CN114126157A - Wireless sensor network-based adaptive dimming intelligent street lamp monitoring system and method - Google Patents
Wireless sensor network-based adaptive dimming intelligent street lamp monitoring system and method Download PDFInfo
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Abstract
The invention discloses a wireless sensor network-based adaptive dimming intelligent street lamp monitoring system and a method, wherein the system comprises a street lamp node sensing control module, a logic control module, a wireless communication module and a system terminal, the street lamp node sensing control module is arranged on a street lamp post and used for controlling dimming of a street lamp, collecting information such as electric quantity, light intensity and traffic flow and sending the collected information to the logic control module, the logic control module receives the uploaded data and then sends the data to the system terminal through the wireless communication module, and the system terminal adopts a dimming algorithm to analyze and process the uploaded data. The invention utilizes the data communication capability of the wireless sensor network, adopts a banded non-uniform clustering network topology structure to realize data transmission, and improves the monitoring range and the positioning precision of the system in a data fusion mode; the improved self-adaptive light path lamp illumination algorithm based on K-means + + and a support vector machine is provided, and the lamp light of the street lamp is comprehensively adjusted.
Description
Technical Field
The invention relates to an intelligent dimming street lamp monitoring technology, in particular to a wireless sensor network-based self-adaptive dimming intelligent street lamp monitoring system and method.
Background
The street lamps are important components of urban road illumination, and along with the development of smart cities, the expansion of urban roads and the travel demands of people, the number of urban road street lamps in each region is increased progressively at a linear speed. And in the remote street in city, the daily flow of people and the traffic is rare relatively, and at this moment, if the street lamp still uses the constant light illumination every day, will cause the waste of unnecessary electric power resource certainly, and in the long run, also have not little loss to the life-span of street lamp. However, currently, the street lamp switch control modes in most urban areas still adopt 3 modes of manual operation, time switch and induction control. The problems of electric energy waste and low intelligent degree of illumination are easily caused by adopting a manual control mode. Although the energy-saving effect is improved by the mode of regularly controlling the dimming level according to the pedestrian flow and the vehicle flow in different time periods, the method still has a large promotion space. Compared with the former 2 control modes, the induction type control method combines light control and traffic flow induction, the intelligent degree of illumination is improved, but the electric energy is wasted in different degrees due to single dimming level.
The meaning of the street lamp monitoring system of the smart city not only lies in responding to the call of the smart city, and saving the city operation cost to a certain extent, but also can improve the city management and service level through advanced technology and design concept, and make a contribution to creating a green ecological high-tech living environment.
Disclosure of Invention
The invention aims to provide an intelligent self-adaptive dimming street lamp monitoring system and method based on a wireless sensor network, which are used for solving the problems that the existing street lamp wastes manpower, is complex in operation, wastes energy and the like.
The technical scheme for realizing the purpose of the invention is as follows: a wireless sensor network-based adaptive dimming intelligent street lamp monitoring system comprises a street lamp node sensing control module, a logic control module, a wireless communication module and a system terminal;
the street lamp node sensing control module is arranged on a street lamp post and used for controlling the dimming of the street lamp, acquiring the information of electric quantity, light intensity and traffic flow and sending the acquired information to the logic control module;
the logic control module receives the uploaded data and sends the data to the system terminal through the wireless communication module, the system terminal analyzes and processes the uploaded data by adopting a dimming algorithm, a processing result is fed back to the street lamp node sensing control module, the street lamp is dimmed, relevant information is displayed on a system interface, and if the street lamp node sensing control module is fault information, the system sends positioning information.
A wireless sensor network-based adaptive dimming intelligent street lamp monitoring method comprises the following steps:
the street lamp node sensing control module acquires the electric quantity, light intensity and traffic flow information of the street lamp nodes and sends the acquired information to the logic control module;
after receiving the uploaded data, the logic control module sends the data to a system terminal through a wireless communication module;
the system terminal analyzes and processes the uploaded data by adopting a dimming algorithm, feeds back a processing result to the street lamp node sensing control module, dims the street lamp and displays related information on a system interface, and if the street lamp is fault information, the system sends positioning information; the dimming algorithm is an improved adaptive light path lamp lighting algorithm based on K-means + + and supporting a vector machine, and comprises the following steps:
step S101, a system terminal is initialized firstly;
step S102, inputting and reading ambient illuminance and traffic flow data of the street lamp to be dimmed;
step S103, performing cluster analysis on the light intensity illumination and traffic flow data, and dividing the light intensity illumination and the traffic flow into response levels;
step S104, obtaining a characteristic vector (illuminance grade, traffic flow grade and time) after clustering analysis;
s105, performing inter-class separability measurement on different classes, and establishing an initial operation form to obtain a directed acyclic graph topological structure;
step S106, establishing different kernel function support vector machine classifiers matched with corresponding dimming levels;
step S107, generating six classification models of dimming 20%, dimming 40%, dimming 60%, dimming 80% and 100% lighting, and training the models by using a part of collected data;
step S108, executing step S109 if the model generated in step S104 is matched with the model generated in step S104, and executing step S107 if the model is not matched with the model generated in step S;
step S109, carrying out SVM dimming prediction by using the other part of collected data;
and step S110, the system terminal sends out a dimming command to control the street lamp and can check related information on an interface.
