CN107884319B - System and method for calibrating dust sensor - Google Patents
System and method for calibrating dust sensor Download PDFInfo
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- 239000000428 dust Substances 0.000 title claims abstract description 61
- 238000000034 method Methods 0.000 title claims abstract description 21
- 239000013618 particulate matter Substances 0.000 claims abstract description 31
- 230000007613 environmental effect Effects 0.000 claims abstract description 21
- 238000012937 correction Methods 0.000 claims abstract description 15
- 239000002245 particle Substances 0.000 claims description 10
- 238000012549 training Methods 0.000 claims description 5
- 238000005259 measurement Methods 0.000 description 13
- 238000004590 computer program Methods 0.000 description 4
- 239000007789 gas Substances 0.000 description 4
- 238000000149 argon plasma sintering Methods 0.000 description 3
- 239000003344 environmental pollutant Substances 0.000 description 3
- 231100000719 pollutant Toxicity 0.000 description 3
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
- CBENFWSGALASAD-UHFFFAOYSA-N Ozone Chemical compound [O-][O+]=O CBENFWSGALASAD-UHFFFAOYSA-N 0.000 description 1
- 238000004378 air conditioning Methods 0.000 description 1
- 238000003915 air pollution Methods 0.000 description 1
- 229910021529 ammonia Inorganic materials 0.000 description 1
- 230000005250 beta ray Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 238000012888 cubic function Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000003546 flue gas Substances 0.000 description 1
- -1 formaldehyde, benzene series Chemical class 0.000 description 1
- 239000003517 fume Substances 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 244000005700 microbiome Species 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
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- 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
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- 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
- G01N15/075—Investigating concentration of particle suspensions by optical means
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- 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
- G01N2015/0096—Investigating consistence of powders, dustability, dustiness
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Abstract
The application provides a system for calibrating a dust sensor, comprising: a data acquisition unit configured to acquire output values of the dust sensor under a plurality of environmental variables and particulate matter standard values at a plurality of time points; a determining unit configured to determine a calibration function from the acquired data; a measuring unit configured to measure an output value of the dust sensor and a plurality of environmental variables in real time, and obtain a particulate matter calibration value based on the calibration function; and a correction unit configured to correct the particulate matter calibration value. The application also provides a method for calibrating the dust sensor. The application provides a process of calibrating dust sensor gathers various environment variable values simultaneously to can carry out more accurate calibration to dust sensor.
Description
Technical Field
The application relates to the field of data analysis, in particular to a system and a method for calibrating a dust sensor.
Background
With the advancement of industrial development and modern progress, global air pollution is increasingly serious, and indoor air quality of life and work of people is gradually deteriorated. It is counted that people are in various indoor environments for up to 70% -80% of the time, and urban residents are in the indoor time even up to 90%. The concentration of pollutants such as indoor formaldehyde, benzene series, ammonia, ozone and the like is far higher than that of the indoor pollutants. In reality, PM2.5 in the room is not inferior to that in the outdoor. The main sources of indoor PM2.5 include flue gas, microorganisms, kitchen fumes, air conditioning, etc., while also being affected by outdoor PM 2.5.
The accurate measurement of dust particles is the first step of dust pollutant control, and plays a very basic and key role in pollution control. The measurement principles of the common dust particle sensor are three: light scattering, weighing and beta-ray methods. The light scattering principle includes infrared and laser principles. Light scattering sensors are relatively inexpensive and easy to install and use compared to the other two sensors, but are susceptible to interference from environmental variables relative to other measurement methods.
Reference 1 (CN 104596904 a) discloses a dust concentration measuring method of a laser dust sensor, comprising the steps of: calibrating a laser dust sensor at normal temperature, and recording three indexes influencing dust measurement concentration during calibration; the voltage value of the photoelectric sensor after passing through the primary amplifier, the comparison voltage value after passing through a blocking capacitor and a secondary amplifier and the fan rotating speed; correcting factors such as attenuation of the optical path device, temperature and humidity change and the like; correcting the rotation speed of a fan in the laser dust sensor; thereby obtaining the real-time dust measurement concentration of the laser dust sensor.
