WO2020043031A1 - 一种大气污染监测传感器校准和协同工作的方法 - Google Patents
一种大气污染监测传感器校准和协同工作的方法 Download PDFInfo
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Definitions
- the invention relates to a method for calibrating and cooperating air pollution monitoring 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 less than 1 micron), PM2.5 (particles with aerodynamic particle size less than 2.5 microns) in the atmosphere ), 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 aforementioned small monitoring stations and handheld devices all use air quality sensors to measure pollutants in the atmosphere.
- 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. Compared with large-scale precision instruments or manual monitoring methods, air quality sensors also have lower accuracy, poor stability, large errors, and require 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, large noise, and low measurement accuracy.
- the core device is easily affected by various environmental factors, and fluctuations easily cause misjudgment.
- a station with the ability to monitor the atmospheric environment 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, usually expressed by the letter ⁇ .
- the calibration coefficient refers to a correction coefficient used to calibrate and correct the deviation of the data set of the sensor.
- Particulate matter measured by light scattering method is susceptible to 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 regular manual maintenance. Staff go to the site to clean up 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 problems such as inaccuracy, complex calibration and high cost.
- the present invention provides a method for calibrating and cooperating air pollution monitoring sensors.
- Multi-data mutual calibration and comparison are adopted between environmental sensors to achieve complementary data deviations and mutual verification, and improve the reliability, consistency, and stability of sensors. Precision and life.
- the credibility weight factor is introduced to evaluate its credibility, and then the calibration is performed after the evaluation.
- the credibility weight factor is affected by the distance between the reference site and the calibrated station or the geographical center, the geographic position of the reference site, the evaluation of the reference site by other sites, the stability of the reference site, and other influencing factors. Makes the urban pollution monitoring data more reliable, and also makes the monitoring station calibration more accurate.
- the first calibration method proposed by the present invention is to calibrate the ⁇ data set and the ⁇ data set based on the ⁇ data set.
- the base ⁇ data set is determined by analyzing the data of the ⁇ data set.
- Methods for analyzing alpha datasets include direct average method, average method after removing the highest and lowest values, Kalman filter, Bayesian estimation, D-S evidence reasoning, artificial neural network and other methods.
- the beta data set is compared with the benchmark alpha data set to obtain a calibration coefficient for the beta data set, which is used to calibrate the beta data set.
- a calibration coefficient of the ⁇ data set is obtained, which is used to calibrate the ⁇ data set.
- 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. 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 the ⁇ dataset can be weighted according to the distance from the ⁇ site to the ⁇ site, the closer the distance is, the greater the weight; if the ⁇ site is within a certain distance from the ⁇ site, the weighted average value is taken as the calibration target accurate value. For the ⁇ site, data within a certain distance from the ⁇ site is taken as valid data to participate in the calibration calculation.
- the calibration coefficient of the ⁇ 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 beta data set is calibrated.
- the gamma data set is considered to be a valid reference value after calibration.
- the data set of the ⁇ site and the data set of the passing ⁇ site are used to calculate the calibration coefficient.
- the calibration method can select linear calibration or non-linear calibration.
- the distance can be 500m, 1km, 2km, 5km.
- the high accuracy device After ranking the ⁇ data set and the ⁇ data set with accuracy, the high accuracy device is calibrated to the low accuracy device.
- the third calibration method proposed by the present invention is to calibrate the ⁇ data set and the ⁇ data set by accuracy, and then calibrate from a device with high accuracy to a device with low accuracy.
- the ⁇ data set and the ⁇ data set are compared with the ⁇ data set to obtain an accuracy index.
- the comparison method may be a correlation coefficient, a ratio average, and the like.
- the accuracy is ranked from high to low, and the lower-ranked data set is calibrated.
- the calibration method uses the first method. Recalculate accuracy after calibration to rank.
- the accuracy 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, Accuracy calculations are performed using the average of the alpha data set for the entire city. For the ⁇ mobile station, when the ⁇ mobile station moves to a certain range of the ⁇ national control station, the accuracy calculation is performed. 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 the fixed monitoring station When the monitoring data of the 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 determination of the abnormal data may be that the contrast coefficient exceeds the set range, that is, the data of the site is determined to be abnormal.
