CN104254165A - Decision-tree-based intelligent automatic streetlamp control system - Google Patents
Decision-tree-based intelligent automatic streetlamp control system Download PDFInfo
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- CN104254165A CN104254165A CN201310265145.XA CN201310265145A CN104254165A CN 104254165 A CN104254165 A CN 104254165A CN 201310265145 A CN201310265145 A CN 201310265145A CN 104254165 A CN104254165 A CN 104254165A
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- 238000003066 decision tree Methods 0.000 title claims abstract description 41
- 230000007613 environmental effect Effects 0.000 claims abstract description 27
- 238000011217 control strategy Methods 0.000 claims abstract description 25
- 238000005286 illumination Methods 0.000 claims description 6
- 238000010801 machine learning Methods 0.000 claims description 5
- 238000012937 correction Methods 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 3
- 230000009897 systematic effect Effects 0.000 claims description 3
- 238000000034 method Methods 0.000 description 9
- 238000013461 design Methods 0.000 description 6
- 230000033228 biological regulation Effects 0.000 description 5
- 238000010276 construction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
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- 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
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B20/00—Energy efficient lighting technologies, e.g. halogen lamps or gas discharge lamps
- Y02B20/40—Control techniques providing energy savings, e.g. smart controller or presence detection
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Abstract
The invention discloses a decision-tree-based intelligent automatic streetlamp control system comprising streetlamps, node controllers, central controllers and a streetlamp control unit. The streetlamps, the node controllers, the central controllers and the streetlamp control unit are connected in order. A two-way link is arranged between each central controller and the streetlamp control unit. Each central controller is provided with a camera and a sensor. The streetlamp control unit comprises a self-learning module and a control strategy auto-decision module. The cameras and the sensors are used for acquiring environmental data of a streetlamp site. The central controllers feed the environmental data back to the streetlamp control unit. The self-learning module and the control strategy auto-decision module perform automatic configurations according to the environmental data. The streetlamp control unit automatically controls brightness or on-off of the streetlamps. The decision-tree-based intelligent automatic streetlamp control system allows damp utilization needs of passengers to be met according to actual conditions of a road section and saves electric energy.
Description
Technical field
The present invention relates to a kind of Lighting Control Assembly, specifically, relate to a kind of street lamp control system.
Background technology
Road lamp switch and brightness adjustment control can effectively electrical energy saving, because a lot of street lamp was all turn on light, so there is not the problem when will carrying out switch lamp and brightness regulation to street lamp all night in the past.Along with the development of hardware and software engineering, carry out meticulous control and management to street lamp to become a reality, but all the needing of the Street lamps control of existing street lamp control system is planned in advance, although switch and the brightness regulation of street lamp no longer need manual intervention, the strategy of Street lamps control or needs rely on manually to be planned and completes.
Existing control method is generally carry out Manual definition's control time point, carry out the switch of street lamp and the operation of brightness regulation, there is subjectivity in the method, actual conditions environmentally can not carry out the control of street lamp, when causing some period illumination sufficient, street lamp light yellow, and when some period illumination is very low, street lamp but fails normally to open, and the manpower of management is also very high.
Refer to Fig. 1 and Fig. 2, in prior art, whole system is by Street lamps control unit, Centralized Controller, Node Controller, street lamp, the tree topology of classification is adopted to build, wherein Street lamps control unit is to Centralized Controller, it is unidirectional link between Centralized Controller to Node Controller, transmitted control signal to Centralized Controller by Street lamps control unit respectively, Centralized Controller transmits control signal to Node Controller, thus realization controls the switch of street lamp and brightness.
Centralized Controller is unidirectional to the transfer of data between Node Controller, Node Controller to street lamp, can only transmission of control signals, cannot gather the situation of node.Arranged by section switching time of artificial mode to street lamp, dynamic adjustments street lamp brightness cannot be carried out according to the brightness value of actual environment, there is certain irrationality, need more manpower to safeguard Street lamps control unit, with the stable operation of safeguards system simultaneously.
