CN111556630A - Intelligent lamp self-adaptive scene recognition system and method based on Bayesian network - Google Patents
Intelligent lamp self-adaptive scene recognition system and method based on Bayesian network Download PDFInfo
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- H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
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Abstract
The invention provides an intelligent lamp self-adaptive scene recognition system and method based on a Bayesian network, and relates to the technical field of machine learning and Internet of things. The system comprises a light sensor, a distance sensor, an infrared sensor, a raspberry group, a data collection and management module, an intelligent lamp self-adaptive scene recognition module and an intelligent lamp dynamic adjustment module. The method can predict scenes based on the environment information and the regulation and control information of the user on the intelligent lamp, and enables the intelligent lamp system to self-learn according to habits of people, so that people can live and work in a comfortable illumination environment no matter what scene, and individuation and self-adaptive regulation of the intelligent lamp control system are realized.
Description
Technical Field
The invention relates to the field of machine learning and the Internet of things, in particular to an intelligent lamp self-adaptive scene recognition system and method based on a Bayesian network.
Background
With the rapid development of the internet of things and cloud computing, related research and application of smart homes have become one of the hot spots concerned by the industry. In the intelligent home system, the sensor, the controller, the interface, the application and the controlled equipment are connected with each other through a network, and a user can locally or remotely control the home equipment through a control terminal such as a mobile phone and the like and can also realize automatic switching and adjustment of the intelligent equipment through setting in the intelligent home system. In the intelligent home environment, the intelligent device can be directly connected and controlled through WIFI or Bluetooth generally, and can also be remotely controlled through networking. In recent years, devices such as smart speakers can acquire more extended functions through networking.
In order to improve the operation convenience and the intelligence level of the intelligent home system, automatic control based on scenes has become an intelligent home control mode commonly adopted by many manufacturers. The user carries out the tasks to be executed in the scene through the creation of the scene and the definition of the trigger condition of the scene. When the home system detects that the scene triggering condition is met, the execution task corresponding to the scene can be automatically triggered, and the automatic switching and adjustment of the home equipment are completed, so that the times of manual operation of a user are reduced, and the convenience and the intelligence level of the home system are improved. At present, in a scene-based smart home system, the triggering condition of a scene generally needs to be manually set by a resident, and is relatively simple. For example, a getting-up scene and a sleeping scene are defined by setting the trigger time, and a getting-home scene and a getting-away scene are set by using the trigger time or the infrared sensor monitoring value. However, the definition of the daily life scene is often more complex and dynamic, and it is difficult to ensure the accuracy of the control of the household equipment by simply setting the conditions.
The purpose of the smart home system is to provide convenience to the user and to ensure the comfort of the user in use. People's daily life intelligent lighting system receives more and more attention because the purpose of intelligent lighting system is not only for people bring light, also provides people with more suitable light and more convenient operation. The intelligent lighting system mainly takes automatic control as a main part and takes manual control as an auxiliary part. The intelligent lamp can be controlled autonomously under the condition of reducing human intervention, and the comfortable brightness of a user is achieved. However, since the demand of each user for the smart lamp system and the deviation of the environmental status lead to the lack of sufficient personalization of the smart home, the study on the personalization of the smart lamp has become a focus of general attention in the research and industrial fields. Because the relation between the control demand of each intelligent lamp and the environment state is different, the use scene needs to be identified, and the intelligent lamp is controlled by the use scene, so that the overall intelligent level and the working efficiency of the intelligent home system in application are improved.
However, the existing intelligent lamp system cannot monitor indoor and outdoor light intensity in real time and automatically adjust the brightness of light, and can only manually switch scenes by manually setting certain scenes, so that the self-adaptive adjustment of the intelligent lamp control system cannot be realized. The user consumes time and labor to set the desk lamp, so that the working efficiency is reduced, and electricity consumption under certain conditions is wasted. Therefore, no mature intelligent lamp control system can meet the self-adaptive scene identification requirement at present.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an intelligent lamp self-adaptive scene recognition system and method based on a Bayesian network.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
on one hand, the invention provides an intelligent lamp self-adaptive scene recognition system based on a Bayesian network, which comprises a light sensor, a distance sensor, an infrared sensor, a raspberry group, a data collection and management module, an intelligent lamp self-adaptive scene recognition module and an intelligent lamp dynamic regulation module;
light sensor, distance sensor and infrared sensor install on the intelligent lamp, and raspberry group is connected to its output, the raspberry group is connected through TCP with the intelligent lamp.