- a credibility weight factor is introduced to evaluate its credibility, and then the calibration is performed after the evaluation.
- the credibility weight factor is affected by the distance between the reference site and the calibrated station or the geographical center, the geographic position of the reference site, the evaluation of the reference site by other sites, the stability of the reference site, and other influencing factors.
- a credibility weight factor is introduced to evaluate their credibility.
- the credibility weight factor is affected by the distance between the reference site and the calibrated station or the geographical center, the geographic position of the reference site, the evaluation of the reference site by other sites, the stability of the reference site, and other influencing factors.
- the credibility weight factor is positively related to the distance factor, geographical location factor, other site evaluation factors, and stability factor.
- a credibility weight factor calculation formula is:
- the credibility of the data monitored by a monitoring site decreases with increasing distance from the monitored area to the monitoring site, and the same applies to the monitoring data of the high-precision site.
- the data fusion results of multiple sites in the spatial range are considered.
- the present invention proposes an evaluation describing the effective area of a monitoring site.
- the method includes several weighting factors to indicate the spatial impact weights of the data collected by the data site when real monitoring is performed in an area in the city, and then describes the data impact range of the site. Or data valid range.
- the distance factor between the reference station and the calibrated station can also be considered, and a distance factor is introduced.
- the distance factor f 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 by the data obtained by monitoring stations in a certain area.
- the other embodiment of the distance factor is a specific location.
- the pollution data is composed of monitoring data from several monitoring stations that are close to each other. 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, and 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 distance factor calculation formula is:
- d represents the distance from the geometric center point of the area to each station in the area or the distance from the specific location to each monitoring point.
- the base station is located in a long-term fixed wind direction area, which may cause the fixed station to fail to represent the air quality in the measurement area.
- the factor factor should be appropriately reduced.
- Pollutants discharged from surrounding pollution sources are not the main pollutants monitored by the monitoring station: there are pollution sources around the monitoring station, but the pollutants emitted by the pollution source are not the primary pollutants monitored by the monitoring station.
- the data obtained by the Chinese control station will also be affected by factors such as equipment aging and the reliability will change. Therefore, it is necessary to use data from other nearby stations (national control stations, fixed micro stations, vehicles) to analyze this.
- One point of the national control station to evaluate, evaluate its accuracy, and give weight.
- the reliability of the data generated by the site at a certain moment cannot be determined, and only extreme anomalies can be ruled out.
- the data change trend of the site from other surrounding stations of the same level is significantly different, one reason may There is a pollution source nearby. Another reason may be that the monitoring equipment at the site is abnormal.
- the specific method is: take data from several monitoring stations within a certain distance around the monitoring station, which can be within 10 kilometers, and average them. And average Perform the following calculation with the monitoring value M of the monitoring station to obtain the ratio ⁇ , then the magnitude of ⁇ can indicate whether other nearby devices, such as a fixed micro station or a mobile monitoring station, have a similar data change trend with the data obtained by the station. And the above f e ( ⁇ ) relationship can get the weight of the site for this influencing factor.
- M in the formula represents the monitoring value of the monitoring station.
- the site data is significantly abnormal.
- the PM10 data is less than the PM2.5 data.
- the PM10 data at the site will be manually checked and screened.
- the reliability of the equipment will decrease during this period of time.
- You can set a reliability factor which can be expressed by the cumulative number of abnormalities in a period of time, such as Counted monthly, the initial value is 1, and the reliability factor decreases by 0.1 each time.
- whether the national control station participates in the calibration can be determined according to the reliability weight of the national control station.
- n is the number of times that the site has abnormal data within a certain period of time.
- the abnormal situation and judgment are as follows.
- a period of time can be one month, one week, one day, and other time periods.
- the evaluation method can be a way of directly ranking the credibility weight factor, or a threshold limit.
- the direct ranking method is to arrange the credibility weight factors from large to small. The closer the credibility weight factor is to 1, the higher the ranking, the more credible the site or data. Select the top 10%, 20%, or a certain percentage of the credibility weight factor, or exclude the bottom 10%, 20%, or a certain percentage of the credibility weight factor.
- the station or data corresponding to the confidence weight factor can be used for calibration calculation.
