Intelligent chessboard chess moving method and device based on language large model, equipment and medium

Abstract

The invention relates to the technical field of artificial intelligence and voice processing, and provides an intelligent chessboard chess moving method and device based on a language large model, equipment and a medium, and the method comprises the steps: receiving chess moving voice information inputted by a user; identifying the chess moving voice information through a language large model to determine a target chess moving instruction corresponding to the chess moving voice information; and executing a corresponding chess moving operation according to the target chess moving instruction. According to the technical scheme, the real chess pieces can be controlled through voice recognition, accurate control over the chessboard is achieved, user operation is facilitated, and the use experience of a user is improved.

Description

[Technical field]

[0001] The present application relates to the field of artificial intelligence and speech processing technology, and in particular to an intelligent chessboard chess-playing method, device, equipment and medium based on a large language model. [Background technology]

[0002] In today's society, chess has always been a popular mental activity. Especially for the elderly who cannot participate in sports activities, chess activities can not only kill time, but also prevent Alzheimer's disease. Although mobile phones can also play chess games, mobile phones lack physical experience, and some elderly or disabled people are unable to operate the chessboard due to mobility problems. Therefore, if voice recognition can be used to control real chess pieces, the above problems can be solved. However, it is difficult for users to use accurate natural language to achieve precise control of the chessboard. [Summary of the invention]

[0003] The embodiments of the present application provide a method, device, equipment and medium for playing chess on an intelligent chessboard based on a large language model, aiming to solve technical problems existing in related technologies such as the inability to accurately control the chessboard through voice.

[0004] In a first aspect, an embodiment of the present application provides an intelligent chessboard chess playing method based on a language large model, which is used in an intelligent chessboard system, including:

[0005] Receive the chess move voice information input by the user;

[0006] Recognize the chess-moving voice information through a large language model to determine a target chess-moving instruction corresponding to the chess-moving voice information;

[0007] According to the target move instruction, the corresponding move operation is executed.

[0008] In one embodiment, optionally, the chess move voice information is recognized by a large language model to determine a target chess move instruction corresponding to the chess move voice information, including:

[0009] Performing voice recognition on the chess-moving voice information to obtain corresponding chess-moving text information;

[0010] Analyzing the chess move text information through the language big model to determine whether the target chess piece corresponding to the chess move text information and the target position corresponding to the target chess piece can be determined;

[0011] In response to determining the target chess piece and the target position corresponding to the target chess piece, generating a corresponding target move instruction according to the target chess piece and the target position corresponding to the target chess piece;

[0012] In response to the inability to determine the target chess piece and / or the target position corresponding to the target chess piece, generating a corresponding target query instruction;

[0013] Output the target inquiry voice information corresponding to the target inquiry instruction.

[0014] In one embodiment, optionally, the method further comprises:

[0015] Receiving a response voice message corresponding to the target inquiry voice message input by a user;

[0016] Performing voice recognition on the response voice information to obtain corresponding response text information;

[0017] Analyzing the response text information by using the language big model to determine the target chess piece corresponding to the response text information and the target position corresponding to the target chess piece;

[0018] According to the target chess piece and the target position corresponding to the target chess piece, a corresponding target move instruction is generated.

[0019] In one embodiment, optionally, analyzing the chess move text information by the language big model to determine whether a target chess piece corresponding to the chess move text information and a target position corresponding to the target chess piece can be determined includes:

[0020] Analyzing the chess move text information through the language big model to determine whether the target chess piece corresponding to the chess move text information can be determined;

[0021] In response to being able to determine the target chess piece corresponding to the chess move text information, obtaining a preset chessboard rule corresponding to the smart chessboard;

[0022] According to the preset chessboard rules, determining the movable range corresponding to the target chess piece;

[0023] Analyze the chess move text information to determine whether the pre-moved position corresponding to the target chess piece can be determined;

[0024] In response to determining the pre-moved position corresponding to the target chess piece, determining whether the pre-moved position is within the movable range;

[0025] In response to determining that the pre-moved-in position is within the movable range, the pre-moved-in position is determined as the target position.

[0026] In one embodiment, optionally, the method further comprises:

[0027] In response to determining that the pre-moved position is not within the movable range, it is determined that the target position corresponding to the target chess piece cannot be determined.

