CN105277228A - Multi-sensor data fusion method for vegetable waste fermentation - Google Patents
Multi-sensor data fusion method for vegetable waste fermentation Download PDFInfo
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- CN105277228A CN105277228A CN201410225769.3A CN201410225769A CN105277228A CN 105277228 A CN105277228 A CN 105277228A CN 201410225769 A CN201410225769 A CN 201410225769A CN 105277228 A CN105277228 A CN 105277228A
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Abstract
The invention relates to a multi-sensor data fusion method for vegetable waste fermentation. According to the invention, measured values of different types of sensors in a vegetable waste fermentation reactor are fused and judged by adopting a Bayes theory. The multi-sensor data fusion method comprises the steps of firstly carrying out first-level data fusion on measured values of each measurement point of a temperature sensor, a pH-value sensor and a pressure sensor through a Bayes formula; and then carrying out second-level data fusion on first-level fusion target values of temperature, pH values and pressure through the Bayes formula so as to acquire a final judgment result. The fusion method provided by the invention greatly improves the accuracy and the credibility of measurement and monitoring in vegetable waste fermentation reaction.
Description
Technical field
Notebook data fusion method is mainly applicable to the real-time monitoring of vegetable castoff sweat.
Background technology
The process of vegetable castoff fermentation is comparatively complicated, relates to multiple measurands such as temperature, pressure, pH value, and simultaneously in order to improve the levels of precision of measurement, each measurand has again multiple corresponding sensor and measures.Certain scientific and effective data fusion is carried out to the measured value of these variety classes different sensors and calculates the problem becoming field of modern detection.Notebook data fusion method have employed Bayes theory and just well solves problems.
Summary of the invention
Notebook data fusion method adopts Bayes theory to process multiple numerical value of each sensor measurement in vegetable castoff fermentation reactor, improves the accuracy of fermentation reaction.
This method adopts two-stage data fusion, first order data fusion is the fusion of each measurement numerical value of same species sensor, the second level data fusion fusion results of variety classes sensor is again merged on the basis of first order data fusion, form final result of determination, reflection sweat.
Theoretical according to the target identification of Bayes reasoning, first order data fusion is as prior probability using the measured value of one of them of certain class sensor, other measured value of sensors with auxiliary electrode utilizes Bayes formula as the conditional probability under this prior probability, obtains the target posterior probability of each sensor.
Second level data fusion utilizes Bayes formula to calculate as prior probability and conditional probability the posterior probability of first order data fusion gained more again, draws the target posterior probability of variety classes sensor, reaches the object that objective result judges.
By implementing notebook data fusion method, more reasonably can control the sweat in vegetable castoff reactor, greatly improving the accuracy that monitoring is measured in vegetable castoff fermentation.
Accompanying drawing explanation
Accompanying drawing is the Fusion process flow diagram of vegetable castoff fermentation.
In figure: D11 ... each measured value of D1n---temperature sensor, D21 ... each measured value of D2n---pH sensor, D31 ... each measured value of D3n---pressure transducer, D1---temperature sensor one-level fusion results, D2---pH sensor one-level fusion results, D3---pressure transducer one-level fusion results, D---sensor two level fusion result.
embodiment
With reference to Figure of description, following explanation is done to the sweat of data fusion method of the present invention in vegetable castoff reactor.Concrete operations are: the measured value D11 of each temperature sensor ... D1n carries out data processing by Bayes formula and permeates a desired value D1 as temperature sensor one-level fusion results.The measured value D21 of each pH sensor ... D2n carries out data processing by Bayes formula and permeates a desired value D2 as pH sensor one-level fusion results.The measured value D31 of each pressure transducer ... D3n carries out data processing by Bayes formula and permeates a desired value D3 as pressure transducer one-level fusion results.Then, the first order fusion results of various kinds of sensors is carried out data processing by Bayes formula again and is fused to desired value D as second level fusion results, draw final judgement.
