WO2019210722A1 - 一种出租车和公交车协同监测时选择公交线路的方法 - Google Patents

一种出租车和公交车协同监测时选择公交线路的方法 Download PDF

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Publication number
WO2019210722A1
WO2019210722A1 PCT/CN2019/074043 CN2019074043W WO2019210722A1 WO 2019210722 A1 WO2019210722 A1 WO 2019210722A1 CN 2019074043 W CN2019074043 W CN 2019074043W WO 2019210722 A1 WO2019210722 A1 WO 2019210722A1
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Prior art keywords
monitoring
sensor
module
data
detection
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PCT/CN2019/074043
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English (en)
French (fr)
Inventor
司书春
刘善文
许军
刘利凝
宏宇 许
马姗姗
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山东诺方电子科技有限公司
山东省济南生态环境监测中心
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Priority claimed from PCT/IB2018/055531 external-priority patent/WO2019150182A1/zh
Application filed by 山东诺方电子科技有限公司, 山东省济南生态环境监测中心 filed Critical 山东诺方电子科技有限公司
Priority to CN201980003739.9A priority Critical patent/CN111033221B/zh
Priority to GB2012917.7A priority patent/GB2587708B/en
Priority to NO20210238A priority patent/NO20210238A1/en
Priority to CN201980043485.3A priority patent/CN112352147B/zh
Priority to NO20210245A priority patent/NO20210245A1/en
Priority to CN201980043012.3A priority patent/CN112368563B/zh
Priority to GB2102609.1A priority patent/GB2599747B/en
Priority to CN201980043006.8A priority patent/CN112639439B/zh
Priority to CN201980042987.4A priority patent/CN112703384B/zh
Priority to PCT/CN2019/097593 priority patent/WO2020020259A1/zh
Priority to CN201980042791.5A priority patent/CN112654851B/zh
Priority to NO20210246A priority patent/NO20210246A1/en
Priority to PCT/CN2019/097592 priority patent/WO2020020258A1/zh
Priority to PCT/CN2019/097594 priority patent/WO2020020260A1/zh
Priority to GBGB2102430.2A priority patent/GB202102430D0/en
Priority to PCT/CN2019/097595 priority patent/WO2020020261A1/zh
Priority to NO20210242A priority patent/NO20210242A1/en
Priority to GBGB2102429.4A priority patent/GB202102429D0/en
Priority to NO20210247A priority patent/NO20210247A1/en
Priority to GBGB2102431.0A priority patent/GB202102431D0/en
Priority to CN201980043011.9A priority patent/CN112334754B/zh
Priority to GBGB2102427.8A priority patent/GB202102427D0/en
Priority to PCT/CN2019/097590 priority patent/WO2020020256A1/zh
Priority to PCT/CN2019/097591 priority patent/WO2020020257A1/zh
Publication of WO2019210722A1 publication Critical patent/WO2019210722A1/zh
Priority to US17/156,667 priority patent/US20210140930A1/en
Priority to US17/156,665 priority patent/US20210148879A1/en

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Definitions

  • the invention relates to a method for determining a missed detection rate of a mobile monitoring and a rated quantity of a mobile monitoring vehicle, and belongs to the technical field of environmental monitoring.
  • Air pollutant monitoring equipments are either insufficient in quantity or unscientific in spatial distribution, and cannot continue to meet the technical requirements of urban environmental air monitoring, and thus need to be added or adjusted.
  • Atmospheric environmental monitoring is a process for determining the types and concentrations of pollutants in the atmosphere, observing their temporal and spatial distribution and changing laws.
  • the main pollutants monitored are sulfur dioxide, nitrogen oxides, ozone, carbon monoxide, PM 1 and PM 2.5 in the atmosphere.
  • 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 environmental protection department can use these data for environmental decision-making, environmental management, and pollution prevention; the public can take personal protection according to environmental data and arrange life reasonably.
  • atmospheric monitoring equipment mainly has fixed monitoring stations and mobile monitoring equipment.
  • the current fixed monitoring sites are mainly divided into large fixed monitoring stations (large stations) and small monitoring stations (small stations).
