CN107358778A - A kind of fire-alarm of combination KNN algorithms - Google Patents
A kind of fire-alarm of combination KNN algorithms Download PDFInfo
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- CN107358778A CN107358778A CN201710630768.0A CN201710630768A CN107358778A CN 107358778 A CN107358778 A CN 107358778A CN 201710630768 A CN201710630768 A CN 201710630768A CN 107358778 A CN107358778 A CN 107358778A
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- 238000012545 processing Methods 0.000 claims abstract description 41
- 239000000779 smoke Substances 0.000 claims abstract description 21
- 238000012544 monitoring process Methods 0.000 claims abstract description 6
- 210000004027 cell Anatomy 0.000 claims description 13
- 210000000352 storage cell Anatomy 0.000 claims description 11
- 238000005259 measurement Methods 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 4
- 230000005611 electricity Effects 0.000 claims description 4
- 230000005855 radiation Effects 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 2
- 238000010801 machine learning Methods 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000012549 training Methods 0.000 description 4
- 238000000034 method Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 235000019504 cigarettes Nutrition 0.000 description 2
- 230000004807 localization Effects 0.000 description 2
- 239000003595 mist Substances 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000011157 data evaluation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 239000008187 granular material Substances 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000000060 site-specific infrared dichroism spectroscopy Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/06—Electric actuation of the alarm, e.g. using a thermally-operated switch
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/10—Actuation by presence of smoke or gases, e.g. automatic alarm devices for analysing flowing fluid materials by the use of optical means
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/08—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using communication transmission lines
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B29/00—Checking or monitoring of signalling or alarm systems; Prevention or correction of operating errors, e.g. preventing unauthorised operation
- G08B29/18—Prevention or correction of operating errors
- G08B29/185—Signal analysis techniques for reducing or preventing false alarms or for enhancing the reliability of the system
- G08B29/188—Data fusion; cooperative systems, e.g. voting among different detectors
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/12—Actuation by presence of radiation or particles, e.g. of infrared radiation or of ions
- G08B17/125—Actuation by presence of radiation or particles, e.g. of infrared radiation or of ions by using a video camera to detect fire or smoke
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B29/00—Checking or monitoring of signalling or alarm systems; Prevention or correction of operating errors, e.g. preventing unauthorised operation
- G08B29/18—Prevention or correction of operating errors
- G08B29/185—Signal analysis techniques for reducing or preventing false alarms or for enhancing the reliability of the system
- G08B29/186—Fuzzy logic; neural networks
Abstract
The present invention relates to a kind of fire-alarm of combination KNN algorithms, belong to safety monitoring technology field.The present invention includes central processing module, Smoke Sensor, alarm module, memory module, photographing module, point-type step differential temp type detector, locating module.Wherein KNN theories of algorithm are simple, it is easy to realize.The present invention can not only be quickly and accurately positioned out the particular location of fire alarm, and the assigned number set is sent in the form of short message, and it can also recognize the situation of scene of fire by memory module in the very first time, effectively and rationally to organize to rescue, casualties and property loss are reduced to greatest extent.
Description
Technical field
The present invention relates to a kind of fire-alarm of combination KNN algorithms, belong to safety monitoring technology field.
Background technology
Fire serious threat the life of people, and the development to our security of the lives and property and country causes huge
Loss.Security against fire is the cardinal task of each enterprise, and the emphasis of fire-fighting work is mainly aimed at prevention, combined prevention with fire fighting.
Substantial amounts of smoke particle can be all usually produced when fire occurs, so the concentration of smoke particle turns into judges whether that fire occurs
The important evidence of calamity.Meanwhile temperature can be caused to raise rapidly during fire generation, front and rear temperature difference on fire is very big.Generally fire
Calamity scene is all relative closure, it is difficult to the concrete condition of scene of fire is observed from the external world in the very first time, can not be efficiently accurate
Really organize to rescue.Smoke alarm is widely used that on present society, with the pollution of air, or some specific fields
Institute, the granule density in air is higher, so as to easily reach predetermined value, produces the situation of false alarm.Will not although can ensure that
Fail to report, but the accuracy rate of its alarm reduces, it is also possible to the great loss caused by wrong report.
So being badly in need of one kind can accurately alarm, and the fire-alarm of the situation of scene of fire can be grasped in the very first time.
