WO2020021344A1 - Procédé d'étalonnage collaboratif de capteur environnemental - Google Patents

Procédé d'étalonnage collaboratif de capteur environnemental Download PDF

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Publication number
WO2020021344A1
WO2020021344A1 PCT/IB2019/051244 IB2019051244W WO2020021344A1 WO 2020021344 A1 WO2020021344 A1 WO 2020021344A1 IB 2019051244 W IB2019051244 W IB 2019051244W WO 2020021344 A1 WO2020021344 A1 WO 2020021344A1
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Prior art keywords
data
data set
calibration
sensor
station
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PCT/IB2019/051244
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English (en)
Chinese (zh)
Inventor
司书春
许军
秀福 万
帅帅 贾
一平 刘
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山东诺方电子科技有限公司
司书春
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Priority claimed from PCT/IB2018/055531 external-priority patent/WO2019150182A1/fr
Application filed by 山东诺方电子科技有限公司, 司书春 filed Critical 山东诺方电子科技有限公司
Priority to CN201980006118.6A priority Critical patent/CN112567241A/zh
Priority to PCT/CN2019/102420 priority patent/WO2020043031A1/fr
Priority to CN201980089835.XA priority patent/CN113728220B/zh
Priority to CN201980089854.2A priority patent/CN113330283B/zh
Priority to PCT/CN2019/102419 priority patent/WO2020043030A1/fr
Publication of WO2020021344A1 publication Critical patent/WO2020021344A1/fr

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    • 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

