CN105067768A - Multi-component mixed gas quantitative recognition system for dangerous chemical detection - Google Patents
Multi-component mixed gas quantitative recognition system for dangerous chemical detection Download PDFInfo
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- 238000001514 detection method Methods 0.000 title claims abstract description 8
- 239000000126 substance Substances 0.000 title abstract description 7
- 238000000034 method Methods 0.000 claims abstract description 20
- 238000013528 artificial neural network Methods 0.000 claims description 9
- 238000000513 principal component analysis Methods 0.000 claims description 9
- 238000005516 engineering process Methods 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 230000004044 response Effects 0.000 claims description 4
- 230000000694 effects Effects 0.000 claims description 3
- 238000012880 independent component analysis Methods 0.000 claims description 3
- 230000001537 neural effect Effects 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000003909 pattern recognition Methods 0.000 claims description 3
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Abstract
The invention discloses a multi-component mixed gas quantitative recognition system for dangerous chemical detection, and relates to the technical field of detection and examination. The system is characterized by comprising a data layer, a characteristic layer, a classification layer, and a decision making layer; wherein the data layer, the characteristic layer, the classification layer, and the decision making layer are connected in sequence. The system utilizes the cross sensitivity of gas sensors to generate a high-dimensional corresponding mode to mixed gas; and then processes the obtained data by combining a corresponding mode recognition method so as to recognize the mixed gas.
Description
Technical field
The present invention relates to inspection technical field, be specifically related to a kind of quantitative judge system of the Diversity gas for harmful influence detection.
Background technology
Along with the continuous expansion of industrial and agricultural production scale; the demand of global threat chemicals (abbreviation harmful influence) constantly increases; the disaster accident that the links such as the production of harmful influence, transport, storage and use occur also constantly increases; the generation of harmful influence accident can cause huge disaster usually, and this is determined by following characteristics:
The first, harmful influence accident has in all contingent feature of links, and special analysis was done by relevant department, harmful influence is managed from producing to, transport, store, use, 6 discarded links none can escape by luck from accident.The accident of transit link is maximum, has accounted for 41% of total number of accident.
The second, harmful influence accident has volatile feature under hot and humid condition.
3rd, harmful influence accident has the feature of linksystem, and the harmful influence overwhelming majority is inflammable and explosive, and harmful influence accident often causes the consequence of series connection mutually between damaged in collision, leakage, burning, blast, pollution.
4th, harmful influence accident has rescue, gives treatment to the feature that difficulty is large and consequence is serious, and harmful influence generally all has special physics, chemical property, and the chemicals performance varied adds the difficulty of rescue, treatment work.Therefore, harmful influence is rescued is usually fire extinguishing, rescue the multiple means such as people, sterilization, evacuating personnel, detection carries out simultaneously.
According to statistics, in the production scale of harmful influence, Chinese Enterprises can produce 45000 kinds of chemicals in 2007, wherein had the kind that 3823 kinds belong to government and strictly supervise, and also had 335 kinds to be put into severe poisonous chemicals catalogue and implemented management.Along with China's rapid development of economy, the safety problem of harmful influence and the research of regulation technique also more urgent.
In international and domestic bibliographical information up to now, not yet find around hazardous chemical safety problem based on the relevant signals process of sensor network and the research of information fusion technology aspect.
Summary of the invention
The object of this invention is to provide a kind of quantitative judge system of the Diversity gas for harmful influence detection; it utilizes the cross-sensitivity of gas sensor to produce the response modes of higher-dimension to mixed gas; and in conjunction with corresponding mode identification method, the data obtained are processed, thus realize the identification to mixed gas.
In order to solve the problem existing for background technology, the present invention is by the following technical solutions: it comprises data Layer, characteristic layer, classification layer, decision-making level, and data Layer, characteristic layer, classification layer, decision-making level are connected successively.
The present invention comprises following job step:
In pretreatment process, metering circuit is converted to various gas sensor signal the voltage signal being convenient to acquisition and processing, the raw voltage signals of collection is converted to the gas response signal that physical significance is clearer and more definite;
At data preprocessing phase, adopt the technology such as normalization, denoising and signal compensation to a certain degree can remove noise and the baseline wander of compensation sensor;
In signal compensation, adopt mark ratioing technigue X=(S
gas-s
o)/S
o, have effect to the drift of additivity and product noise;
Feature extraction comprises two parts task, one is compress information, two is reduce the relevance between data, principal component analysis (PCA) (PCA) is a kind of typical feature extracting method, feature little for variance contribution ratio is removed by it, reach dimensionality reduction object, and eliminate the correlativity between sample data, make the neural computing of next stage more simple efficient; In addition Independent Component Analysis can be adopted to be used for feature extraction, and its correlativity can removed between data can remove again the higher order dependencies between data;
Pattern-recognition layer adopts reverse transfers neural network (BP) to realize the estimation of gas composition and concentration value, BP neural network full name is error backward propagation method, it is a kind of nonparametric learning method, zmodem, Nonlinear Processing ability is strong, is suitable for Gas Sensor Array Signals process;
The output of neural network and warning threshold value at different levels contrast and realize alarm level and adjudicate by last Situation Assessment.