[0028] In one embodiment, optionally, executing a corresponding chess move operation according to the target chess move instruction includes:

[0029] Using a preset instruction example library as a prompt word of the language macro model, the target chess move instruction is decomposed into at least one target chess piece move instruction according to the execution order through the language macro model;

[0030] Execute each target chess move instruction in sequence to complete the chess move operation corresponding to the target chess move instruction.

[0031] In one embodiment, optionally, the method further comprises:

[0032] Receive at least one preset chess move instruction and decomposed sub-instructions corresponding to the chess move instruction, wherein the chess move instruction includes a chess move operation and a chess move position;

[0033] The at least one chess move instruction and the decomposed sub-instructions corresponding to the chess move instruction are stored in a corresponding preset instruction example library.

[0034] In a second aspect, the embodiment of the present application provides an intelligent chessboard chess-playing device based on a language large model, comprising:

[0035] An information receiving module is used to receive the chess move voice information input by the user;

[0036] A recognition module, used to recognize the chess-moving voice information through a large language model to determine a target chess-moving instruction corresponding to the chess-moving voice information;

[0037] An execution module is used to execute corresponding chess-moving operations according to the target chess-moving instructions.

[0038] According to a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned intelligent chessboard moving method based on a large language model when executing the computer program.

[0039] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned intelligent chessboard moving method based on the language large model are implemented.

[0040] In the scheme implemented by the above-mentioned intelligent chessboard chess-playing method, device, equipment and medium based on the language big model, the chess-playing voice information input by the user can be received; the chess-playing voice information is recognized by the language big model to determine the target chess-playing instruction corresponding to the chess-playing voice information; and the corresponding chess-playing operation is executed according to the target chess-playing instruction. Through the above-mentioned technical scheme of the present invention, it is possible to control the real chess pieces through voice recognition and realize precise control of the chessboard, thereby facilitating user operation and improving the user experience.

Brief Description of the Drawings

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A schematic diagram of an intelligent chessboard system according to an embodiment of the present application is shown.

[0043] Figure 2 A schematic flow chart of an intelligent chessboard move method based on a large language model according to an embodiment of the present application is shown.

[0044] Figure 3 A schematic flow chart of step S202 in an intelligent chessboard move method based on a large language model according to an embodiment of the present application is shown.

[0045] Figure 4 A schematic flow chart of an intelligent chessboard move method based on a large language model according to another embodiment of the present application is shown.

[0046] Figure 5 A schematic flow chart of step S302 in an intelligent chessboard move method based on a large language model according to an embodiment of the present application is shown.

[0047] Figure 6 A block diagram of an intelligent chessboard chess-playing device based on a large language model according to an embodiment of the present application is shown.

[0048] Figure 7 A block diagram of a computer device according to an embodiment of the present application is shown. [Specific implementation method]

[0049] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0050] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0051] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0052] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0053] It should be noted that the embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence 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.

[0054] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0055] The present disclosure provides a possible application scenario, which includes an intelligent chessboard system. The intelligent chessboard system includes but is not limited to chessboard systems such as go, chess, and international chess. Figure 1 As shown, taking chess as an example, the intelligent chessboard system includes a chessboard, chess pieces, a processing mechanism and an actuator. Among them, the chessboard is covered by an electronic press-type contact film. When the chess pieces are placed on the chessboard, the electronic press-type contact film can sense the placement of all chess pieces. The chess pieces are chess pieces with NFC, where the NFC marks the relevant information of the chess pieces, such as the name and color of the chess pieces. The chess pieces can also be printed on both sides, so there is no need to distinguish the front and back of the chess pieces.

[0056] The processing mechanism is arranged under the chessboard and may include a processor, which is used to: receive chess-moving voice information input by a user; and recognize the chess-moving voice information through a large language model to determine a target chess-moving instruction corresponding to the chess-moving voice information.

[0057] The actuator is arranged under the chessboard, including: a motor, a horizontal scroll axis 1, a vertical scroll axis 2, and a plurality of magnetic blocks 3 respectively arranged on the horizontal scroll axis and the vertical scroll axis. The magnetic block has an NFC card reading function and can read the NFC information of the chess piece through the chessboard. The horizontal axis and the vertical axis can be pulled by the motor so that the magnetic block can reach any position on the back of the chessboard. When the magnetic block is powered on, the chess piece can be attracted through the chessboard and moved to any position on the chessboard.

