CN111242278A - Composite smoke sense low-false-alarm method based on intelligent algorithm - Google Patents
Composite smoke sense low-false-alarm method based on intelligent algorithm Download PDFInfo
- Publication number
- CN111242278A CN111242278A CN201911386004.7A CN201911386004A CN111242278A CN 111242278 A CN111242278 A CN 111242278A CN 201911386004 A CN201911386004 A CN 201911386004A CN 111242278 A CN111242278 A CN 111242278A
- Authority
- CN
- China
- Prior art keywords
- smoke
- neural network
- sensor
- false alarm
- method based
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000000779 smoke Substances 0.000 title claims abstract description 71
- 238000000034 method Methods 0.000 title claims abstract description 20
- 239000002131 composite material Substances 0.000 title claims abstract description 17
- 238000013528 artificial neural network Methods 0.000 claims abstract description 46
- 230000035807 sensation Effects 0.000 claims abstract description 20
- 238000012544 monitoring process Methods 0.000 claims abstract description 10
- 238000001514 detection method Methods 0.000 claims abstract description 7
- 238000012795 verification Methods 0.000 claims abstract description 6
- 230000002068 genetic effect Effects 0.000 claims description 12
- 230000006870 function Effects 0.000 claims description 9
- 230000003044 adaptive effect Effects 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 3
- 238000001035 drying Methods 0.000 claims description 3
- 230000035772 mutation Effects 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 238000002790 cross-validation Methods 0.000 abstract description 3
- 239000002245 particle Substances 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 3
- 239000000428 dust Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 239000000443 aerosol Substances 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000011664 signaling Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0036—General constructional details of gas analysers, e.g. portable test equipment concerning the detector specially adapted to detect a particular component
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0036—General constructional details of gas analysers, e.g. portable test equipment concerning the detector specially adapted to detect a particular component
- G01N33/004—CO or CO2
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method or the display, e.g. intermittent measurement or digital display
- G01N33/0063—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method or the display, e.g. intermittent measurement or digital display using a threshold to release an alarm or displaying means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- 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
- G08B17/103—Actuation by presence of smoke or gases, e.g. automatic alarm devices for analysing flowing fluid materials by the use of optical means using a light emitting and receiving device
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Pathology (AREA)
- Biochemistry (AREA)
- Biophysics (AREA)
- Immunology (AREA)
- Theoretical Computer Science (AREA)
- Medicinal Chemistry (AREA)
- Food Science & Technology (AREA)
- Combustion & Propulsion (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Mathematical Physics (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Software Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Physiology (AREA)
- Genetics & Genomics (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Dispersion Chemistry (AREA)
- Fire-Detection Mechanisms (AREA)
Abstract
The invention relates to a composite smoke sensation low false alarm method based on an intelligent algorithm, which comprises the steps of respectively initializing smoke sensations, arranging a humidity sensor and a gas sensor in a matching manner, and obtaining an initialization sample data set A, B, C based on the three; and constructing a BP neural network, taking the sets A and B as input samples, taking the set C as a verification set, training the BP neural network, after the training is finished, putting the humidity-sensitive sensor in the smoke sensor, integrally setting the humidity-sensitive sensor and the smoke sensor, obtaining data of the smoke sensor and the humidity-sensitive sensor in real time, outputting the data based on the trained BP neural network, and alarming or continuously monitoring based on an output result. The method establishes multiple sensing channels, and by acquiring detection values of multiple channels, performing cross validation and establishing a model, juxtaposing all the channels, and acquiring data to directly perform intelligent identification on smoke sensing conditions. The model of the invention has high adaptability, good robustness, high monitoring automation degree, small interference of false alarm and small probability of false alarm.
Description
Technical Field
The present invention relates to signaling or calling devices; an instruction-transmitting device; the technical field of alarm devices, in particular to a composite smoke sensation low-false alarm method based on an intelligent algorithm.
Background
The smoke detector is a smoke detector alarm or a smoke alarm, realizes fire prevention by monitoring the concentration of smoke, and is widely applied to various fire alarm systems.