- the way to limit the threshold is to set a certain threshold (thresholds can be 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, etc.), select a credibility weight factor that exceeds this threshold, or exclude below this
- a threshold credibility weight factor, the selected station or data corresponding to the credibility weight factor remaining after screening can be used for calibration calculation.
- y c is the baseline data after correction or screening
- the benchmark data may be unprocessed data or revised or filtered benchmark data (y c )
- x is the data to be calibrated
- ⁇ is the contrast coefficient
- ⁇ c is the corrected contrast coefficient
- c is a calibration coefficient, and c may be ⁇ , ⁇ c, or a contrast coefficient after other mathematical operations.
- the stability factor ⁇ is the ratio of the number of base station data to the total number of base station data in the set interval. If ⁇ is greater than the set percentage (the set percentage can be 80%, 90%, and other percentages), the base station data set is considered stable, and the higher the ⁇ , the more stable the data set.
- the set interval is the range given to the reference data within the set T time range.
- the mathematical expression of the set interval is (Yu ⁇ Y, Y + u ⁇ Y).
- Y can be determined by the average of the base station data within the T time range, Median, mode and other statistical methods, u is the interval coefficient.
- the stability coefficient can also be related to the variance of the reference data in the set interval for the set T time range
- the stability coefficient can also be related to the standard deviation of the reference data within the set T time range.
- the steps for multiple calibrations of mobile monitoring stations based on the regional data of a single reference station are:
- a mobile monitoring device may pass through the same reference station multiple times. Each time this device passes this reference station, a comparison is performed to obtain the ⁇ value of the comparison.
- the steps for multiple calibrations of mobile monitoring stations based on the regional data of multiple reference stations are:
- a mobile monitoring device may pass through the area covered by multiple reference stations multiple times. Every time this device passes this area, it is compared with the average reference data of the passing time in this area to get the corresponding value of ⁇ .
- the reference value of the region can be obtained by using the normalized calculation method.
- the procedure for calibrating other fixed stations based on the area data of a single base station is as follows.
- the reference station that meets the standard is used as a reference, and every interval, the reference station data is used as a reference to perform a comparison with the fixed station to be calibrated to obtain the corresponding ⁇ value.
- a mobile monitoring device may pass through the area covered by multiple reference stations multiple times. Every time this device passes this area, it is compared with the average reference data of the passing time in this area to get the corresponding value of ⁇ .
- the reference value of the region can be obtained by using the normalized calculation method.
- the time range of the data used for calibration needs to be limited.
- the process of the mobile monitoring device performing the calibration operation is periodic (for example, once a month). Between calibration periods, the device should get a set number of valid ⁇ values for Calculation (the set number can be 10 times, 100 times, etc., or once every hour, every 5 hours, etc.).
- the value of ⁇ obtained in a calibration cycle should be as uniform as possible. For example, if you perform a calibration operation once a month, you need to get at least one At the same time, each time ⁇ value should be obtained within a uniformly dispersed time. For example, if 12 sets of comparison data are obtained, these 12 sets of comparison data are obtained every 2 hours instead of 1 hour. 12 sets of comparative data.
- each monitoring unit can be calibrated separately using the reference station. You can also use the base station to calibrate one of the monitoring units first, and then calibrate the other units from the calibrated unit.
- Figure 1 is a schematic diagram of the composition of a calibration system
- FIG. 2 is a schematic diagram of calibrating the ⁇ data set and the ⁇ data set based on the ⁇ data set;
- FIG. 3 is a schematic diagram of calibrating the ⁇ data set based on the ⁇ data set
- FIG. 4 is a schematic diagram of a calibration area range
- 10 is the base station
- 20 is the fixed monitoring station
- 30 is the mobile monitoring station
- 40 is the data center
- 50 is the user
- 101 is the base station 1
- 102 is the base station 2
- 103 is the base station 3
- 201 is No. 1 fixed calibrated station
- 202 is No. 2 fixed calibrated station
- 203 is No. 3 fixed calibrated 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 station data is calibrated one by one to get the calibration coefficient, and the above data is counted in the following table:
- the denominator (reference data) is A
- the numerator (calibrated data) is B 3 .