Disclosure of Invention
However, environmental variables such as humidity and wind have a large influence on the measurement values of the dust sensors of the laser and infrared type. In the prior art, various real-time environment variables are not obtained when the measured value of the dust sensor is calibrated, and the influence of different environment conditions on the measured value of the sensor cannot be considered, so that the measured value has larger deviation under different environment conditions.
The application proposes that various environmental variables (such as temperature, humidity, air pressure, wind power, etc.) are acquired in real time when using a dust sensor. During calibration, the influence of various environment variables on the calibration function is considered. In the process of real-time measurement, the final measured value of the dust sensor is corrected through monitoring of wind power.
Specifically, according to one aspect of the present application, there is provided a system for calibrating a dust sensor, comprising: a data acquisition unit configured to acquire output values of the dust sensor under a plurality of environmental variables and particulate matter standard values at a plurality of time points; a determining unit configured to determine a calibration function from the acquired data; a measuring unit configured to measure an output value of the dust sensor and a plurality of environmental variables in real time, and obtain a particulate matter calibration value based on the calibration function; and a correction unit configured to correct the particulate matter calibration value.
In one embodiment, the plurality of environmental variables includes temperature, humidity, and air pressure.
In one embodiment, the determining unit is configured to: a calibration function is determined from the acquired data such that, for the determined calibration function, the sum of the squares of the differences between the particulate matter standard value and the calibration function value is minimized at each point in time in the training set.
In one embodiment, the calibration function comprises a polynomial function.
In one embodiment, the correction unit is configured to: and correcting the particulate matter calibration value according to the rotating speed of the fan and the wind power.
According to another aspect of the present application, there is provided a method for calibrating a dust sensor, comprising: acquiring output values of the dust sensor and particle standard values of the dust sensor under a plurality of environment variables at a plurality of time points; determining a calibration function according to the acquired data; measuring the output value of the dust sensor and a plurality of environment variables in real time, and obtaining a particulate matter calibration value based on the calibration function; and correcting the particulate matter calibration value.
In one embodiment, the plurality of environmental variables includes temperature, humidity, and air pressure.
In one embodiment, the calibration function is determined from the acquired data such that for the determined calibration function, the sum of the squares of the differences between the particle calibration value and the calibration function value is minimized at each point in time in the training set.
In one embodiment, the calibration function comprises a polynomial function.
In one embodiment, the particulate matter calibration value is modified based on fan speed and wind force.
The application provides the in-process to calibrating dust sensor, gathers various environment variable value simultaneously, accumulates calibration data, considers the influence of different environment variable to dust sensor measured value to can carry out more accurate calibration to dust sensor.
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The foregoing and other features of the present application will become more apparent from the following detailed description, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a block diagram illustrating a system for calibrating a dust sensor according to one embodiment of the present application.
FIG. 2 is a flow chart illustrating a method for calibrating a dust sensor according to one embodiment of the present application.
Detailed Description
The principles and implementations of the present application will become apparent from the following description of specific embodiments thereof, taken in conjunction with the accompanying drawings. It should be noted that the present application should not be limited to the specific embodiments described below. In addition, detailed descriptions of well-known techniques not related to the present application are omitted for the sake of brevity.
Fig. 1 is a block diagram illustrating a system 10 for predicting indoor air quality according to one embodiment of the present application. As shown in fig. 1, the system 10 includes a data acquisition unit 110, a determination unit 120, a measurement unit 130, and a correction unit 140.
The data acquisition unit 110 is configured to: at a plurality of time points, output values of the dust sensor under a plurality of environment variables and particle standard values are acquired. For example, the plurality of environmental variables includes temperature, humidity, and air pressure.
