WO2021036543A1 - Procédé d'actionnement automatique utilisant des mégadonnées et une intelligence artificielle, et système robotisé - Google Patents

Procédé d'actionnement automatique utilisant des mégadonnées et une intelligence artificielle, et système robotisé Download PDF

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WO2021036543A1
WO2021036543A1 PCT/CN2020/101535 CN2020101535W WO2021036543A1 WO 2021036543 A1 WO2021036543 A1 WO 2021036543A1 CN 2020101535 W CN2020101535 W CN 2020101535W WO 2021036543 A1 WO2021036543 A1 WO 2021036543A1
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data
preset
job
model
type
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PCT/CN2020/101535
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English (en)
Chinese (zh)
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朱定局
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南京智慧光信息科技研究院有限公司
大国创新智能科技(东莞)有限公司
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Publication of WO2021036543A1 publication Critical patent/WO2021036543A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/0088—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the invention relates to the field of information technology, in particular to an automatic operation method and a robot system based on big data and artificial intelligence.
  • the automatic operation methods in the prior art include vehicles (such as excavators, bulldozers, etc.), aircraft (such as spraying aircraft, bombing aircraft, etc.). Such as airplanes), boats (such as fishing boats, etc.), etc., used in the execution of operational tasks, such as forward, backward, left, right, or a combination of multiple methods; automatic operation under the existing technology
  • the switching of the operation mode generally adopts manual switching, which shows that the degree of automation of the operation mode in the prior art is insufficient and the intelligence is low.
  • an embodiment of the present invention provides an automatic operation method, and the method includes:
  • the job type obtaining step is used to obtain the job type to which the work device belongs;
  • the data acquisition step is used to acquire preset types of data related to the job
  • the preset model obtaining step is used to obtain the preset model
  • the model calculation step is used to input the preset type of data related to the job into the preset model, and the output calculated by the preset model is used as a recommended job mode.
  • the method further includes:
  • the system control step is used to control the working device according to the recommended working mode.
  • the preset type is preset by the user or obtained from the knowledge base;
  • the data of the preset type includes data related to the selection of the operation mode
  • the data related to the job includes data of the job and environmental data of the job;
  • the preset type of data related to the job is the preset type of current data or recent data related to the job or data within a recent preset time period.
  • the step of obtaining the preset model includes:
  • the historical big data obtaining step is used to obtain valid historical big data of all jobs of the type to which the job belongs;
  • the corresponding data acquisition step is used to acquire preset types of data in the effective historical big data and their corresponding operation methods
  • the deep learning model initialization step is used to initialize the deep learning model
  • the unsupervised training step is used to use the preset type of data in the effective historical big data as the input of the deep learning model, and perform unsupervised training on the deep learning model;
  • the supervised training step is used to use the preset types of data in the historical big data and their corresponding operation modes as the input and output of the deep learning model, respectively, and perform unsupervised training on the deep learning after unsupervised training.
  • Model supervised training ;
  • the preset model generation step is used to obtain the deep learning model after supervised training as the preset model.
  • the step of obtaining the preset model includes:
  • the historical big data obtaining step is used to obtain valid historical big data of all jobs of the type to which the job belongs;
  • the corresponding data acquisition step is used to acquire preset types of data in the effective historical big data and their corresponding operation methods
  • the model data setting step is configured to use the preset type of data and the corresponding operation mode in the effective historical big data as the to-be-matched data of the preset model and the corresponding to-be-recommended data respectively;
  • the model calculation steps include:
  • the matching step is used to perform fuzzy matching of the data related to the job of the preset type with each of the to-be-matched data in the preset model;
  • the recommendation step is used to obtain the recommended data corresponding to the selected data to be matched from the preset model as the output calculated by the preset model, and use the output as the recommended operation mode.
  • the effective historical big data includes historical big data of operations that are operated by humans and have at least one common operation mode with the operation; or/and unmanned operations and the selection effect of the operation mode satisfies preset conditions and is consistent with
  • the job has historical big data of jobs in at least one common job mode.
  • the operation is a vehicle-mounted operation;
  • the vehicle includes an unmanned vehicle;
  • the preset type of data related to the operation includes road condition data of the road section where the vehicle is currently located, and data of the road section where the vehicle is currently located.
