WO2019056267A1 - Procédé de prise de décision interactive hiérarchique, terminal interactif et serveur en nuage - Google Patents

Procédé de prise de décision interactive hiérarchique, terminal interactif et serveur en nuage Download PDF

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Publication number
WO2019056267A1
WO2019056267A1 PCT/CN2017/102746 CN2017102746W WO2019056267A1 WO 2019056267 A1 WO2019056267 A1 WO 2019056267A1 CN 2017102746 W CN2017102746 W CN 2017102746W WO 2019056267 A1 WO2019056267 A1 WO 2019056267A1
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Prior art keywords
attribute
module
grading
feature
target object
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PCT/CN2017/102746
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English (en)
Chinese (zh)
Inventor
廉士国
刘兆祥
王宁
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达闼科技（北京）有限公司
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Priority to CN201780001795.XA priority Critical patent/CN107820619B/zh
Priority to PCT/CN2017/102746 priority patent/WO2019056267A1/fr
Publication of WO2019056267A1 publication Critical patent/WO2019056267A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
    • G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/174—Facial expression recognition
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; 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 OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00—Speaker identification or verification

Definitions

  • the present application relates to the field of robot interaction, and specifically relates to a hierarchical interaction decision method, an interactive terminal, and a cloud server.
  • the blind person is given a reminder voice, for example, when the name or gender is recognized, the voice prompt “Before your friend Xiao Ming” and “There is a woman in front”.
  • the welcome robot recognizes the guests according to the machine vision and greets them by voice. For example, “Hello! Dear VIP customer Zhang Xiaoming”, “Hello, Ms!”. This kind of interaction can bring a user-friendly experience and improve service quality.
  • the prior art interactive terminal directly interacts according to various object features recognized by the machine vision, so that interactions based on advanced machine vision may also occur in some scenes, for example, due to light, angle or occlusion. People can't ensure that male and female, unable to detect human expression and age, and other machine intelligence can't judge, if the interactive user is a female, and the interactive terminal says "Hello, sir! will be caused by gender recognition errors. .
  • the present application provides a hierarchical interaction decision method, an interactive terminal, and a cloud server, which pre-sets the attribute classification of the target object, and prioritizes the attribute rankings to form a multi-level hierarchical neural network. Attribute judgment based on the target object features recognized by machine vision and robot semantic understanding Break and output the interactive decision-making basis for the current target object information, and try to identify the more detailed attributes of the person and the object, making the interactive terminal more intelligent and flexible, and improving the user experience.
  • the present application provides the following technical solutions.
  • an embodiment of the present application provides a hierarchical interaction decision method, including the following steps:
  • the judgment result of the attribute grading of the current attribute grading and the following priority is output as the basis of the interactive decision.
  • an embodiment of the present application further provides an interaction terminal, including an information acquisition module, an identification module, an attribute module, a determination module, and an output module.
  • the information acquiring module is configured to acquire target object information, and the identifying module is configured to identify the target object feature;
  • the attribute module is configured to obtain a corresponding attribute classification according to the target object feature, and prioritize the attribute classification;
  • the determining module is configured to perform a stepwise attribute determination on the target object feature according to the prioritized order of the attribute ranking, and the target object feature is hierarchically migrated to a higher priority attribute when the current attribute classification is satisfied by the classification criterion;
  • the output module is used to output the current attribute grading and the subordinate attribute grading judgment result as the basis of the interaction decision.
  • the embodiment of the present application further provides a cloud server, including a receiving module, an attribute module, a determining module, an output module, and a sending module.
  • the receiving module is configured to receive a target object feature that is sent by the interaction terminal and is identified according to the acquired target object information;
  • the attribute module is configured to obtain a corresponding attribute classification according to the target object feature, and prioritize the attribute classification;
  • the judging module is configured to perform hierarchical attribute determination based on the priority order of the attribute ranking based on the target object feature, and the target object module is hierarchically migrated to a higher priority attribute when the current attribute classification grading standard is satisfied;
  • the output module is used for output when the target object module does not meet the grading criteria of the current attribute hierarchy.
  • the current attribute grading and the judgment result of the subordinate attribute grading are used as the basis for the interactive decision;
  • the sending module is configured to send the basis.
  • the embodiment of the present application further provides an electronic device, including:
  • At least one processor and,
  • a memory communicatively coupled to the at least one processor, a communication component, an audio data collector, and a video data collector;
  • the memory stores instructions executable by the at least one processor, the instructions being invoked by the at least one processor to invoke data of the audio data collector and the video data collector, and establishing a connection with the cloud server through the communication component to enable the At least one processor is capable of performing the method as described above.
