WO2020199963A1 - Procédé pour robot de commande d'aliment pour identifier l'intention de commande d'aliment d'un utilisateur, et robot - Google Patents

Procédé pour robot de commande d'aliment pour identifier l'intention de commande d'aliment d'un utilisateur, et robot Download PDF

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Publication number
WO2020199963A1
WO2020199963A1 PCT/CN2020/080725 CN2020080725W WO2020199963A1 WO 2020199963 A1 WO2020199963 A1 WO 2020199963A1 CN 2020080725 W CN2020080725 W CN 2020080725W WO 2020199963 A1 WO2020199963 A1 WO 2020199963A1
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WO
WIPO (PCT)
Prior art keywords
ordering
intention
user
robot
processor
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PCT/CN2020/080725
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English (en)
Chinese (zh)
Inventor
彭瑞刚
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时时同云科技(成都)有限责任公司
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Publication of WO2020199963A1 publication Critical patent/WO2020199963A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/18—Speech classification or search using natural language modelling
    • G10L15/1822—Parsing for meaning understanding
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/26—Speech to text systems
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G10L2015/221—Announcement of recognition results
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G10L2015/225—Feedback of the input speech

Definitions

  • the present disclosure relates to the technical field of robot intention recognition, and in particular, to a method and a robot for an ordering robot to recognize a user's ordering intention.
  • the application publication number is CN 109146717 A and the publication date of the application publication date is 2019.01.04 discloses a method for self-service ordering and delivery of food in an unmanned restaurant; the method includes: the user obtains it through his own personal mobile terminal The identification information provided by the dining table; the user opens the login interface of the ordering system through his own personal mobile terminal and logs into the ordering system; the ordering system authenticates the received identification information, and the ordering system provides the ordering interface to the user The user orders food and forms an order in the ordering system; the ordering system automatically shares the information with the restaurant robot, which receives the order and identification information, and picks up the food in the back kitchen according to the order of the order; The robot locates the table position according to the position information of the table in the identification information, and delivers the meal ordered by the user to the designated meal. ; This disclosure realizes the formation of intelligent automatic management between the user order and the meal, reduces the number of restaurant waiters, and helps reduce restaurant operating costs.
  • this unmanned restaurant self-service ordering and delivery method does not have a model training function, which makes it difficult to configure voice conversion, and the machine often fails to recognize the ordering intention.
  • the present disclosure provides a method and a robot for an ordering robot to recognize a user’s ordering intention, which can effectively overcome the existing technology that does not have a model training function, resulting in voice conversion. Configuration is difficult, and the machine often fails to recognize the defects of ordering intentions.
  • the present disclosure provides a method for a food ordering robot to recognize a user's ordering intention, which includes the following steps:
  • the ordering robot recognizes the user's ordering intention expressed by voice through the intention recognition model.
  • the present disclosure also provides an ordering robot using the above method for recognizing a user’s ordering intention.
  • the ordering robot is provided with a controller, a wireless communication module, a voice recognition module, a speaker and a display screen; wherein, wireless communication
  • the module, voice recognition module, speaker and display screen are all electrically connected to the controller.
  • the present disclosure also provides an electronic device, which includes:
  • At least one processor and,
  • a memory communicatively connected with the at least one processor; wherein,
  • the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute any of the foregoing first aspect or any implementation of the first aspect A method for the ordering robot to recognize the user's ordering intention.
  • the present disclosure also provides a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium stores at least one executable instruction, and the executable instruction is used to make a processor execute the aforementioned first aspect or the first A method for the ordering robot in any implementation manner of the aspect to recognize the user's ordering intention.
  • the present disclosure also provides a computer program product.
  • the computer program product includes a calculation program stored on a non-transitory computer-readable storage medium.
  • the computer program includes program instructions that, when executed by a processor, cause the The processor executes the method for the ordering robot in the foregoing first aspect or any implementation of the first aspect to recognize the user's ordering intention.
  • the present disclosure provides a method and robot for an ordering robot to recognize the user's ordering intention, and the beneficial effect produced is: creatively applying the method of natural language text classification to the ordering robot
  • the field of intention recognition in the ordering stage greatly improves the robot's intelligence. It does not need to be divided into verbs and nouns manually. It has a model training function. After the model is trained, the corresponding intention can be accurately identified for different sentences. Greatly increase the flexibility and simplicity of configuration and optimize the user experience.
  • Figure 1 is a schematic flow diagram of the disclosed method
  • FIG. 2 is a schematic diagram of the system structure of the ordering robot of the present disclosure
  • FIG. 3 is a schematic structural diagram of an electronic device 30 provided by an embodiment of the disclosure.
  • a method for an ordering robot to recognize a user's ordering intention includes the following steps:
  • Step 1 Intent preparation.
  • the developer summarizes the intent of ordering according to business characteristics
  • Step 2 Data preparation, summarizing and calibrating the intent of daily ordering terms
  • Step three model training, select and train an ordering intention recognition model according to the number of intentions and the scale of training data
  • Step 4 online application, the ordering robot obtains the voice information of the user's order, converts the text through voice recognition technology, enters the text into the intention recognition model, obtains the probability corresponding to each intention, and applies the appropriate probability threshold set according to the business characteristics Determine the intention of the conversation;
  • Step five online optimization, for the situation where the user’s ordering intention cannot be identified through comparison or the identified ordering intention is wrong, training data is added, and steps 3 and 4 are repeated to make the intention recognition more and more accurate.
  • the user's ordering intention cannot be recognized, that is, the online intention recognition score is low, and the preset probability threshold is not reached.