Definitions

  • the present invention relates to a method for collaborative calibration of environmental sensors, and belongs to the field of environmental monitoring.
  • the monitoring indicators of atmospheric pollutants in environmental monitoring are sulfur dioxide, nitrogen oxides, ozone, carbon monoxide, PM1 (particles with aerodynamic particle size smaller than 1 micron), PM2.5 (aerodynamic particle size with smaller than 2.5 micron) in the atmosphere Particles), PM10 (particles with aerodynamic particle size less than 10 microns), PM100 (particles with aerodynamic particle size less than 100 microns), and VOCs (volatile organic compounds) or TVOC (total volatile organic compounds).
  • the atmospheric environment monitoring system can collect and process the monitored data, and timely and accurately reflect the regional ambient air quality status and changes.
  • the atmospheric environment monitoring equipment mainly includes fixed monitoring stations and mobile monitoring equipment.
  • the current fixed monitoring stations are mainly divided into large fixed monitoring stations and small stations.
  • Mobile monitoring equipment mainly includes special atmospheric environmental monitoring vehicles, drones, and handheld devices.
  • the above-mentioned small monitoring stations and handheld devices use air quality sensors to measure atmospheric pollutants.
  • Small sensors have the characteristics of low cost, miniaturization, and online monitoring, and can be used on a large scale.
  • the air quality sensor itself may cause errors due to inconsistencies between the measured values and the true values for various reasons.
  • air quality sensors Compared with large precision instruments or manual monitoring methods, air quality sensors have the characteristics of lower accuracy, poor stability, large errors, and frequent calibration.
  • the laser scattering method for air pollution particulate matter sensors has a broad market prospect because of its low cost and portability.
  • the portable analysis device using the scattering method has disadvantages such as poor measurement consistency, high noise, and low measurement accuracy.
  • the core device is easily affected by various environmental factors, and fluctuations easily cause misjudgment.
  • the sensor data changes suddenly and sharply being able to intelligently determine whether the change is due to a sensor failure or sudden pollution will greatly improve the reliability of the data and is of great value for ensuring the quality of environmental monitoring data.
  • Y data set the monitoring data of the mobile monitoring station
  • Fixed monitoring station A station with the ability to monitor the atmospheric environment. It can be a national control station, a calibration station, or a grid-based microstation.
  • Mobile monitoring station a monitoring station equipped with atmospheric environmental monitoring equipment and capable of moving. It can be a social vehicle equipped with miniature monitoring equipment, or it can be a professional atmospheric environment monitoring vehicle.
  • Contrast coefficient A quantity that indicates the degree of linear correlation between variables. It is generally represented by the letter tl.
  • the calibration coefficient in the present invention refers to a correction coefficient used for calibrating and correcting a deviation of a data set of a sensor. Particulate matter measured by light scattering method is easily affected by environmental factors, such as humidity and other factors. There are also many ways to improve sensor accuracy.
  • the current monitoring station calibration method mainly uses manual maintenance on a regular basis. Staff go to the site to clean and maintain the equipment, and carry standard equipment and standard gas to manually calibrate the sensors on site. Or simply make coefficient corrections to the monitoring equipment. These calibration methods have different levels of inaccuracy, complicated calibration and high cost. To address the above-mentioned shortcomings, the present invention provides a method for coordinated calibration of environmental sensors. Multiple sensors are used to calibrate and compare environmental sensors to achieve complementary data deviations.
  • Calibration method proposed by the present invention relates from (X stations of a data set from the data set p p monitoring stations and Y in the Y data set from the stations; first screened as a calibration reference based on a data set; This can be a subset of the a dataset; it can also be a subset of the (3 dataset or a subset of the Y dataset.
  • the benchmark data set As a benchmark data set, certain conditions must be met. First of all, when the benchmark data set is used to calibrate other monitoring stations with some distance, its data should be stable for a period of time without significant data fluctuations. Due to the natural diffusion of the atmosphere, this kind of data is stable for a period of time. It is the stability of air quality within a certain range, and the benchmark data set selected at this time should be able to represent data within a certain range.
  • the first calibration method proposed by the present invention is to calibrate the radon data set and the Y data set based on the a data set.
  • the data of the a data set are analyzed to determine the benchmark a data set.
  • Analyze data Set methods include direct average method, average method after removing the highest and lowest values, Kalman filter, Bayesian estimation, DS evidence reasoning, artificial neural network and other methods.
  • a calibration coefficient of the radon data set is obtained by comparing the radon data set with the benchmark a data set, which is used to calibrate the P data set.
  • a calibration coefficient of the Y dataset is obtained, which is used to calibrate the Y dataset.
  • the comparison method can be linear calibration, non-linear calibration, or other calibration methods.
  • multiple calibration coefficients are generally calculated, and calibration coefficients whose coefficients differ by less than a certain value are taken as valid calibration coefficients, and the average of these valid calibration coefficients is used as the final calibration coefficient to calibrate the calibration object.
  • the calibration coefficient may also need to take into account the spatial distribution.
  • the calibration coefficients of (3 datasets can be weighted according to the distance from (3 sites to a site), the closer the distance is, the greater the weight; if the opposite site is within a certain distance from a site, the weighted average is accurate as the calibration target.
  • Value Take the data within a certain distance from Site A to Site Y as valid data to participate in the calibration calculation.
  • the calibration coefficients of the Y data set can also be determined according to different data intervals of the data, that is, multiple calibration coefficients are set in different data intervals. In the selection of calibration coefficients in different intervals, direct average method, average method after removing the highest value and the lowest value can still be used.
  • the calibration P data set is based on the Y data set, and the calibration (3 data set. When the distance between the mobile monitoring station and the fixed monitoring station is less than the calibration trigger distance t, the Y data set of the mobile monitoring station is used as the reference Compare the 3 data set of the fixed monitoring station with the Y data set of the mobile monitoring station to obtain a calibration coefficient, and use the calibration coefficient and other calibration factors to calibrate the fixed station.
  • the calibration method can choose linear calibration or non-linear calibration, and the calibration trigger
  • the distance t can be 500m, 1km, 2km, 5km, etc.
  • calibrate other Y data sets Based on the selected Y data set, calibrate other Y data sets.