  • the embodiment of the present application further provides a non-transitory computer readable storage medium, where the computer-readable storage medium is stored, where the computer-executable instructions are used to cause a computer to execute the above The method described.
  • the embodiment of the present application further provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, when When the program instructions are executed by the computer, the computer is caused to perform the method as described above.
  • the utility model has the beneficial effects that the hierarchical interaction decision method, the interaction terminal and the cloud server provided by the embodiments of the present application pre-set a reasonable attribute classification of the target object, prioritize the attribute classifications, and form a multi-level hierarchical neural network.
  • the target object features are judged step by step, and the optimal decision basis for the current target object information is outputted, so that the interactive terminal is more intelligent and flexible, and the user experience is improved.
  • the target object such as the attributes of the person and the object, is determined as much as possible, and more interactive decision-making basis is provided for the actual interactive application scenario, thereby improving the user's deep interactive experience.
  • FIG. 1 is a system architecture diagram of hierarchical interaction decision provided by an embodiment of the present application.
  • FIG. 2 is a block diagram of an interaction terminal provided by an embodiment of the present application.
  • FIG. 3 is a main flowchart of hierarchical interaction decision of an interactive terminal according to an embodiment of the present application.
  • FIG. 4 is an overall flowchart of hierarchical interaction decision of an interactive terminal according to an embodiment of the present application.
  • FIG. 5 is a flowchart of step-by-step determination of a face recognition embodiment of an interactive terminal according to an embodiment of the present application
  • FIG. 6 is a multi-level hierarchical neural network diagram of an interaction terminal according to an embodiment of the present application.
  • FIG. 7 is a flowchart of step-by-step determination of a vehicle identification embodiment of an interactive terminal according to an embodiment of the present application.
  • FIG. 8 is a flowchart of step-by-step judgment of an audio recognition embodiment of an interactive terminal according to an embodiment of the present application
  • FIG. 9 is a schematic diagram of a cloud server module according to an embodiment of the present application.
  • FIG. 10 is a flowchart of a method for implementing hierarchical interaction decision on a cloud server side provided by an embodiment of the present application
  • FIG. 11 is a schematic diagram showing the hardware structure of an electronic device according to the hierarchical interaction decision method provided by the embodiment of the present application.
  • the hierarchical interaction decision method, the interaction terminal and the cloud server provided by the embodiments of the present application pre-set attribute classification of the target object, prioritize the attribute classification, form a multi-level hierarchical neural network, and recognize and recognize according to machine vision and robot semantics.
  • the target object feature performs attribute judgment step by step, and outputs the interaction decision basis with the most information about the current target object, so that the interactive terminal is more intelligent and flexible, and improves the user experience.
  • the hierarchical interactive decision content of the present application includes: acquiring target object information, identifying the target object feature; acquiring corresponding attribute classification according to the target object feature, prioritizing the attribute ranking; and ranking the priority order according to the attribute classification, based on the target
  • the object feature performs step-by-step attribute judgment, and when the current attribute classification grading standard is satisfied, the attribute is graded to a higher priority attribute; when the current attribute grading grading standard is not satisfied, the current attribute grading and the following priority attribute grading result are outputted.
  • the basis for interactive decision making is performed by: acquiring target object information, identifying the target object feature; acquiring corresponding attribute classification according to the target object feature, prioritizing the attribute ranking; and ranking the priority order according to the attribute classification, based on the target
  • the object feature performs step-by-step attribute judgment, and when the current attribute classification grading standard is satisfied, the attribute is graded to a higher priority attribute; when the current attribute grading grading standard is not satisfied, the current
  • the prioritization of the attribute ranking may be starting from a primary attribute to an advanced attribute, and starting from the primary attribute to the advanced attribute based on the target object feature for progressive attribute determination. It can be understood that the stepwise attribute judgment based on the target object feature may also be judged step by step from the advanced attribute to the primary attribute, as long as the deeper result can be recognized as the result output of the system, thereby identifying as much as possible. More detailed attributes of characters and objects to support deep interaction.
  • the hierarchical interactive decision-making method, the interactive terminal and the cloud server of the present application are machine-optimized decision-making methods based on confidence priority, which realizes optimal recognition of characters and objects, and tries to identify more detailed attributes of characters and objects, thereby realizing terminal and person. Friendly interaction between.
  • each interaction device is connected to the cloud server 100.