The content of the invention
The technical problem to be solved in the present invention is：The present invention provides a kind of fire-alarm of combination KNN algorithms, for
Solution alarm accuracy rate is not high, and the situation of scene of fire can not be grasped in the very first time, can not accurate judgement specific position on fire
The problem of putting, and can not be just notify related personnel in time in the Initial Stage of Fire stage the problem of.
The technical scheme is that：A kind of fire-alarm of combination KNN algorithms, including central processing module 1, smog pass
Sensor 2, alarm module 3, memory module 4, photographing module 5, point-type step differential temp type detector 6, locating module 7；
The central processing module module 1 connects multiple Smoke Sensors 2, alarm module 3 by electric signal, memory module 4, taken the photograph
As module 5, multiple point-type step differential temp type detectors 6, locating module 7；
The multiple Smoke Sensor 2 is used to measure the smokescope in respective position range respectively, and is obtained measured
Data send central processing module 1 to；
The alarm module 3 includes buzzer and SIM card, after central processing module 1 sends alarm command, will be reported by SIM card
Alert information and specific alert locations are sent to the assigned number pre-set；
The memory module 4 is divided for dynamic storage cell and non-volatile memory cells, when detect fire occurs when data
It is saved in non-volatile memory cells, is stored data into when being not detected by fire in dynamic storage cell；
The photographing module 5 includes infrared pick-up head and monitoring camera, and infrared pick-up hair goes out infrared radiation object,
Diffusing reflection occurs for infrared ray, and the camera that is monitored receives, so as to form video image.
The central processing module 1 passes through electric signal and multiple Smoke Sensors 2 and multiple point-type step differential temp type detectors 6
It is connected, only when the smokescope that Smoke Sensor 2 measures exceedes preset value, while point-type step differential temp type detector 6 is surveyed
The temperature rate-of-rise measured can just send alarm command also above preset value, central processing module 1, otherwise continue to monitor.
The central processing module 1 includes DSP, arm processor, A/D converter, electricity accumulating unit, graphics processing unit, nothing
Line transmitter unit and communication unit.
Infrared camera in the multiple Smoke Sensor 2 and photographing module 5 is equally spaced in the area monitored
In domain.
The locating module 7 realizes positioning by detecting the intensity of Wi-Fi signal with reference to KNN algorithms, and in centre
When reason module 1 sends alarm command, positional information is sent to assigned number by alarm module 3 by the form of short message.
The memory module 4 is divided for overlayable dynamic storage cell and non-volatile memory cells, when central processing mould
, can be by the smokescope data that in-site measurement obtains and the video image storage that photographing module 5 obtains after block 1 sends alarm command
Data and video when uploading into non-volatile memory cells and simultaneously and be saved in high in the clouds, and not sending alarm command can be followed
It is stored in dynamic storage cell to ring cover, when resulting video image is not clear enough, the figure in central processing module 1
As processing module can carry out image enhancement processing.
The localization method of locating module 7, first pass through Wi-Fi Info radio network informations and obtain near current location
RSSI received signal strength indicator device information, local AP undetermined signal intensity and physical address are measured, recycle machine
Learn related matching algorithm, measured data is contrasted with the data stored in a program, searches one group and measurement allusion quotation
The data of type matching, the particular location of tested point is estimated using KNN algorithms.