The invention utilizes the data communication ability of the wireless sensor network, realize the cooperative work of the multinode, and improve the monitoring range and detection precision of the system through the way of data fusion; (2) according to the invention, a banded non-uniform clustering network topology structure is adopted for street lamp distribution characteristics, and inter-cluster communication is balanced by adopting non-uniform competition radius concept periodic election cluster head nodes, so that cluster head nodes close to a sink node have smaller competition, the influence of a 'hot zone' problem in multi-hop routing of a wireless sensor network on a system is reduced, and the clustering network is suitable for large-scale networking of street lamps, has the characteristics of good network expansibility, convenience for energy management, load balance, resource allocation and the like; (3) aiming at the problem that the existing street lamp control technology control and street lamp dimming strategy are too single to achieve lighting as required in a complex and changeable environment, the invention provides an improved adaptive light path lamp lighting algorithm based on K-means + + and a support vector machine, so that the light of the street lamp is comprehensively adjusted, and the waste of power resources is reduced.
Drawings
Fig. 1 is a structural frame diagram of a node of an adaptive dimming intelligent street lamp monitoring system based on a wireless sensor network.
Fig. 2 is a schematic diagram of a strip-shaped non-uniform clustering network topology structure of the wireless sensor network-based adaptive dimming intelligent street lamp monitoring system.
Fig. 3 is a flow chart of an improved adaptive dimming path lamp illumination algorithm based on K-means + + and a support vector machine for the adaptive dimming intelligent street lamp monitoring system based on the wireless sensor network.
Detailed Description
As shown in fig. 1, an adaptive dimming intelligent street lamp monitoring system based on a wireless sensor network includes a street lamp node sensing control module, a logic control module, a wireless communication module and a system terminal;
the street lamp node sensing control module is arranged on a street lamp post and used for controlling the dimming of the street lamp, acquiring the information of electric quantity, light intensity and traffic flow and sending the acquired information to the logic control module;
the logic control module receives the uploaded data and sends the data to the system terminal through the wireless communication module, the system terminal analyzes and processes the uploaded data by adopting a dimming algorithm, a processing result is fed back to the street lamp node sensing control module, the street lamp is dimmed, and related information is displayed on a system interface, if the street lamp node sensing control module is fault information, the system sends positioning information to remind a manager;
the street lamp node sensing control module is used for collecting, amplifying and filtering electric quantity, light intensity and traffic flow information of the surrounding environment of the street lamp, exchanging data among nodes and uploading the collected data to the logic control module through a Zigbee network; the logic control module is used for the workflow control, data acquisition and analysis, data fusion and wireless communication management of the nodes; the wireless communication module is used for the communication function between the logic control module and the system terminal; the system terminal is used for man-machine interaction and storing various collected information.
The street lamp node sensing control module mainly comprises a CC2530 minimum system, a traffic microwave detection circuit, a light intensity acquisition circuit, a 0-10V dimming circuit and a fault detection circuit; the CC2630 wireless radio frequency chip is a new generation of double ARM core-32 bit CPU chip, can form a larger and more stable network, and supports the transmission of data packets with larger capacity; and a strip-shaped non-uniform clustering network topology structure as shown in fig. 2 is adopted to realize data transmission, wherein the nodes in the cluster are used for collecting data and forwarding the data to the cluster head nodes, and the cluster head nodes play the role of routers and finally forward the data to the sink nodes. The monitoring range and the positioning precision of the system are improved by the sink node in a data fusion mode. The traffic microwave detection circuit is used as a sensitive unit for sensing traffic in a road, the 0-10V dimming circuit is used for responding to a dimming command sent by a system terminal to enable the street lamp to automatically dim light, and the fault detection circuit is used for detecting whether the street lamp is in fault or not.
The logic control module mainly comprises an STM32 processor, a peripheral circuit thereof and a BDS positioning module; the STM32 processor and the peripheral circuit thereof complete the work flow control and data fusion of the street lamp node sensing control module and the connection management of Zigbee and GPRS; the BDS positioning module is used for determining the position of a fault circuit.
The wireless communication module mainly realizes connection between a Zigbee network and wifi and sends the acquired and processed data to the system terminal for further processing and displaying.