  • the interactive terminal 100 can be smart glasses 110, can be a robot 120, can be a smart terminal 130, or can be a smart helmet 140 or the like.
  • the interactive terminal collects information about the opposite target object, such as picture image information or audio and audio information; and hierarchically and adaptively identifies the target object and the target object feature from the target object information; based on the identified target object and the target object The feature, the interactive terminal device sends corresponding interaction information to the person; the user responds to the interactive terminal and the like.
  • information about the opposite target object such as picture image information or audio and audio information
  • hierarchically and adaptively identifies the target object and the target object feature from the target object information based on the identified target object and the target object
  • the interactive terminal device sends corresponding interaction information to the person; the user responds to the interactive terminal and the like.
  • the priority order of the determination result is evaluated and the confidence analysis is performed.
  • the priority order of the attribute classification of the different attributes is: L0 (person) > L1 (sex) > L2 (person name) > L3 (expression). That is, first, it is judged whether or not the person can be identified, and the personless target object is output, and if possible, the gender is further recognized, and if the gender is confirmed, the name is recognized again.
  • the difficulty of identifying different attributes can be sorted according to the recognition algorithm of different attributes under the same conditions, such as the recognition rate of inputting the same image data. For example, it is often difficult to identify a person's name, gender recognition is difficult to face detection, or sort according to the mutual inclusion relationship between attributes. For example, to identify a gender, it is necessary to first detect the presence of a face. Among them, the discrimination of each attribute depends on the corresponding confidence. For example, when L0 recognizes a face, only the confidence exceeds W0, it is considered that the person is recognized; when L1 identifies the gender, only when the confidence exceeds W1, the gender can be recognized. When L2 recognizes a person's name, only when the confidence exceeds W2, the name recognition is successful.
  • the embodiment relates to an interactive terminal.
  • the attribute module, the judgment module, and the confidence module for implementing the hierarchical interaction decision are set in the interaction terminal.
  • the interaction terminal includes an information acquisition module 20, an identification module 22, an attribute module 30, a determination module 40, a confidence module 42, an output module 50, and an interaction module 60.
  • the information acquisition module 20 acquires target object information, and the recognition module 22 identifies the target object feature.
  • the attribute module 30 obtains a corresponding attribute ranking according to the target object feature, and the attribute ranking is prioritized from the primary attribute to the advanced attribute.
  • the judging module 40 starts from the primary attribute to the advanced attribute, and performs stepwise attribute determination based on the target object feature, and the target object feature is hierarchically migrated to the higher priority attribute when the current attribute classification is satisfied.
  • the output module 50 outputs the current attribute grading and the subordinate attribute grading judgment result as the basis of the interaction decision.
  • the confidence module is configured to perform a confidence analysis on the judgment result of each attribute classification.
  • the judging module is based on the judgment result of all subordinate attribute grading in the judgment of the superior attribute grading.
  • the target object information includes image information as well as audio information.
  • the attribute module 30 classifies the attributes of different groups.
  • the attribute module 30 is a face attribute module.
  • the information acquisition module 20 acquires image information of a face, and the recognition module 22 identifies a face feature based on the image information.
  • the face attribute module is configured to obtain a corresponding attribute ranking according to the facial feature. For example, the priority order of the attribute ranking is: person, gender, person name, and expression.
  • the determining module 40 starts from the attribute of the person to the expression attribute, and performs stepwise attribute determination based on the facial feature.
  • the confidence module 42 starts from the attribute of the person to the expression attribute, and performs level-by-level confidence determination based on the facial feature. When the face feature satisfies the grading standard of the current attribute grading, the attribute is migrated to a higher priority attribute;
  • the output module 50 When the facial feature does not satisfy the grading standard of the current attribute grading, the output module 50 outputs all the judgment results of the current attribute grading and the attribute grading of the following priority levels as the basis of the interactive decision.
  • the person and its attributes are identified from the screen according to the confidence level. Identification of attributes for people, such as people, names, genders, expressions, etc. From the perspective of confidence, the processing unit of the interactive terminal makes intelligent analysis and decision making on the screen:
  • T and D are detection thresholds, which can be used as a confidence level for judging the existence of a human face.
  • the attribute detection algorithm is used to identify attributes such as the gender of the person corresponding to the face image.
  • the gender identification algorithm as an example.
  • the name is detected by the face recognition technology to determine whether the detected face is a pre-stored or registered face, that is, the face similarity S ⁇ C, where C is a similarity threshold, which can be used as a confidence level for determining the name.