  • the operation is a ship-borne operation; the ship includes an unmanned ship; the preset type of data related to the operation includes the sea condition data of the current segment of the ship, and the current navigation of the ship.
  • the exhaust pollution control index data of the segment, the noise control index data of the current segment of the ship, the meteorological data of the current segment of the ship, the wind data of the current segment of the ship, the current demand data of the ship One or more of the energy data required by the current different types of operations of the ship, the remaining energy of the ship, the ship type of the ship, and the current preset data of the ship.
  • the operation is an airborne operation of an aircraft;
  • the aircraft includes a drone;
  • the preset type of data related to the operation includes weather data of the flight segment where the aircraft is currently located, One of the noise pollution control index data of the flight segment, the wind data of the current flight segment, the energy data required for the different types of operations of the aircraft, the remaining energy of the aircraft, the model, and the current preset data of the aircraft Kind or several.
  • an embodiment of the present invention provides a system, wherein the system executes the steps in the automatic operation method of any one of the first aspect; the system includes a robot system.
  • a preset model is obtained by learning from historical big data, and then the preset model and current data are used to calculate the current operation mode that should be used, and the historical data and current data include the data and data of the operation to which the operating system belongs.
  • Environmental data so that the obtained preset model and recommended operation mode are more in line with the needs of the operation and the environment, and are more efficient. Therefore, the embodiment of the present invention can make the switching of the operation mode more intelligent and efficient.
  • the switching of the automatic operation mode in the prior art generally adopts manual switching. It can be seen that the degree of automation of the operation mode in the prior art is insufficient and the intelligence is low.
  • the automatic operation method and robot system based on big data and artificial intelligence provided by the embodiment of the present invention include: obtaining the operation type to which the operation device belongs, obtaining the data related to the operation of the preset type, obtaining the preset model, The data related to the operation of the predetermined type is input to the predetermined model, and the output calculated by the predetermined model is used as the recommended operation mode.
  • the above method and system improve the intelligence and efficiency of automatic operation mode switching through automatic operation technology based on big data and artificial intelligence.
  • FIG. 1 is a flowchart of an automatic operation method provided by Embodiment 2 of the present invention.
  • Embodiment 5 of the present invention is a flowchart of the steps of obtaining a preset model provided by Embodiment 5 of the present invention.
  • FIG. 5 is a functional block diagram of an automatic operation system provided by Embodiment 11 of the present invention.
  • FIG. 6 is a functional block diagram of a preset model module provided by Embodiment 13 of the present invention.
  • FIG. 7 is a functional block diagram of a preset model module provided by Embodiment 14 of the present invention.
  • FIG. 8 is a functional block diagram of a model calculation module provided by Embodiment 14 of the present invention.
  • An automatic operation method includes a job type acquisition step S100, a data acquisition step S200, a preset model acquisition step S300, a model calculation step S400, and a system control step S500.
  • the job type obtaining step S100 is used to obtain the job type to which the work device belongs. Therefore, the operation mode of the working device of the type is determined according to the operation type, and the preset type of data related to the operation related to the operation mode is determined.
  • the data acquisition step S200 is used to acquire preset types of data related to the job. Therefore, the recommended operation mode is calculated according to the data related to the operation of the preset type.
  • the preset model obtaining step S300 is used to obtain a preset model. Thereby, a correspondence relationship between the preset type of data related to the operation and the operation mode is established through the preset model.
  • the input format of the preset model is a preset type of data format related to the job
  • the output format is the data format of the job mode
  • the data format of the job mode can use a digital format, and each job mode is encoded into A number
  • the operation method (that is, the operation method) includes the use of forward, backward, left turn, right turn and other more complicated operation methods related to the operation task, as well as one or more mixed operation methods;
  • the model calculation step S400 is used to input the preset type of data related to the job into the preset model, and the output calculated by the preset model is used as a recommended job mode. Therefore, a recommended operation mode is provided for controlling the operating system.
  • the output is the output of the preset model
  • the method according to embodiment 1 further includes a system control step S500, as shown in FIG. 1.
  • System control step S500 is used to control the working device according to the recommended working mode; thereby enabling the working system to operate in a better way, thereby improving the intelligence and efficiency of the working system of the working .