[0058] like Figure 1 As shown, the horizontal scroll axis can be offset by a motor. The vertical scroll axis can be offset by a motor. The chessboard surface can be traversed by the horizontal scroll axis and the vertical scroll axis.

[0059] Taking chess as an example, when the smart chessboard is initialized, the chess pieces will be placed. The chess pieces are laid out flat on the chessboard. At this time, the electronic press-type contact film on the surface of the chessboard can scan the coordinates of all the chess pieces on the chessboard. The contact film notifies the motor of the x-axis and y-axis coordinates of all the chess pieces. The motor drives the magnetic blocks to read the information of all the chess pieces in turn, and then formulates a placement order plan. The magnetic blocks are conductive, and the motor absorbs the chess pieces through the magnetic blocks and moves all the chess pieces to the correct placement position. When the chess pieces are placed, the game begins, and the chess moves can begin. In the chess moves, two users can play against each other, or one user can choose to play against the smart chessboard. In the chess game, the following technical solutions are used.

[0060] See also Figure 2 , Figure 2 A schematic flow chart of an intelligent chessboard move method based on a large language model according to an embodiment of the present application is shown.

[0061] like Figure 2 As shown, the process of the intelligent chessboard chess-playing method based on the language large model according to one embodiment of the present application includes:

[0062] Step S201, receiving chess move voice information input by a user;

[0063] Step S202, recognizing the chess-moving voice information through a large language model to determine a target chess-moving instruction corresponding to the chess-moving voice information;

[0064] like Figure 3 As shown, in one embodiment, optionally, step S202 includes:

[0065] Step S301, performing voice recognition on the chess move voice information to obtain corresponding chess move text information;

[0066] Speech recognition technology is also known as Automatic Speech Recognition (ASR). Its goal is to convert human speech vocabulary content into computer-readable input, such as keystrokes, binary codes, and strings. It is different from speaker recognition and speaker confirmation, which attempt to identify or confirm the speaker of the speech rather than the vocabulary content contained therein.

[0067] Step S302, analyzing the chess move text information through the language big model to determine whether the target chess piece corresponding to the chess move text information and the target position corresponding to the target chess piece can be determined;

[0068] The target chess piece is the chess piece that the user wants to move through voice, and the target position is the position coordinates to which the user wants to move the chess piece.

[0069] Step S303, in response to determining the target chess piece and the target position corresponding to the target chess piece, generating a corresponding target move instruction according to the target chess piece and the target position corresponding to the target chess piece;

[0070] The target move instruction includes a target chess piece, a target move operation and a corresponding move position.

[0071] Step S304, in response to the inability to determine the target chess piece and / or the target position corresponding to the target chess piece, generating a corresponding target query instruction;

[0072] In this step, if the target chess piece or the position corresponding to the target chess piece cannot be determined, the move cannot be completed. At this time, a corresponding target query instruction can be generated to further inquire the user about the chess piece to be moved or the position of the chess piece to be moved.

[0073] Step S305: outputting the target inquiry voice information corresponding to the target inquiry instruction.

[0074] Take chess as an example. For example, the user's initial voice command is: "The horse on the right moves forward to capture the opponent's soldier across the river." At this time, if the target chess piece "horse" and the target position "capture the opponent's soldier across the river" can be uniquely determined, the corresponding target move instruction can be generated, and the corresponding move operation can be performed, so that the horse on the right moves forward to capture the opponent's soldier across the river. However, if there are two target chess pieces "horses" on the right, at this time, the target chess pieces cannot be determined, and the corresponding target query instruction can be generated based on the current chessboard information. For example, "Is the horse to be moved the horse at position (2,1) or the horse at position (2,4)?"

[0075] Step S203, executing a corresponding chess move operation according to the target chess move instruction.

[0076] like Figure 4 As shown, in one embodiment, optionally, the method further includes:

[0077] Step S401, receiving a response voice message corresponding to the target inquiry voice message input by the user;

[0078] Step S402, performing voice recognition on the response voice information to obtain corresponding response text information;

[0079] Step S403, analyzing the response text information by using the language macro model to determine the target chess piece corresponding to the response text information and the target position corresponding to the target chess piece;

[0080] Step S404, generating a corresponding target move instruction according to the target chess piece and the target position corresponding to the target chess piece.

