CN110913542A - Street lamp controller, street lamp control system and street lamp control method - Google Patents
Street lamp controller, street lamp control system and street lamp control methodDownload PDFInfo
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- CN110913542A CN110913542ACN201911191271.9ACN201911191271ACN110913542ACN 110913542 ACN110913542 ACN 110913542ACN 201911191271 ACN201911191271 ACN 201911191271ACN 110913542 ACN110913542 ACN 110913542A
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- 238000000034methodMethods0.000titleclaimsabstractdescription28
- 238000012544monitoring processMethods0.000claimsabstractdescription30
- 238000001514detection methodMethods0.000claimsabstractdescription26
- 238000012545processingMethods0.000claimsabstractdescription17
- 238000004458analytical methodMethods0.000claimsabstractdescription7
- 238000010223real-time analysisMethods0.000claimsabstractdescription7
- 238000012423maintenanceMethods0.000claimsdescription18
- 238000013135deep learningMethods0.000claimsdescription8
- 230000003993interactionEffects0.000claimsdescription4
- 238000004891communicationMethods0.000abstractdescription9
- 238000010586diagramMethods0.000description2
- 238000012549trainingMethods0.000description2
- 206010033799ParalysisDiseases0.000description1
- 230000009286beneficial effectEffects0.000description1
- 230000006870functionEffects0.000description1
- 238000012986modificationMethods0.000description1
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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
Description
Technical Field
The embodiment of the invention relates to the technical field of street lamp control equipment, in particular to a street lamp controller, a street lamp control system and a street lamp control method.
Background
Most of the existing street lamps are directly controlled by an upper computer. The street lamp controller takes an upper computer as a controller, and the control of all street lamps depends on the upper computer. When the street lamp body is used, usually, the street lamp collects road surface information and sends the road surface information to the upper computer, and the upper computer analyzes and judges whether pedestrians exist or not, so that the street lamp is controlled to be lightened. However, the existing centralized system architecture increases the number of street lamps, road information at the ends of the street lamps is increased, frequent communication is needed between the street lamps and the upper computer, huge load is caused to the upper computer, data processing pressure borne by the upper computer is also increased in multiples, the processing capacity of the upper computer becomes a bottleneck of the whole control system, and when the upper computer is down, the whole control system is in a paralysis state.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is to provide a street lamp controller, which can effectively reduce the working strength of an upper computer and improve the control efficiency.
The technical problem to be solved by the embodiment of the invention is to provide a street lamp control system, which can effectively reduce the working strength of an upper computer and improve the control efficiency.
The embodiment of the invention further aims to solve the technical problem of providing a street lamp control method which can effectively reduce the working strength of an upper computer and improve the control efficiency.
In order to solve the above technical problem, an embodiment of the present invention provides the following technical solutions: a street lamp controller is connected with a correspondingly controlled street lamp in a mounting way, and comprises:
the storage module is used for receiving and storing monitoring data which is acquired and uploaded by the corresponding controlled street lamps, and the monitoring data comprises road video images;
and the analysis processing module is used for carrying out real-time analysis processing on the road surface video image by adopting a pre-trained pedestrian detection model when the visibility of the surrounding environment of the street lamp is lower than a preset threshold value, and sending a lamp lighting instruction to control the street lamp to be lighted when the pedestrian is judged to exist in the road surface video image.
Further, the pedestrian detection model is an SSD detection model based on deep learning.
Further, the street lamp controller further comprises:
and the feedback module is used for uploading the monitoring data uploaded by the street lamp to a corresponding upper computer in an idle time period, and the monitoring data further comprises street lamp fault data and use information.
On the other hand, in order to further solve the above technical problem, an embodiment of the present invention provides the following technical solutions: a street lamp control system comprises an upper computer for man-machine interaction and a plurality of street lamp controllers, wherein one ends of the street lamp controllers are connected with the upper computer, the other ends of the street lamp controllers are connected with street lamps correspondingly controlled, the street lamp controllers are any one of the street lamp controllers, and the upper computer receives road surface video images collected by the street lamps and uploaded by the street lamp controllers.