  • the preset type is preset by the user or obtained from the knowledge base;
  • the data of the preset type includes data related to the selection of the operation mode
  • the data related to the job includes data of the job and environmental data of the job;
  • the preset type of data related to the job is the preset type of current data or recent data related to the job or data within a recent preset time period.
  • the preset model acquisition step S300 includes historical big data acquisition step S311, corresponding data acquisition step S312, deep learning model initialization step S313, unsupervised training step S314, supervised training step S315, and preset model generation step S316, as shown in the figure 2 shown.
  • the historical big data acquisition step S311 is used to acquire valid historical big data of all jobs of the type to which the job belongs; the historical big data includes the big data collected so far;
  • Corresponding data acquisition step S312 is used to acquire a preset type of data related to the job and its corresponding operation mode in the effective historical big data, wherein the preset type of data related to the job is Preset types of historical data;
  • the deep learning model initialization step S313 is used to initialize the deep learning model
  • the unsupervised training step S314 is used to use the preset type of data related to the job in the effective historical big data as the input of the deep learning model, and perform unsupervised training on the deep learning model;
  • the supervised training step S315 is used to use the preset type of data related to the job and its corresponding job mode in the historical big data as the input and output of the deep learning model, respectively.
  • the deep learning model after training performs supervised training;
  • the preset model generation step S316 is used to obtain the deep learning model after supervised training as the preset model.
  • the historical big data can be obtained online through the network or obtained from a large historical database.
  • the effective historical big data includes preset types of data related to the operation and the corresponding operation mode; the effective historical big data is collected in the past for a long period of time.
  • the preset type of data related to the job and its corresponding job mode are collected by each collected job at each time it is collected. It is necessary to collect each collected job at each time it is collected.
  • the type of the job includes jobs (including vehicles) that have at least one common operation mode with the job.
  • the preset model obtaining step S300 includes historical big data obtaining step S321, corresponding data obtaining step S322, and model data setting step S323, as shown in FIG. 3.
  • Historical big data acquisition step S321 is used to acquire valid historical big data of all jobs of the type of the job
  • Corresponding data acquisition step S322 is used to acquire a preset type of data related to the job and its corresponding operation mode in the effective historical big data; wherein the preset type of data related to the job is Preset types of historical data;
  • the model data setting step S323 is used to use the preset type of data related to the job and its corresponding job mode in the effective historical big data as the to-be-matched data of the preset model and its corresponding Data to be recommended.
  • the model calculation step S400 includes a matching step S421, a selection step S422, and a recommendation step S423, as shown in FIG. 4.
  • the matching step S421 is used for the fuzzy matching of the data related to the job of the preset type with each of the data to be matched in the preset model;
  • Selecting step S422 is for selecting the data to be matched in the preset model that has the greatest degree of matching with the input preset type of data related to the job;
  • the recommendation step S423 is used to obtain the data to be recommended corresponding to the selected data to be matched from the preset model as an output calculated by the preset model, and use the output as a recommended operation mode.
  • Embodiment 4 uses big data and deep learning technology
  • embodiment 5 uses big data and its recommendation technology.
  • the effective historical big data includes historical big data of operations (for example, vehicles) that are "manned (for example, driving)" and "have at least one common operation mode with the operation". Because humans are intelligent, the operation mode chosen by manned operations (such as driving) is more credible, while the historical big data of unmanned operations such as drones, unmanned vehicles, and unmanned ships may not be available.
  • the manned operation includes the selection of a manned operation mode.
  • the effective historical big data also includes "unmanned operation (for example, unmanned driving)" and "the selection effect of the operation mode satisfies the preset conditions” and “has at least one common operation mode with the operation” (for example, The historical big data of driving, sailing, and flying).
  • the selection effect of the work mode satisfies the preset condition, including that the user score of the selection effect of the work mode is greater than the preset threshold.
  • the unmanned operation includes the selection of an unmanned operation mode.
  • the operation is a vehicle-mounted operation;
  • the vehicle includes an unmanned vehicle;
  • the preset type of data related to the operation includes the road condition data of the road section where the vehicle is currently located, the exhaust pollution control index data of the current road section, the noise control index data of the current road section, and the road section data of the current road section.