Summary of the invention
The object of the present invention is to provide a kind of street lamp intelligent automatic control system based on decision tree, dynamic adjustments street lamp brightness can be carried out according to the situation Dominating paths lamp switch of actual environment and brightness value, thus decrease human cost, what meet passerby uses lamp needs, the conservation of power energy.
To achieve these goals, the technical solution adopted in the present invention is as follows:
A kind of street lamp intelligent automatic control system based on decision tree, comprise street lamp, Node Controller, Centralized Controller and Street lamps control unit, described street lamp, described Node Controller, described Centralized Controller is connected successively with described Street lamps control unit, be two-way link between described Centralized Controller and described Street lamps control unit, described Centralized Controller is equiped with camera and transducer, described Street lamps control unit has self-learning module and control strategy automatic decision module, described camera and described transducer gather the site environment data of described street lamp, described environmental data is fed back to described Street lamps control unit by described Centralized Controller, described self-learning module and described control strategy automatic decision module carry out corresponding automation configuration according to described environmental data, described Street lamps control unit controls automatically to the brightness of described street lamp or switch.
Further, described camera and described transducer can gather vehicle fleet size, intensity of illumination, temperature, humidity, the errors in visibility data in described street lamp site environment section.
Further, its identification code and described environmental data are passed to described Street lamps control unit by wireless network by described Centralized Controller.
Further, the control operation instruction of described Street lamps control unit is passed to described Node Controller by the mode of power carrier by described Centralized Controller, and described Node Controller carries out regulable control to the brightness of described street lamp or switch.
Further, described self-learning module adopts decision Tree algorithms, in conjunction with the environmental aspect in the section, described environmental data analysis current described street lamp place that described Centralized Controller returns.
Further, when the operation of road lamp management person to described Street lamps control unit adjusts and revise, the information of described adjustment and correction will pass to described self-learning module.
Further, described self-learning module adopts the decision-tree model of machine learning, builds Street lamps control decision-tree model, and described Street lamps control decision-tree model is passed to described control strategy automatic decision module.
Further, after described self-learning module has learnt all Street lamps control conditions by described Street lamps control decision-tree model, systematic evaluation is automatic control mode by turnkey console by described road lamp management person.
Further, in described automatic control mode, the described environmental data at described street lamp scene is directly passed to described control strategy automatic decision module, and described control strategy automatic decision module carries out automatic classification to the described environmental data obtained, and determines the control operation instruction of described street lamp.
Further, described control operation instruction is passed to Street lamps control module by described control strategy automatic decision module, and described control operation instruction is delivered to described Centralized Controller by wireless network by described Street lamps control module.
Compared with prior art, for two-way link designs between Centralized Controller of the present invention and described Street lamps control unit, video sensing device is increased at described Centralized Controller, the getting sth into one's head property of artificial design road lamp switch and brightness regulation can be reduced, what meet passerby according to the actual conditions in section uses lamp needs, the conservation of power energy.
The present invention introduces described control strategy automatic decision module and described self-learning module, by machine learning and automatic decision method are incorporated in described Street lamps control unit, give street lamp control system wisdom, in the process that street lamp control system can be allowed to use in reality, possess the intelligence of the mankind gradually, thus realize unattended operation or manage without the need to the street lamp Automated condtrol of manual intervention.
Accompanying drawing explanation
Fig. 1 is the logical construction schematic diagram of street lamp control system in prior art;
Fig. 2 is the structural representation of street lamp control unit in prior art;
Fig. 3 is logical construction schematic diagram of the present invention;
Fig. 4 is the structural representation of Street lamps control unit of the present invention;
Fig. 5 is the construction process schematic diagram of decision tree of the present invention.
Embodiment
Below in conjunction with the drawings and specific embodiments, the street lamp intelligent automatic control system that the present invention is based on decision tree is described further.
