may be:

[0096]

[0097] Among them, 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.

[0098] Specifically, the condition matrix corresponding to the game conditions can be used to assign values ​​to the preset decision function to obtain the decision tree of the target level, and then the game content corresponding to all game levels can be input into the decision tree model, and then the decision tree model can be used to identify the association relationship between each of the game levels.

[0099] In an embodiment of the present invention, combining the game levels according to the association relationship to obtain a first game frame includes: screening out game levels that are associated with each other; combining the screened game levels in the order of the association relationship to obtain an intermediate frame; and randomly inserting game levels that are not associated with each other into the intermediate frame to obtain the first game frame.

[0100] Specifically, when there are more than two game levels, some of the game levels may be associated with each other, but some of the levels may not be associated with any other levels.

[0101] For example, there are level A, level B and level C, among which, between level A and level B, "the entry condition of level B is to obtain the reward item for passing level A", and there is no association between level C and level A and level B.

[0102] Therefore, in order to achieve efficient combination of game levels, levels with associated relationships among the game levels may be screened out, and then the screened game levels may be combined in the order of the associated relationships between the levels to obtain an intermediate framework.

[0103] Specifically, since the correlation between the game levels without correlation relationship and other levels is low, after the combination of the game levels with correlation relationship is completed, the game levels without correlation relationship can be randomly inserted into the middle frame to obtain the first game frame.

[0104] S4. Obtain user data of the game object, construct a data summary of the user data, and filter out a game scene that matches the data summary from a preset scene pool.

[0105] In an embodiment of the present invention, the user data includes data such as the age, gender, education level, occupation, etc. of the game object; the user data can be uploaded by the game object to a preset data form or page.

[0106] In one of the practical application scenarios of the present invention, since the generated first game framework is merely obtained by combining multiple game levels according to an association relationship, the degree of matching between the generated first game framework and the game object is low. In order to achieve accurate configuration of the game object, the user data of the game object can be analyzed to filter out game scenes matching the game object from a preset scene pool, thereby improving the adaptability of the finally generated game framework and the game object.

[0107] In detail, the scene pool includes a variety of different game scenes, which are created in advance by analyzing and selecting user groups of different ages, genders, occupations, and education levels.

[0108] In an embodiment of the present invention, the construction of a data summary of the user data includes: splitting the user data into multiple data words, and converting each of the data words into a word vector; adding position coding information to the word vector according to the order of the position of each data word in the user data to obtain a coding vector; using a preset summary extraction model to perform convolution and pooling processing on each of the coding vectors for a preset number of times to obtain vector features; calculating the word probability value of the word vector corresponding to each of the coding vectors as a summary word based on the vector features; and aggregating the word vectors whose word probability values ​​are greater than a preset probability threshold as the data summary of the user data.

[0109] In detail, the user data can be split into several data segmentations using preset segmentation tools such as jieba, SnowNLP, PKUse, THULAC, etc.; or, the user data can be split into several data segmentations using the word_tokenize() method in the NLTK library in Python.

[0110] Exemplarily, the following word_tokenize() method may be used to split the user data into several data tokens:

[0111] from nltk.tokenize import word_tokenize

[0112] data="y"

[0113] print(word_tokenize(data))

[0114] Wherein, y is the user data.

[0115] Specifically, each of the data word segments can be encoded into a word vector using an encoder with a word encoding function, such as a transformer encoder, a word2vec encoder, and an Autoencoder encoder.

[0116] 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-mentioned encoding method, and add the encoded position information to the word segmentation vector to obtain an encoding vector, so as to reflect the position information of different word segmentation vectors in the encoding vector, thereby improving the accuracy of subsequent analysis of the user data.

[0117] Furthermore, the summary extraction model includes but is not limited to a Convolutional Neural Network (CNN) model and a Support Vector Machines (SVM) model with summary word recognition function; the keyword analysis model includes but is not limited to a Conditional Random Field (CRF) model and an LSTM (Long short-term memory) model with keyword recognition function.

[0118] In detail, the activation function preset in the summary extraction model can be used to calculate the probability value of the word segmentation vector corresponding to each encoding vector being a summary word according to the vector feature, and the word segmentation vectors whose word probability values ​​are greater than a preset probability threshold are aggregated as the data summary of the user data.

[0119] In the embodiment of the present invention, the step of selecting a game scene matching the data summary from a preset scene pool is consistent with the step of selecting a game level matching the semantic matrix from a predetermined game level pool in S2, and will not be elaborated here.

[0120] S5. Obtain scene data of the game scene, and perform scene configuration on the first game framework according to the scene data to obtain a second game framework.

[0121] In an embodiment of the present invention, the game scene data includes data such as game element style, interface color tone, NPC dressing style, game voice style, etc.

[0122] In detail, the step of obtaining the scene data of the game scene is consistent with the step of obtaining the user's game configuration requirements in S1, and will not be repeated here.