  • Mobile monitoring equipment mainly includes dedicated atmospheric environment monitoring vehicles, drones and handheld devices.
  • the large fixed monitoring station is equivalent to an independent laboratory that monitors and analyzes multiple levels of contaminants in the environment through expensive and sophisticated instruments.
  • the large fixed monitoring station is characterized by monitoring the types of pollutants and high precision.
  • large-scale fixed monitoring sites have large investment, and the conventional investment is in the order of one million to ten million. It requires high financial support. Therefore, the number of large-scale fixed monitoring stations will not be large and cannot be spread out on a large scale. Therefore, it can only be selected. Sex and viable location for construction.
  • large fixed monitoring stations also have high requirements for site selection.
  • the site needs a large area to accommodate large equipment.
  • the equipment needs temperature and humidity control.
  • a large number of professional and high-quality personnel are required to use the instrument, analyze the data and maintain the instrument.
  • the data obtained from the super station can only be single-point inference, it is difficult to find other super stations nearby to verify.
  • Small monitoring stations reduce the cost of gridding and batching by integrating low-cost, small-sized sensors.
  • the small monitoring station also features power consumption (powered by solar energy) and easy installation.
  • power consumption powered by solar energy
  • the accuracy and consistency of monitoring data at small stations needs to be improved and sufficient operational support is required.
  • small monitoring sites cover a wide range, they are still fixed monitoring with limited flexibility.
  • the dedicated atmospheric environment monitoring vehicle is a vehicle equipped with a sampling system, a pollutant monitoring instrument, a meteorological parameter observer, a data processing device, and other auxiliary equipment. It is a mobile monitoring station that complements the ground-based monitoring station.
  • the atmospheric environment monitoring vehicle can be taken to the scene where the pollution accident occurs or the suspect point is sampled and measured, so as to grasp the pollution situation in time, and its use is not limited by time, place and season.
  • Atmospheric environmental monitoring vehicles require full-time driving and require specialists to operate the relevant equipment. It is expensive and cannot be used on a large scale.
  • UAV air pollution monitoring is a way to monitor the atmospheric environment using a drone equipped with miniaturized atmospheric monitoring equipment.
  • the air pollution monitoring of drones can realize the stereoscopic monitoring of air pollution in high-altitude vertical sections, with wide monitoring range and high monitoring efficiency.
  • high airflow may be turbulent, and drone propellers may also cause airflow disturbances, which may affect the monitoring results.
  • the use of drones to monitor air pollution also requires professional operations.
  • the above document discloses a method for constructing an online monitoring system for atmospheric characteristic pollution.
  • the coverage is fixed.
  • the data of the monitoring point can only represent the pollution situation near the monitoring point.
  • the pollution situation away from the monitoring point can only be indirectly calculated; especially the monitoring point
  • the “three-nothing area” between the two belongs to the area that is obviously missing.
  • Figure 2 shows the limitations of fixed grid placement.
  • a significant feature of mobile monitoring is that the mobile device continuously changes the monitoring position with the operation of the moving vehicle, and basically only monitors the area along the traffic network.
  • the moving path is a traffic route; its coverage is the traffic network in the entire area.
  • each monitoring device is the entire road network, which is a continuous "line” or "band".
  • the “line” coverage of the larger data distribution obtained is more representative of the overall air pollution of a city.
  • the data of each monitored location comes from the same or the same set of monitoring equipment
  • the data for each monitored location comes from many different monitoring devices.
  • the pollution situation at a certain location within the road network can be measured multiple times in different time periods by different mobile devices (mounted on the moving vehicle).
  • the data reliability of the monitoring device can be indirectly evaluated through the correlation between the monitoring data of different devices.
  • the invention provides a method for determining the missed detection rate of mobile monitoring on the premise of ensuring the objectivity of urban air pollutant monitoring data.
  • the invention utilizes a large number of randomly operated social vehicles to carry hidden installation monitoring devices as mobile monitoring vehicles, assisting with fixed monitoring devices, and realizing monitoring of real-time pollutant distribution and changes in urban areas, as long as the mobile monitoring devices reach a certain level. Quantity, then the monitoring data generated by these mobile devices can objectively reflect the real situation of urban air pollution distribution and pollution level.