Refer to Fig. 3 and Fig. 4, the invention discloses a kind of street lamp intelligent automatic control system based on decision tree, comprise street lamp, Node Controller, Centralized Controller and Street lamps control unit, described street lamp, described Node Controller, described Centralized Controller is connected successively with described Street lamps control unit, be two-way link between described Centralized Controller and described Street lamps control unit, described Centralized Controller is equiped with camera and transducer, described Street lamps control unit has self-learning module and control strategy automatic decision module, described camera and described transducer gather the site environment data of described street lamp, described environmental data is fed back to described Street lamps control unit by described Centralized Controller, described self-learning module and described control strategy automatic decision module carry out corresponding automation configuration according to described environmental data, described Street lamps control unit controls automatically to the brightness of described street lamp or switch.
Refer to Fig. 3, be two-way link between described Centralized Controller and described Street lamps control unit, the control operation instruction that described Street lamps control unit sends can be passed to described Node Controller by the mode of power carrier by described Centralized Controller, and described Node Controller carries out regulable control to the brightness of described street lamp or switch; Simultaneously, the described camera that described Centralized Controller is installed and described transducer can gather vehicle fleet size, intensity of illumination, temperature, humidity, errors in visibility data in described street lamp site environment section, by wireless network (GSM/3G), its identification code and described environmental data are passed to described Street lamps control unit.
Refer to Fig. 4 and Fig. 5, described self-learning module adopts decision Tree algorithms, in conjunction with the environmental aspect in the section, described environmental data analysis current described street lamp place that described Centralized Controller returns.Described decision Tree algorithms is the induced learning algorithm based on example, and it infers the classifying rules of decision tree representation from one group of out of order, random tuple.Described decision Tree algorithms adopts top-down recursive fashion, carries out the comparison of property value at the inside node of decision tree, and according to different property values from this node to inferior division, leaf node is the class that will learn to divide.A paths from root to leaf node just correspond to a conjunction rule, and whole decision tree just correspond to one group of expression formula rule of extracting.
Refer to Fig. 3 and Fig. 4, the described environmental data that road lamp management person is returned by described Centralized Controller, switch or brightness adjustment operation are carried out to the described street lamp in corresponding section.Described road lamp management person operates and passes to described self-learning module with corresponding described environmental data by described Street lamps control unit accordingly, described self-learning module adopts the decision-tree model of machine learning, build Street lamps control decision-tree model, and described Street lamps control decision-tree model is passed to described control strategy automatic decision module.When the operation of road lamp management person to described Street lamps control unit adjusts and revise, the information of described adjustment and correction also will pass to described self-learning module.Described self-learning module is constantly learnt by described Street lamps control decision-tree model, until after having learnt all Street lamps control conditions, systematic evaluation is automatic control mode by turnkey console by described road lamp management person.In described automatic control mode, the described environmental data at described street lamp scene is directly passed to described control strategy automatic decision module, described control strategy automatic decision module carries out automatic classification to the described environmental data obtained, and determines the control operation instruction of described street lamp.Described control operation instruction is passed to Street lamps control module by described control strategy automatic decision module, and described control operation instruction is delivered to described Centralized Controller by wireless network by described Street lamps control module.
The Automated condtrol process that the street lamp intelligent automatic control system that the present invention is based on decision tree realizes street lamp is as follows:
First, the described camera described Centralized Controller installed and described transducer gather vehicle fleet size, intensity of illumination, temperature, humidity, errors in visibility data in described street lamp site environment section; Secondly, its identification code and described environmental data are passed to the described control strategy automatic decision module of described Street lamps control unit by described Centralized Controller by wireless network (GSM/3G); Then, described control strategy automatic decision module, according to the decision model preset, determines corresponding Street lamps control operational order; Then, described control operation instruction is passed to Street lamps control module by described control strategy automatic decision module, and described control operation instruction is delivered to described Centralized Controller by wireless network by described Street lamps control module; Finally, described control operation instruction is passed to described Node Controller by the mode of power carrier by described Centralized Controller, and described Node Controller carries out regulable control to the brightness of described street lamp or switch.
For two-way link designs between Centralized Controller of the present invention and described Street lamps control unit, video sensing device is increased at described Centralized Controller, the getting sth into one's head property of artificial design road lamp switch and brightness regulation can be reduced, what meet passerby according to the actual conditions in section uses lamp needs, the conservation of power energy.