- the denominator (reference data) is B 2
- the numerator (calibrated data) is B 3
- other calculations of the calibration coefficients are performed by analogy.
- the accuracy of the specified contrast coefficient is within the range of 0.95-1.05, and the calibration is not performed.
- the contrast coefficient is between 1.05-1.2, and the calibration is performed.
- the contrast coefficient is greater than 1.2 to not perform the calibration.
- the alarm indicates 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 is not calibrated. For the equipment with the correlation coefficient less than 0.9, the calibration is performed with the goal of reaching the benchmark data set. In principle, the equipment with the highest accuracy is used for calibration As a benchmark, if the device with the highest accuracy is a ⁇ fixed station, the calibration starts from the stations around the ⁇ fixed station until all are completed. If the device with the highest accuracy is a ⁇ mobile station, it passes by its surroundings. The stations are calibrated for priority calibration objects until all are completed.
- the calibration range is determined by the proportional average coefficient. Equipment with a proportional coefficient between 0.9 and 1.1 is not calibrated. Equipment with a proportional coefficient in other ranges is calibrated with the goal of reaching the benchmark data set. In principle, the one with the highest accuracy is ranked first.
- the device is the calibration benchmark. If the device with the highest accuracy is a ⁇ fixed station, the calibration starts from the stations around the ⁇ fixed station until all are completed. If the device with the highest accuracy is a ⁇ mobile station, the The stations passing by it are calibrated as priority calibration objects until all are completed.
- FIG. 2 there are reference stations 1, 2, 3, and 4 in the area, and two calibrated stations ⁇ 1 and ⁇ 1. Take the monitoring values of the four national control base stations and the fixed microstations at the four moments T1, T2, T3, and T4, and calculate the average value of the monitoring data of each fixed national control base station as shown in the following table. Calculate the calibrated microstations based on the data in the table The ratios ⁇ to the average value of the national control reference station are 1.2, 1.1, 1.1, 1.04, respectively.
- 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.
- the contrast coefficient is within the range of 1-1.2, and the calibration coefficient is the average value of the contrast coefficients between 1 and 1.2; if the contrast coefficient is above 1.2, the average value is taken after removing the maximum value of the contrast coefficient.