The data collection and management module collects information of the current environment and user behaviors, and comprises a light sensor, a distance sensor and an infrared sensor, wherein the light sensor, the distance sensor and the infrared sensor collect illumination information of natural light of the current environment, the illumination information of an intelligent lamp, the distance between a user and the intelligent lamp and whether a person controls the intelligent lamp in a room where the intelligent lamp is located;
the intelligent lamp self-adaptive scene recognition module comprises an environment data processing module, a model training module and a scene classification module; the environment data processing module processes the information collected by the data collection module and provides the processed data to the model training module; the model training module performs Bayesian network training; the scene classification module uses a trained Bayesian network to perform scene classification on the current environment information and the user behavior;
the intelligent lamp dynamic adjusting module comprises a model decision module, a network dynamic updating module and an intelligent lamp control module; the model decision module combines a Bayesian network and a fuzzy set for common decision, and the network dynamic updating module acquires current environmental information and user control information under the condition of scene output failure or user adjustment and performs negative feedback updating on the Bayesian network; and the intelligent lamp control module controls the brightness and the color temperature of the intelligent lamp according to the scene output by the Bayesian network.
The fuzzy set is a rule base for clarifying user scenes or environment scenes which are not acquired by the original Bayesian network in the training process.
On the other hand, the intelligent lamp self-adaptive scene recognition method based on the Bayesian network is realized by the intelligent lamp self-adaptive scene recognition system based on the Bayesian network, and comprises the following steps: the method comprises the following steps:
step 1: collecting environmental data in a room where the intelligent lamp is located by using a light sensor, a distance sensor and an infrared sensor, and storing the collected data and the data of the intelligent lamp controlled by a user into a database of a raspberry group;
step 2: performing escape analysis on the environmental data and the user control information stored in the database in the step 1, and extracting the numerical value acquired by each sensor and the control information of the user at the corresponding time point;
and step 3: constructing a Bayesian network by using a search-based network structured learning algorithm according to the data extracted in the step 2, deploying the Bayesian network to a raspberry party and training the network;
step 3.1: performing state division on the data in the step 2, and determining the weight occupied by each sensor data type through evaluation factors;
step 3.2: determining incidence relation according to the weight obtained in the step 3.1, obtaining correlation relation between data states, and constructing a Bayesian network by using a search-based network structured learning-K2 algorithm;
step 3.3: the bayesian network in step 3.2 is trained.
And 4, step 4: judging the environment of the intelligent lamp according to the Bayesian network obtained in the step 3 by combining the fuzzy set and the fuzzy rule, and performing model joint decision to output a scene;
step 4.1: inputting the environment data into the Bayesian network obtained in the step 3, skipping to the step 5 if the output scene is successful, and skipping to the step 4.2 if the output scene is failed;
step 4.2: if the scene output fails, dynamically adjusting the Bayesian network by using a fuzzy rule base, and outputting the judgment scene again;
and 5: controlling the intelligent lamp according to the scene information output in the step 4, and realizing the self-adaptation of the scene of the intelligent lamp;
adopt the produced beneficial effect of above-mentioned technical scheme to lie in:
the invention provides an intelligent lamp self-adaptive scene recognition system and method based on a Bayesian network.
Drawings
Fig. 1 is a block diagram of an intelligent lamp adaptive scene recognition system based on a bayesian network according to an embodiment of the present invention;
fig. 2 is a flowchart of an intelligent lamp adaptive scene recognition method based on a bayesian network according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a smart lamp system provided by an embodiment of the invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings.
In order to meet the requirements of people on different brightness in the situations of work, rest and the like, an intelligent lamp system capable of automatically regulating and controlling user habits and current environment information needs to be generated. By means of machine learning and internet of things technology, scenes can be predicted based on environment information and regulation and control information of a user on an intelligent lamp, the intelligent lamp system can learn by itself according to habits of people, the purpose that people can live and work in a comfortable illumination environment no matter what scenes are achieved is achieved, electricity waste of various illumination scenes is reduced, fatigue is relieved, eye vision is protected, meanwhile work efficiency is improved, and personalization and self-adaptive regulation of the intelligent lamp control system are achieved.
The method of this example is as follows.