  • features for discriminating "face”, “gender”, and "person name” can be iteratively calculated from a picture, such as based on a multi-layer neural network principle.
  • the bottom layer of the neural network is calculated for discriminating No is the face feature of the face 1
  • the middle layer calculates the face feature 2 and the face feature 3 from the feature 1
  • the upper layer can be calculated based on the features of the next layer, for example, calculating the person from the face feature 2
  • the face feature 3 is used to discriminate a specific "person name”.
  • the terminal device Based on the identified person and its attributes, the terminal device sends a corresponding interaction signal to the person;
  • step 2) Based on the output of step 2), the intelligent machine responds accordingly.
  • the smart helmet If the output is ⁇ 1, Face>, the smart helmet emits a sound "someone in front";
  • the smart helmet If the output is ⁇ 1, Male>, the smart helmet emits a sound "There is a man in front”;
  • the smart helmet makes a sound "There is a lady in front”
  • the welcome robot makes a sound "Hello! What can I help you?";
  • the welcome robot makes a sound "Hello! Sir!”;
  • the welcome robot makes a sound "Hello! Ms!”;
  • the welcome robot sounds "Hello! VIP Customer NameInfo!.
  • the intelligent identification and decision-making process can be implemented in the interactive terminal or on the cloud server side.
  • the specific solution implemented in the cloud server refers to Embodiment 3.
  • the interactive terminal needs to collect the collected object information, such as an image or Audio data is transmitted to the cloud server.
  • the attribute module 30 is a vehicle attribute module.
  • the information acquisition module 20 acquires image information of the vehicle, and the recognition module 22 identifies the vehicle feature based on the image information.
  • the vehicle attribute module obtains a corresponding attribute ranking based on the vehicle characteristics. For example, the prioritization of the attribute hierarchy is: car, color, model, brand, and style.
  • the judging module 40 starts from the vehicle attribute to the style attribute, and performs stepwise attribute determination based on the vehicle characteristic
  • the confidence module 42 starts from the vehicle attribute to the style attribute, and performs stepwise confidence determination based on the vehicle characteristic, the vehicle
  • the attribute is migrated to a higher priority attribute
  • the output module 50 When the vehicle feature does not meet the grading criteria of the current attribute grading, the output module 50 outputs all the judgment results of the current attribute grading and the attribute grading of the following priority levels as the basis for the interactive decision.
  • the hierarchical decision method can be based on different target object features, and different features are extracted from the original input image or audio for different levels of decision.
  • the specific classification packet can be pre-stored in the interactive terminal.
  • the image information may be various, and may include vehicle recognition, fruit recognition, animal recognition, and the like in addition to face recognition.
  • the attribute module 30 is a sound attribute module.
  • the information acquisition module 20 acquires target object audio information, and the recognition module 22 identifies an audio feature of the target object based on the audio information.
  • the sound attribute module obtains a corresponding attribute level according to the audio feature of the target object. For example, the order of prioritization of the attribute hierarchy is: vocals, languages, keywords, and semantics.
  • the determining module 40 starts from the vocal attribute to the semantic attribute, and performs stepwise attribute determination based on the audio feature of the target object.
  • the confidence module 42 starts from the vocal attribute to the semantic attribute, and performs a step-by-step confidence judgment based on the audio feature of the target object.
  • the output module 50 outputs all the judgment results of the current attribute grading and the attribute grading of the following priority levels as the basis for the interactive decision.
  • the judgment result can be optimized based on the recognition rate of the algorithm, and the corresponding interactive output is given.
  • the order of decision making can be: vocal, language, keywords and semantics.
  • the interactive terminal can say "Hello!”; if the language is recognized, the intelligent interactive terminal can say “Hello!” in the corresponding language; if the keyword “finance” is recognized, the intelligent interactive terminal can say "There is the Bank of China's latest financial information, I don't know if you are interested.” If you identify the customer's intention "I want to understand high interest rate management,” the interactive terminal can say "high interest rate financial information is as follows".
  • the embodiment relates to a cloud server 100, wherein an attribute module, a determining module, and a confidence module for implementing hierarchical interaction decision are disposed in the cloud server 100.
  • the cloud server includes a receiving module 102 , a sending module 104 , an attribute module 130 , a determining module 140 , an output module 150 , and a confidence module 142 .
  • the receiving module 102 receives the target object feature identified by the interactive terminal according to the acquired target object information.
  • the attribute module 130 acquires a corresponding attribute ranking according to the target object feature, and the attribute ranking is prioritized from the primary attribute to the advanced attribute.