- vehicle No. 1 is located within the area of 5km around monitoring stations No. 1, 2, and 3, and vehicle No. 2 has no place. In the area within 5km around monitoring stations 1, 2 and 3, the mobile monitoring equipment No. 1 starts the calibration process, and the mobile testing equipment No. 2 does not start the calibration process.
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Abstract
Description
异常情况 | 判定 |
停电导致无数据输出 | 无输出超过1小时 |
维护原因导致的无数据输出 | 无输出超过1小时 |
网络故障导致数据异常 | 无输出超过30分钟, |
数据异常:PM 2.5数据大于PM 10数据 | 数据异常时间超过1小时 |
特殊极端情况 | 条件 |
湿度 | 湿度>90% |
湿度 | 湿度<90% |
温度 | 温度>40℃ |
温度 | 温度<40℃ |
极端天气 | 沙尘暴、暴雨、暴风雪等 |
PM 2.5极端值 | PM 2.5>500 |
PM 2.5极端值 | PM 2.5<20 |
Claims (11)
- 一种大气污染监测传感器校准和协同工作的方法,所述方法涉及来自于α监测站的α数据集、来自于β监测站的β数据集和来自于γ监测站的γ数据集;其步骤为:1)首先获取α数据集、β数据集、γ数据集中至少两种;2)选定作为校准依据的基准数据集,以及被校准数据集;3)应用可信度权重因子(F c)对选定的基准数据集的数据进行评价和筛选;4)使用基准站修正计算公式对筛选后的基准站数据集进行校准得到修正后的基准数据集和修正后的基准数据(y c);5)从基准数据集得到基准数据(y),从被校准数据集得到被校准数据(x);6)依据对比系数计算公式得到对比系数(η)或修正后对比系数(η c);7)计算校准系数(c);被校准监测站采用校准系数(c)进行校准;所述基准数据集中的数据满足稳定系数(λ)的要求,所述稳定系数λ为设定区间内的基准站数据数量占总基准站数据数量的比值,所述可信度权重因子与距离因子(f d)、地理位置因子(f l)、其他站点评价(f e)、稳定性因子(f s)正相关;所述基准数据应用归一化法进行修正,归一化计算公式为:其中y c为经过修正后的基准数据;y′为未经修正的基准站数据;n为达到标准的基准站个数;所述的对比系数(η)的计算公式为:其中,x为被校准数据,y为基准数据,η为对比系数;所述可信度权重因子(F c)的计算方法为:F c=f(f d,f l,f e,f s)所述的稳定系数(λ)的取值范围为至少80%;所述的稳定系数(λ)的计算公式为:所述稳定系数(λ)计算公式中的基准站数据的数量选取方法包括:1)如果设定T时间范围内的基准数据方差<方差设定值B,则落入设定区间;2)如果设定T时间范围内的基准数据标准差<方差设定值C,则落入设定区间。
- 一种大气污染监测传感器校准和协同工作的方法,所述方法涉及来自于α监测站的α数据集、来自于β监测站的β数据集和来自于γ监测站的γ数据集;其步骤为:1)首先获取α数据集、β数据集、γ数据集中至少两种;2)选定作为校准依据的基准数据集,以及被校准数据集;3)计算基准数据集的可信度权重因子(F c);4)使用基准站修正计算公式对筛选后的基准站数据校准得到修正后的基准数据(y c);5)从基准数据集得到基准数据(y),从被校准数据集得到被校准数据(x);6)依据对比系数计算公式得到对比系数(η)或修正后对比系数(η c);7)计算校准系数(c);被校准监测站采用校准系数(c)进行校准;所述基准数据集中的数据满足稳定系数(λ)的要求,所述稳定系数λ为设定区间内的基准站数据数量占总基准站数据数量的比值,所述可信度权重因子与距离因子(f d)、地理位置因子(f l)、其他站点评价(f e)、稳定性因子(f s)正相关;所述基准数据应用归一化法进行修正,归一化计算公式为:其中y c为经过修正后的基准数据;y′为未经修正的基准站数据;n为达到标准的基准站个数;所述的对比系数(η)的计算公式为:其中,x为被校准数据,y为基准数据,η为对比系数;所述可信度权重因子(F c)的计算方法为:F c=f(f d,f l,f e,f s)所述的稳定系数(λ)的取值范围为至少80%;所述的稳定系数(λ)的计算公式为:所述稳定系数(λ)计算公式中的基准站数据的数量选取方法包括:1)如果设定T时间范围内的基准数据方差<方差设定值B,则落入设定区间;2)如果设定T时间范围内的基准数据标准差<方差设定值C,则落入设定区间。
- 如权利要求1或2所述的方法,其特征在于,所述基准数据集是α数据集的子集、β数据集的子集或者γ数据集的子集;被校准数据集是β数据集的子集或者γ数据集的子集。
- 如权利要求1或2所述的方法,其特征在于,所述基准数据集是一个α数据集的子集;所述被校准数据集是一个β数据集的子集或者一个γ数据集的子集;在计算校准系数(c)时,以距离因子f d做修正。
- 如权利要求1或2所述的方法,其特征在于,所述步骤2)中,所述被校准数据集来自于β监测站;所述作为校准依据的基准数据集来自于被校准监测站附近的多个移动监测站的γ数据集;选取与被校准监测站的数据集同一时段的基准数据子集,合并为基准数据集。
- 如权利要求1或2所述的方法,其特征在于,所述步骤2)中,所述被校准数据集来自于γ监测站;所述作为校准依据的基准数据集来自于被校准监测站附近的多个移动监测站的γ数据集;选取与被校准监测站的数据集同一时段的基准数据子集,合并为基准数据集。