  • Environmental monitoring needs to ensure that the monitoring data is objective and effective.
  • Environmental pollution, especially atmospheric pollution has a large variation with time and space compared with other types of pollution. Monitoring based on these characteristics is important for obtaining monitoring results that correctly reflect the actual situation of atmospheric pollution.
  • the temporal and spatial distribution of air pollutants and their concentrations are closely related to the distribution of pollutant sources, emissions and topography, geomorphology and meteorology.
  • the types of pollutants, the law of discharge and the nature of pollutants are different, and their temporal and spatial distribution characteristics are also different.
  • the level of atmospheric pollutants in the same place is rapidly fluctuating.
  • There is a concept of temporal resolution in air pollution monitoring that requires a change in pollutant concentration to be reflected within a specified time. For example, some acutely hazardous contaminants require a resolution of 3 min; some chemical aerosols, such as ozone, respond to the respiratory tract with a resolution of 10 min.
  • the invention realizes atmospheric pollutant monitoring on a mobile monitoring vehicle by deploying a plurality of atmospheric pollutant monitoring devices in a large amount, and real-time transmission of position and monitoring data, which can realize monitoring from fixed point to full road network and extensive geographical coverage, reflecting urban pollution.
  • the real situation of the object is realized by deploying a plurality of atmospheric pollutant monitoring devices in a large amount, and real-time transmission of position and monitoring data, which can realize monitoring from fixed point to full road network and extensive geographical coverage, reflecting urban pollution. The real situation of the object.
  • Figure 1 shows the system composition of the present invention.
  • the system includes a mobile monitoring vehicle, a monitoring center, and a fixed monitoring station equipped with monitoring equipment.
  • the invention proposes to reasonably increase the density of equipment placement and realize the coverage of motor vehicle roads in urban areas.
  • the monitoring equipment needs to transmit data in real time in seconds, and continuous monitoring can be realized by multi-vehicle relay for 24 hours, and the data is processed by the monitoring center.
  • the extensive use of monitoring equipment has made up for the shortage of the number of fixed monitoring stations, providing data support for grid supervision.
  • Mobile monitoring vehicles equipped with on-line monitoring equipment for atmospheric pollutants are continuously moving within the city, and the concentration of pollutants, watering and road damage in all corners can be monitored in real time, so that air pollution monitoring can cover every community and every road segment. Avoid dead ends and blind spots.
  • the system can also access the data of the fixed monitoring site to make the monitoring data more complete.
  • FIG. 6 shows the change of pollutant concentration in the same place with time.
  • T 0 to T 6 indicate the time from the start of pollution to the dissipation.
  • the concentration of pollutants corresponding to T 3 is the largest, and the air pollution changes rapidly with time.
  • the extensive use of monitoring equipment compensates for the lack of fixed monitoring points at each monitoring station and provides data support for gridded supervision.
  • Mobile monitoring vehicles that have installed a large number of on-board air pollutant online monitoring systems are constantly moving within the city, real-time monitoring of pollutant concentrations, watering and road damage in all corners, so that air pollution monitoring can cover every community, each The road section avoids dead ends and blind spots.
  • the program proposes a model for the number of mobile monitoring vehicles equipped with on-line monitoring equipment for atmospheric pollutants, and the specific quantity is determined according to the average missed detection rate of the monitoring.
  • the average miss rate represents the probability of detection of air pollution in a monitored area.
  • Method 1 Measured by the density of monitored vehicles per square kilometer
  • the method still focuses on the number of monitoring devices relative to the density of a region.
  • the average miss rate is related to the density of monitoring equipment in the city, and the density of monitoring equipment is expressed in terms of the number of vehicles per square kilometer.
  • the density of monitoring equipment is expressed in terms of the number of vehicles per square kilometer.
  • the average rate of detection of pollutants that are quickly dissipated in a small range will be high.
  • the higher the density of placement the lower the average rate of detection. reduce.
  • the number of monitoring devices placed reaches a certain value, if the delivery density reaches n 0 , the average missed detection rate will be reduced to m 0 .