  • the determining module 140 starts from the primary attribute to the advanced attribute, and performs stepwise attribute determination based on the target object feature, and the target object feature is hierarchically migrated to a higher priority attribute when the current attribute classification is satisfied; the target object feature
  • the output module 150 outputs all the judgment results of the current attribute grading and the subordinate attribute grading as the basis of the interactive decision.
  • the sending module 104 sends the basis to the connected interactive terminal, and the interactive terminal performs a certain depth interaction with the user based on the received interaction decision.
  • the confidence module 142 performs a confidence analysis on the determination result of each attribute ranking.
  • the judging module 140 is based on the judgment result of all subordinate attribute gradings in the superior attribute grading judgment.
  • FIG. 10 a flow chart of implementing a hierarchical interaction decision method on the cloud server 100 side is shown.
  • Step 301 The receiving module receives, by the interaction terminal, a target object feature that is identified according to the acquired target object information.
  • Step 302 The attribute module acquires a corresponding attribute classification according to the target object feature, and the attribute level is prioritized from the primary attribute to the advanced attribute.
  • Step 303 The judging module starts from the primary attribute to the advanced attribute, and performs stepwise attribute determination based on the target object feature.
  • Step 304 The confidence module performs a confidence analysis on the judgment result of each attribute classification.
  • Step 305 Whether the confidence threshold grading standard is met
  • Step 306 Whether the grading standard of the current attribute grading is satisfied
  • Step 307 The target object feature is hierarchically migrated to a higher priority attribute when the grading standard of the current attribute grading is satisfied;
  • Step 308 When the target object feature does not meet the grading standard of the current attribute grading, the judgment result of the current attribute grading and the following attribute grading is output as the basis of the interactive decision;
  • Step 309 The sending module is configured to send the basis.
  • the embodiment relates to a hierarchical interaction decision method, which mainly includes the following steps:
  • Step 101 Acquire target object information, where the target object information includes image information and audio information;
  • Step 102 Identify the target object feature.
  • Step 103 Acquire a corresponding attribute ranking according to the target object feature, where the attribute ranking is prioritized from the primary attribute to the advanced attribute;
  • Step 104 Starting from the primary attribute to the advanced attribute, performing hierarchical attribute determination based on the target object feature, and the superior attribute leveling is based on the judgment result of all the lower attribute attributes in the judgment;
  • Step 105 The target object feature is hierarchically migrated to a higher priority attribute when the grading standard of the current attribute grading is satisfied;
  • Step 106 When the target object feature does not meet the grading standard of the current attribute grading, the judgment result of the attribute grading of the current attribute grading and the following priority is output as the basis of the interaction decision.
  • the hierarchical interaction decision method further includes a confidence analysis step when performing stepwise attribute determination based on the target object feature.
  • Step 201 Perform a confidence analysis on the judgment result of each attribute classification to ensure the recognition accuracy rate
  • Step 203 Whether the grading standard of the current attribute grading is satisfied, for example, whether the face is recognized, and if the face is recognized, it is further determined whether the character gender, etc.;
  • Step 205 Whether the confidence threshold grading criterion is met, for example, whether the face meets the threshold grading standard of the face image data, or whether the gender satisfies the set image feature threshold, such as the length of the hair;
  • Step 207 The target object feature is hierarchically migrated to a higher priority attribute when the grading standard of the current attribute grading is satisfied;
  • Step 209 When the target object feature does not satisfy the grading standard of the current attribute grading, the judgment result of the attribute grading of the current attribute grading and the following priority is output as the basis of the interaction decision.
  • the hierarchical interactive decision method, the interactive terminal and the cloud server of the embodiment pre-set the attribute grading of the target object, prioritize the attribute grading, and perform attribute judgment step by step according to the target object features recognized by the machine vision and the robot semantic understanding.
  • the interactive decision basis for the current target object information is outputted, so that the interactive terminal is more intelligent and flexible, and the user experience is improved.
  • the interactive content between the interactive terminal and the user is more rich and interesting, and the recognition person is taken as an example: when the camera is facing the camera under the condition of good illumination and close proximity, the name of the person can be recognized; Half face, or side-to-machine camera, can only identify the gender of the person; when facing the camera, it can only identify whether it is an individual, or take the identification of the vehicle as an example: the blind person walks with the guide helmet on the side of the road Sometimes it is possible to identify the model and color, and sometimes only recognize the color. Interactive content is more interesting and interesting.