  • the Y data set of the selected mobile monitoring station is compared with the Y data set of other mobile monitoring stations based on the Y data set of the selected mobile monitoring station to obtain Calibration coefficient, use fixed calibration coefficient and other calibration factors to calibrate fixed stations.
  • Calibration method can choose linear calibration or non-linear calibration, calibration trigger distance t Be a 500m, lkm s 2km s 5km equidistant calibration third embodiment the present invention set forth Ranking calibration data set to the P and Y to the credibility of the data set ranked by confidence to high confidence device Low equipment calibration.
  • the reliability can be the comparison coefficient between the reference data and the number being calibrated, or other parameters of the monitoring equipment such as the calibration time factor (representing the time since the last calibration), the stability factor, and so on.
  • the P data set and the Y data set are compared with the a data set to obtain a credibility index.
  • the comparison method can be a correlation coefficient, a proportional average, and the like.
  • the reliability index is ranked from high to low, and the lower-ranked data set is calibrated.
  • the first method is used for the calibration method. Recalculate the confidence level after calibration to rank. For the P fixed station, select a national control station within a certain range for credibility calculation.
  • the credibility can be the average of multiple national control stations, or it can be calculated by weighted average based on the distance as a weight; in the case where there is no national control station within a certain range Calculate the credibility with the average value of the a dataset for the entire city.
  • the reliability calculation is performed on the data after mobile station Y moves to a certain range of national control station a. After ranking, the higher-ranked data set is compared with the lower-ranked data set, the calibration coefficient is calculated, and the higher-ranked data set is used to calibrate the lower-ranked data set.
  • the monitoring data of a fixed monitoring station When the monitoring data of a fixed monitoring station is abnormal, it can communicate with the mobile monitoring station to control the working state of the sensors of the mobile station, and increase the monitoring frequency and data return frequency.
  • the abnormal data can be judged by the contrast coefficient exceeding the setting range, that is, the abnormal data of the station is determined.
  • x is the calibrated data
  • y c is the corrected reference data
  • ri is the contrast coefficient
  • the calibration coefficient is the median of the contrast coefficient.
  • the calibration coefficient is the mode of the contrast coefficient.
  • the calibration coefficient is the value of the comparison coefficient calculated by other probability methods.
  • the distance factor between the reference station and the calibrated station can also be considered, and a distance factor is introduced.
  • the distance factor / d is used to consider the influence of the distance factor between the monitored point and the monitoring station on the reliability of the monitoring data.
  • the distance factor can be normalized by the inverse ratio of the distance from the geometric center point of the area to each monitoring point.
  • the distance factor can be obtained from the data obtained by monitoring stations in a certain area.
  • the pollution data consists of monitoring data from several nearby monitoring stations. These monitoring data can have different weights for pollution data at a specific location.
  • the weight is normalized by the inverse ratio of the distance from the specific location to each monitoring point.
  • the weight is the distance factor.
  • d represents the distance from the geometric center point of the area to each station in the area or the distance from the specific position to each monitoring point
  • the set value of the distance is represented by A.
  • the distance factor is 1. After exceeding the set distance A, the farther the distance is, the smaller the weight of the monitoring station data is, and the closer the distance is, the greater the weight of the monitoring station data is.
  • the formula for calculating the distance factor is:, d> A 0 ⁇ d ⁇ A d represents the distance from the geometric center point of the area to each station in the area, or from the specific location to each monitoring point.
  • the parameter K is a distance weight parameter.
  • the reference value data can also be obtained through normalization. Apply the normalized base station correction calculation formula to correct the base station data.
  • the normalized calculation formula: y c is the revised baseline data,
  • n is the number of reference stations that meet the standard. In the case where there is only one reference station that meets the standard, the correction of the reference data is calculated as follows:
  • the stability coefficient 2 is the ratio of the number of base station data in the set interval to the total base station data. If 2 is greater than the set percentage (the set percentage can be 80%, 90%, and other percentages), the base station data set is considered stable. A higher A indicates a more stable data set.
  • the setting interval is the range given to the reference data within the setting T time range. The mathematical expression of the setting interval is
  • Y can be obtained from statistical methods such as the average, median, and mode of the base station data within the time range of T, "" is the interval coefficient.
  • the number of base station data that falls within the set interval within the T time range is the number of base station data that falls within the set interval within the T time range
  • the stability coefficient can also be related to the variance of the reference data in the set time range. , Then it is unstable. If the base is set within the T time range, according to the variance ⁇ Variance setting, B, then it is stable.
  • C) The stability factor can also be related to the standard deviation of the reference data within the set 1T time range. Set the standard deviation of the reference data within the T time range, and the variance is set to ⁇ C, then it is unstable. If the standard deviation of the reference data within the T time range is set to ⁇ Variance setting, ⁇ C, it is stable. For mobile monitoring stations to be used as a calibration reference, they need to have sufficient credibility.
  • the low-frequency sensor disclosed in the earlier application PCT / IB2018 / 05531 discloses air pollution detection equipment.
  • the air pollution detection equipment that is, the mobile monitoring station in this article, includes a main control module and a detection module.
  • the detection module uses at least four The sub-sensor units constitute a sensor module; when the main control module detects a suspected abnormality in one of the sub-sensor units, and judges that the suspected abnormal sensor is an abnormal sensor, isolates the abnormal sensor, and the abnormal sensor In the isolation zone, the multi-core sensor module continues to work normally after it is downgraded.
  • the air pollution detection device includes a main control module and a detection module; the detection module includes at least two similar sub-sensor units to form a sensor module; and the sub-sensor units work At normal operating frequency.
  • the detection module further includes at least one sub-sensor unit similar to the sensor module to form a low-frequency calibration module; the sub-sensor unit in the low-frequency calibration module operates at a frequency much lower than the operating frequency of the sensor module. Therefore, the low-frequency calibration module is also called a low-frequency group.