- 如权利要求1或2所述的方法,其特征在于,所述步骤2)中,先选取距离 相近的一组β监测站和移动监测站;然后对各个β监测站和移动监测站按可信度排序;选取可信度最低的监测站作为被校准监测站;选取一段时间满足稳定系数要求的数据子集作为被校准数据集;选取与被校准设备的数据集同一时段的若干个可信度高的监测站的基准数据子集,合并为基准数据集。
- 如权利要求1所述的方法,其特征在于,当n=1时,归一化计算公式为:y c=y-f d×(x-y)
- 如权利要求1所述的方法,其特征在于,所述步骤3)的筛选方法包括:1)可信度权重因子进行从大到小进行排列,选取排名前一定比例的可信度权重因子,所述一定比例是10%、20%、30%、40%、50%、60%、70%、80%、90%之一,选取出的可信度权重因子所对应的站点或者数据用于校准计算;2)排除排名在后一定比例的可信度权重因子,所述一定比例是10%、20%、30%、40%、50%、60%、70%、80%、90%之一,排除后剩下的可信度权重因子所对应的站点或者数据用于校准计算。
- 如权利要求1所述的方法,其特征在于,所述步骤3)的筛选方法包括:1)选取超过一定阈值的可信度权重因子,所述一定阈值是0.1、0.2、0.3、0.4、0.5、0.6、0.7、0.8、0.9之一,选取出的可信度权重因子所对应的站点或者数据用于校准计算;2)排除低于一定阈值的可信度权重因子,所述一定阈值是0.1、0.2、0.3、0.4、0.5、0.6、0.7、0.8、0.9之一,排除后剩下的可信度权重因子所对应的站点或者数据用于校准计算。
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113536395A (zh) * | 2021-07-16 | 2021-10-22 | 四川新网银行股份有限公司 | 一种银行可信数据校验方法 |
Families Citing this family (19)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111595743A (zh) * | 2020-05-26 | 2020-08-28 | 陈国蓬 | 一种建筑工程环境监测系统及其监测方法 |
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CN116501108B (zh) * | 2023-06-26 | 2023-09-01 | 玖影软件(沈阳)有限公司 | 一种智慧档案柜的温度控制方法 |
CN116773756B (zh) * | 2023-08-24 | 2023-10-20 | 甘肃赛迈科能源科技有限公司 | 一种大气环境中有害气体含量的监测预警方法及系统 |
CN117647273B (zh) * | 2024-01-30 | 2024-03-29 | 东营航空产业技术研究院 | 一种提高传感器测量精度的方法 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20130074575A1 (en) * | 2011-09-16 | 2013-03-28 | Siemens Aktiengesellschaft | Method and test device for field calibration of a gas detector |
CN106525674A (zh) * | 2016-10-31 | 2017-03-22 | 天津大学 | 一种便携式大气颗粒物浓度仪器测量数据的校准方法 |
CN107063955A (zh) * | 2017-04-18 | 2017-08-18 | 击风科技(北京)有限公司 | 空气颗粒物检测仪校准方法及管理系统 |
CN107612999A (zh) * | 2017-09-20 | 2018-01-19 | 广东先河科迪隆科技有限公司 | 大气网格化精准监控系统 |
Family Cites Families (25)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5296910A (en) * | 1992-10-05 | 1994-03-22 | University Of Akransas | Method and apparatus for particle analysis |
US8410945B2 (en) * | 2002-06-11 | 2013-04-02 | Intelligent Technologies International, Inc | Atmospheric monitoring |
US8309024B2 (en) * | 2008-04-23 | 2012-11-13 | Enerize Corporation | Methods and systems for non-destructive determination of fluorination of carbon powders |
CN102507412B (zh) * | 2011-11-07 | 2014-07-02 | 中国石油集团川庆钻探工程有限公司 | 碳酸盐岩油藏等效模型的裂缝-基质渗透率级差判别方法 |
US9040932B2 (en) * | 2011-11-16 | 2015-05-26 | Canberra Industries, Inc. | Surface contamination monitoring system and method |
EP3194930B1 (en) * | 2014-09-19 | 2023-07-19 | 3datx Corporation | Particulate matter/number synchronization measurement device |
CN104280070B (zh) * | 2014-10-16 | 2017-02-01 | 北京中恒电国际信息技术有限公司 | 大数据云服务集中环境监测平台 |