[0123] In one of the actual application scenarios of the present invention, since the first game framework is obtained by configuring multiple game levels according to the game configuration requirements of the user, the first game framework meets the game configuration requirements of the user, but the game content is oriented to the game object. Furthermore, in order to achieve bilateral matching between the user and the game object, the first game framework can be scene configured according to the game scene relative to the game object to achieve bilateral matching between the game object and the user.

[0124] In the embodiment of the present invention, the step of configuring the first game framework according to the scene data to obtain the second game framework includes: parameter encoding the scene data to obtain scene parameters; and assigning parameters to the first game framework using the scene parameters to obtain the second game framework.

[0125] In detail, a preset encoder may 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) and DAE (denoising autoencoder).

[0126] Specifically, the scene parameters obtained after encoding can be assigned to the first game framework to obtain a second game framework with bilateral matching between the user and the game object.

[0127] The embodiment of the present invention analyzes the game configuration requirements of the user to select the game levels that meet the user's requirements from the level pool, and combines the levels into a first game framework according to the association relationship between the levels, thereby achieving adaptation to the configuration party's requirements, and disassembles the game into multiple levels for combination, thereby reducing the overall coupling of the game and improving the flexibility of the game configuration; at the same time, the user data of the game object is analyzed to select the game scene that meets the game object, and the first game framework is assigned according to the game scene to obtain a second game framework with a game scene corresponding to the game object, thereby achieving adaptation to the game object; that is, the bilateral adaptation of the configuration party's requirements and the user data is completed at the same time, thereby improving the matching degree of the game with all parties. Therefore, the game resource configuration method, device, electronic device and computer-readable storage medium based on bilateral adaptation proposed by the present invention can solve the problem that the development of teaching games is relatively complicated and the matching degree between the developed games and the game objects is low.

[0128] like Figure 4 , which is a functional module diagram of a game resource configuration device based on bilateral adaptation provided by an embodiment of the present invention.

[0129] The game resource configuration device 100 based on bilateral adaptation of the present invention can be installed in an electronic device. According to the functions to be implemented, the game resource configuration device 100 based on bilateral adaptation can 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 module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0130] In this embodiment, the functions of each module / unit are as follows:

[0131] The demand analysis module 101 is used to obtain the game configuration requirements of the user, extract the game publishing object from the game configuration requirements, and construct a semantic matrix of the game configuration requirements;

[0132] The level screening module 102 is used to screen the game levels matching the semantic matrix from a predetermined game level pool;

[0133] The first configuration module 103 is used to 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;

[0134] The scene screening module 104 is used to obtain the user data of the game object, construct a data summary of the user data, and screen out game scenes matching the data summary from a preset scene pool;

[0135] The second configuration module 105 is used to obtain scene data of the game scene, and perform scene configuration on the first game framework according to the scene data to obtain a second game framework.

[0136] In detail, each module described in the game resource configuration device 100 based on bilateral adaptation in the embodiment of the present invention is used in the same manner as described above. Figures 1 to 3 The game resource configuration method based on bilateral adaptation has the same technical means as described in the previous section and can produce the same technical effects, so I will not go into details here.

[0137] like Figure 5 , is a schematic diagram of the structure of an electronic device for implementing a game resource configuration method based on bilateral adaptation provided by an embodiment of the present invention.

[0138] 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 a game resource configuration program based on bilateral adaptation.

[0139] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. 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 executes or executes programs or modules stored in the memory 11 (for example, executing a game resource configuration program based on bilateral adaptation, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device and process data.

[0140] 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, disk, optical disk, etc. In some embodiments, the memory 11 can 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 can also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 can also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of 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 is to be output.

[0141] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0142] The communication interface 13 is used for communication between the above-mentioned 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 (such as 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, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0143] The figure only shows an electronic device with components. Those skilled in the art will 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 shown in the figure, or combine certain components, or arrange the components differently.

[0144] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption 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 other arbitrary components. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0145] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0146] The game resource configuration program based on bilateral adaptation stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0147] Obtaining the user's game configuration requirements, extracting the game publishing object from the game configuration requirements, and constructing a semantic matrix of the game configuration requirements;

[0148] Screening a game level that matches the semantic matrix from a predetermined game level pool;

[0149] Acquire game content of each of the game levels, identify associations between each of the game levels according to the game content, and combine the game levels according to the associations to obtain a first game framework;

[0150] Acquire user data of the game object, construct a data summary of the user data, and filter out a game scene that matches the data summary from a preset scene pool;

[0151] The scene data of the game scene is obtained, and the scene configuration of the first game framework is performed according to the scene data to obtain a second game framework.

[0152] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0153] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0154] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0155] Obtaining the user's game configuration requirements, extracting the game publishing object from the game configuration requirements, and constructing a semantic matrix of the game configuration requirements;

[0156] Screening a game level that matches the semantic matrix from a predetermined game level pool;

[0157] Acquire game content of each of the game levels, identify associations between each of the game levels according to the game content, and combine the game levels according to the associations to obtain a first game framework;

[0158] Acquire user data of the game object, construct a data summary of the user data, and filter out a game scene that matches the data summary from a preset scene pool;

[0159] The scene data of the game scene is obtained, and the scene configuration of the first game framework is performed according to the scene data to obtain a second game framework.