  • the sensor module is also called a high-frequency group.
  • the operating frequency of the sensor module is 10 times or more than that of the low-frequency calibration module.
  • the ratio of the working frequency of the high-frequency group to the low-frequency group is called the high-frequency and low-frequency ratio, and can be selected as: 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8: 1 , 9: 1, 10: 1, 15: 1, 20: l o
  • the working frequency of the low frequency group can be consistent with the rhythm of abnormal judgment. That is, when it is necessary to determine whether there is an abnormal phenomenon of the sub-sensor in the sensor module, the low-frequency group performs the detection work.
  • the accuracy of its data can be restored by calibration; that is, the sub-sensor that is not attenuated or has a very low attenuation is used to calibrate the high attenuation.
  • Child sensor During the operation of the sensor module, every certain period of time, such as 1 day, 1 week, or 1 month, use the low-frequency group detection data as a reference, calibrate the high-frequency group detection data, and the calibration coefficients can use the high-frequency group sensor detection data. The ratio of the average value to the average value of the detection data of the low frequency group.
  • Isolation and Recovery Prior application PCT / IB2018 / 05531 also discloses a set of methods to identify the working status of sub-sensors and isolate and restore the sub-sensors.
  • the sensor module obtains a set of detection data at a time, and the main control module filters out data that is suspected to be abnormal from this set of data, and then determines whether the corresponding sub-sensor meets the isolation condition. After determining that the sub-sensor is an abnormal sub-sensor, the abnormal sub-sensor is classified into the isolation area; after determining that the sub-sensor suspected to be abnormal does not satisfy the isolation condition, the sub-sensor continues to work normally. It is judged whether the sub-sensor entering the isolation zone can heal itself.
  • the frequency reduction work processing is performed on the self-healing sub-sensor. Sub-sensors that cannot heal themselves will stop working and notify the operation and maintenance party for repair or replacement.
  • the main control module detects the output data of the sub-sensor to determine whether it has reached the recovery condition. The sub-sensor that has reached the recovery condition is detached from the isolation area and resumes work. The output data participates in sensor module data or the main control Data calculation; judge again whether the abnormal sensor that does not meet the recovery conditions can heal itself. After isolating the abnormal sub-sensors in the sensor module, the average value of the remaining sub-sensor output data is used as the output result of the sensor module, and the sensor module can continue to be used normally.
  • FIG. 1 is a schematic diagram of a calibration system
  • FIG. 2 is a schematic diagram of a calibration data set and a Y data set based on a data set
  • FIG. 3 is a schematic diagram of a calibration data set based on a data set
  • FIG. 4 It is a schematic diagram of the calibration area range.
  • Fig. 5 is a flowchart of calibrating the radon data set and the Y data set based on the a data set
  • Fig. 6 is a flowchart of calibrating the radon data set based on the Y data set
  • the Y data set is used as a basis to calibrate the Y data set.
  • Figure 8 is the flowchart for ranking calibration.
  • 10 is the reference station
  • 20 is the fixed monitoring station
  • 30 is the mobile monitoring station
  • 40 is the data center
  • 50 is
  • 101 is the No. 1 base station
  • 102 is the No. 2 base station
  • 103 is the No. 3 base station
  • 201 is the No. 1 fixed calibration station
  • 202 is the No. 2 fixed calibration station
  • 203 is the No. 3 fixed calibration station.
  • Calibration station, 301 is No. 1 mobile calibrated station
  • 302 is No. 2 mobile calibrated station
  • 501 is the calibration range of the base station.
  • the calibrated stations are sorted according to their credibility and used The most accurate micro-station data is calibrated one by one to get the calibration coefficients, and the above data is counted in the following table:
  • the spatial distribution may also need to be considered during calibration.
  • the station to be calibrated is ranked lower.
  • P 4 150
  • the contrast coefficient is within the range of 0.95-1.05 with high credibility, and no calibration is performed; the contrast coefficient is between 1.05-1.2, and calibration is performed; the contrast coefficient is greater than 1.2 to not perform calibration, and the equipment may have a serious failure.
  • the control system will be alerted to indicate that the monitoring equipment needs manual maintenance.
  • the calibration range is determined by the correlation coefficient, and the equipment with the correlation coefficient greater than 0.9 will not be calibrated. For the equipment with the correlation coefficient less than 0.9, the calibration will be performed with the goal of reaching the benchmark data set.
  • the device with the highest reliability is used for calibration As a benchmark, if the device with the highest credibility is a fixed station, calibration is performed from the stations around the fixed station until all is completed. If the device with the highest credibility is a Y mobile station, the The stations passing by it are calibrated as priority calibration objects until they are all completed.
  • the calibration range is determined by the proportional average coefficient.
  • Equipment with a proportional coefficient of 0.9 to 1.1 is not calibrated, and equipment with a proportional coefficient in other ranges is used to reach the benchmark.
  • the data set is calibrated for the target.
  • the device with the highest credibility is used as the calibration benchmark. If the device with the highest credibility is a fixed station, the calibration is started from the stations around the fixed station. Until all is completed, if the device with the highest credibility is the Y mobile station, the stations passing by around it are used as priority calibration objects for calibration until all are completed.
  • Embodiment 5 As shown in FIG. 2, there are reference stations No. 1, 2, and 3 in the area, and two calibrated stations (31, yl. Take four state-controlled reference stations at four moments, Tl, T2, T3, and T4. And the monitoring values of the fixed micro-stations, the average values of the monitoring data of each fixed national control base station are calculated as shown in the table below. 1.04.
  • the calibration coefficient can also be determined according to different data intervals of the data, that is, multiple calibration coefficients are set in different data intervals. In the selection of calibration coefficients in different intervals, the direct average method can still be used, after removing the highest and lowest values Methods such as averaging method. In this embodiment, it is specified that the contrast coefficient is within the range of 1-1.2, and the calibration coefficient is the average value of the contrast coefficient between 1-1.2; if the contrast coefficient is above 1.2, the maximum value of the contrast coefficient is removed. Take the average.
  • the seventh embodiment provides that the calibration procedure is started when the mobile monitoring equipment enters a range of 5 km around the reference station.
  • vehicle No. 1 is located within a 5 km range around No. 1, 2, and 3 monitoring stations, and No. 2 If the vehicle is not within a 5km area around monitoring stations 1, 2, and 3, the mobile monitoring device No. 1 starts the calibration process, and the mobile testing device No. 2 does not start the calibration process.