[0081] In this embodiment, for the above example, a corresponding target query instruction is generated according to the current chessboard information. For example, "Is the horse to be moved the horse at position (2,1) or the horse at position (2,4)?", and the user answers "the horse at position (2,1)" according to the query instruction. Then, according to the response information, the target chess piece and the corresponding target position can be determined, and the target move instruction corresponding to "the horse at position (2,1) eats the opponent's cross-river soldier" is generated, thereby completing the move operation.

[0082] like Figure 5 As shown, in one embodiment, optionally, step S302 includes:

[0083] Step S501, analyzing the chess move text information through the language big model to determine whether the target chess piece corresponding to the chess move text information can be determined;

[0084] Step S502, in response to determining the target chess piece corresponding to the chess move text information, obtaining a preset chessboard rule corresponding to the smart chessboard;

[0085] In this embodiment, corresponding preset chessboard rules can be set for different types of chessboards, for example, for chess, corresponding rules can be set, and for go, corresponding rules can be set. In this way, it can be determined whether the position where the user wants to move the chess piece complies with the chess move rules according to the rules.

[0086] Step S503, determining the movable range corresponding to the target chess piece according to the preset chessboard rule;

[0087] In this embodiment, the movable range corresponding to the target chess piece is determined according to the preset chessboard rules and the current chessboard state, and all the current positions where the target chess piece can move. The current chessboard state includes the chessboard states of the user and the opponent, such as the chess pieces on the chessboard and their corresponding positions.

[0088] Step S504, analyzing the chess move text information to determine whether the pre-moved position corresponding to the target chess piece can be determined;

[0089] The pre-moved position is the position where the user wants the chess piece to be moved.

[0090] Step S505, in response to determining the pre-moved position corresponding to the target chess piece, determining whether the pre-moved position is within the movable range;

[0091] Step S506 : in response to determining that the pre-moved-in position is within the movable range, determining the pre-moved-in position as the target position.

[0092] In this embodiment, if a certain position on the chessboard does not meet the rules of moving chess, for example, the position cannot be moved, or it is the opponent's position, or there is an existing chess piece, etc., then it does not belong to the movable range corresponding to the target chess piece, that is, it is not within the movable range corresponding to the target chess piece. The target chess piece can only be moved within its movable range, and other positions cannot be moved.

[0093] In one embodiment, optionally, the method further comprises:

[0094] In response to determining that the pre-moved position is not within the movable range, it is determined that the target position corresponding to the target chess piece cannot be determined.

[0095] In this embodiment, if the pre-moved position is not within the movable range, it means that the target chess piece cannot execute the voice information currently issued by the user, that is, the target position corresponding to the target chess piece is incorrect or inoperable. At this time, it can be determined that the target position corresponding to the target chess piece cannot be determined, so as to output the target inquiry voice information to the user, thereby realizing communication and dialogue with the user, further determining the user's intention, and improving the intelligence and convenience of the chessboard.

[0096] In one embodiment, optionally, step S203 includes:

[0097] Using a preset instruction example library as a prompt word of the language macro model, the target chess move instruction is decomposed into at least one target chess piece move instruction according to the execution order through the language macro model;

[0098] In this embodiment, the language model can use the instruction examples in the preset instruction example library as prompt words to decompose the target move instruction into sub-instructions. For example, a complete target move instruction can be decomposed into multiple sub-instructions:

[0099] 1){operation: take, position: xx};

[0100] 2){operation: move and drop, position: xx};

[0101] 3){operation: take, position: xx};

[0102] 4){operation: move and drop, position: xx};

[0103] Execute each target chess move instruction in sequence to complete the chess move operation corresponding to the target chess move instruction.

[0104] In this way, decomposing the target move instructions not only facilitates execution, but also increases the flexibility of the execution steps, and can be applied to a variety of chessboard scenarios.

[0105] In one embodiment, optionally, the method further comprises:

[0106] Receive at least one preset chess move instruction and decomposed sub-instructions corresponding to the chess move instruction, wherein the chess move instruction includes a chess move operation and a chess move position;

[0107] The at least one chess move instruction and the decomposed sub-instructions corresponding to the chess move instruction are stored in a corresponding preset instruction example library.

[0108] In this embodiment, at least one move instruction and the decomposed sub-instructions corresponding to the move instruction can be preset, so as to facilitate the large model to decompose and execute the move instruction according to the preset instruction instance.

[0109] When generating executable sub-instructions with the help of the big model, we use the following sentence structure to enable the big model to refer to the given example.