  • a set of highest average missed detection rate indicators M 0 In order to ensure the objectivity of a city's air pollution monitoring data, it is necessary to set a set of highest average missed detection rate indicators M 0 , and correspondingly, a set of minimum delivery density indicators N 0 can be measured. As the average missed detection rate is higher, the objectivity of the monitoring data is worse, so we expect to reduce the average missed detection rate; in general, the average missed detection rate of atmospheric pollutants to be monitored should be controlled below 50%; Cost problem, the actual average miss rate is between 20% and 50%.
  • the miss detection rate should correspond to a specific monitorable data, including but not limited to PM 1 , PM 2.5 , PM 10 , PM 100 , sulfur dioxide, nitrogen oxides, ozone, carbon monoxide, VOCs (volatile organic compounds) or TVOC.
  • the average miss rate can be expressed as a set of indicators as follows:
  • the density of placement also requires a set of indicators to reflect the density of specific pollution monitoring equipment:
  • the monitoring device placement density of PM 10 should be greater than the monitoring device placement density of PM 2.5 .
  • the highest average missed detection rate index M 0 and its corresponding minimum delivery density index N 0 are expressed as follows:
  • the miss detection rate of the air pollutant can be effectively reduced.
  • the method for determining the missed detection rate of urban air pollutant monitoring data proposed by the present invention is as follows:
  • a monitoring system consisting of a fixed monitoring station, a monitoring center and a mobile monitoring vehicle is established in a monitoring area; the mobile monitoring vehicle is equipped with an air pollutant monitoring device; and a set of highest average leakage in the monitoring area is determined.
  • Probability indicator M 0 a probability indicator for determining whether a leakage is a leakage is a leakage.
  • the difference between a fixed point and a moving point is that the monitoring area of a single point is different.
  • the fixed point can only cover a fixed-size area, and the moving point changes with time.
  • the coverage area is variable, and can cover a long time in a period of time (for example, one day).
  • the spatial extent of the fixed point in the case of satisfying the amount of data, the number of moving points covering the whole city is very different from the fixed point. Therefore, the estimation of the number of deployed cities in the whole city is different from the fixed point, and cannot be fixed. Point calculation theory to solve.
  • the data volume of the national control station for air pollution monitoring is mainly based on one hour.
  • the evaluation of urban air quality is mainly based on daily average data, while the mobile monitoring stations are mainly based on minute or second data.
  • Mobile monitoring can achieve the effect of multiple fixed monitoring stations.
  • the vehicle mobile monitoring is carried out in the form of continuous travel monitoring, it achieves high coverage.
  • vehicles such as taxis are equipped with mobile monitoring equipment, they are more flexible and random. It is impossible to take measures in advance to affect air quality, and the data is more convincing.
  • Method 2 Measure by the distribution of the number of monitored units on each road segment of the traffic network
  • the road network of an area (such as the urban area of a certain city) is divided into a single road section unit according to a certain granularity, that is, the length unit (such as every 100 meters, or 200 meters); we put the mobile monitoring equipment (in working state)
  • the length unit such as every 100 meters, or 200 meters.
  • every 15 minutes or 30 minutes is used as a timing unit, and in one timing unit, multiple passes of the same mobile device are counted only once. The data of different monitoring vehicles is accumulated.
  • the timing unit can be 15 minutes, 30 minutes, or 1 hour.
  • Counting period with a timing unit as the deadline, when a monitoring vehicle enters a certain section unit, it triggers a new counting period; during the counting period, the same monitoring vehicle enters the section unit and is no longer counted in the number of detections. .
  • the counting period ends after a timer unit is stored. The same monitoring vehicle can only have up to one counting cycle per segment unit; a new counting period cannot be triggered until the end of the previous counting period.
  • Figure 11 is a statistical distribution diagram of the number of times of monitoring (taxi) in a city area obtained by the above method according to the road segment unit.
  • Expected indicator It is an indicator used to assess whether the collected amount of air pollution detection data within a city's regional road network can reflect the overall pollution status. This indicator is characterized by two important parameters: coverage, predetermined number of detections.
  • Coverage The ratio of the number of road segments that have reached the predetermined number of inspections to the total number of road segments.
  • the optional value range is between 50% and 90%. Generally, 70 to 80% is selected as easy.