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Abstract

L'invention concerne un procédé d'étalonnage collaboratif de capteur environnemental, se rapportant au domaine de la surveillance environnementale. Le procédé concerne un ensemble de données α provenant d'une station de surveillance α, un ensemble de données β provenant d'une station de surveillance β, et un ensemble de données γ provenant d'une station de surveillance γ, le capteur environnemental de la station de surveillance fait appel au procédé de comparaison d'étalonnage mutuel de données multiples, réalise une complémentarité de déviation de données et une vérification mutuelle, améliore la fiabilité, la cohérence et la précision du capteur.
PCT/IB2019/051244 2018-07-25 2019-02-15 Procédé d'étalonnage collaboratif de capteur environnemental WO2020021344A1 (fr)

Priority Applications (5)

Application Number Priority Date Filing Date Title
CN201980006118.6A CN112567241A (zh) 2018-07-25 2019-02-15 环境传感器协同校准方法
PCT/CN2019/102420 WO2020043031A1 (fr) 2018-08-25 2019-08-25 Procédé d'étalonnage et de coordination de travail destiné à des capteurs de surveillance de pollution atmosphérique
CN201980089835.XA CN113728220B (zh) 2018-08-25 2019-08-25 一种大气污染监测传感器校准和协同工作的方法
CN201980089854.2A CN113330283B (zh) 2018-08-25 2019-08-25 大气污染检测设备数据可信度评价及校准方法
PCT/CN2019/102419 WO2020043030A1 (fr) 2018-08-25 2019-08-25 Procédé d'évaluation et d'étalonnage de crédibilité de données pour dispositif de surveillance de pollution de l'air

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
IBPCT/IB2018/055531 2018-07-25
PCT/IB2018/055531 WO2019150182A1 (fr) 2018-02-01 2018-07-25 Système de capteur multicœur, et son procédé d'isolation et de récupération
CN201810976246 2018-08-25
CN2018109762460 2018-08-25

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