[0110] “

[0111] Please refer to the following example to generate executable sub-instructions based on the given chess instructions.

[0112] {Example 1}

[0113] {Example 2}

[0114] ”

[0115] The following takes the intelligent chessboard system as a chessboard system and the chessboard mode as a human-computer game model as an example to explain the above technical features of the present invention in detail. The chessboard mode can also be a two-player game mode.

[0116] 1) Placing chess pieces: Lay the chess pieces flat on the chessboard. The electronic press-type contact film on the surface of the chessboard can scan the coordinates of all the chess pieces on the chessboard. The contact film notifies the motor of the x-axis and y-axis coordinates of all the chess pieces. The motor drives the magnetic block to read the information of all the chess pieces in turn, and then formulates a placement order. The magnetic block conducts electricity, and the motor absorbs the chess pieces through the magnetic block and moves all the chess pieces to the correct placement position.

[0117] 2) Player moves: When the chess pieces are placed, the game begins and the move phase begins. Players can control the chess pieces through voice commands (for example: horse 7 to 2, cannon 8 to 6). When the micro-host detects the player's voice command, it determines whether the player's moving position complies with the rules of the game and whether there are enemy pieces at the position reached. If so, the enemy pieces are moved out of the chessboard range through the magnetic block and then the player's own pieces are moved to the corresponding position. Then it determines whether the game is over (the general or the general is captured);

[0118] 3) Enemy moves: When the player has finished making a move and the game is not over, it is the enemy's turn to make a move. At this time, the smart chessboard receives the player's move instruction, recalculates the next step, and sends the instruction to the motor to move the player's chess piece to the corresponding position through the magnetic block. It determines whether there is an enemy chess piece. If there is, the enemy chess piece is moved out of the chessboard range through the magnetic block before moving the player's chess piece to the corresponding position. According to the rules of the game, it reminds the player whether he has been checked and determines whether the game is over (the general or the general will be captured).

[0119] 4) End: When the king or general is captured, the game ends and the chess pieces are placed again.

[0120] Through the above technical solution of the present invention, a more intelligent chess game experience can be provided for users. For example, it can automatically identify the position and state of chess pieces and respond accordingly according to the user's operation. In addition, it can also provide some intelligent functions, such as automatically saving game records, automatically analyzing the trend of chess games, and providing chess game recommendations, etc. These functions can help users better understand and master the chess game and improve their chess game level.

[0121] In addition, the technical solution of the present invention is highly scalable and can be customized and upgraded according to user needs. For example, it can support a variety of different board games, including go, chess, and international chess, and users can choose different boards to play according to their preferences. In addition, it can also support multi-player and online game functions, allowing users to communicate and learn from more players and improve their game level.

[0122] In short, the intelligent chessboard game method has many advantages, and can provide users with a more friendly, beautiful and realistic game experience. It also has strong scalability and intelligent functions, which can meet the different needs of users.

[0123] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0124] Figure 6 A block diagram of an intelligent chessboard chess-playing device based on a large language model according to an embodiment of the present application is shown.

[0125] like Figure 6 As shown, in the second aspect, the embodiment of the present application provides an intelligent chessboard chess playing device 60 based on a language large model, comprising:

[0126] The information receiving module 61 is used to receive the chess move voice information input by the user;

[0127] A recognition module 62 is used to recognize the chess-moving voice information through a large language model to determine a target chess-moving instruction corresponding to the chess-moving voice information;

[0128] The execution module 63 is used to execute the corresponding chess move operation according to the target chess move instruction.

[0129] In one embodiment, optionally, the identification module includes:

[0130] A speech recognition unit, used to perform speech recognition on the chess move speech information to obtain corresponding chess move text information;

[0131] An analysis unit, used to analyze the chess move text information through the language large model to determine whether a target chess piece corresponding to the chess move text information and a target position corresponding to the target chess piece can be determined;

[0132] A first generating unit, configured to generate a corresponding target move instruction according to the target chess piece and the target position corresponding to the target chess piece in response to determining the target chess piece and the target position corresponding to the target chess piece;

[0133] A second generating unit, configured to generate a corresponding target query instruction in response to failure to determine the target chess piece and / or the target position corresponding to the target chess piece;

[0134] The output unit is used to output the target inquiry voice information corresponding to the target inquiry instruction.