The present invention introduces described control strategy automatic decision module and described self-learning module, by machine learning and automatic decision method are incorporated in described Street lamps control unit, give street lamp control system wisdom, in the process that street lamp control system can be allowed to use in reality, possess the intelligence of the mankind gradually, thus realize unattended operation or manage without the need to the street lamp Automated condtrol of manual intervention.
The street lamp intelligent automatic control system that the present invention is based on decision tree, by constantly obtaining and learn operating process and the decision-making of road lamp management person, can replace road lamp management person gradually and automatically be controlled street lamp by machine completely, decrease human cost; And the Street lamps control solution bank of described self-learning module accumulation, directly can being shared by other sections and use, without the need to manually observing detection for a long time to all sections, just automatically can realize the automatic deployment of optimum energy-saving scheme.
More than describe preferred embodiment of the present invention in detail, should be appreciated that those of ordinary skill in the art just design according to the present invention can make many modifications and variations without the need to creative work.Therefore, all technical staff in the art according to the present invention's design on prior art basis by logic analysis, reasoning or according to the available technical scheme of limited experiment, all should by among the determined protection range of these claims.
Claims (10)
1. the street lamp intelligent automatic control system based on decision tree, comprise street lamp, Node Controller, Centralized Controller and Street lamps control unit, it is characterized in that: described street lamp, described Node Controller, described Centralized Controller is connected successively with described Street lamps control unit, be two-way link between described Centralized Controller and described Street lamps control unit, described Centralized Controller is equiped with camera and transducer, described Street lamps control unit has self-learning module and control strategy automatic decision module, described camera and described transducer gather the site environment data of described street lamp, described environmental data is fed back to described Street lamps control unit by described Centralized Controller, described self-learning module and described control strategy automatic decision module carry out corresponding automation configuration according to described environmental data, described Street lamps control unit controls automatically to the brightness of described street lamp or switch.
2., as claimed in claim 1 based on the street lamp intelligent automatic control system of decision tree, it is characterized in that: described camera and described transducer can gather vehicle fleet size, intensity of illumination, temperature, humidity, errors in visibility data in described street lamp site environment section.
3., as claimed in claim 1 based on the street lamp intelligent automatic control system of decision tree, it is characterized in that: its identification code and described environmental data are passed to described Street lamps control unit by wireless network by described Centralized Controller.
4. as claimed in claim 1 based on the street lamp intelligent automatic control system of decision tree, it is characterized in that: the control operation instruction of described Street lamps control unit is passed to described Node Controller by the mode of power carrier by described Centralized Controller, described Node Controller carries out regulable control to the brightness of described street lamp or switch.
5. as claimed in claim 1 based on the street lamp intelligent automatic control system of decision tree, it is characterized in that: described self-learning module adopts decision Tree algorithms, in conjunction with the environmental aspect in the section, described environmental data analysis current described street lamp place that described Centralized Controller returns.
6., as claimed in claim 1 based on the street lamp intelligent automatic control system of decision tree, it is characterized in that: when the operation of road lamp management person to described Street lamps control unit adjusts and revise, the information of described adjustment and correction will pass to described self-learning module.
7. as claimed in claim 6 based on the street lamp intelligent automatic control system of decision tree, it is characterized in that: described self-learning module adopts the decision-tree model of machine learning, build Street lamps control decision-tree model, and described Street lamps control decision-tree model is passed to described control strategy automatic decision module.
8. as claimed in claim 7 based on the street lamp intelligent automatic control system of decision tree, it is characterized in that: after described self-learning module has learnt all Street lamps control conditions by described Street lamps control decision-tree model, systematic evaluation is automatic control mode by turnkey console by described road lamp management person.
9. as claimed in claim 8 based on the street lamp intelligent automatic control system of decision tree, it is characterized in that: in described automatic control mode, the described environmental data at described street lamp scene is directly passed to described control strategy automatic decision module, described control strategy automatic decision module carries out automatic classification to the described environmental data obtained, and determines the control operation instruction of described street lamp.
