CN102255965A - Test platform for multi-sensor fused Internet of things (IOT) based on rough set BP (Back Propagation) neural network - Google Patents
Test platform for multi-sensor fused Internet of things (IOT) based on rough set BP (Back Propagation) neural network Download PDFInfo
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- CN102255965A CN102255965A CN2011101877801A CN201110187780A CN102255965A CN 102255965 A CN102255965 A CN 102255965A CN 2011101877801 A CN2011101877801 A CN 2011101877801A CN 201110187780 A CN201110187780 A CN 201110187780A CN 102255965 A CN102255965 A CN 102255965A
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Abstract
The invention discloses a test platform for a multi-sensor fused Internet of things (IOT) based on a rough set BP (Back Propagation) neural network. The test platform comprises a host system, a front-end node and an IOT wireless control system, wherein the front-end node comprises a smog sensor, a temperature sensor, a CO (Carbon Monoxide) gas sensor, a humidity sensor, a glass sensor and a RFID (Radio Frequency Identification Device) sensor. According to the technical scheme of the invention, an multi-sensor IOT fusion algorithm based on the rough set BP neural network can be tested on a practical IOT test platform, multiple sensors are used for monitoring various characteristic quantities (such as vibration, temperature, humidity, pressure, flow, and the like), and the information of the sensors is fused, thereby acquiring consistent explanation, description and verification of a target.
Description
Technical field
The present invention relates to Internet of Things multisensor test platform, relate in particular to a kind of Multi-sensor Fusion Internet of Things test platform based on rough set BP neural net.
Background technology
The normal multisensor that uses is monitored (as vibration, temperature, humidity, pressure, flow etc.) to the various features amount in actual Internet of things system, and the information of these transducers is merged, and obtains compatibility of goals and explains and description.Its core is to select suitable blending algorithm, requires to have the ability of robustness, parallel processing, also will guarantee arithmetic speed and given precision.And artificial neural net is to be interconnected by a large amount of basic neurons to form, can carry out distributed parallel handles and non-linear conversion, have powerful study and sum up the function of concluding, processing speed is fast, the advantage that data space is little exactly can satisfy the requirement of multisensor syste to blending algorithm.When using neural net as data fusion model, the input information of network is the various measurement parameters of multisensor to target, and the output of network is to the pattern recognition of target or classification results or other response results.
Also have, traditional neural net also has certain limitation aspect fusion, and it does not possess the preprocessing function to the input sample space.When input feature vector amount dimension was big, neural net is complex structure not only, and the training time prolongs greatly, and real-time is also bad.
At above defective, rough set is incorporated in the Fusion Model of neural net, utilize rough set attribute reduction and do not change the characteristics of classification capacity and the advantage of neural network concurrent disposal ability and powerful fault-tolerant ability, earlier the input feature vector amount is carried out the dimensionality reduction operation, eliminate redundant attributes,, reach and simplify network configuration again via neural metwork training, accelerate network convergence speed, improve the purpose of real-time.
Summary of the invention
The objective of the invention is at above algorithm, a kind of Multi-sensor Fusion Internet of Things experiment porch is provided, can move and verify the feasibility of this algorithm fully.
For achieving the above object, the present invention is achieved through the following technical solutions:
A kind of Multi-sensor Fusion Internet of Things test platform based on rough set BP neural net is made up of host computer system, front end node and Internet of Things wireless control system; The Internet of Things wireless control system comprises: core processor, radio node module and radio-frequency module; Core processor is by radio-frequency module wireless connections radio node module; Core processor is SD card, camera, WIFI module, inductive pick-up and the audio frequency peripheral hardware of access control main frame in parallel inside respectively; Core processor carries out wireless connections by gsm module and user mobile phone; Front end node comprises: Smoke Sensor, temperature sensor, CO gas sensor, temperature sensor, glass sensor, RFID transducer.
The radio node inside modules is provided with the node module core processor, gather the data message or the state of various kinds of sensors by the node module core processor, and send to main control system, and realize respective operations according to the control command of main control system issue by radio-frequency module.
Host computer system comprises: LCDs, VGA module, camera module, mobile module, USB mouse/keyboard.
Adopt technical scheme of the present invention, make the blending algorithm based on the BP neural net of rough set of Internet of Things multisensor in an actual Internet of Things test platform, carry out testing experiment, use multisensor that the various features amount is monitored (as vibration, temperature, humidity, pressure, flow etc.), and the information of these transducers merged, obtain compatibility of goals and explain, describe and checking.
Description of drawings
With embodiment the present invention is described in further detail with reference to the accompanying drawings below.
Fig. 1 is the theory structure schematic diagram of a kind of Multi-sensor Fusion Internet of Things test platform based on rough set BP neural net of the embodiment of the invention;
Fig. 2 is based on the multisensor radio node part theory structure schematic diagram of rough set BP neural net in a kind of Internet of Things experiment porch of the embodiment of the invention.
Among the figure:
1, core processor; 2, radio node module; 3, radio-frequency module; 4, gsm module; 5, SD card; 6, camera; 7, WIFI module; 8, inductive pick-up; 9, audio frequency peripheral hardware; 10, user mobile phone; 21, node module core processor; 22, Smoke Sensor; 23, temperature sensor; 24, CO gas sensor; 25, temperature sensor; 26, glass sensor; 27, RFID transducer.