The present invention operation principle be：
This alarm is applied to the places such as each factory, market, school, residential block.Central processing module 1 passes through electric signal and smog
Sensor 2, alarm module 3, memory module 4, photographing module 5, point-type step differential temp type detector 6, locating module 7 are connected.To cigarette
Mist sensor 2 and point-type step differential temp type detector 6 set a concentration threshold and temperature rate-of-rise threshold value respectively, when detected value is same
When being more than or equal to the two threshold values, central processing module 1 sends alarm command, while by the smokescope and video at scene
Image storage is into the non-volatile memory cells in memory module 4 and synchronized upload is to high in the clouds, hereafter in real time by live feelings
Condition preserves until the data detected are less than the threshold value set.After sending alarm command, locating module will be first according to each
The data-evaluation such as the position of Smoke Sensor and smokescope, rate of rise in temperature goes out point of origin position, then by particular location
Assigned number is sent in the form of short message together with warning message, facilitates relevant staff rapidly to organize to rescue.Positioning
Module is to first pass through Wi-Fi Info to obtain RSSI information near current location, using the Wi-Fi Manager class libraries increased income,
Facilitate application program and Wi-Fi connection, all AP focuses in the range of equipment communication can pass through packaged Wi-Fi scannings
Function scanning is arrived, while can also show SSID, MAC Address, IP and the RSSI of quantified processing of access point.Recycle machine
Learn related matching algorithm, measured data is contrasted with the data stored in a program, searches one group and measurement allusion quotation
The data of type matching.RSSI from j-th AP is received by tested point and i-th of reference point is received from j-th of AP
RSSI value calculate Euclidean distance dis.Each reference point has two parameters, and first is in a certain reception in the position
Wi-Fi1 signal intensity, second is signal intensity in another reception Wi-Fi2 in the position.Then KNN algorithms are utilized
Select K reference point from small to large in dis, calculate the seat of tested point using averaging method by reference to the actual coordinate of point
Mark, and then estimate the position of tested point.The Thoughts of KNN algorithms are：Training sample set is obtained by pretreatment, and will instruction
Practice sample set to be saved in memory module, and each data have a label in sample set, in sample set each data with it is affiliated
Corresponding relation of classifying is known, after inputting the test sample data of no label, according to the Euclidean distance dis degree being previously set
Amount calculates each test sample and the distance of training sample, selects K closest training samples, is then based on ballot side
Test sample labeled as the most classification of occurrence number in K training sample, is extracted the most like data of feature in sample set by formula
Tag along sort.
The beneficial effects of the invention are as follows：
1st, Smoke Sensor used herein and point-type step differential temp type detector monitor simultaneously, only reach both default simultaneously
It during value, can just start alarm module, double shield, improve the accuracy of alarm；
2nd, the particular location and warning message of fire are sent to assigned number in the very first time of alarm, are easy to related work people
Member's efficiently tissue rescue.This alarm has electricity accumulating unit to can fully ensure that its normal use, and graphics processing unit can be with
The video image inadequate to definition carries out image procossing；
3rd, the smokescope of scene of fire and video image are preserved, beneficial to analysis the reason for fire occurs afterwards and
Investigation.Power storage module in central processing module has fully ensured that the normal use of in case of emergency alarm；
4th, different places can be directed to and environment uses the sensor of different types.The theory of KNN algorithms is simple, realizes and holds
Easily.
Brief description of the drawings
Fig. 1 is the overall structure block diagram of the present invention；
Fig. 2 is the flow chart of the KNN algorithms of the present invention；
Fig. 3 is the alarm flow figure of the present invention.
Each label in Fig. 1：1- central processing modules；2- smoke alarms；3- alarm modules；4- memory modules；5- is imaged
Module；6- point-type step differential temp type detectors；7- locating modules.
Embodiment
Below in conjunction with the accompanying drawings and specific embodiment, the invention will be further described.
Embodiment 1：As Figure 1-3, a kind of fire-alarm of combination KNN algorithms, including central processing module 1, cigarette
Mist sensor 2, alarm module 3, memory module 4, photographing module 5, point-type step differential temp type detector 6, locating module 7；
The central processing module module 1 connects multiple Smoke Sensors 2, alarm module 3 by electric signal, memory module 4, taken the photograph
As module 5, multiple point-type step differential temp type detectors 6, locating module 7；
The multiple Smoke Sensor 2 is used to measure the smokescope in respective position range respectively, and is obtained measured
Data send central processing module 1 to；
The alarm module 3 includes buzzer and SIM card, after central processing module 1 sends alarm command, will be reported by SIM card
Alert information and specific alert locations are sent to the assigned number pre-set；
The memory module 4 is divided for dynamic storage cell and non-volatile memory cells, when detect fire occurs when data
It is saved in non-volatile memory cells, is stored data into when being not detected by fire in dynamic storage cell；
The photographing module 5 includes infrared pick-up head and monitoring camera, and infrared pick-up hair goes out infrared radiation object,
Diffusing reflection occurs for infrared ray, and the camera that is monitored receives, so as to form video image.
Preferably, the central processing module 1 passes through electric signal and multiple Smoke Sensors 2 and multiple point-type step differential temp types
Detector 6 is connected, only when the smokescope that Smoke Sensor 2 measures exceedes preset value, while the detection of point-type step differential temp type
Temperature rate-of-rise measured by device 6 can just send alarm command, otherwise continue also above preset value, central processing module 1
Monitoring.