On one hand, the invention provides an intelligent lamp self-adaptive scene recognition system based on a Bayesian network, as shown in FIG. 1, comprising a light sensor, a distance sensor, an infrared sensor, a raspberry pi, a data collection and management module, an intelligent lamp self-adaptive scene recognition module and an intelligent lamp dynamic adjustment module;
the light sensor, the distance sensor and the infrared sensor are arranged on the intelligent lamp, the output end of the light sensor, the distance sensor and the infrared sensor is connected with the raspberry pie, the raspberry pie is connected with the intelligent lamp through a TCP, and the schematic diagram of the intelligent lamp system provided by the embodiment of the invention is shown in FIG. 3.
The data collection and management module is used for collecting information such as current environment, user behaviors and the like, carrying light, distance and infrared sensors on a nodemcu chip through GPIO pins, the nodemcu chip is connected with a raspberry pie through a self-contained wifi module to establish tcp (transmission control protocol), collecting illumination information of natural light of the current environment, illumination information of an intelligent lamp, the distance between the user and the intelligent lamp, whether people exist around the intelligent lamp and control information of the user to the intelligent lamp every 5s, and storing the collected data in a database of the raspberry pie in a json mode;
the intelligent lamp self-adaptive scene recognition module comprises an environment data processing module, a model training module and a scene classification module; the environment data processing module is used for transferring the environment information and the user control information collected in the raspberry group database, extracting the environment data and the user control information corresponding to the time point, and providing the processed data to the model training module; the model training module is used for training the Bayesian network, dividing scene categories such as learning, rest and the like according to data provided by the environment data processing module, constructing the Bayesian network by using a search-based network structured learning-K2 algorithm, and automatically deploying the Bayesian network on a raspberry party to train the Bayesian network; the scene classification module is used for carrying out scene classification on the current environment information and the user behavior by using the trained Bayesian network;
the intelligent lamp dynamic adjusting module comprises a model decision module, a network dynamic updating module and an intelligent lamp control module; the model decision module combines a Bayesian network on a raspberry derivative with a fuzzy set for making a decision, wherein the fuzzy set is a rule base for clarifying user scenes or environmental scenes which are not acquired by the Bayesian network in a training process; the embodiment adopts the calculation of the relative difference between the network output value and the scene to obtain the output more conforming to the current scene; the network dynamic updating module is used for acquiring current environment information and user control information under the condition that scene output fails or a user frequently adjusts, and performing negative feedback updating on the Bayesian network by using a fuzzy rule; the intelligent lamp control module is used for controlling the brightness and the color temperature of the intelligent lamp according to the scene output by the Bayesian network.
On the other hand, the invention also provides an intelligent lamp self-adaptive scene recognition method based on the Bayesian network, which is realized by the intelligent lamp self-adaptive scene recognition system based on the Bayesian network; as shown in fig. 2, the method comprises the following steps:
step 1: carrying the light, the distance and the infrared sensor on a nodemcu chip by using a GPIO pin, and storing environment data and user control information in a room where the intelligent lamp is located into a raspberry group database every 5s through a wifi module of the chip;
step 2: performing escape analysis on the environmental data and the user control information stored in the database in the step 1, and extracting the numerical value acquired by each sensor and the control information of the user at the corresponding time point;
and step 3: according to the data processed in the step 2, a Bayesian network is constructed on the raspberry side by using a search-based network structured learning algorithm, and the network is trained; the method comprises the following specific steps;
step 3.1: and (3) performing state division on the data in the step (2), and determining the weight occupied by each sensor data type through evaluation factors.
Step 3.2: and determining incidence relations according to the weights obtained in the step 3.1, obtaining correlation relations among data states, and constructing the Bayesian network by using a search-based network structured learning-K2 algorithm.
Step 3.3: the bayesian network in step 3.2 is trained.
And 4, step 4: judging the environment of the intelligent lamp according to the Bayesian network obtained in the step 3 and a fuzzy set and a fuzzy rule set in advance on the raspberry, and performing model joint decision-making to output a scene; the method comprises the following specific steps;
step 4.1: inputting the environment data into the Bayesian network obtained in the step 3, skipping to the step 5 if the output scene is successful, and skipping to the step 4.2 if the output scene is failed;
step 4.2: and (5) when the scene output fails, dynamically adjusting the Bayesian network by using the fuzzy rule base, and outputting the judgment scene again.
And 5: transmitting the scene information output in the step 4 to the intelligent lamp through tcp connection of the raspberry pi and the intelligent lamp, so as to realize self-adaptation of the scene of the intelligent lamp;
finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions and scope of the present invention as defined in the appended claims.