The system terminal adopts QT platform design development and embeds improved adaptive light path lamp illumination algorithm based on K-means + + and support vector machine to realize man-machine interaction, and the algorithm comprises:
1) collecting ambient illuminance and traffic flow data of different time periods and road sections, and preprocessing the illuminance and traffic flow data due to the overlarge change range of the illuminance and the traffic flow; clustering the illuminance and traffic flow data by using a K-means + + algorithm to divide the illuminance and traffic flow data into different grades;
2) taking the preprocessed data as new characteristic data Xi＝{x1,x2,x3}, characteristic attribute x1、x2、x3Respectively representing the illumination intensity level, the traffic flow level and the time period;
3) the method comprises the following steps of improving the speed of solving the optimal solution of a support vector machine by adopting a minimum sequence optimization algorithm, constructing a six-classification light modulation model based on the support vector machine of the directed acyclic graph by using the support vector machine of linear, polynomial and Gaussian kernel functions, realizing multi-classification on the basis of two classifications of the support vector machine according to the algorithm principle, constructing a classification hyperplane between every two classifications by using the algorithm, and constructing the classification hyperplanes into a directed acyclic graph structure; the algorithm starts to classify from a top root node, and then determines the classification direction according to the output result of classification until the samples are classified into six classes;
4) classifying the sample data into six classes based on a directed acyclic graph support vector machine classification method, and attaching a dimming level label to the sample data according to the actual situation; six categories respectively represent lamp turning off, dimming 20%, dimming 40%, dimming 60%, dimming 80% and 100% lamp turning on, wherein different brightness is realized by PWM;
5) the final classification result of the directed acyclic graph directly influences the classification effect when the arrangement sequence of the classes is different, so that the separability measurement needs to be carried out on different classes; evaluating the separation property among various classes by adopting class distribution-based inter-class separation measure, establishing an initial operation form, rearranging the class sequence to obtain a directed acyclic graph topological structure;
6) in order to guarantee the classification effect, different kernel functions are used to guarantee the classification precision during classification, through tests, linear kernel functions are used during construction of 2vs3, 3vs4, 4vs5 and 5vs6 classifiers, Gaussian kernel functions are used during construction of 1vs3 and 1vs2 classifiers, and the polynomial kernel functions are used by other classifiers to remarkably improve the classification precision and enable dimming to be more accurate and intelligent.
The invention also provides a monitoring method based on the monitoring system, which comprises the following steps:
the street lamp node sensing control module acquires the electric quantity, light intensity and traffic flow information of the street lamp nodes and sends the acquired information to the logic control module;
after receiving the uploaded data, the logic control module sends the data to a system terminal through a wireless communication module;
the system terminal analyzes and processes the uploaded data by adopting a dimming algorithm, feeds back a processing result to the street lamp node sensing control module, dims the street lamp and displays related information on a system interface, and if the street lamp is fault information, the system sends positioning information; as shown in fig. 3, the dimming algorithm is a modified adaptive dimming path lamp lighting algorithm based on K-means + + and a support vector machine, and the algorithm includes:
step S101, a system terminal is initialized firstly;
step S102, inputting and reading ambient illuminance and traffic flow data of the street lamp to be dimmed;
step S103, performing cluster analysis on the light intensity illumination and traffic flow data, and dividing the light intensity illumination and the traffic flow into response levels;
step S104, obtaining a characteristic vector (illuminance grade, traffic flow grade and time) after clustering analysis;
s105, performing inter-class separability measurement on different classes, and establishing an initial operation form to obtain a directed acyclic graph topological structure;
step S106, establishing different kernel function support vector machine classifiers matched with corresponding dimming levels;
step S107, generating six classification models of dimming 20%, dimming 40%, dimming 60%, dimming 80% and 100% lighting, and training the models by using a part of collected data;
step S108, executing step S109 if the model generated in step S104 is matched with the model generated in step S104, and executing step S107 if the model is not matched with the model generated in step S;
step S109, carrying out SVM dimming prediction by using the other part of collected data;
and step S110, the system terminal sends out a dimming command to control the street lamp and can check related information on an interface.
Claims (7)
1. A wireless sensor network-based self-adaptive dimming intelligent street lamp monitoring system is characterized by comprising a street lamp node sensing control module, a logic control module, a wireless communication module and a system terminal;
the street lamp node sensing control module is arranged on a street lamp post and used for controlling the dimming of the street lamp, collecting the information of electric quantity, light intensity and traffic flow and sending the collected information to the logic control module;
the logic control module receives the uploaded data and sends the data to the system terminal through the wireless communication module, the system terminal analyzes and processes the uploaded data through a dimming algorithm, a processing result is fed back to the street lamp node sensing control module, dimming is carried out on the street lamp, relevant information is displayed on a system interface, and if the street lamp node sensing control module is fault information, the system sends positioning information.