10. as claimed in claim 9 based on the street lamp intelligent automatic control system of decision tree, it is characterized in that: described control operation instruction is passed to Street lamps control module by described control strategy automatic decision module, and described control operation instruction is delivered to described Centralized Controller by wireless network by described Street lamps control module.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104881674A (en) * | 2015-03-19 | 2015-09-02 | 智联通建筑科技（北京）有限公司 | Identification method and system as well as estimation method and system for lamp on-off state |
| CN105472817A (en) * | 2015-12-29 | 2016-04-06 | 苏州市职业大学 | Environmental change-based intelligent lighting controller for dynamically adjusting LED street lamp |
| CN106594661A (en) * | 2017-01-10 | 2017-04-26 | 武汉大学 | LED multi-lamp-cap illumination control device powered by storage battery |
| CN107087326A (en) * | 2017-05-25 | 2017-08-22 | 胡汉 | A kind of Intelligent indoor lamp lighting system |
| CN108200690A (en) * | 2018-01-10 | 2018-06-22 | 安徽极光照明工程有限公司 | A kind of light intensity self-adaptive formula is scattered formula LED light lamp light control system |
| CN108200704A (en) * | 2018-01-02 | 2018-06-22 | 刘博洋 | A kind of street lamp control system and control method |
| CN109327944A (en) * | 2018-09-14 | 2019-02-12 | 中国联合网络通信集团有限公司 | Intelligent lighting controls method and system |
| CN111083861A (en) * | 2019-11-20 | 2020-04-28 | 万翼科技有限公司 | Street lamp optimization control method and device, computer equipment and storage medium |
| CN112085076A (en) * | 2020-08-27 | 2020-12-15 | 河北智达光电科技股份有限公司 | Decision-making method and device based on smart street lamp big data and terminal |
| CN113721496A (en) * | 2021-07-01 | 2021-11-30 | 上海乾隆节能科技有限公司 | Centralized controller host, centralized controller extension slave, street lamp centralized controller system of extension |
| CN114554665A (en) * | 2020-11-25 | 2022-05-27 | 武汉市奥拓智能科技有限公司 | Street lamp group control system and method |
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Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104881674A (en) * | 2015-03-19 | 2015-09-02 | 智联通建筑科技（北京）有限公司 | Identification method and system as well as estimation method and system for lamp on-off state |
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| CN105472817A (en) * | 2015-12-29 | 2016-04-06 | 苏州市职业大学 | Environmental change-based intelligent lighting controller for dynamically adjusting LED street lamp |
| CN106594661B (en) * | 2017-01-10 | 2019-02-26 | 武汉大学 | A kind of more lamp cap illumination control apparatus of LED with storage battery power supply |
| CN106594661A (en) * | 2017-01-10 | 2017-04-26 | 武汉大学 | LED multi-lamp-cap illumination control device powered by storage battery |
| CN107087326A (en) * | 2017-05-25 | 2017-08-22 | 胡汉 | A kind of Intelligent indoor lamp lighting system |
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| CN111083861A (en) * | 2019-11-20 | 2020-04-28 | 万翼科技有限公司 | Street lamp optimization control method and device, computer equipment and storage medium |
| CN111083861B (en) * | 2019-11-20 | 2022-01-25 | 万翼科技有限公司 | Street lamp optimization control method and device, computer equipment and storage medium |
| CN112085076A (en) * | 2020-08-27 | 2020-12-15 | 河北智达光电科技股份有限公司 | Decision-making method and device based on smart street lamp big data and terminal |
| CN114554665A (en) * | 2020-11-25 | 2022-05-27 | 武汉市奥拓智能科技有限公司 | Street lamp group control system and method |
| CN114554665B (en) * | 2020-11-25 | 2023-08-29 | 武汉市奥拓智能科技有限公司 | Street lamp grouping control system and method |
| CN113721496A (en) * | 2021-07-01 | 2021-11-30 | 上海乾隆节能科技有限公司 | Centralized controller host, centralized controller extension slave, street lamp centralized controller system of extension |
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