  • the ordering robot in step 4 is equipped with a controller, the controller model is STM32F103, and the controller is powered by a small battery; the ordering robot in step 4 is equipped with a wireless communication module, and the wireless communication module model is URS-GPRS- 730, the wireless communication module is electrically connected to the controller; the ordering robot in step 4 is equipped with a voice recognition module, the model of the speech recognition module is LD3320, and the speech recognition module is electrically connected to the controller; the ordering robot in step 4 is equipped with The speaker for the voice feedback information is electrically connected to the controller; the ordering robot in step 4 is provided with a display screen for confirming the ordering information for customers, and the display screen is electrically connected to the controller.
  • the developer summarizes the intent of ordering according to the business characteristics and completes the intent preparation; then summarizes and calibrates the intent of the daily ordering language to complete the data preparation; secondly, select the appropriate machine learning according to the number of intents and the scale of training data Model, train the intention classification model, and complete the model training; again, the ordering robot obtains the voice information of the user’s order, converts the voice signal into text through the voice recognition module, and enters the text into the intention recognition model to obtain the probability corresponding to each intent.
  • the controller controls the wireless communication module to send the ordering information to the background for processing To complete the ordering work;
  • the intent recognition scheme of the existing general platform is to configure a certain intent.
  • it is necessary to configure the verb and noun corresponding to the intent such as "lai a bowl of noodles" in the intent of ordering.
  • the verb "lai” and nouns need to be configured during configuration.
  • "A bowl of noodles” is extremely inconvenient to configure.
  • One more word in the middle cannot be recognized.
  • This disclosure only needs to tell the model that "coming a bowl of noodles” is an ordering intention during the data preparation stage, and does not need to be manually divided into
  • the verb "lai” and the noun "one bowl of noodles" after training the model, it can accurately identify the corresponding intentions for the different sentences like "lai one noodle", which greatly increases the flexibility and simplicity of configuration, and optimizes Improve the user experience.
  • This disclosure has disclosed the models of the controller, the wireless communication module, and the voice recognition module.
  • the internal structure and pin functions of the electrical components involved in this disclosure are the common knowledge of those skilled in the art, and those skilled in the art have the ability to establish The circuit connection relationship between the above-mentioned electrical components, in addition, the use of a wireless communication module to establish a wireless connection is common knowledge of those skilled in the art.
  • the present disclosure provides a method and a robot for an ordering robot to recognize a user's ordering intention, which produces beneficial effects: creatively applying the method of natural language field text classification to the field of intention recognition at the ordering stage of the ordering robot, It greatly improves the robot's intelligence. It does not need to be manually split into verbs and nouns. It has a model training function. After the model is trained, the corresponding intent can be accurately identified for different sentences, which greatly increases the flexibility and configuration Convenience, optimized user experience.
  • the electronic device 30 includes at least one processor 301 (for example, a CPU), at least one input and output interface 304, a memory 302, and at least one communication bus 303 for implementing The connection and communication between these components.
  • the at least one processor 301 is configured to execute the computer instructions stored in the memory 302, so that the at least one processor 301 can execute any of the foregoing embodiments of the method for ordering robots to recognize the user's ordering intention.
  • the memory 302 is a non-transitory memory (non-transitory memory), which may include volatile memory, such as high-speed random access memory (RAM: Random Access Memory), and may also include non-volatile memory (non-volatile memory) , Such as at least one disk storage.
  • volatile memory such as high-speed random access memory (RAM: Random Access Memory)
  • non-volatile memory non-volatile memory
  • the communication connection with at least one other device or unit is realized through at least one input and output interface 304 (which may be a wired or wireless communication interface).
  • the memory 302 stores a program 3021
  • the processor 301 executes the program 3021, which is used to execute any of the aforementioned food ordering robots in the method embodiment for recognizing the user's ordering intention.
  • the electronic device can exist in many forms, including but not limited to:
  • Mobile communication equipment This type of equipment is characterized by mobile communication functions, and its main goal is to provide voice and data communications.
  • Such terminals include: smart phones (such as iPhone), multimedia phones, functional phones, and low-end phones.
  • Ultra-mobile personal computer equipment This type of equipment belongs to the category of personal computers, has calculation and processing functions, and generally also has mobile Internet features.
  • Such terminals include: PDA, MID and UMPC devices, such as iPad.
  • Portable entertainment equipment This type of equipment can display and play multimedia content.
  • Such devices include: audio, video players (such as iPod), handheld game consoles, e-books, as well as smart toys and portable car navigation devices.
  • Specific server a device that provides computing services.
  • the composition of a server includes a processor, hard disk, memory, system bus, etc.
  • the server is similar to a general computer architecture, but due to the need to provide highly reliable services, it is High requirements in terms of performance, reliability, security, scalability, and manageability.
  • the description is relatively simple, and for related parts, please refer to the partial description of the method embodiment.
  • a "computer-readable medium” can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, device, or device or in combination with these instruction execution systems, devices, or devices.
  • computer readable media include the following: electrical connections (electronic devices) with one or more wiring, portable computer disk cases (magnetic devices), random access memory (RAM), Read only memory (ROM), erasable and editable read only memory (EPROM or flash memory), fiber optic devices, and portable compact disk read only memory (CDROM).
  • the computer-readable medium may even be paper or other suitable media on which the program can be printed, because it can be used, for example, by optically scanning the paper or other media, and then editing, interpreting, or other suitable media if necessary. The program is processed in a manner to obtain the program electronically and then stored in the computer memory.
  • multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.
  • a logic gate circuit for implementing logic functions on data signals
  • Discrete logic circuits application-specific integrated circuits with suitable combinational logic gates
  • PGA programmable gate array
  • FPGA field programmable gate array