Under normal conditions, the optical maze in the smoke sensation meets the smoke and triggers the photoelectric sensor to give an alarm.
However, since the sensor is sensitive to extremely tiny smoke particles, dust accumulation exists in the optical labyrinth after the time for installing the smoke sensor is long, dust may be blown up when airflow passes through the smoke sensor, and the smoke sensor is mistaken for the smoke to cause false alarm; in addition to this, aerosol like water vapor, as small particles, may also cause false smoke sensations.
In order to solve the problem, in the prior art, a smoke sensing device and a semiconductor temperature sensing device are adopted in some composite smoke sensing and temperature sensing fire detectors to form a multi-component composite detector, which enhances the false alarm recognition rate to a certain extent, but still needs manual judgment, and is low in efficiency.
Disclosure of Invention
The invention solves the problems that in the prior art, tiny particles in the air and in the sensor are easy to cause false alarm of smoke feeling, and meanwhile, a combined smoke-sensing and temperature-sensing fire detector for the tiny particles needs excessive manual participation, so that the working efficiency is low, and provides an optimized method for low false alarm of combined smoke feeling based on an intelligent algorithm.
The invention adopts the technical scheme that a composite smoke sense low false alarm method based on an intelligent algorithm comprises the following steps:
step 1: initializing smoke feeling, and obtaining an initialization sample data set A;
step 2: setting a humidity-sensitive sensor in cooperation with smoke detection to obtain an initialization sample data set B;
and step 3: setting a gas sensor in cooperation with smoke sensation to obtain an initialization sample data set C;
and 4, step 4: constructing a BP neural network, taking the sets A and B as input samples, taking the set C as a verification set, and training the BP neural network;
and 5: a humidity-sensitive sensor is arranged in the smoke sensor;
step 6: acquiring data of smoke sensors and humidity sensors in real time, and outputting the data based on the trained BP neural network;
and 7: and alarming or continuously monitoring based on the output result.
Preferably, in step 3, the gas sensor is a CO sensor.
Preferably, in step 4, the BP neural network is a genetic algorithm-based BP neural network.
Preferably, in the step 4, constructing the BP neural network based on the genetic algorithm comprises the following steps:
step 4.1: initializing the population number N;
step 4.2: determining an adaptive function and a control parameter;
step 4.3: carrying out selection, crossing and mutation operations, and feeding back a result to the BP neural network;
step 4.4: and if the optimal weight estimation is obtained, constructing a BP neural network based on a genetic algorithm, otherwise, returning to the step 4.3.
Preferably, in said step 4.2, the function
Where e is the maximum estimation error of the improved BP neural network, Yi is the actual output, and Ci is the desired output.
Preferably, a penalty term is included in the error function of the BP neural network.
Preferably, in step 7, if the output result is that the moisture is excessive, a drying process is performed.
Preferably, a fan is arranged in the smoke sensor.
The invention relates to an optimized composite smoke sensation low false alarm method based on an intelligent algorithm, which comprises the steps of respectively initializing smoke sensations, arranging a humidity sensor and a gas sensor in a matching manner, and obtaining an initialization sample data set A, B, C based on the three; and constructing a BP neural network, taking the sets A and B as input samples, taking the set C as a verification set, training the BP neural network, after the training is finished, putting the humidity-sensitive sensor in the smoke sensor, integrally setting the humidity-sensitive sensor and the smoke sensor, obtaining data of the smoke sensor and the humidity-sensitive sensor in real time, outputting the data based on the trained BP neural network, and alarming or continuously monitoring based on an output result.
The method establishes multiple sensing channels, and by acquiring detection values of multiple channels, performing cross validation and establishing a model, juxtaposing all the channels, and acquiring data to directly perform intelligent identification on smoke sensing conditions.
The model of the invention has high adaptability, good robustness, high monitoring automation degree, small interference of false alarm and small probability of false alarm.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The present invention is described in further detail with reference to the following examples, but the scope of the present invention is not limited thereto.