Further, the upper computer is also used for correspondingly generating maintenance information according to the street lamp fault data and the use information uploaded by each street lamp controller and correspondingly issuing the maintenance information to each street lamp controller for prestoring.
In another aspect, to further solve the above technical problem, an embodiment of the present invention provides the following technical solutions: a street light control method, the method comprising:
receiving and storing monitoring data which is acquired and uploaded by a corresponding controlled street lamp, wherein the monitoring data comprises a road video image;
when the visibility of the surrounding environment of the street lamp is lower than a preset threshold value, a pre-trained pedestrian detection model is adopted to analyze and process the road video image in real time, and a lamp lighting instruction is sent to control the street lamp to be lighted when the pedestrian is judged to exist in the road video image.
Further, the pedestrian detection model is an SSD detection model based on deep learning.
Further, the method further comprises:
and uploading the monitoring data uploaded by the street lamp to a corresponding upper computer in an idle time period, wherein the monitoring data further comprises street lamp fault data and use information.
Further, the method further comprises:
and the upper computer correspondingly generates maintenance information according to the street lamp fault data and the use information uploaded by each street lamp controller and correspondingly issues the maintenance information to each street lamp controller for pre-storage.
After the technical scheme is adopted, the embodiment of the invention at least has the following beneficial effects: according to the embodiment of the invention, the storage module of each street lamp controller firstly receives and stores the road surface video image which is acquired and uploaded by the corresponding controlled street lamp, then the analysis processing module automatically adopts the pre-trained pedestrian detection model to perform real-time analysis processing on the road surface video image when the visibility of the surrounding environment of the street lamp is lower than the preset threshold value, and sends the lighting instruction to control the lighting of the street lamp when the pedestrian is judged to exist in the road surface video image, so that the lighting of the street lamp can be automatically controlled without passing through an upper computer, the communication data volume of the upper computer in busy hours is greatly reduced, the working pressure of the upper computer is effectively reduced, and the street lamp control efficiency is higher.
Drawings
Fig. 1 is a schematic block diagram of a single street lamp, a single street lamp controller, and a connection between an upper computer according to an alternative embodiment of the street lamp controller of the present invention.
Fig. 2 is a schematic block diagram of a system architecture of an alternative embodiment of the street lamp control system of the present invention.
Detailed Description
The present application will now be described in further detail with reference to the accompanying drawings and specific examples. It should be understood that the following illustrative embodiments and description are only intended to explain the present invention, and are not intended to limit the present invention, and features of the embodiments and examples in the present application may be combined with each other without conflict.
As shown in fig. 1, an alternative embodiment of the present invention provides astreet lamp controller 1, which is installed and connected to astreet lamp 3 to be controlled correspondingly, where thestreet lamp controller 1 includes:
thestorage module 10 is used for receiving and storing monitoring data acquired and uploaded by the correspondingly controlledstreet lamps 3, wherein the monitoring data comprises road video images;
and theanalysis processing module 12 is configured to, when the visibility of the surrounding environment of thestreet lamp 3 is lower than a predetermined threshold value, perform real-time analysis processing on the road video image by using a pre-trained pedestrian detection model, and send a light-on instruction to control thestreet lamp 3 to be turned on when it is determined that a pedestrian exists in the road video image.
According to the embodiment of the invention, thestorage module 10 of eachstreet lamp controller 1 firstly receives and stores the road surface video image which is acquired and uploaded by the corresponding controlledstreet lamp 3, then theanalysis processing module 12 automatically adopts the pre-trained pedestrian detection model to perform real-time analysis processing on the road surface video image when the visibility of the surrounding environment of the street lamp is lower than the preset threshold value, and sends the lighting instruction to control the lighting of thestreet lamp 3 when the pedestrian is judged to exist in the road surface video image, so that the lighting of thestreet lamp 3 can be automatically controlled without passing through the upper computer 5, the communication data volume of the upper computer 5 in busy hours is greatly reduced, the working pressure of the upper computer is effectively reduced, and the street lamp control efficiency is higher.