Accompanying drawing illustrates:
Fig. 1 is structural representation of the present invention.
Embodiment
With reference to Fig. 1, for the quantitative judge system of the Diversity gas that harmful influence detects, it comprises data Layer, characteristic layer, classification layer, decision-making level, and data Layer, characteristic layer, classification layer, decision-making level are connected successively, and comprise following job step:
In pretreatment process, metering circuit is converted to various gas sensor signal the voltage signal being convenient to acquisition and processing, the raw voltage signals of collection is converted to the gas response signal that physical significance is clearer and more definite;
At data preprocessing phase, adopt the technology such as normalization, denoising and signal compensation to a certain degree can remove noise and the baseline wander of compensation sensor;
In signal compensation, adopt mark ratioing technigue X=(S
gas-s
o)/S
o, have effect to the drift of additivity and product noise;
Feature extraction comprises two parts task, one is compress information, two is reduce the relevance between data, principal component analysis (PCA) (PCA) is a kind of typical feature extracting method, feature little for variance contribution ratio is removed by it, reach dimensionality reduction object, and eliminate the correlativity between sample data, make the neural computing of next stage more simple efficient; In addition Independent Component Analysis can be adopted to be used for feature extraction, and its correlativity can removed between data can remove again the higher order dependencies between data;
Pattern-recognition layer adopts reverse transfers neural network (BP) to realize the estimation of gas composition and concentration value, BP neural network full name is error backward propagation method, it is a kind of nonparametric learning method, zmodem, Nonlinear Processing ability is strong, is suitable for Gas Sensor Array Signals process;
The output of neural network and warning threshold value at different levels contrast and realize alarm level and adjudicate by last Situation Assessment.
Claims (2)
1., for the quantitative judge system of the Diversity gas of harmful influence detection, it is characterized in that comprising data Layer, characteristic layer, classification layer, decision-making level, data Layer, characteristic layer, classification layer, decision-making level are connected successively.
2. the quantitative judge system of the Diversity gas for harmful influence detection according to claim 1, is characterized in that comprising following job step:
In pretreatment process, metering circuit is converted to various gas sensor signal the voltage signal being convenient to acquisition and processing, the raw voltage signals of collection is converted to the gas response signal that physical significance is clearer and more definite;
At data preprocessing phase, adopt the technology such as normalization, denoising and signal compensation to a certain degree can remove noise and the baseline wander of compensation sensor;
In signal compensation, adopt mark ratioing technigue X=(S
gas-s
o)/S
o, have effect to the drift of additivity and product noise;
Feature extraction comprises two parts task, one is compress information, two is reduce the relevance between data, principal component analysis (PCA) (PCA) is a kind of typical feature extracting method, feature little for variance contribution ratio is removed by it, reach dimensionality reduction object, and eliminate the correlativity between sample data, make the neural computing of next stage more simple efficient; In addition Independent Component Analysis can be adopted to be used for feature extraction, and its correlativity can removed between data can remove again the higher order dependencies between data;
Pattern-recognition layer adopts reverse transfers neural network (BP) to realize the estimation of gas composition and concentration value, BP neural network full name is error backward propagation method, it is a kind of nonparametric learning method, zmodem, Nonlinear Processing ability is strong, is suitable for Gas Sensor Array Signals process;
The output of neural network and warning threshold value at different levels contrast and realize alarm level and adjudicate by last Situation Assessment.
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| CN201510457724.3A CN105067768A (en) | 2015-07-30 | 2015-07-30 | Multi-component mixed gas quantitative recognition system for dangerous chemical detection |
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| CN201510457724.3A CN105067768A (en) | 2015-07-30 | 2015-07-30 | Multi-component mixed gas quantitative recognition system for dangerous chemical detection |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107132315A (en) * | 2017-05-12 | 2017-09-05 | 盐城工学院 | Signal recognition method, device and volatile organic matter detection device |
| CN109116042A (en) * | 2018-09-04 | 2019-01-01 | 中国农业大学 | A kind of network communication electronic nose detection system and detection method |
| CN114399883A (en) * | 2021-11-30 | 2022-04-26 | 宏大爆破工程集团有限责任公司 | A kind of blasting equipment transportation monitoring and early warning system and method |
-
2015
- 2015-07-30 CN CN201510457724.3A patent/CN105067768A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107132315A (en) * | 2017-05-12 | 2017-09-05 | 盐城工学院 | Signal recognition method, device and volatile organic matter detection device |
| CN109116042A (en) * | 2018-09-04 | 2019-01-01 | 中国农业大学 | A kind of network communication electronic nose detection system and detection method |
| CN114399883A (en) * | 2021-11-30 | 2022-04-26 | 宏大爆破工程集团有限责任公司 | A kind of blasting equipment transportation monitoring and early warning system and method |
| CN114399883B (en) * | 2021-11-30 | 2023-09-26 | 宏大爆破工程集团有限责任公司 | Blasting equipment transportation monitoring and early warning system and method |
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Application publication date: 20151118 |