Claims (6)
1. key point of the present invention is to utilize Bayes theory to realize carrying out twice data fusion to the measured value of the temperature sensor in vegetable castoff fermentation reactor, pH sensor and pressure transducer, draw final desired value, by desired value, result is judged, greatly improve the confidence level to ferment control and accuracy.
2. data fusion new method according to claim 1, is characterized in that utilizing Bayes theory to carry out first order data fusion to each measured value of the temperature sensor in vegetable castoff fermentation reactor, draws thermometric desired value.
3. data fusion new method according to claim 1, is characterized in that utilizing Bayes theory to carry out first order data fusion to each measured value of the pH sensor in vegetable castoff fermentation reactor, draws the desired value that pH value is measured.
4. data fusion new method according to claim 1, is characterized in that utilizing Bayes theory to carry out first order data fusion to each measured value of the pressure transducer in vegetable castoff fermentation reactor, draws tonometric desired value.
5. data fusion new method according to claim 1, is characterized in that utilizing Bayes theory to carry out second level data fusion to the desired value of first order data fusion gained temperature, pH value and pressure, draws final goal value, judge.
6. data fusion new method according to claim 1, is characterized in that utilizing Bayes theory to realize result judgement to carrying out multi-stage data fusion except the above other various kinds of sensors in vegetable castoff fermentation reactor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN201410225769.3A CN105277228A (en) | 2014-05-27 | 2014-05-27 | Multi-sensor data fusion method for vegetable waste fermentation |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201410225769.3A CN105277228A (en) | 2014-05-27 | 2014-05-27 | Multi-sensor data fusion method for vegetable waste fermentation |
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| CN105277228A true CN105277228A (en) | 2016-01-27 |
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| CN201410225769.3A Pending CN105277228A (en) | 2014-05-27 | 2014-05-27 | Multi-sensor data fusion method for vegetable waste fermentation |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030088381A1 (en) * | 2001-06-25 | 2003-05-08 | Henry Manus P. | Sensor fusion using self evaluating process sensors |
| CN101252677A (en) * | 2007-10-19 | 2008-08-27 | 西安交通大学 | Object tracking method based on multi-optical spectrum image sensor |
| CN101706650A (en) * | 2009-02-01 | 2010-05-12 | 杨厚荣 | Multi-sensor system |
| CN102147468A (en) * | 2011-01-07 | 2011-08-10 | 西安电子科技大学 | Bayesian theory-based multi-sensor detecting and tracking combined processing method |
| CN102288224A (en) * | 2011-07-28 | 2011-12-21 | 农业部规划设计研究院 | Organic solid waste aerobic fermentation engineering key parameter data acquisition system |
| CN102876816A (en) * | 2012-07-23 | 2013-01-16 | 江苏大学 | Fermentation process statue monitoring and controlling method based on multi-sensor information fusion |
-
2014
- 2014-05-27 CN CN201410225769.3A patent/CN105277228A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030088381A1 (en) * | 2001-06-25 | 2003-05-08 | Henry Manus P. | Sensor fusion using self evaluating process sensors |
| CN101252677A (en) * | 2007-10-19 | 2008-08-27 | 西安交通大学 | Object tracking method based on multi-optical spectrum image sensor |
| CN101706650A (en) * | 2009-02-01 | 2010-05-12 | 杨厚荣 | Multi-sensor system |
| CN102147468A (en) * | 2011-01-07 | 2011-08-10 | 西安电子科技大学 | Bayesian theory-based multi-sensor detecting and tracking combined processing method |
| CN102288224A (en) * | 2011-07-28 | 2011-12-21 | 农业部规划设计研究院 | Organic solid waste aerobic fermentation engineering key parameter data acquisition system |
| CN102876816A (en) * | 2012-07-23 | 2013-01-16 | 江苏大学 | Fermentation process statue monitoring and controlling method based on multi-sensor information fusion |
Non-Patent Citations (1)
| Title |
|---|
| 安居白等: "一个Bayes数据融合模型及其在发酵控制中的应用", 《大连轻工业学院学报》 * |
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Application publication date: 20160127 |