CN104410992A (zh) * | 2014-10-30 | 2015-03-11 | 重庆邮电大学 | 分布式传感网络基于信任的态势数据融合方法 |
CN104410981B (zh) * | 2014-11-06 | 2017-12-29 | 广东工业大学 | 一种无线传感器网络中信标节点可信度评估方法 |
CN104697568B (zh) * | 2015-02-13 | 2015-11-11 | 中国人民解放军海军工程大学 | 一种船用机电产品的混合型可靠性试验方法 |
CN105092783B (zh) * | 2015-05-08 | 2017-07-11 | 中国科学院遥感与数字地球研究所 | 一种大气污染监测设备、方法、系统以及终端设备 |
CN106325144A (zh) * | 2015-07-06 | 2017-01-11 | 湖州庆渔堂农业科技有限公司 | 一种水产养殖监控系统的监测数据校准方法及系统 |
CN106644862B (zh) * | 2016-09-12 | 2023-08-29 | 山东诺方电子科技有限公司 | 一种传感器、基于该传感器的监测站及监测站的监测方法 |
CN106442881B (zh) * | 2016-09-20 | 2018-11-20 | 北京市农林科学院 | 一种城市森林环境中的空气质量监测方法 |
CN106526710A (zh) * | 2016-10-19 | 2017-03-22 | 陈文飞 | 一种雾霾预测方法及装置 |
CN106768032A (zh) * | 2016-12-06 | 2017-05-31 | 水利部交通运输部国家能源局南京水利科学研究院 | 一种提高大坝安全自动化监测数据可靠性的处理方法 |
CN107066831B (zh) * | 2017-05-19 | 2021-04-23 | 君晟合众(北京)科技有限公司 | 一种区域综合环境评价方法、装置及系统 |
IT201700064056A1 (it) * | 2017-06-09 | 2018-12-09 | Sense Square S R L S | Mappatura delle sorgenti d’inquinamento atmosferico e tracciamento degli inquinanti mediante l’utilizzo di reti di monitoraggio della qualità dell’aria ad alta risoluzione spazio-temporale |
CN107493317B (zh) * | 2017-06-30 | 2020-03-31 | 重庆交通大学 | 基于bds的桥梁结构相对形变监测系统与方法 |
CN108414682A (zh) * | 2018-01-29 | 2018-08-17 | 东莞理工学院 | 一种基于无线传感器网络的空气质量监测数据快速校准方法 |
CN108413855B (zh) * | 2018-02-24 | 2019-12-10 | 第一拖拉机股份有限公司 | 一种电子卡规的校准及评定方法 |
CN108732316A (zh) * | 2018-07-06 | 2018-11-02 | 天津澜禹笙环保科技有限公司 | 一种基于云计算平台的恶臭智能监测系统 |
CN109034252B (zh) * | 2018-08-01 | 2020-10-30 | 中国科学院大气物理研究所 | 空气质量站点监测数据异常的自动化识别方法 |
CN109061067A (zh) * | 2018-08-24 | 2018-12-21 | 北京环丁环保大数据研究院 | 一种共享单车采集的空气质量数据校正方法和装置 |
CN109298136B (zh) * | 2018-10-11 | 2019-10-15 | 北京大学 | 空气质量评价方法、装置、设备和存储介质 |
-
2019
- 2019-08-25 WO PCT/CN2019/102419 patent/WO2020043030A1/zh active Application Filing
- 2019-08-25 WO PCT/CN2019/102420 patent/WO2020043031A1/zh active Application Filing
- 2019-08-25 CN CN201980089854.2A patent/CN113330283B/zh active Active
- 2019-08-25 CN CN201980089835.XA patent/CN113728220B/zh active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20130074575A1 (en) * | 2011-09-16 | 2013-03-28 | Siemens Aktiengesellschaft | Method and test device for field calibration of a gas detector |
CN106525674A (zh) * | 2016-10-31 | 2017-03-22 | 天津大学 | 一种便携式大气颗粒物浓度仪器测量数据的校准方法 |
CN107063955A (zh) * | 2017-04-18 | 2017-08-18 | 击风科技(北京)有限公司 | 空气颗粒物检测仪校准方法及管理系统 |
CN107612999A (zh) * | 2017-09-20 | 2018-01-19 | 广东先河科迪隆科技有限公司 | 大气网格化精准监控系统 |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113536395A (zh) * | 2021-07-16 | 2021-10-22 | 四川新网银行股份有限公司 | 一种银行可信数据校验方法 |
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