The invention relates to a composite smoke sense low false alarm method based on an intelligent algorithm, which comprises the following steps.
Step 1: and initializing smoke feeling and obtaining an initialization sample data set A.
Step 2: and (4) setting a humidity sensor in cooperation with the smoke sensor to obtain an initialization sample data set B.
And step 3: and setting a gas sensor in cooperation with the smoke sensation to obtain an initialization sample data set C.
In the step 3, the gas sensor is a CO sensor.
In the present invention, the initialization sample data set in steps 1 to 3 may collect data of smoke sensations under various use conditions, for example, a group of data is obtained with a brand-new smoke sensation, a group of data is obtained with a smoke sensation of three months, and the like, and finally all the groups of data are gathered together to form the initialization sample data set A, B, C.
In the present invention, in order to maintain the operation accuracy of the network for a single product, the elements in the sample data set A, B, C are collected from the same or a class of smoke-sensitive products, and are collected in a continuous tracking manner.
In the invention, the CO sensor can be comparatively
And 4, step 4: and (3) constructing a BP neural network, taking the sets A and B as input samples, taking the set C as a verification set, and training the BP neural network.
In the step 4, the BP neural network is a genetic algorithm-based BP neural network.
In the step 4, the construction of the BP neural network based on the genetic algorithm comprises the following steps:
step 4.1: initializing the population number N;
step 4.2: determining an adaptive function and a control parameter;
in said step 4.2, function
Where e is the maximum estimation error of the improved BP neural network, Yi is the actual output, and Ci is the desired output.
Step 4.3: carrying out selection, crossing and mutation operations, and feeding back a result to the BP neural network;
step 4.4: and if the optimal weight estimation is obtained, constructing a BP neural network based on a genetic algorithm, otherwise, returning to the step 4.3.
And the error function of the BP neural network comprises a penalty term.
In the invention, a BP neural network is adopted for prediction operation, the BP neural network is a multi-layer feedforward neural network, signals are propagated forwards and errors are propagated backwards, and the weight relation between input and output is found out by utilizing the existing data so as to carry out simulation.
In the invention, N is the power N of 2.
In the invention, further, the BP neural network is optimized by a genetic algorithm, the weight and the threshold of the BP neural network can be optimized, each individual in a population comprises all the weight and the threshold of one network, the individual calculates the individual fitness value through a fitness function, and the genetic algorithm finds out the individual corresponding to the optimal fitness value through selection, intersection and variation operations, so that the BP neural network obtains the optimal individual by the genetic algorithm, assigns values to the initial weight and the threshold of the network, and the detection accuracy is higher.
In the invention, in order to avoid overfitting and further increase the robustness of the network, a penalty term is added into the loss function, so that the loss function is added with a constraint term compared with a common BP neural network; the setting of the penalty term is well known in the art, and can be set by a person skilled in the art according to the requirement.
And 5: a humidity sensor is placed in the smoke sensor.
Step 6: and acquiring data of the smoke sensor and the humidity sensor in real time, and outputting the data based on the trained BP neural network.
In the invention, after the network construction is completed, the humidity-sensitive sensor is arranged in the new smoke sensor, and the humidity-sensitive sensor and the normal optical maze detect together and output the result of network judgment so as to confirm whether to trigger alarm or not.
And 7: and alarming or continuously monitoring based on the output result.
In the step 7, if the output result is that the water vapor is too much, drying treatment is performed.
A fan is arranged in the smoke sensor.
In the present invention, when an excessive sensing value of the humidity sensor is included in the output result, the inside of the smoke sensor should be dried first.
In the invention, the fan is generally a small axial flow fan, and the wind direction faces outwards from the inside of the smoke sensor when the fan is started.
According to the invention, by respectively initializing smoke senses and arranging a humidity sensor and a gas sensor in a matching manner, an initialization sample data set A, B, C is obtained based on the three sensors; and constructing a BP neural network, taking the sets A and B as input samples, taking the set C as a verification set, training the BP neural network, after the training is finished, putting the humidity-sensitive sensor in the smoke sensor, integrally setting the humidity-sensitive sensor and the smoke sensor, obtaining data of the smoke sensor and the humidity-sensitive sensor in real time, outputting the data based on the trained BP neural network, and alarming or continuously monitoring based on an output result.