Embodiment
As shown in Figure 1, multisensor test platform of the present invention comprises: host computer system, front end node and Internet of Things wireless control system, its Internet of Things wireless control system is made up of ARM9 core processor 1, radio node module 2 and radio-frequency module 3, ARM9 core processor 1 is arranged at main control system inside, core processor 1 outside be provided with radio-frequency module 3 and therewith radio-frequency module 3 be reciprocal relation, core processor 1 is by radio-frequency module 3 wireless connections radio node modules 2; Core processor 1 is SD card 5, camera 6, WIFI module 7, inductive pick-up 8 and the audio frequency peripheral hardware 9 of access control main frame in parallel inside respectively, and in addition, core processor 1 carries out wireless connections by gsm module 4 and user mobile phone 10.The groundwork principle: main control system realizes that by core processor 1 control radio-frequency module 3 information of radio nodes gathers and handles, and utilize the mode of note and multimedia message to notify user mobile phone 10 and receive the note order by gsm module 4 and carry out subsequent treatment, realize the Internet of Things controlled in wireless with this.
The host computer system of Internet of Things test platform of the present invention comprises: LCDs, can also expand peripheral apparatus such as VGA module, camera module, mobile module, USB mouse/keyboard; Front end node comprises: Smoke Sensor 22, temperature sensor 23, CO gas sensor 24, temperature sensor 25, glass sensor 26, RFID transducer 27.
As shown in Figure 2, in the Internet of Things test platform of the present invention, radio node module 2 inside are provided with MSP430 core processor 21, this MSP430 core processor 21 connects Smoke Sensor 22 respectively, temperature sensor 23, CO gas sensor 24, temperature sensor 25, glass sensor 26, RFID transducer 27, this radio node module 2 is gathered the data message or the state of various kinds of sensors based on MSP430 core processor design and by MSP430, and sends to main control system and can realize respective operations by the control command that radio-frequency module receive main frame by radio-frequency module 3.
Claims (3)
1. the Multi-sensor Fusion Internet of Things test platform based on rough set BP neural net is characterized in that: be made up of host computer system, front end node and Internet of Things wireless control system; The Internet of Things wireless control system comprises: core processor, radio node module and radio-frequency module; Described core processor is by radio-frequency module wireless connections radio node module; Described core processor is SD card, camera, WIFI module, inductive pick-up and the audio frequency peripheral hardware of access control main frame in parallel inside respectively; Described core processor carries out wireless connections by gsm module and user mobile phone; Described front end node comprises: Smoke Sensor, temperature sensor, CO gas sensor, temperature sensor, glass sensor, RFID transducer.
2. the Multi-sensor Fusion Internet of Things test platform based on rough set BP neural net according to claim 1, it is characterized in that: described radio node inside modules is provided with the node module core processor, gather the data message or the state of various kinds of sensors by described node module core processor, and send to main control system, and realize respective operations according to the control command of main control system issue by radio-frequency module.
3. the Multi-sensor Fusion Internet of Things test platform based on rough set BP neural net according to claim 1, it is characterized in that: described host computer system comprises: LCDs, VGA module, camera module, mobile module, USB mouse/keyboard.
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| CN103826328A (en) * | 2014-03-03 | 2014-05-28 | 昆山杰普软件科技有限公司 | Distributed sensing network experimental device |
| CN104089656A (en) * | 2014-07-17 | 2014-10-08 | 北京物资学院 | Storage yard coal spontaneous combustion detection method and device |
| US20140337263A1 (en) * | 2013-05-07 | 2014-11-13 | Iotelligent Technology Ltd Inc | Architecture for implementing an improved neural network |
| WO2016071783A1 (en) * | 2014-11-07 | 2016-05-12 | International Business Machines Corporation | Synaptic neural network core based sensor system |
| CN108520311A (en) * | 2018-03-07 | 2018-09-11 | 中国地质大学（武汉） | In conjunction with the haze prediction model method for building up and system of SOFM nets and BP neural network |
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| CN101968648A (en) * | 2010-10-19 | 2011-02-09 | 无锡泛太科技有限公司 | Wireless control system of Internet of things |
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| CN101110106A (en) * | 2007-06-21 | 2008-01-23 | 上海交通大学 | Multiple sensor information amalgamation method combining rough set and neural network |
| CN101968648A (en) * | 2010-10-19 | 2011-02-09 | 无锡泛太科技有限公司 | Wireless control system of Internet of things |
| CN201854436U (en) * | 2010-10-19 | 2011-06-01 | 无锡泛太科技有限公司 | Wireless control system for Internet of things |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US20140337263A1 (en) * | 2013-05-07 | 2014-11-13 | Iotelligent Technology Ltd Inc | Architecture for implementing an improved neural network |
| US10235621B2 (en) * | 2013-05-07 | 2019-03-19 | Iotelligent Technology Ltd Inc | Architecture for implementing an improved neural network |
| CN103826328A (en) * | 2014-03-03 | 2014-05-28 | 昆山杰普软件科技有限公司 | Distributed sensing network experimental device |
| CN104089656A (en) * | 2014-07-17 | 2014-10-08 | 北京物资学院 | Storage yard coal spontaneous combustion detection method and device |
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| US11010660B2 (en) | 2014-11-07 | 2021-05-18 | International Business Machines Corporation | Synaptic neural network core based sensor system |
| CN108520311A (en) * | 2018-03-07 | 2018-09-11 | 中国地质大学（武汉） | In conjunction with the haze prediction model method for building up and system of SOFM nets and BP neural network |
| CN108520311B (en) * | 2018-03-07 | 2021-05-28 | 中国地质大学（武汉） | Haze prediction model establishing method and system combining SOFM (software on a programmable) network and BP (back propagation) neural network |
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