Claims (4)
1. The utility model provides an intelligence lamp self-adaptation scene identification system based on bayesian network which characterized in that: the system comprises a light sensor, a distance sensor, an infrared sensor, a raspberry group, a data collection and management module, an intelligent lamp self-adaptive scene recognition module and an intelligent lamp dynamic adjustment module;
the light sensor, the distance sensor and the infrared sensor are arranged on the intelligent lamp, the output end of the light sensor, the distance sensor and the infrared sensor is connected with the raspberry pie, and the raspberry pie is connected with the intelligent lamp through a TCP;
the data collection and management module collects information of the current environment and user behaviors, and comprises a light sensor, a distance sensor and an infrared sensor, wherein the light sensor, the distance sensor and the infrared sensor collect illumination information of natural light of the current environment, the illumination information of an intelligent lamp, the distance between a user and the intelligent lamp and whether a person controls the intelligent lamp in a room where the intelligent lamp is located;
the intelligent lamp self-adaptive scene recognition module comprises an environment data processing module, a model training module and a scene classification module; the environment data processing module processes the information collected by the data collection module and provides the processed data to the model training module; the model training module performs Bayesian network training; the scene classification module uses a trained Bayesian network to perform scene classification on the current environment information and the user behavior;
the intelligent lamp dynamic adjusting module comprises a model decision module, a network dynamic updating module and an intelligent lamp control module; the model decision module combines a Bayesian network and a fuzzy set for common decision, and the network dynamic updating module acquires current environmental information and user control information under the condition of scene output failure or user adjustment and performs negative feedback updating on the Bayesian network; and the intelligent lamp control module controls the brightness and the color temperature of the intelligent lamp according to the scene output by the Bayesian network.
2. An intelligent lamp adaptive scene recognition method based on a Bayesian network is realized by the intelligent lamp adaptive scene recognition system of the Bayesian network in claim 1, and is characterized in that: the method comprises the following steps:
step 1: collecting environmental data in a room where the intelligent lamp is located by using a light sensor, a distance sensor and an infrared sensor, and storing the collected data and the data of the intelligent lamp controlled by a user into a database of a raspberry group;
step 2: performing escape analysis on the environmental data and the user control information stored in the database in the step 1, and extracting the numerical value acquired by each sensor and the control information of the user at the corresponding time point;
and step 3: constructing a Bayesian network by using a search-based network structured learning algorithm according to the data extracted in the step 2, deploying the Bayesian network to a raspberry party and training the network;
and 4, step 4: judging the environment of the intelligent lamp according to the Bayesian network obtained in the step 3 by combining the fuzzy set and the fuzzy rule, and performing model joint decision to output a scene;
and 5: and (4) controlling the intelligent lamp according to the scene information output in the step (4) to realize the self-adaptation of the scene of the intelligent lamp.
3. The intelligent Bayesian network-based lamp adaptive scene recognition method as recited in claim 2, wherein the step 3 comprises the steps of:
step 3.1: performing state division on the data in the step 2, and determining the weight occupied by each sensor data type through evaluation factors;
step 3.2: determining incidence relation according to the weight obtained in the step 3.1, obtaining correlation relation between data states, and constructing a Bayesian network by using a search-based network structured learning-K2 algorithm;
step 3.3: the bayesian network in step 3.2 is trained.