  • the track section can be replaced with a region.
  • Other current preset data include control index data for noise and speed during combat.
  • the operation is a ship-borne operation;
  • the ship includes an unmanned ship;
  • the preset type of data related to the operation includes the sea state data of the current flight segment of the ship, the exhaust pollution control index data of the current flight segment, the noise control index data of the current flight segment, and the current Meteorological data of the flight segment, wind data of the current flight segment, current other demand data, current energy data required for different types of operations, remaining energy of the ship, ship type, current other preset data, etc.
  • the flight segment can be replaced with sea area.
  • Other current preset data include control index data for noise, speed, etc. during combat.
  • the operation is an airborne operation of an aircraft;
  • the aircraft includes an unmanned aerial vehicle;
  • the preset type of data related to the operation includes weather data of the flight segment where the aircraft is currently located, noise pollution control index data of the flight segment currently located, wind data of the current flight segment, and current different types of operations One or more of required energy data, remaining energy of the aircraft, model, current other preset data, and so on.
  • the flight segment can be replaced with airspace.
  • Other current preset data include control index data for noise, speed, etc. during combat.
  • An automatic operation system includes a task type acquisition module 100, a data acquisition module 200, a preset model acquisition module 300, and a model calculation module 400.
  • the job type acquiring module 100 is used to acquire the job type to which the working device belongs.
  • the data acquisition module 200 is configured to acquire preset types of data related to the job.
  • the preset model obtaining module 300 is used to obtain a preset model.
  • the model calculation module 400 is configured to input the preset type of data related to the operation into the preset model, and the output calculated by the preset model is used as a recommended operation mode.
  • the system according to the embodiment 10 further includes a system control module 500, as shown in FIG. 5.
  • the system control module 500 is configured to control the working device according to the recommended working mode. Specifically, it is determined whether the recommended work mode is consistent with the current work mode: if yes, a control instruction to continue the current work mode is sent to the work device; if not, it is sent to the work device to switch the current work mode to the recommended work Way of control instructions.
  • the preset type is preset by the user or obtained from the knowledge base;
  • the data of the preset type includes data related to the selection of the operation mode
  • the data related to the job includes data of the job and environmental data of the job;
  • the preset type of data related to the job is the preset type of current data or recent data related to the job or data within a recent preset time period.
  • the preset model acquisition module 300 includes a historical big data acquisition module 311, a corresponding data acquisition module 312, a deep learning model initialization module 313, an unsupervised training module 314, a supervised training module 315, and a preset model generation module 316, as shown in the figure 6 shown.
  • the historical big data acquisition module 311 is configured to acquire valid historical big data of all jobs of the type to which the job belongs;
  • Corresponding data acquisition module 312 configured to acquire preset types of data related to the operation in the effective historical big data and the corresponding operation mode
  • the deep learning model initialization module 313 is used to initialize the deep learning model
  • the supervised training module 315 is configured to use the preset type of data related to the job and its corresponding job mode in the historical big data as the input and output of the deep learning model, respectively.
  • the deep learning model after training performs supervised training;
  • the preset model generation module 316 is configured to obtain the deep learning model after supervised training as the preset model.
  • the preset model acquisition module 300 includes a historical big data acquisition module 321, a corresponding data acquisition module 322, and a model data setting module 323, as shown in FIG. 7.
  • the historical big data acquisition module 321 is configured to acquire valid historical big data of all jobs of the type to which the job belongs;
  • Corresponding data acquisition module 322 configured to acquire preset types of data related to the operation in the effective historical big data and the corresponding operation mode
  • the model data setting module 323 is configured to use the preset type of data related to the job and the corresponding job mode in the effective historical big data as the to-be-matched data of the preset model and its corresponding Data to be recommended.
  • the model calculation module 400 includes a matching module 421, a selection module 422, and a recommendation module 423, as shown in FIG. 8.
  • the matching module 421 is configured to perform fuzzy matching of the data related to the job of the preset type with each of the data to be matched in the preset model;
  • the selecting module 422 is configured to select the data to be matched in the preset model that has the greatest degree of matching with the input preset type of data related to the job;
  • the recommendation module 423 is configured to obtain the recommended data corresponding to the selected data to be matched from the preset model as an output calculated by the preset model, and use the output as a recommended operation mode.