In specific implementation, it can be understood that theroad controller 1 collects road video images 24 hours in a whole day corresponding to the controlledstreet lamps 3, and theanalysis processing module 12 of theroad controller 1 only analyzes and processes the road video images in real time when the visibility of the surrounding environment of thestreet lamps 3 is lower than a predetermined threshold, so that thestreet lamps 3 can monitor the road in real time, and on the other hand, the lighting of thestreet lamps 3 can be effectively controlled.
In an optional embodiment of the present invention, the pedestrian detection model is a deep learning based SSD (single shot multi box Detector) detection model. In the embodiment, the SSD detection model based on deep learning is adopted, and the SSD detection model is generated through a large amount of training in advance, so that pedestrians can be efficiently and accurately identified in the road video image.
In yet another optional embodiment of the present invention, thestreet light controller 1 further comprises:
and thefeedback module 14 is used for uploading the monitoring data uploaded by thestreet lamp 3 to a corresponding upper computer in an idle time period, wherein the monitoring data further comprises street lamp fault data and use information. In this embodiment, thefeedback module 14 uploads the monitoring data uploaded by thestreet lamps 3 to the corresponding upper computer 5 in an idle time period, and the monitoring data further includes street lamp fault data and use information, so that the communication resources in an idle time are reasonably utilized, the communication data amount of the upper computer 5 in a busy time is effectively reduced, and the upper computer 5 is convenient to process and manually interact, so that the running state of eachstreet lamp 3 can be known in real time.
On the other hand, in order to further solve the above technical problem, an embodiment of the present invention provides the following technical solutions: a street lamp control system comprises an upper computer 5 used for man-machine interaction and a plurality ofstreet lamp controllers 3, wherein one ends of the street lamp controllers are connected with the upper computer 5, the other ends of the street lamp controllers are connected withstreet lamps 3 correspondingly controlled, thestreet lamp controllers 1 are any one of the street lamp controllers, and the upper computer 5 receives road surface video images collected by thestreet lamps 3 and uploaded by thestreet lamp controllers 1. The street lamp control system provided by the embodiment of the invention adopts thestreet lamp controller 1, so that the working pressure of an upper computer is effectively reduced, and the street lamp control efficiency is higher.
In another optional embodiment of the present invention, the upper computer 5 is further configured to generate maintenance information according to the street lamp fault data and the usage information uploaded by eachstreet lamp controller 1, and issue the maintenance information to eachstreet lamp controller 1 for pre-storage. In the embodiment, the street lamp fault data and the use information uploaded by thestreet lamp controller 1 are analyzed and processed through the upper computer 5, the maintenance information is correspondingly generated for thestreet lamp controller 1, and the maintenance personnel can conveniently correspondingly maintain thestreet lamp 3 according to the timely checking of the maintenance information.
In another aspect, to further solve the above technical problem, an embodiment of the present invention provides the following technical solutions: a street light control method, the method comprising:
s1: receiving and storing monitoring data which is acquired and uploaded by the correspondingly controlledstreet lamps 3, wherein the monitoring data comprises road surface video images;
s2: when the visibility of the surrounding environment of thestreet lamp 3 is lower than a preset threshold value, a pre-trained pedestrian detection model is adopted to perform real-time analysis processing on the road video image, and a lamp lighting instruction is sent to control thestreet lamp 3 to be lighted when it is judged that pedestrians exist in the road video image.