  • the number of scheduled detections When the number of detections recorded by a road segment unit reaches a certain value within one day, the road segment unit is recognized as having received the detection coverage; otherwise, the road segment unit is regarded as the missing detection road segment unit.
  • the predetermined number of detections can be selected from 5 to 10 times.
  • Missing section unit When a certain section of the road unit does not reach the predetermined number of detections in one day, the section unit is regarded as a missing section unit.
  • an average miss detection rate indicator m is actually set.
  • each expected indicator may be significant.
  • the average missed detection rate can be expressed as:
  • m (PM2.5, 80%, 10); its meaning: 80% of the segment units can record at least 10 tests (number of tests) within 24 hours in a city area.
  • the invention discloses a method for determining the rated quantity of a mobile monitoring vehicle.
  • the method takes the traffic road network of an urban area as the focus of attention, and realizes the installation of the air pollution detecting device by the mobile monitoring vehicle, especially the taxi.
  • Air quality monitoring of the urban area comprises the following steps:
  • the traffic road network is decomposed by the road segment unit; the database of each road segment unit is established and initialized; the database includes the link unit number, the link unit location information, the link unit detection record (detection device number, The time when the mobile monitoring vehicle enters the road segment unit and the cumulative number of passes of the mobile monitoring vehicle passing through the road segment unit (initial value is “0”))
  • Taxi drivers generally have a regional orientation.
  • the number of taxis participating in the model construction should not be too small. Generally speaking, there should be at least 50 vehicles.
  • the bus usually has a fixed route, a fixed coverage area, a fixed working time, and a fixed attendance rate. Therefore, the bus is a special case of a taxi.
  • the route with a small probability of taxi selection will serve as a blinding effect. When it reaches the same missed rate indicator, it helps to reduce the total number of mobile monitoring points.
  • the amount of data monitored by one bus will have the effect of multiple taxis, and the calculation of the missed detection rate is to meet the data of the small probability route.
  • the minimum requirements are derived, so the combination of the two will greatly reduce the total number of vehicles monitored by the city and save resources.
  • the bus usually runs back and forth along a fixed line; then the curve of the bus line is actually a smooth line corresponding to the monitoring number model based on the link unit.
  • the bus lines B1 and B2 are evenly distributed on their respective running routes on their respective passing section units.
  • bus line B1 contains a part of the road section (shaded part), which just falls in the area where the taxi is rarely seen.
  • the bus line B1 and the taxi cooperate to complete the monitoring of a certain pollutant, the bus line B1 can help cover the missing section of the taxi that is difficult to cover.
  • the combination of the bus line B1 and the taxi can significantly increase the coverage of the pollution detection as a whole.
  • the bus line B2 because its path is all located in the coverage of the taxi, the installation of the detecting device on the B2 does not significantly increase the coverage of the pollution detection as a whole.
  • the invention discloses a method for selecting a bus line participating in monitoring when a taxi and a bus cooperate to monitor:
  • the traffic road network is decomposed by the road segment unit; the database of each road segment unit is established and initialized; the database includes the link unit number, the link unit location information, the link unit detection record (detection device number, Time to enter the road segment, cumulative number of passes (initial value is "0"))
  • the horizontal axis thereof is consistent with the horizontal axis of the taxi detection number curve model; according to the operation plan of the bus line, the corresponding road segment unit is given the number of detection times.
  • the bus line B1 can cover the road segment unit represented by b 1 , the rated number of taxis can be reduced from the original 290 to 140.
  • the air pollution detecting device includes a main control module and a detecting module; the detecting module uses at least four sub sensor units to form a sensor module; when the main control module finds One of the sub-sensor units has a suspected abnormality, and after determining that the suspected abnormal sub-sensor is an abnormal sub-sensor, the abnormal sub-sensor is isolated, the abnormal sub-sensor is classified into the isolation area, and the multi-core sensor module continues to be normal after being degraded. jobs.
  • the present application further discloses another air pollution detecting device, which comprises a main control module and a detecting module; the detecting module comprises at least two homogeneous sub sensor units to form a sensor module; the sub sensor unit works At normal operating frequency.
  • the detection module further comprises 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 works far below the operating frequency of the sensor module. Therefore, the low frequency calibration module is also called a low frequency group.