  • FIG. 11 is a hardware structure of an electronic device 600 for hierarchical hierarchical decision making according to an embodiment of the present application.
  • the electronic device 600 includes:
  • One or more processors 610, a memory 620, an audio data collector 630, a video data collector 640, a communication component 650, and a display unit 660 are illustrated by one processor 610 in FIG.
  • the output of the audio data collector is the input of an audio recognition module, and the output of the video data collector identifies the input of the module.
  • the memory 620 stores instructions executable by the at least one processor 610, the instructions being invoked by the at least one processor to invoke data of the audio data collector and the video data collector, and the communication component 650 establishes a connection with the cloud server. To enable the at least one processor to perform the hierarchical interaction decision method.
  • the processor 610, the memory 620, the display unit 660, and the human-machine interaction unit 630 may be connected by a bus or other means, and the connection by a bus is taken as an example in FIG.
  • the memory 620 is a non-volatile computer readable storage medium, and can be used for storing a non-volatile software program, a non-volatile computer executable program, and a module, such as a program corresponding to the hierarchical interactive decision method in the embodiment of the present application.
  • An instruction/module for example, the identification module 22, the attribute module 30, the determination module 40, the confidence module 42 and the interaction module 60 shown in FIG. 2).
  • the processor 610 executes various functional applications and data processing of the server by running non-volatile software programs, instructions, and modules stored in the memory 620, that is, implementing the hierarchical interaction decision method in the above method embodiments.
  • the memory 620 may include a storage program area and an storage data area, wherein the storage program area may store an operating system, an application required for at least one function; the storage data area may store data created according to usage of the interactive terminal, and the like.
  • memory 620 can include high speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device.
  • memory 620 can optionally include memory remotely located relative to processor 610, which can be connected to the robotic interactive electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • the one or more modules are stored in the memory 620, and when executed by the one or more processors 610, perform a hierarchical interaction decision method in any of the above method embodiments, for example, performing the above described FIG.
  • steps 101 to 106 of the method the method steps 201 to 209 in FIG. 4 described above are executed, and the identification module 22, the attribute module 30, the determination module 40, the confidence module 42 and the interaction module 60 and the diagram in FIG. 2 are implemented. 9 functions of the attribute module 130, the determination module 140, the confidence module 142, and the transmission module 104.
  • the electronic device of the embodiment of the present application exists in various forms, including but not limited to:
  • Mobile communication equipment This type of equipment is characterized by its mobile communication function.
  • Three-dimensional display devices These devices can display and play multimedia content. Such devices include: virtual reality helmets, enhanced display helmets, or enhanced display glasses.
  • Server A device that provides computing services.
  • the server consists of a processor, a hard disk, a memory, a system bus, etc.
  • the server is similar to a general-purpose computer architecture, but because of the need to provide highly reliable services, processing power and stability High reliability in terms of reliability, security, scalability, and manageability.
  • Embodiments of the present application provide a non-transitory computer readable storage medium storing computer-executable instructions that are executed by one or more processors, for example, to perform the above
  • the method steps 101 to 106 in FIG. 3 described above, the method steps 201 to 209 in FIG. 4 described above are performed, and the identification module 22, the attribute module 30, the determination module 40, the confidence module 42 and the interaction in FIG. 2 are implemented.
  • the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, ie may be located A place, or it can be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

Abstract

L'invention concerne un procédé de prise de décision interactive hiérarchique comprenant les étapes suivantes consistant : à obtenir des informations concernant un objet cible (101) ; à identifier une caractéristique de l'objet cible (102) ; à obtenir un grade d'attribut correspondant en fonction de la caractéristique de l'objet cible, et à hiérarchiser le grade d'attribut (103) ; à réaliser, selon une séquence de priorité du grade d'attribut, une détermination d'attribut par grade sur la caractéristique de l'objet cible (104) ; si la caractéristique de l'objet cible satisfait une norme de notation du grade d'attribut courant, à faire migrer la caractéristique de l'objet cible vers un niveau d'attribut plus élevé (105) ; et si la caractéristique de l'objet cible ne satisfait pas à une norme de notation du grade d'attribut courant, à délivrer en sortie un résultat de détermination du grade d'attribut courant et un grade d'attribut d'une priorité inférieure en tant que base de prise de décision interactive (106).
PCT/CN2017/102746 2017-09-21 2017-09-21 Procédé de prise de décision interactive hiérarchique, terminal interactif et serveur en nuage WO2019056267A1 (fr)

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