In one example, the output values of the dust sensor (e.g., laser dust sensor or infrared dust sensor) can be calibrated in different temperature ranges, humidity ranges and air pressure ranges to obtain corresponding temperature values Tt, humidity values Ht, air pressure values Pt, air force Wt, output values x of the dust sensor at different time points t t Standard value y of particulate matter t 。
The determination unit 120 is configured to determine a calibration function from the acquired data. In one embodiment, the determining unit 120 may determine the calibration function according to the data acquired by the data acquiring unit 110 such that the sum of the squares of the differences of the particle standard value and the calibration function value at each point in time in the training set is minimum for the determined calibration function. The calibration function may be expressed as follows:
min f ∑ t ||y t -f(x t ，T t ，H t ，P t )|| 2 (1)
that is, the determination unit 120 determines the calibration function f that minimizes the sum of squares in the above formula.
The measurement unit 130 is configured to measure the output value of the dust sensor and a plurality of environmental variables in real time and obtain a particulate matter calibration based on the calibration functionValues. For example, a dust sensor is used for real-time measurement to obtain a real-time measurement value x r Real-time temperature value T r Real-time humidity value H r And a real-time air pressure value P t Substituting the measured values into a calibration function to obtain real-time particulate matter calibration values
PM 1 ＝f(x T ，T T ，II T ，P t )
The correction unit 140 is configured to correct the particulate matter calibration value. For example, the correction unit 140 may correct the particulate matter calibration value according to the rotation speed and the wind power of the fan to obtain the corrected particulate matter concentration value PM 2 。
An example of an application for determining the calibration function is described in detail below.
At a temperature value T which is not considered t Humidity value H t And air pressure value P t In the case of (a), the output value x of the dust sensor t And particle standard value y t The relationship of (2) may be represented by a polynomial function, such as a cubic function as given below:
the values of the coefficients a, b, c need to be determined by equations (1) and (2) during calibration.
Due to the temperature value T t And air pressure value P t Less influence on the measured value when T is considered t And P t When two small correction terms can be added to equation (2):
when the humidity value is H t At lower levels, humidity has less effect on the measurement. And when the humidity value is H t Above a certain concentration, humidity has a greater influence on the measured value. Therefore, when considering the effect of humidity on the measured value, a piecewise consideration is required. That is to say,
when H t ＜H d When a small correction term is added to equation (3):
when H t ≥H d Consider, when adding a polynomial correction term to equation (3):
wherein H is d Is the threshold value of humidity, which can be reasonably determined by the specific case.
An example of an application of the correction of the particulate matter standard value is described in detail below.
When the fan speed is at the standard speed and no wind is present, the gas flow rate into the dust sensor is standard stable (assuming that the standard flow rate is L d ). At this time, there is no deviation in the measurement value of the dust sensor, that is, PM 2 ＝PM 1 。
In wind W t When the flow rate of the gas entering the dust sensor is large, the flow rate of the gas entering the sensor needs to be corrected according to the wind power. For example, when the inlet hole of the sensor is in the vertical direction, the larger the wind force is, the smaller the gas flow rate entering the sensor is, and the PM 2 Is required to be in PM 1 On the basis of adding compensation for intake air flow, i.e. PM 2 ＝ρ(W t )×PM 1 。
When the wind power W t ＜W d At ρ (W) t )＝1；
When the wind power W t ≥W d At the time p (W) t ) > 1, and becomes larger as the wind force increases.
Wherein W is d The threshold value for judging the wind power can be reasonably determined according to specific conditions.
In the embodiment, various environmental variable values are collected simultaneously in the process of calibrating the dust sensor, and the influence of different environmental variables on the measured value of the dust sensor is considered, so that the dust sensor can be calibrated more accurately.
FIG. 2 is a flow chart illustrating a method for calibrating a dust sensor according to one embodiment of the present application. As shown in fig. 2, the method 20 begins at step S210.
In step S220, output values of the dust sensor under a plurality of environmental variables and particulate matter standard values are acquired at a plurality of time points. For example, the plurality of environmental variables includes temperature, humidity, and air pressure.
In step S230, a calibration function is determined from the acquired data. The calibration function can be described, for example, in the above equation (1).