According to the embodiment of the invention, by the method, the road surface video image collected and uploaded by the corresponding controlledstreet lamp 3 is received and stored, then the pre-trained pedestrian detection model is adopted to analyze and process the road surface video image in real time when the visibility of the surrounding environment of the street lamp is lower than the preset threshold value by oneself, and the lighting instruction is sent out to control the lighting of thestreet lamp 3 when the pedestrian is judged to exist in the road surface video image, so that the lighting of thestreet lamp 3 can be automatically controlled without passing through the upper computer 5, the communication data volume of the upper computer 5 in busy hours is greatly reduced, the working pressure of the upper computer is effectively reduced, and the street lamp control efficiency is higher.
In another optional embodiment of the invention, the pedestrian detection model is a deep learning based SSD detection model. In the embodiment, the SSD detection model based on deep learning is adopted, and the SSD detection model is generated through a large amount of training in advance, so that pedestrians can be efficiently and accurately identified in the road video image.
In an optional embodiment of the invention, the method further comprises:
s3: and uploading the monitoring data uploaded by thestreet lamp 3 to a corresponding upper computer 5 in an idle time period, wherein the monitoring data further comprises street lamp fault data and use information.
According to the method, the monitoring data uploaded by thestreet lamps 3 are uploaded to the corresponding upper computer 5 in an idle time period, the monitoring data further comprise street lamp fault data and use information, idle communication resources are reasonably utilized, busy communication data volume of the upper computer 5 is effectively reduced, processing and manual interaction of the upper computer 5 are facilitated, and therefore the running state of eachstreet lamp 3 can be known in real time.
In yet another optional embodiment of the invention, the method further comprises:
s4: the upper computer 5 correspondingly generates maintenance information according to the street lamp fault data and the use information uploaded by eachstreet lamp controller 1 and correspondingly issues the maintenance information to eachstreet lamp controller 1 to be prestored.
According to the method, the street lamp fault data and the use information uploaded by thestreet lamp controller 1 are analyzed and processed, the maintenance information is correspondingly generated and prestored in thestreet lamp controller 1, and the maintenance personnel can conveniently check the maintenance information in time to correspondingly maintain thestreet lamp 3.
The functions described in the embodiments of the present invention may be stored in a storage medium readable by a computing device if they are implemented in the form of software functional modules or units and sold or used as independent products. Based on such understanding, part of the contribution of the embodiments of the present invention to the prior art or part of the technical solution may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computing device (which may be a personal computer, a server, a mobile computing device, a network device, or the like) to execute all or part of the steps of the method described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (9)
1. A street lamp controller is connected with a correspondingly controlled street lamp in an installing way, and is characterized by comprising:
the storage module is used for receiving and storing monitoring data which is acquired and uploaded by the corresponding controlled street lamps, and the monitoring data comprises road video images;
and the analysis processing module is used for carrying out real-time analysis processing on the road surface video image by adopting a pre-trained pedestrian detection model when the visibility of the surrounding environment of the street lamp is lower than a preset threshold value, and sending a lamp lighting instruction to control the street lamp to be lighted when the pedestrian is judged to exist in the road surface video image.
2. The street light controller of claim 1, wherein the pedestrian detection model is a deep learning based SSD detection model.
3. The street light controller of claim 1, further comprising:
and the feedback module is used for uploading the monitoring data uploaded by the street lamp to a corresponding upper computer in an idle time period, and the monitoring data further comprises street lamp fault data and use information.
4. A street lamp control system comprises an upper computer for man-machine interaction and a plurality of street lamp controllers, wherein one end of each street lamp controller is connected with the upper computer, and the other end of each street lamp controller is connected with a street lamp to be correspondingly controlled, the street lamp controllers are as claimed in any one of claims 1 to 3, and the upper computer receives road surface video images collected by the street lamps and uploaded by the street lamp controllers.
5. The street lamp control system according to claim 4, wherein the upper computer is further configured to generate maintenance information according to the street lamp fault data and the usage information uploaded by each street lamp controller, and issue the maintenance information to each street lamp controller for pre-storage.