[0160] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0161] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0162] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0163] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0164] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0165] The blockchain referred to in this invention is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0166] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the 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.

[0167] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0168] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A game resource configuration method based on bilateral adaptation, characterized in that: The method comprises: Obtaining the user's game configuration requirements, extracting the game publishing object from the game configuration requirements, and constructing a semantic matrix of the game configuration requirements; Screening a game level that matches the semantic matrix from a predetermined game level pool; Constructing a decision tree model for identifying the association between each of the game levels, obtaining the game content of each of the game levels, inputting the game content into the decision tree model for identification, obtaining the association between each of the game levels, and combining the game levels according to the association to obtain a first game framework, including: screening out game levels that have an association relationship with each other, combining the screened game levels according to the order of the association relationship to obtain an intermediate framework, and randomly inserting game levels that have no association relationship with each other into the intermediate framework to obtain the first game framework; Acquire user data of the game publishing object, construct a data summary of the user data, and filter out game scenes matching the data summary from a preset scene pool; The scene data of the game scene is obtained, and the scene configuration of the first game framework is performed according to the scene data to obtain a second game framework.

2. The game resource configuration method based on bilateral adaptation according to claim 1, characterized in that: The constructing of the semantic matrix of the game configuration requirements includes: Performing word segmentation processing on the game configuration requirements to obtain requirement word segmentation; Counting the segmentation frequency of each segmentation in the demand segmentation, selecting the demand segmentation whose 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 concatenated into a vector matrix, and the vector matrix is ​​determined to be a semantic matrix of the game configuration requirement.

3. The game resource configuration method based on bilateral adaptation as claimed in claim 1, characterized in that: The step of selecting a game level that matches the semantic matrix from a predetermined game level pool includes: Obtaining a level tag for each game level in the game level pool; Calculating the distance value between the semantic matrix and each of the level labels one by one; The game level corresponding to the level tag whose distance value is less than the preset distance threshold is selected, and the selected game level is determined to be a game level matching the semantic matrix.

4. The game resource configuration method based on bilateral adaptation as claimed in claim 1, characterized in that: The construction of a decision tree model for identifying the association relationship between each of the game levels includes: Selecting one of the game levels as the target level one by one from the game levels; Filtering the game conditions of the target level from the game content of the target level by using a regular expression; Constructing a condition matrix corresponding to the game conditions, and using the condition matrix to assign values ​​to a preset decision function to obtain a decision tree for the target level; Gather the decision trees corresponding to all game levels to obtain a decision tree model.

5. The game resource configuration method based on bilateral adaptation as claimed in claim 1, characterized in that: The step of constructing a data summary of the user data comprises: Splitting the user data into multiple data tokens, and converting each of the data tokens into a token vector; Add position coding information to the word segmentation vector according to the order of the position of each data word in the user data to obtain a coding vector; Using a preset summary extraction model, each of the encoding vectors is subjected to a preset number of convolution and pooling processes to obtain vector features; Calculate the word probability value of the word segmentation vector corresponding to each of the encoding vectors as a summary word according to the vector features; The word segmentation vectors whose word probability values ​​are greater than a preset probability threshold are aggregated as a data summary of the user data.

6. The game resource configuration method based on bilateral adaptation according to any one of claims 1 to 5, characterized in that: The step of configuring the first game framework according to the scene data to obtain a second game framework includes: Performing parameter encoding on the scene data to obtain scene parameters; The first game frame is assigned parameters using the scene parameters to obtain a second game frame.

7. A game resource configuration device based on bilateral adaptation, characterized in that: The device comprises: A demand analysis module is used to obtain the game configuration requirements of the user, extract the game publishing object from the game configuration requirements, and construct a semantic matrix of the game configuration requirements; A level screening module, used to screen game levels matching the semantic matrix from a predetermined game level pool; A first configuration module is used to construct a decision tree model for identifying the association relationship between each of the game levels, obtain the game content of each of the game levels, input the game content into the decision tree model for identification, obtain the association relationship between each of the game levels, and combine the game levels according to the association relationship to obtain a first game framework, including: screening out game levels that have an association relationship with each other, combining the screened game levels according to the order of the association relationship to obtain an intermediate framework, and randomly inserting game levels that have no association relationship with each other into the intermediate framework to obtain the first game framework; A scene screening module, used to obtain user data of the game publishing object, construct a data summary of the user data, and screen out game scenes matching the data summary from a preset scene pool; The second configuration module is used to obtain scene data of the game scene, and perform scene configuration on the first game framework according to the scene data to obtain a second game framework.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the game resource configuration method based on bilateral adaptation as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the game resource configuration method based on bilateral adaptation as described in any one of claims 1 to 6 is implemented.

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