The method establishes multiple sensing channels, and by acquiring detection values of multiple channels, performing cross validation and establishing a model, juxtaposing all the channels, and acquiring data to directly perform intelligent identification on smoke sensing conditions.
The model of the invention has high adaptability, good robustness, high monitoring automation degree, small interference of false alarm and small probability of false alarm.
Claims (8)
1. A composite smoke detection low false alarm method based on an intelligent algorithm is characterized in that: the method comprises the following steps:
step 1: initializing smoke feeling, and obtaining an initialization sample data set A;
step 2: setting a humidity-sensitive sensor in cooperation with smoke detection to obtain an initialization sample data set B;
and step 3: setting a gas sensor in cooperation with smoke sensation to obtain an initialization sample data set C;
and 4, step 4: constructing a BP neural network, taking the sets A and B as input samples, taking the set C as a verification set, and training the BP neural network;
and 5: a humidity-sensitive sensor is arranged in the smoke sensor;
step 6: acquiring data of smoke sensors and humidity sensors in real time, and outputting the data based on the trained BP neural network;
and 7: and alarming or continuously monitoring based on the output result.
2. The composite smoke sensation low false alarm method based on the intelligent algorithm according to claim 1, characterized in that: in the step 3, the gas sensor is a CO sensor.
3. The composite smoke sensation low false alarm method based on the intelligent algorithm according to claim 1, characterized in that: in the step 4, the BP neural network is a genetic algorithm-based BP neural network.
4. The composite smoke sensation low false alarm method based on the intelligent algorithm according to claim 3, characterized in that: in the step 4, the construction of the BP neural network based on the genetic algorithm comprises the following steps:
step 4.1: initializing the population number N;
step 4.2: determining an adaptive function and a control parameter;
step 4.3: carrying out selection, crossing and mutation operations, and feeding back a result to the BP neural network;
step 4.4: and if the optimal weight estimation is obtained, constructing a BP neural network based on a genetic algorithm, otherwise, returning to the step 4.3.
5. The composite smoke sensation low-false-alarm method based on the intelligent algorithm according to claim 4, characterized in that: in said step 4.2, function
Where e is the maximum estimation error of the improved BP neural network, Yi is the actual output, and Ci is the desired output.
6. The composite smoke sensation low false alarm method based on the intelligent algorithm according to claim 3, characterized in that: and the error function of the BP neural network comprises a penalty term.
7. The composite smoke sensation low false alarm method based on the intelligent algorithm according to claim 1, characterized in that: in the step 7, if the output result is that the water vapor is too much, drying treatment is performed.