In step S240, the output value of the dust sensor and a plurality of environmental variables are measured in real time, and a particulate matter calibration value is obtained based on the calibration function. For example, a dust sensor is used for real-time measurement to obtain a real-time measured value, a real-time temperature value, a real-time humidity value and a real-time air pressure value, and the measured values are substituted into a calibration function to obtain a real-time particulate matter calibration value.
In step S250, the particulate matter calibration value is corrected. For example, the particulate matter calibration value may be corrected according to the fan rotation speed and the wind power, to obtain a corrected particulate matter concentration value.
Finally, the method 20 ends at step S260.
It should be understood that the above-described embodiments of the present application may be implemented by software, hardware, or a combination of both software and hardware. For example, the various components within the system in the above embodiments may be implemented by a variety of devices including, but not limited to: analog circuitry, digital circuitry, a general purpose processor, digital Signal Processing (DSP) circuitry, a programmable processor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a programmable logic device (CPLD), and so forth.
In addition, those skilled in the art will appreciate that the initial parameters described in the embodiments of the present application may be stored in a local database, may be stored in a distributed database, or may be stored in a remote database.
Furthermore, embodiments of the present application disclosed herein may be implemented on a computer program product. More specifically, the computer program product is one of the following: having a computer readable medium encoded thereon with computer program logic that, when executed on a computing device, provides relevant operations to implement the above-described aspects of the present application. The computer program logic, when executed on at least one processor of a computing system, causes the processor to perform the operations (methods) described in embodiments of the present application. Such an arrangement of the present application is typically provided as software, code and/or other data structures arranged or encoded on a computer readable medium, such as an optical medium (e.g., CD-ROM), floppy disk or hard disk, or other a medium such as firmware or microcode on one or more ROM or RAM or PROM chips, or as downloadable software images in one or more modules, shared databases, etc. The software or firmware or such configuration may be installed on a computing device to cause one or more processors in the computing device to perform the techniques described in embodiments of the present application.
Although the present application has been shown above in connection with the preferred embodiments thereof, it will be understood by those skilled in the art that various modifications, substitutions and changes may be made thereto without departing from the spirit and scope of the application. Accordingly, the present application should not be limited by the above-described embodiments, but should be defined by the appended claims and equivalents thereof.
Claims (6)
1. A system for calibrating a dust sensor, comprising:
a data acquisition unit configured to; acquiring output values of the dust sensor and particle standard values of the dust sensor under a plurality of environment variables at a plurality of time points;
a determining unit configured to determine a calibration function from the acquired output value and the particulate matter standard value such that, for the determined calibration function, a sum of squares of differences of the particulate matter standard value and the calibration function value is minimum at each point in time in the training set;
a measuring unit configured to measure an output value of the dust sensor and a plurality of environmental variables in real time, and obtain a particulate matter calibration value based on the calibration function; and
a correction unit configured to correct the particulate matter calibration value according to the fan rotation speed and the wind power,
wherein the plurality of environmental variables includes humidity, an
Wherein when the humidity value is smaller than the threshold value of humidity, a small correction term is added in the calibration function, and when the humidity value is greater than or equal to the threshold value of humidity, a correction term of a polynomial is added in the calibration function.
2. The system of claim 1, wherein the plurality of environmental variables further comprises temperature and air pressure.
3. The system of claim 1, wherein the calibration function comprises a polynomial function.
4. A method for calibrating a dust sensor, comprising:
acquiring output values of the dust sensor and particle standard values of the dust sensor under a plurality of environment variables at a plurality of time points;
determining a calibration function according to the acquired output value and the particulate matter standard value, so that the sum of the squares of the differences of the particulate matter standard value and the calibration function value at each time point in the training set is minimum for the determined calibration function;
measuring the output value of the dust sensor and a plurality of environment variables in real time, and obtaining a particulate matter calibration value based on the calibration function; and
correcting the particle calibration value according to the rotating speed of the fan and the wind power,
wherein the plurality of environmental variables includes humidity, an
Wherein when the humidity value is smaller than the threshold value of humidity, a small correction term is added in the calibration function, and when the humidity value is greater than or equal to the threshold value of humidity, a correction term of a polynomial is added in the calibration function.