  • the effective historical big data includes historical big data of operations (including vehicles) that are "manned (including driving)” and “have at least one common operation mode with the operation” or/and “unmanned operations (including Unmanned driving)” and “the selection effect of the operation mode satisfies the preset conditions” and “has at least one common operation mode with the operation” (including transportation) historical big data.
  • the system according to embodiment 10 includes:
  • the operation is a vehicle-mounted operation;
  • the vehicle includes an unmanned vehicle;
  • the preset type of data related to the operation includes the road condition data of the road section where the vehicle is currently located, the exhaust pollution control index data of the current road section, the noise control index data of the current road section, and the road section data of the current road section.
  • the track section can be replaced with a region.
  • Other current preset data include control index data for noise and speed during combat.
  • the operation is a ship-borne operation;
  • the ship includes an unmanned ship;
  • the preset type of data related to the operation includes the sea state data of the current flight segment of the ship, the exhaust pollution control index data of the current flight segment, the noise control index data of the current flight segment, and the current One or more of meteorological data of the flight segment, wind data of the current flight segment, current other demand data, current energy data required for different types of operations, ship type, current other preset data, and so on.
  • the flight segment can be replaced with sea area.
  • Other current preset data include control index data for noise, speed, etc. during combat.
  • the operation is an airborne operation of an aircraft;
  • the aircraft includes an unmanned aerial vehicle;
  • the preset type of data related to the operation includes weather data of the flight segment where the aircraft is currently located, noise pollution control index data of the flight segment currently located, wind data of the current flight segment, and current different types of operations One or more of required energy data, remaining energy of the aircraft, model, current other preset data, and so on.
  • the flight segment can be replaced with airspace.
  • Other current preset data include control index data for noise, speed, etc. during combat.
  • a robot system is provided, and the robots are respectively configured with the systems described in Embodiment 10 to Embodiment 18.
  • the methods and systems in the foregoing embodiments can be executed and deployed on computers, servers, cloud servers, supercomputers, robots, embedded devices, electronic devices, and so on.

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Abstract

L'invention concerne un procédé d'actionnement automatique utilisant des mégadonnées et une intelligence artificielle, et un système robotisé. Le procédé consiste : à acquérir le type d'actionnement d'un dispositif d'actionnement ; à acquérir un type prédéfini de données relatives à l'actionnement ; à acquérir un modèle prédéfini ; à entrer le type prédéfini de données relatives à l'actionnement dans le modèle prédéfini ; et à effectuer un calcul afin d'obtenir une sortie au moyen du modèle prédéfini, et à utiliser la sortie comme mode d'actionnement recommandé. Le procédé et le système utilisent une technologie d'actionnement automatique utilisant des mégadonnées et une intelligence artificielle pour effectuer une commutation entre des modes d'actionnement automatique plus intelligents et efficaces.