8. The composite smoke sensation low false alarm method based on the intelligent algorithm according to claim 7, characterized in that: a fan is arranged in the smoke sensor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911386004.7A CN111242278A (en) | 2019-12-29 | 2019-12-29 | Composite smoke sense low-false-alarm method based on intelligent algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911386004.7A CN111242278A (en) | 2019-12-29 | 2019-12-29 | Composite smoke sense low-false-alarm method based on intelligent algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111242278A true CN111242278A (en) | 2020-06-05 |
Family
ID=70875803
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911386004.7A Pending CN111242278A (en) | 2019-12-29 | 2019-12-29 | Composite smoke sense low-false-alarm method based on intelligent algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111242278A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112614298A (en) * | 2020-12-09 | 2021-04-06 | 杭州拓深科技有限公司 | Composite smoke sensation monitoring method based on intra-class interaction constraint layering single classification |
| CN113362560A (en) * | 2021-05-28 | 2021-09-07 | 蚌埠依爱消防电子有限责任公司 | Photoelectric smoke sensing detection method for accurately identifying fire smoke |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201117044Y (en) * | 2007-11-20 | 2008-09-17 | 华南理工大学 | High-rise building fire forecast device based on light and temperature composite intelligent monitoring |
| CN103869379A (en) * | 2014-03-24 | 2014-06-18 | 东南大学 | Magnetometer correcting method with optimized and modified BP neural network based on genetic algorithm |
| CN109034388A (en) * | 2018-07-27 | 2018-12-18 | 湖北中烟工业有限责任公司 | A kind of prediction model of cigarette material and mainstream smoke constituents based on Genetic Algorithm Optimized Neural Network |
-
2019
- 2019-12-29 CN CN201911386004.7A patent/CN111242278A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201117044Y (en) * | 2007-11-20 | 2008-09-17 | 华南理工大学 | High-rise building fire forecast device based on light and temperature composite intelligent monitoring |
| CN103869379A (en) * | 2014-03-24 | 2014-06-18 | 东南大学 | Magnetometer correcting method with optimized and modified BP neural network based on genetic algorithm |
| CN109034388A (en) * | 2018-07-27 | 2018-12-18 | 湖北中烟工业有限责任公司 | A kind of prediction model of cigarette material and mainstream smoke constituents based on Genetic Algorithm Optimized Neural Network |
Non-Patent Citations (1)
| Title |
|---|
| 孙占鹏等: "多传感器室内环境监测系统", 《传感器与微系统》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112614298A (en) * | 2020-12-09 | 2021-04-06 | 杭州拓深科技有限公司 | Composite smoke sensation monitoring method based on intra-class interaction constraint layering single classification |
| CN113362560A (en) * | 2021-05-28 | 2021-09-07 | 蚌埠依爱消防电子有限责任公司 | Photoelectric smoke sensing detection method for accurately identifying fire smoke |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111627181B (en) | Comprehensive pipe rack fire early warning method fusing multi-source parameters and gradient information thereof | |
| CN112987675B (en) | Method, device, computer equipment and medium for anomaly detection | |
| US5483222A (en) | Multiple sensor apparatus and method | |
| US11579002B2 (en) | Sensor fusion for fire detection and air quality monitoring | |
| CN111242278A (en) | Composite smoke sense low-false-alarm method based on intelligent algorithm | |
| US20200300707A1 (en) | Method, apparatus and system for passive infrared sensor framework | |
| CN102265227A (en) | Method and apparatus for creating state estimation models in machine condition monitoring | |
| CN107355940A (en) | Control method, the apparatus of air conditioning and the air-conditioning device of the apparatus of air conditioning | |
| CN105975991B (en) | A kind of improved extreme learning machine fire kind recognition methods | |
| CN111613037B (en) | Method for reducing composite smoke sense false alarm based on intelligent algorithm | |
| WO2021043126A1 (en) | System and method for event recognition | |
| KR20200130657A (en) | Gas sensing device and method for operating a gas sensing device | |
| CN107036948A (en) | Detect the methods, devices and systems of dust concentration | |
| CN111915833A (en) | Fire multi-element detection device and detection method in limited space | |
| CN113627521A (en) | Intelligent logistics unmanned aerial vehicle abnormal behavior identification method based on isolated forest method | |
| Baek et al. | Intelligent multi-sensor detection system for monitoring indoor building fires | |
| CN110927217A (en) | Food freshness identification method based on electronic nose system and electronic nose system | |
| KR102313465B1 (en) | Multi-sensor based air quality status mobile notification system and method | |
| Nakıp et al. | Multi-sensor fire detector based on trend predictive neural network | |
| CN112037106B (en) | Data anomaly analysis method based on feature cross-correlation and probability density | |
| Hamami et al. | Classification of air pollution levels using artificial neural network | |
| CN113362560B (en) | Photoelectric smoke sensing detection method for accurately identifying fire smoke | |
| Wen et al. | Design of an intelligent alarm system based on multi-sensor data fusion | |
| CN111127814B (en) | Fire alarm identification method and related device | |
| CN112984712B (en) | Control method of fresh air system, fresh air system and computer readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200605 |