Preferably, the central processing module 1 includes DSP, arm processor, A/D converter, electricity accumulating unit, image procossing
Unit, wireless transmitting unit and communication unit.
Preferably, the infrared camera in the multiple Smoke Sensor 2 and photographing module 5, which is equally spaced, is being supervised
In the region of control.
Preferably, the locating module 7 realizes positioning by detecting the intensity of Wi-Fi signal with reference to KNN algorithms, and
When central processing module 1 sends alarm command, positional information is sent to specified number by alarm module 3 by the form of short message
Code.
Preferably, the memory module 4 is divided for overlayable dynamic storage cell and non-volatile memory cells, works as center
, can be by the smokescope data that in-site measurement obtains and the video figure that photographing module 5 obtains after processing module 1 sends alarm command
Data and video when uploading into non-volatile memory cells and simultaneously as storage and is saved in high in the clouds, and not sending alarm command
Just it is stored in dynamic storage cell to recyclable covering, when resulting video image is not clear enough, central processing module 1
In image processing module can carry out image enhancement processing.
Preferably, the localization method of locating module 7, it is attached to first pass through Wi-Fi Info radio network informations acquisition current location
Near RSSI received signal strength indicator device information, local AP undetermined signal intensity and physical address are measured, recycled
The related matching algorithm of machine learning, measured data is contrasted with the data stored in a program, searches one group and survey
The data of amount typical case's matching, the particular location of tested point is estimated using KNN algorithms.
The specific embodiment of the present invention is explained in detail above in conjunction with accompanying drawing, but the present invention is not limited to above-mentioned reality
Example is applied, can also be on the premise of present inventive concept not be departed from those of ordinary skill in the art's possessed knowledge
Various changes can be made.
Claims (7)
- A kind of 1. fire-alarm of combination KNN algorithms, it is characterised in that：Including central processing module（1）, Smoke Sensor （2）, alarm module（3）, memory module（4）, photographing module（5）, point-type step differential temp type detector（6）, locating module（7）；The central processing module module（1）Multiple Smoke Sensors are connected by electric signal（2）, alarm module（3）, storage mould Block（4）, photographing module（5）, multiple point-type step differential temp type detectors（6）, locating module（7）；The multiple Smoke Sensor（2）It is used to measure the smokescope in respective position range respectively, and will be measured To data send central processing module to（1）；The alarm module（3）Including buzzer and SIM card, central processing module（1）After sending alarm command, pass through SIM card Warning message and specific alert locations are sent to the assigned number pre-set；The memory module（4）It is divided into dynamic storage cell and non-volatile memory cells, when detecting generation fire number According to being saved in non-volatile memory cells, stored data into when being not detected by fire in dynamic storage cell；The photographing module（5）Including infrared pick-up head and monitoring camera, infrared pick-up hair goes out infrared radiation thing Diffusing reflection occurs for body, infrared ray, and the camera that is monitored receives, so as to form video image.
- 2. the fire-alarm of combination KNN algorithms according to claim 1, it is characterised in that：The central processing module （1）Pass through electric signal and multiple Smoke Sensors（2）And multiple point-type step differential temp type detectors（6）It is connected, only works as smog Sensor（2）The smokescope measured exceedes preset value, while point-type step differential temp type detector（6）Measured temperature rises Speed is also above preset value, central processing module（1）Alarm command can be just sent, otherwise continues to monitor.
- 3. the fire-alarm of combination KNN algorithms according to claim 1, it is characterised in that：The central processing module （1）Including DSP, arm processor, A/D converter, electricity accumulating unit, graphics processing unit, wireless transmitting unit and communication unit.
- 4. the fire-alarm of combination KNN algorithms according to claim 1, it is characterised in that：The multiple smog sensing Device（2）And photographing module（5）In infrared camera be equally spaced in the region monitored.
- 5. the fire-alarm of combination KNN algorithms according to claim 2, it is characterised in that：The locating module（7）It is logical The intensity of detection Wi-Fi signal is crossed, positioning is realized with reference to KNN algorithms, and in central processing module（1）Send alarm command When, alarm module（3）Positional information is sent to assigned number by the form of short message.