PCT/CN2020/101535 2019-08-29 2020-07-13 Procédé d'actionnement automatique utilisant des mégadonnées et une intelligence artificielle, et système robotisé WO2021036543A1 (fr)

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Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103529805A (zh) * 2013-10-25 2014-01-22 东北大学 一种钢铁企业原料取料设备的优化作业控制方法
CN104850677A (zh) * 2015-03-25 2015-08-19 武汉大学 基于地理信息系统的深度学习元胞自动机仿真分析方法
CN106200604A (zh) * 2016-09-18 2016-12-07 朱超英 一种电控装置、农机系统及其使用方法
WO2017135522A1 (fr) * 2016-02-03 2017-08-10 엘지전자(주) Dispositif d'affichage de type miroir et son procédé de commande
CN107290972A (zh) * 2017-07-05 2017-10-24 三星电子（中国）研发中心 设备控制方法和装置
CN109109863A (zh) * 2018-07-28 2019-01-01 华为技术有限公司 智能设备及其控制方法、装置
CN110488804A (zh) * 2019-08-29 2019-11-22 南京智慧光信息科技研究院有限公司 基于大数据和人工智能的联合导航方法和机器人系统
CN110488803A (zh) * 2019-08-29 2019-11-22 南京智慧光信息科技研究院有限公司 基于大数据和人工智能的制动方法和机器人系统
CN110488828A (zh) * 2019-08-29 2019-11-22 南京智慧光信息科技研究院有限公司 基于大数据和人工智能的航行灯控制方法和机器人系统
CN110502015A (zh) * 2019-08-29 2019-11-26 南京智慧光信息科技研究院有限公司 基于大数据和人工智能的速度控制方法和机器人系统
CN110509913A (zh) * 2019-08-29 2019-11-29 南京智慧光信息科技研究院有限公司 基于大数据和人工智能的混合动力推进方法和机器人系统
CN110737260A (zh) * 2019-08-29 2020-01-31 南京智慧光信息科技研究院有限公司 基于大数据和人工智能的自动作业方法和机器人系统

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103576683B (zh) * 2012-08-03 2016-12-21 中国科学院深圳先进技术研究院 多巡逻机器人的调度方法和系统
CN105215987B (zh) * 2015-10-12 2017-03-22 埃夫特智能装备股份有限公司 一种工业机器人工艺云系统及其工作方法
CN105824970B (zh) * 2016-04-12 2019-05-31 华南师范大学 基于大数据知识库和用户反馈的机器人交互方法和系统
US11087206B2 (en) * 2017-04-28 2021-08-10 Intel Corporation Smart memory handling and data management for machine learning networks
CN107564363B (zh) * 2017-09-05 2019-11-05 百度在线网络技术（北京）有限公司 一种用于驾驶模式切换的方法和装置
CN108629716A (zh) * 2018-06-20 2018-10-09 大国创新智能科技(东莞)有限公司 基于大数据与人工智能的精准复习方法和教育机器人系统
CN109063845B (zh) * 2018-07-15 2021-12-07 大国创新智能科技(东莞)有限公司 基于生成样本的深度学习方法和机器人系统
CN108828948B (zh) * 2018-07-15 2021-06-18 大国创新智能科技(东莞)有限公司 基于深度学习的人工智能作战方法和机器人系统
CN108983788B (zh) * 2018-08-15 2021-03-26 上海海事大学 基于大数据挖掘的无人驾驶环卫车智能控制系统及方法
CN109358614A (zh) * 2018-08-30 2019-02-19 深圳市易成自动驾驶技术有限公司 自动驾驶方法、系统、装置及可读存储介质

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103529805A (zh) * 2013-10-25 2014-01-22 东北大学 一种钢铁企业原料取料设备的优化作业控制方法
CN104850677A (zh) * 2015-03-25 2015-08-19 武汉大学 基于地理信息系统的深度学习元胞自动机仿真分析方法
WO2017135522A1 (fr) * 2016-02-03 2017-08-10 엘지전자(주) Dispositif d'affichage de type miroir et son procédé de commande
CN106200604A (zh) * 2016-09-18 2016-12-07 朱超英 一种电控装置、农机系统及其使用方法
CN107290972A (zh) * 2017-07-05 2017-10-24 三星电子（中国）研发中心 设备控制方法和装置
CN109109863A (zh) * 2018-07-28 2019-01-01 华为技术有限公司 智能设备及其控制方法、装置
CN110488804A (zh) * 2019-08-29 2019-11-22 南京智慧光信息科技研究院有限公司 基于大数据和人工智能的联合导航方法和机器人系统
CN110488803A (zh) * 2019-08-29 2019-11-22 南京智慧光信息科技研究院有限公司 基于大数据和人工智能的制动方法和机器人系统
CN110488828A (zh) * 2019-08-29 2019-11-22 南京智慧光信息科技研究院有限公司 基于大数据和人工智能的航行灯控制方法和机器人系统
CN110502015A (zh) * 2019-08-29 2019-11-26 南京智慧光信息科技研究院有限公司 基于大数据和人工智能的速度控制方法和机器人系统
CN110509913A (zh) * 2019-08-29 2019-11-29 南京智慧光信息科技研究院有限公司 基于大数据和人工智能的混合动力推进方法和机器人系统
CN110737260A (zh) * 2019-08-29 2020-01-31 南京智慧光信息科技研究院有限公司 基于大数据和人工智能的自动作业方法和机器人系统

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