5. The method of claim 4, wherein the plurality of environmental variables further comprises temperature and air pressure.
6. The method of claim 4, wherein the calibration function comprises a polynomial function.
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| CN110426490B (en) * | 2019-07-25 | 2022-02-08 | 北京市劳动保护科学研究所 | Temperature and humidity drift compensation method and device for harmful gas online monitor |
| CN114646102A (en) * | 2022-03-14 | 2022-06-21 | 青岛海尔空调器有限总公司 | Washing air conditioner and purification control method thereof |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2306174A2 (en) * | 2003-03-28 | 2011-04-06 | Inguran, LLC | Apparatus and methods for providing sex-sorted animal sperm |
| CN104596904A (en) * | 2015-01-30 | 2015-05-06 | 武汉四方光电科技有限公司 | Measurement method for dust concentration of laser dust sensor |
| CN105043950A (en) * | 2015-05-19 | 2015-11-11 | 重庆风小六智能技术有限公司 | Laser dust sensor |
| CN105259085A (en) * | 2015-10-20 | 2016-01-20 | 武汉三众和光电科技有限公司 | Dust concentration measurement system of laser dust sensor and dust concentration measurement method |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101354332A (en) * | 2008-09-26 | 2009-01-28 | 北京绿林创新数码科技有限公司 | Laser powder dust detector with humidity continuous self-correcting function and detecting method thereof |
| CN101403692B (en) * | 2008-10-27 | 2010-11-17 | 北京北分麦哈***仪器有限公司 | Laser gas analyzer and its calibration method |
| JP5630674B2 (en) * | 2010-09-30 | 2014-11-26 | 日本電気株式会社 | Information processing apparatus, information processing system, information processing method, program, and recording medium |
| CN102331266A (en) * | 2011-06-13 | 2012-01-25 | 无锡格林通安全装备有限公司 | Multi-parameter gas sensor compensating method |
| US9882065B2 (en) * | 2014-06-27 | 2018-01-30 | Texas Instruments Incorporated | Analog floating-gate atmometer |
| CN204044130U (en) * | 2014-08-01 | 2014-12-24 | 日电（中国）有限公司 | A kind of air pollutants checkout equipment |
| CN205483924U (en) * | 2015-09-25 | 2016-08-17 | 原点生活（北京）科技有限公司 | System for be arranged in detecting air fine particles concentration |
| CN105278578A (en) * | 2015-10-20 | 2016-01-27 | 浪潮电子信息产业股份有限公司 | Inlet air temperature correction method based on rotation speed of fan |
| CN105823714B (en) * | 2016-05-27 | 2019-04-26 | 武汉四方光电科技有限公司 | A kind of wearable dust investigating and method |
| CN105891079A (en) * | 2016-06-27 | 2016-08-24 | 广东美的制冷设备有限公司 | Calibration method and calibration device for dust sensor |
-
2016
- 2016-09-30 CN CN201610872046.1A patent/CN107884319B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2306174A2 (en) * | 2003-03-28 | 2011-04-06 | Inguran, LLC | Apparatus and methods for providing sex-sorted animal sperm |
| CN104596904A (en) * | 2015-01-30 | 2015-05-06 | 武汉四方光电科技有限公司 | Measurement method for dust concentration of laser dust sensor |
| CN105043950A (en) * | 2015-05-19 | 2015-11-11 | 重庆风小六智能技术有限公司 | Laser dust sensor |
| CN105259085A (en) * | 2015-10-20 | 2016-01-20 | 武汉三众和光电科技有限公司 | Dust concentration measurement system of laser dust sensor and dust concentration measurement method |
Non-Patent Citations (1)
| Title |
|---|
| 基于光散射的粉尘浓度测量的研究;胡澄;《洁净与空调技术》(第第3期期);22-25 * |
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