- 6. the fire-alarm of combination KNN algorithms according to claim 3, it is characterised in that：The memory module（4）Point For overlayable dynamic storage cell and non-volatile memory cells, work as central processing module（1）After sending alarm command, it can incite somebody to action The smokescope data and photographing module that in-site measurement obtains（5）Obtained video image storage is into non-volatile memory cells And upload simultaneously and be saved in high in the clouds, and the data and video when not sending alarm command are stored in dynamic with being just recycled covering and deposited In storage unit.
- 7. the fire-alarm of combination KNN algorithms according to claim 3, it is characterised in that：First pass through Wi-Fi Info Radio network information obtains the RSSI received signal strength indicator device information near current location, measures local AP's undetermined Signal intensity and physical address, the related matching algorithm of machine learning is recycled, to number of the measured data with storage in a program According to being contrasted, the data of one group and measurement typical case's matching are searched, the particular location of tested point is estimated using KNN algorithms.
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Cited By (10)
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| CN108764399A (en) * | 2018-05-22 | 2018-11-06 | 东南大学 | A kind of RFID tag sorting technique and device based on kNN |
| CN109003354A (en) * | 2018-05-25 | 2018-12-14 | 淮南万泰电气有限公司 | A kind of substation's wisdom environmental management system |
| CN109272696A (en) * | 2018-08-27 | 2019-01-25 | 出门问问信息科技有限公司 | Electronic equipment |
| CN109300276A (en) * | 2018-07-27 | 2019-02-01 | 昆明理工大学 | A kind of car inside abnormity early warning method based on Fusion |
| CN109443421A (en) * | 2018-09-13 | 2019-03-08 | 东南大学 | A kind of NB-IoT wireless humiture sensor |
| CN110619730A (en) * | 2018-06-20 | 2019-12-27 | 能美防灾株式会社 | Fire detector |
| CN111885491A (en) * | 2020-07-30 | 2020-11-03 | 湖州视远科技有限公司 | Wi-Fi fingerprint-based indoor fire-fighting spraying area positioning system |
| CN113744484A (en) * | 2021-09-01 | 2021-12-03 | 宋久壮 | Fire monitoring device with positioning function |
| CN113762337A (en) * | 2021-07-29 | 2021-12-07 | 国网河北省电力有限公司经济技术研究院 | Initial fire determination method, device, terminal and storage medium |
| CN115171320A (en) * | 2022-06-29 | 2022-10-11 | 万霖消防技术有限公司 | Fire-fighting monitoring method and system |
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| CN108764399B (en) * | 2018-05-22 | 2021-03-19 | 东南大学 | kNN-based RFID tag classification method and device |
| CN108764399A (en) * | 2018-05-22 | 2018-11-06 | 东南大学 | A kind of RFID tag sorting technique and device based on kNN |
| CN109003354A (en) * | 2018-05-25 | 2018-12-14 | 淮南万泰电气有限公司 | A kind of substation's wisdom environmental management system |
| CN109003354B (en) * | 2018-05-25 | 2021-05-04 | 淮南万泰电气有限公司 | Intelligent environment management system for transformer substation |
| CN110619730B (en) * | 2018-06-20 | 2021-07-16 | 能美防灾株式会社 | Fire detector |
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| CN109300276A (en) * | 2018-07-27 | 2019-02-01 | 昆明理工大学 | A kind of car inside abnormity early warning method based on Fusion |
| CN109272696A (en) * | 2018-08-27 | 2019-01-25 | 出门问问信息科技有限公司 | Electronic equipment |
| CN109443421A (en) * | 2018-09-13 | 2019-03-08 | 东南大学 | A kind of NB-IoT wireless humiture sensor |
| CN111885491A (en) * | 2020-07-30 | 2020-11-03 | 湖州视远科技有限公司 | Wi-Fi fingerprint-based indoor fire-fighting spraying area positioning system |
| CN113762337A (en) * | 2021-07-29 | 2021-12-07 | 国网河北省电力有限公司经济技术研究院 | Initial fire determination method, device, terminal and storage medium |
| CN113744484A (en) * | 2021-09-01 | 2021-12-03 | 宋久壮 | Fire monitoring device with positioning function |
| CN115171320A (en) * | 2022-06-29 | 2022-10-11 | 万霖消防技术有限公司 | Fire-fighting monitoring method and system |
| CN115171320B (en) * | 2022-06-29 | 2023-11-14 | 万霖消防技术有限公司 | Fire control monitoring method and system |
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Application publication date: 20171117 |