2. The intelligent street lamp monitoring system based on the wireless sensor network adaptive dimming of claim 1, wherein the street lamp node sensing control module is used for realizing data exchange between nodes and uploading collected data to the logic control module through a Zigbee network.
3. The intelligent street lamp monitoring system based on the wireless sensor network adaptive dimming as claimed in claim 1 or 2, wherein the street lamp node sensing control module comprises a CC2530 minimum system, a traffic microwave detection circuit, a light intensity acquisition circuit, a 0-10V dimming circuit and a fault detection circuit; the CC2530 minimum system adopts a strip-shaped non-uniform clustering network topology structure to realize data transmission, a traffic microwave detection circuit is used as a sensitive unit for sensing traffic in a road, a light intensity acquisition circuit is used for acquiring light intensity, a 0-10V dimming circuit is used for automatically dimming a street lamp, and a fault detection circuit is used for detecting street lamp faults.
4. The adaptive dimming intelligent street lamp monitoring system based on the wireless sensor network as claimed in claim 3, wherein the strip-shaped non-uniform clustering network topology structure comprises cluster nodes, cluster head nodes and sink nodes, the cluster nodes are used for collecting data and forwarding the data to the cluster head nodes, and the cluster head nodes forward the data to the sink nodes.
5. The intelligent street lamp monitoring system based on the wireless sensor network adaptive dimming as claimed in claim 3, wherein the logic control module mainly comprises an STM32 processor and peripheral circuits thereof and a BDS positioning module; the STM32 processor and peripheral circuits thereof are used for completing the workflow control and data fusion of the street lamp node sensing control module and the connection management of Zigbee and GPRS; the BDS positioning module is used for determining the position of a fault circuit.
6. The system of claim 3, wherein the wireless communication module is configured to connect a Zigbee network to wifi and send the collected and processed data to the system terminal for further processing and display.
7. The monitoring method of the wireless sensor network adaptive dimming intelligent street lamp monitoring system based on claim 1 is characterized by comprising the following steps:
the street lamp node sensing control module acquires the electric quantity, light intensity and traffic flow information of the street lamp nodes and sends the acquired information to the logic control module;
after receiving the uploaded data, the logic control module sends the data to a system terminal through a wireless communication module;
the system terminal analyzes and processes the uploaded data by adopting a dimming algorithm, feeds back a processing result to the street lamp node sensing control module, dims the street lamp and displays related information on a system interface, and if the street lamp node sensing control module is fault information, the system sends positioning information; the dimming algorithm is an improved adaptive light path lamp illumination algorithm based on K-means + + and a support vector machine, and comprises the following steps:
step S101, a system terminal is initialized firstly;
step S102, inputting and reading ambient illuminance and traffic flow data of the street lamp to be dimmed;
step S103, performing cluster analysis on the light intensity illumination and traffic flow data, and dividing the light intensity illumination and the traffic flow into response levels;
step S104, obtaining a characteristic vector (illuminance grade, traffic flow grade and time) after clustering analysis;
s105, performing inter-class separability measurement on different classes, and establishing an initial operation form to obtain a directed acyclic graph topological structure;
step S106, establishing different kernel function support vector machine classifiers matched with corresponding dimming levels;
step S107, generating six classification models of dimming 20%, dimming 40%, dimming 60%, dimming 80% and 100% lighting, and training the models by using a part of collected data;
step S108, executing step S109 if the model generated in step S104 is matched with the model generated in step S104, and executing step S107 if the model is not matched with the model generated in step S;
step S109, carrying out SVM dimming prediction by using the other part of collected data;
and step S110, the system terminal sends out a dimming instruction to control the street lamp, and relevant information can be checked on an interface.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104918362A (en) * | 2015-05-13 | 2015-09-16 | 苏州科技学院 | Intelligent monitoring and management system of wind-solar complementary power supply street lamps and device thereof |
| CN110288048A (en) * | 2019-07-02 | 2019-09-27 | 东北大学 | A kind of submarine pipeline methods of risk assessment of SVM directed acyclic graph |
| CN110401262A (en) * | 2019-06-17 | 2019-11-01 | 北京许继电气有限公司 | GIS device state intelligent monitoring system and method based on edge calculations technology |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104918362A (en) * | 2015-05-13 | 2015-09-16 | 苏州科技学院 | Intelligent monitoring and management system of wind-solar complementary power supply street lamps and device thereof |
| CN110401262A (en) * | 2019-06-17 | 2019-11-01 | 北京许继电气有限公司 | GIS device state intelligent monitoring system and method based on edge calculations technology |
| CN110288048A (en) * | 2019-07-02 | 2019-09-27 | 东北大学 | A kind of submarine pipeline methods of risk assessment of SVM directed acyclic graph |
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