[0135] In one embodiment, optionally, the device further comprises:

[0136] A response receiving module, used for receiving a response voice message corresponding to the target inquiry voice message input by a user;

[0137] A response recognition module, used for performing voice recognition on the response voice information to obtain corresponding response text information;

[0138] A response analysis module, used to analyze the response text information through the language large model to determine the target chess piece corresponding to the response text information and the target position corresponding to the target chess piece;

[0139] The instruction generation module is used to generate a corresponding target move instruction according to the target chess piece and the target position corresponding to the target chess piece.

[0140] In one embodiment, optionally, the analysis unit is used to:

[0141] Analyzing the chess move text information through the language big model to determine whether the target chess piece corresponding to the chess move text information can be determined;

[0142] In response to being able to determine the target chess piece corresponding to the chess move text information, obtaining a preset chessboard rule corresponding to the smart chessboard;

[0143] According to the preset chessboard rules, determining the movable range corresponding to the target chess piece;

[0144] Analyze the chess move text information to determine whether the pre-moved position corresponding to the target chess piece can be determined;

[0145] In response to determining the pre-moved position corresponding to the target chess piece, determining whether the pre-moved position is within the movable range;

[0146] In response to determining that the pre-moved-in position is within the movable range, the pre-moved-in position is determined as the target position.

[0147] In one embodiment, optionally, the device further comprises:

[0148] A determination module is used for determining that the target position corresponding to the target chess piece cannot be determined in response to determining that the pre-moved position is not within the movable range.

[0149] In one embodiment, optionally, the execution module includes:

[0150] An instruction decomposition unit, used for using a preset instruction example library as a prompt word of the language macro model, and decomposing the target chess move instruction into at least one target chess piece move instruction according to an execution order through the language macro model;

[0151] The instruction execution unit is used to execute each target chess move instruction in sequence to complete the chess move operation corresponding to the target chess move instruction.

[0152] In one embodiment, optionally, the device further comprises:

[0153] An instruction receiving module, used for receiving at least one preset chess move instruction and decomposed sub-instructions corresponding to the chess move instruction, wherein the chess move instruction includes a chess move operation and a chess move position;

[0154] The storage module is used to store the at least one chess move instruction and the decomposed sub-instructions corresponding to the chess move instruction in a corresponding preset instruction example library.

[0155] In the scheme implemented by the above-mentioned intelligent chessboard chess-playing method, device, equipment and medium based on the language big model, the chess-playing voice information input by the user can be received; the chess-playing voice information is recognized by the language big model to determine the target chess-playing instruction corresponding to the chess-playing voice information; and the corresponding chess-playing operation is executed according to the target chess-playing instruction. Through the above-mentioned technical scheme of the present invention, it is possible to control the real chess pieces through voice recognition and realize precise control of the chessboard, thereby facilitating user operation and improving the user experience.

[0156] According to a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned intelligent chessboard moving method based on a large language model when executing the computer program.

[0157] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned intelligent chessboard moving method based on the language large model are implemented.

[0158] It should be noted that technical personnel in the relevant field can clearly understand that, for the convenience and conciseness of description, the specific working processes of the intelligent chessboard moving device based on the language big model and each module described above can refer to the corresponding processes in the aforementioned embodiment of the intelligent chessboard moving method based on the language big model, and will not be repeated here.

[0159] It should be noted that those skilled in the art can clearly understand that, for the sake of convenience and brevity of description, the specific working processes of the model training device and each module described above can refer to the corresponding processes in the aforementioned embodiment of the intelligent chessboard chess moving method based on the large language model, and will not be repeated here.

[0160] The above-mentioned intelligent chessboard chess-playing device based on the language large model can be implemented in the form of a computer program, and the computer program can be used in Figure 7 Runs on the computer device shown.

[0161] Figure 7 A block diagram of a computer device according to an embodiment of the present application is shown.

[0162] See also Figure 7 The computer device includes a processor, a memory and a network interface connected through a system bus, wherein the memory may include a storage medium and an internal memory.

[0163] The storage medium can store an operating system and a computer program. The computer program includes program instructions, which, when executed, can enable the processor to execute any of the multi-source data-based language large model intelligent chessboard play methods provided in the embodiments of the present application.

[0164] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0165] The internal memory provides an environment for the operation of the computer program in the storage medium. When the computer program is executed by the processor, the processor can execute any infectious disease transmission path analysis method or prediction neural network training method. The storage medium can be non-volatile or volatile.