6. A street lamp control method, characterized in that the method comprises:
receiving and storing monitoring data which is acquired and uploaded by a corresponding controlled street lamp, wherein the monitoring data comprises a road video image;
when the visibility of the surrounding environment of the street lamp is lower than a preset threshold value, a pre-trained pedestrian detection model is adopted to analyze and process the road video image in real time, and a lamp lighting instruction is sent to control the street lamp to be lighted when the pedestrian is judged to exist in the road video image.
7. The street light control system of claim 6, wherein the pedestrian detection model is a deep learning based SSD detection model.
8. The street light control method according to claim 6, characterized in that the method further comprises:
and uploading the monitoring data uploaded by the street lamp to a corresponding upper computer in an idle time period, wherein the monitoring data further comprises street lamp fault data and use information.
9. The street light control method as claimed in claim 8, characterized in that the method further comprises:
and the upper computer correspondingly generates maintenance information according to the street lamp fault data and the use information uploaded by each street lamp controller and correspondingly issues the maintenance information to each street lamp controller for pre-storage.
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| CN201911191271.9ACN110913542A (en) | 2019-11-28 | 2019-11-28 | Street lamp controller, street lamp control system and street lamp control method |
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| CN201911191271.9ACN110913542A (en) | 2019-11-28 | 2019-11-28 | Street lamp controller, street lamp control system and street lamp control method |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR3130354A1 (en) | 2021-12-14 | 2023-06-16 | L'eclairage Technique - Eclatec Sas | Self-configuring and autonomous public lighting device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110133655A1 (en)* | 2006-03-28 | 2011-06-09 | Recker Michael V | Autonomous grid shifting lighting device |
| US8994276B2 (en)* | 2006-03-28 | 2015-03-31 | Wireless Environment, Llc | Grid shifting system for a lighting circuit |
| CN107036019A (en)* | 2017-05-31 | 2017-08-11 | 句容市万福达工艺品厂 | A kind of intelligent road-lamp |
| CN107896407A (en)* | 2018-01-02 | 2018-04-10 | 成都易活动科技有限公司 | A kind of self study road lamp system based on big data |
| CN109152185A (en)* | 2018-10-19 | 2019-01-04 | 中国科学院重庆绿色智能技术研究院 | A kind of multi-source perception intelligent street lamp control system |
| CN109831859A (en)* | 2019-03-15 | 2019-05-31 | 深圳市斯派克光电科技有限公司 | Security protection lighting integrated road lamp system and control method |
- 2019
- 2019-11-28CNCN201911191271.9Apatent/CN110913542A/enactivePending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110133655A1 (en)* | 2006-03-28 | 2011-06-09 | Recker Michael V | Autonomous grid shifting lighting device |
| US8994276B2 (en)* | 2006-03-28 | 2015-03-31 | Wireless Environment, Llc | Grid shifting system for a lighting circuit |
| CN107036019A (en)* | 2017-05-31 | 2017-08-11 | 句容市万福达工艺品厂 | A kind of intelligent road-lamp |
| CN107896407A (en)* | 2018-01-02 | 2018-04-10 | 成都易活动科技有限公司 | A kind of self study road lamp system based on big data |
| CN109152185A (en)* | 2018-10-19 | 2019-01-04 | 中国科学院重庆绿色智能技术研究院 | A kind of multi-source perception intelligent street lamp control system |
| CN109831859A (en)* | 2019-03-15 | 2019-05-31 | 深圳市斯派克光电科技有限公司 | Security protection lighting integrated road lamp system and control method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR3130354A1 (en) | 2021-12-14 | 2023-06-16 | L'eclairage Technique - Eclatec Sas | Self-configuring and autonomous public lighting device |
| EP4199655A1 (en) | 2021-12-14 | 2023-06-21 | L'Eclairage Technique-Eclatec SAS | Self-configuring and self-contained public lighting device |
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