  • sensor modules are also referred to as high frequency groups.
  • the sensor module operates 10 times or more the frequency of the low frequency calibration module.
  • the ratio of the operating frequency of the high frequency group and the low frequency group is called the high frequency 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, 20:1.
  • the operating frequency of the low frequency group can be consistent with the rhythm of the abnormal judgment. That is to say, when it is necessary to judge whether there is a sub-sensor abnormality in the sensor module, the low-frequency group performs the detection work.
  • the low frequency group detection data is used as a reference to calibrate the high frequency group detection data, and the calibration coefficient can use the detection data of the high frequency group sensor.
  • the ratio of the average value to the average value of the low frequency group detection data is obtained.
  • the data weight of the low frequency group can be increased to make a more reliable judgment.
  • a simple solution is that all low frequency group data participate in suspected abnormality judgments with twice the weight.
  • the prior application PCT/IB2018/05531 also discloses a method for identifying the operational status of a sub-sensor and isolating and recovering the sub-sensor.
  • the sensor module obtains a set of detection data at a time, and the main control module filters out the data of the suspected abnormality from the data of the group, and then determines whether the corresponding sub-sensor satisfies 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 the sub-sensor that is suspected of being 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 self-heal.
  • the self-healing sub-sensor is subjected to frequency-down operation, but the data output by the sub-sensor does not participate in the calculation of the output data of the main control module.
  • the main control module detects the data of the output
  • 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.
  • the combination manner of the sensor units in the detecting module is selected, so that the selection of the detecting module matches the distribution characteristics of the air pollutants contacted by the mobile monitoring vehicle.
  • the traditional monitoring methods such as the special monitoring vehicle detection and personnel on-site inspection, the monitoring personnel can control the time and place of monitoring, so that the monitoring is not random and sudden. It is also possible for polluting enterprises to know the time and place of inspection from the operators of these equipments in various ways, so that polluting enterprises can avoid the space for monitoring.
  • This program uses social vehicles such as taxis as mobile monitoring vehicles to monitor atmospheric pollutants. The driving paths and time of social vehicles are not for monitoring purposes. The monitoring locations and time are not subject to human control. At the same time, the monitoring equipment is not controlled by the driver. The monitoring of this method is more random and flexible. After a reasonable amount of monitoring equipment is put into use, the objectivity of the air pollutant monitoring data can be ensured through the processing of big data.
  • polluting enterprises can also find and adjust the production and discharge situation in time; for fixed monitoring stations, polluting enterprises can also target Make corresponding countermeasures, such as changing the sewage outlet.
  • the program will cover the hidden equipment in the taxi dome light or under the overhead light, bus roof, etc., so that the monitored sewage companies and individuals cannot know that nearby equipment is monitoring the air pollutants around them, so that the monitoring data The objectivity is further improved. As shown in Figure 5, the sewage company does not know that the taxi passing by the door is monitoring the pollutants.
  • the system design uses a variety of ways to prevent data tampering, making monitoring more objective.
  • the characteristics of buses are that the routes are relatively fixed, which is beneficial to repeatedly measuring a certain section of the road, and can give more reliable data for more time periods.
  • the buses send more vehicles and the interval time is compared. Evenly, when there are many shifts, it is often a traffic peak, and it is also a period of serious particulate pollution.
  • the characteristic of the taxi is that it has a wide distribution range and a wide time range. It can measure the places that the bus cannot reach, and the measurement time range supplements the time when the bus does not operate.
  • the driving route of the muck is often a road with serious road dust pollution, so that such measurement can focus on monitoring the dusty road section, and it can also measure the dust pollution of the vehicle.
  • the comprehensive data of multiple muck trucks is the road dust background data.
  • the data of the own vehicle includes the background and the pollution of the own vehicle. Through the big data processing, the two types of data can be separated, so that the road and the self-contamination are respectively evaluated. Conducive to control. Long-distance vehicles are characterized by the ability to cover blind spots between cities and to create a wider range of surveillance.
  • the height of the taxi dome lamp is basically the same as the height of the personnel's mouth and nose. It is the height at which the person mainly breathes.