[0166] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0167] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0168] In addition, an embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the method steps in the embodiment of the first aspect.

[0169] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or electronic device can refer to the relevant description in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0170] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0171] It should be understood that, although the terms first, second, etc. may be used to describe the setting unit in the embodiments of the present application, these setting units should not be limited to these terms. These terms are only used to distinguish the setting units from each other. For example, without departing from the scope of the embodiments of the present application, the first setting unit may also be referred to as the second setting unit, and similarly, the second setting unit may also be referred to as the first setting unit.

[0172] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0173] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0174] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or 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 units.

[0175] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0176] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An intelligent chessboard chess playing method based on a large language model, characterized in that: For an intelligent chessboard system, the method comprises: Receive the chess move voice information input by the user; Recognize the chess-moving voice information through a large language model to determine a target chess-moving instruction corresponding to the chess-moving voice information; According to the target move instruction, the corresponding move operation is executed.

2. The method according to claim 1, characterized in that The chess-moving voice information is recognized by a large language model to determine a target chess-moving instruction corresponding to the chess-moving voice information, including: Performing voice recognition on the chess-moving voice information to obtain corresponding chess-moving text information; Analyzing the chess move text information through the language big model to determine whether the target chess piece corresponding to the chess move text information and the target position corresponding to the target chess piece can be determined; In response to determining the target chess piece and the target position corresponding to the target chess piece, generating a corresponding target move instruction according to the target chess piece and the target position corresponding to the target chess piece; In response to the inability to determine the target chess piece and / or the target position corresponding to the target chess piece, generating a corresponding target query instruction; Output the target inquiry voice information corresponding to the target inquiry instruction.

3. The method according to claim 2, characterized in that The method further comprises: Receiving a response voice message corresponding to the target inquiry voice message input by a user; Performing voice recognition on the response voice information to obtain corresponding response text information; Analyzing the response text information by using the language big model to determine the target chess piece corresponding to the response text information and the target position corresponding to the target chess piece; According to the target chess piece and the target position corresponding to the target chess piece, a corresponding target move instruction is generated.

4. The method according to claim 2, characterized in that: The step of analyzing the chess move text information by using the language big model to determine whether a target chess piece corresponding to the chess move text information and a target position corresponding to the target chess piece can be determined includes: Analyzing the chess move text information through the language big model to determine whether the target chess piece corresponding to the chess move text information can be determined; In response to being able to determine the target chess piece corresponding to the chess move text information, obtaining the preset chess board rules and the current chess board state corresponding to the smart chess board; Determine the movable range corresponding to the target chess piece according to the preset chessboard rules and the current chessboard state; Analyze the chess move text information to determine whether the pre-moved position corresponding to the target chess piece can be determined; In response to determining the pre-moved position corresponding to the target chess piece, determining whether the pre-moved position is within the movable range; In response to determining that the pre-moved-in position is within the movable range, the pre-moved-in position is determined as the target position.

5. The method according to claim 4, characterized in that The method further comprises: In response to determining that the pre-moved position is not within the movable range, it is determined that the target position corresponding to the target chess piece cannot be determined.

6. The method according to claim 1, characterized in that The step of executing a corresponding chess move operation according to the target chess move instruction includes: Using a preset instruction example library as a prompt word of the language macro model, the target chess move instruction is decomposed into at least one target chess piece move instruction according to the execution order through the language macro model; Execute each target chess move instruction in sequence to complete the chess move operation corresponding to the target chess move instruction.

7. The method according to claim 6, characterized in that The method further comprises: Receive at least one preset chess move instruction and decomposed sub-instructions corresponding to the chess move instruction, wherein the chess move instruction includes a chess move operation and a chess move position; The at least one chess move instruction and the decomposed sub-instructions corresponding to the chess move instruction are stored in a corresponding preset instruction example library.

8. An intelligent chessboard chess-playing device based on a large language model, characterized in that: include: An information receiving module is used to receive the chess move voice information input by the user; A recognition module, used to recognize the chess-moving voice information through a large language model to determine a target chess-moving instruction corresponding to the chess-moving voice information; An execution module is used to execute corresponding chess-moving operations according to the target chess-moving instructions.

9. A computer device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, wherein the instructions are configured to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Computer executable instructions are stored, and the computer executable instructions are used to execute the method according to any one of claims 1 to 7.

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