4. The intelligent Bayesian network-based lamp adaptive scene recognition method as recited in claim 2, wherein the step 4 comprises the steps of:
step 4.1: inputting the environment data into the Bayesian network obtained in the step 3, skipping to the step 5 if the output scene is successful, and skipping to the step 4.2 if the output scene is failed;
step 4.2: and (5) when the scene output fails, dynamically adjusting the Bayesian network by using the fuzzy rule base, and outputting the judgment scene again.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112074054A (en) * | 2020-08-21 | 2020-12-11 | 博流智能科技（南京）有限公司 | Intelligent lamp color temperature control method and system based on machine learning |
| CN112800501A (en) * | 2021-01-15 | 2021-05-14 | 珠海新势力创建筑设计有限公司 | Method and device for automatically generating ceiling lamp model based on room information |
| CN114529506A (en) * | 2021-12-31 | 2022-05-24 | 厦门阳光恩耐照明有限公司 | Lamplight monitoring method and system based on machine learning |
| CN114549864A (en) * | 2021-12-31 | 2022-05-27 | 厦门阳光恩耐照明有限公司 | Intelligent lamp control method and system based on environment image |
| CN117098270A (en) * | 2023-10-17 | 2023-11-21 | 深圳市天成照明有限公司 | Intelligent control method, device and equipment for LED energy-saving lamp and storage medium |
| CN117440554A (en) * | 2023-12-20 | 2024-01-23 | 深圳市正远科技有限公司 | Scene modeling method and system for realizing LED light source based on digitization |
| CN117615485A (en) * | 2023-12-12 | 2024-02-27 | 江苏圣创半导体科技有限公司 | Intelligent LED lamp control method and system for vending machine |
| CN118393891A (en) * | 2024-05-09 | 2024-07-26 | 中山市名迪电器有限公司 | Intelligent household appliance user behavior sensing and controlling system |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104778495A (en) * | 2014-01-13 | 2015-07-15 | 上海宽带技术及应用工程研究中心 | Bayesian network optimization method based on particle swarm algorithm |
| CN107743331A (en) * | 2017-10-18 | 2018-02-27 | 普联技术有限公司 | Light source control method, device and system |
| CN108052056A (en) * | 2017-12-20 | 2018-05-18 | 中国电子产品可靠性与环境试验研究所 | State monitoring method, the apparatus and system of LED illumination lamp |
| US20180167140A1 (en) * | 2016-12-13 | 2018-06-14 | Maite Brandt-Pearce | Position localization using visible light communication |
| CN109769333A (en) * | 2019-03-29 | 2019-05-17 | 山东建筑大学 | Event driven home furnishings intelligent means of illumination and system |
| CN110113843A (en) * | 2019-05-26 | 2019-08-09 | 中国计量大学上虞高等研究院有限公司 | Lighting control system and light modulation mapping device based on sleep efficiency factor |
| CN110163371A (en) * | 2019-05-26 | 2019-08-23 | 中国计量大学上虞高等研究院有限公司 | Sleep environment dims optimization method and lamps and lanterns regulate and control method |
| CN110687808A (en) * | 2019-10-14 | 2020-01-14 | 苏州数言信息技术有限公司 | Indoor wisdom economizer system based on thing networking and machine learning |
-
2020
- 2020-06-29 CN CN202010604225.3A patent/CN111556630B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104778495A (en) * | 2014-01-13 | 2015-07-15 | 上海宽带技术及应用工程研究中心 | Bayesian network optimization method based on particle swarm algorithm |
| US20180167140A1 (en) * | 2016-12-13 | 2018-06-14 | Maite Brandt-Pearce | Position localization using visible light communication |
| CN107743331A (en) * | 2017-10-18 | 2018-02-27 | 普联技术有限公司 | Light source control method, device and system |
| CN108052056A (en) * | 2017-12-20 | 2018-05-18 | 中国电子产品可靠性与环境试验研究所 | State monitoring method, the apparatus and system of LED illumination lamp |
| CN109769333A (en) * | 2019-03-29 | 2019-05-17 | 山东建筑大学 | Event driven home furnishings intelligent means of illumination and system |
| CN110113843A (en) * | 2019-05-26 | 2019-08-09 | 中国计量大学上虞高等研究院有限公司 | Lighting control system and light modulation mapping device based on sleep efficiency factor |
| CN110163371A (en) * | 2019-05-26 | 2019-08-23 | 中国计量大学上虞高等研究院有限公司 | Sleep environment dims optimization method and lamps and lanterns regulate and control method |
| CN110687808A (en) * | 2019-10-14 | 2020-01-14 | 苏州数言信息技术有限公司 | Indoor wisdom economizer system based on thing networking and machine learning |
Non-Patent Citations (2)
| Title |
|---|
| MURILO DE OLIVEIRA PROVENZI;MARCELO GÖTZ: "Activity Prediction in a Smart Environment Using Bayesian Network and Multi-Agent System", 《2017 VII BRAZILIAN SYMPOSIUM ON COMPUTING SYSTEMS ENGINEERING (SBESC)》 * |
| 聂弘颖: "基于动态贝叶斯网络的交通灯自主智能决策研究", 《 中国优秀硕士学位论文全文数据库》 * |
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| CN117440554A (en) * | 2023-12-20 | 2024-01-23 | 深圳市正远科技有限公司 | Scene modeling method and system for realizing LED light source based on digitization |
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