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Human Computer Interaction (AREA)
  • Multimedia (AREA)
  • Acoustics & Sound (AREA)
  • Artificial Intelligence (AREA)
  • Theoretical Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention concerne un procédé d'interaction homme-machine d'identification d'intention intelligente pour un robot de commande d'aliment, comprenant les étapes suivantes : résumer des intentions de commande d'aliment sur la base de caractéristiques de service (étape 1); étalonner les intentions de commande d'aliment auxquelles des termes de commande d'aliment communs appartiennent (étape 2); sur la base du nombre d'intentions de commande d'aliment résumées et du nombre de termes de commande d'aliment communs, sélectionner et entraîner un modèle d'identification d'intention de commande d'aliment (étape 3); et un robot de commande d'aliment identifiant, au moyen du modèle d'identification d'intention, une intention de commande d'aliment d'un utilisateur exprimée au moyen de la parole (étape 4).
PCT/CN2020/080725 2019-03-29 2020-03-23 Procédé pour robot de commande d'aliment pour identifier l'intention de commande d'aliment d'un utilisateur, et robot WO2020199963A1 (fr)

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CN201910250987.5 2019-03-29
CN201910250987.5A CN109979453A (zh) 2019-03-29 2019-03-29 一种面向点餐机器人的智能意图识别人机交互方法

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CN109979453A (zh) * 2019-03-29 2019-07-05 客如云科技（成都）有限责任公司 一种面向点餐机器人的智能意图识别人机交互方法
CN113326351A (zh) * 2021-06-17 2021-08-31 湖北亿咖通科技有限公司 一种用户意图确定方法及装置

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