  • the taxi is equipped with atmospheric pollutant monitoring equipment to monitor the altitude of the atmosphere, which can effectively reflect the respiratory health of people. High air is important for the treatment of the atmospheric environment.
  • the invention is also beneficial in that the device uses real-time measurement by using atmospheric pollutant sensors in social vehicles such as city buses, long-distance buses, taxis, and mucks. Special venues and professional operators are required, and the requirement for one-time input is lower, which reduces the cost of measurement. At the same time, it reduces the energy consumption and road occupation caused by special vehicles. Ultimately, the occupation of social public resources is reduced, and the cost of monitoring air pollutants is reduced.
  • the air pollutant monitoring equipment includes a detecting module, a main control module and a communication module; the detecting module comprises one or more atmospheric pollutant sensor units; the air pollutant sensor unit is one of the following sensors: PM 1 sensor, PM 2.5 sensor, PM 10 sensor, PM 100 sensor, NOx sensor, O 3 sensor, SO 2 sensor, VOCs sensor or TVOC sensor.
  • the main control module is connected with the power supply of the mobile monitoring vehicle, and the main control module supplies power to the detection module and the communication module on the air pollutant monitoring device.
  • the main control module and the detection module and communication module on the air pollutant monitoring device are also connected through a data interface, and exchange data with the detection module communication module.
  • Figure 1 is a schematic diagram of the system composition of the present invention
  • FIG. 2 is a schematic diagram of a grid pattern of a fixed monitoring station
  • Figure 4 is a schematic diagram showing the basic module composition of the air pollutant monitoring device
  • Figure 5 illustrates the concealment of using social vehicle monitoring
  • Figure 6 shows the characteristics of air pollution, which is time-sensitive
  • Figure 7 shows the relationship between the average missed detection rate and the monitoring equipment placement density indicator
  • FIG. 8 is a schematic diagram of an air pollutant monitoring device including a video capture module
  • FIG. 9 is a schematic diagram of distribution of detected data in a grid in a mobile monitoring mode
  • Figure 10 is a schematic diagram showing the cumulative detection distribution of the "band" road network under the road segment unit model
  • Figure 11 shows the statistical distribution of the number of inspections (taxis) within a 24-hour period in a city area by road segment unit;
  • FIG. 12 is a schematic diagram of a number of detection times in a cooperative monitoring mode of a taxi and a bus;
  • Figure 13 is a schematic diagram of the isolation and recovery process of the high and low frequency multi-core sensor module.
  • the air pollutant monitoring equipment is installed on the social vehicle to monitor the atmospheric environment quality of the vehicle.
  • the air pollutant monitoring equipment has the information transmission function, and the monitored data, location data and time information can be wirelessly returned. Pass to the monitoring center.
  • the air pollutant monitoring equipment can record and collect the road conditions and record the road pollution.
  • the air pollutant monitoring equipment can transmit the collected video to the monitoring center by wireless transmission.
  • the air pollutant monitoring equipment has the function of storing data and video data, and saves the collected data and video data locally.
  • the air pollutant monitoring equipment also has a data transmission interface, which can copy the saved data and video data to the on-site maintenance or staff through local transmission.
  • the monitoring center receives the data returned by the air pollutant monitoring equipment, and the monitoring center stores and processes the data.
  • the monitoring center can also collect data from other types of monitoring equipment, such as collecting data from micro-fixed monitoring stations and collecting data from fixed monitoring stations in nearby countries.
  • the monitoring center integrates the data returned by the air pollutant monitoring equipment of the social vehicle, the collected micro-fixed monitoring station data and the collected data of the nearby national fixed monitoring stations, and generates data lists, data rankings, pollution cloud maps, historical playback data, etc. ways of presenting.
  • the processed data lists, data rankings, and pollution cloud maps are sent to the user terminal through the network, and the user can query and use according to the requirements.
  • the monitoring center can also remotely control the operation of the air pollutant monitoring equipment, such as turning on and off the air pollutant monitoring equipment, turning on and off the video acquisition module, adjusting the monitoring frequency, and correcting the error of the air pollutant monitoring equipment.
  • Obtaining an objective air pollutant monitoring data of a city can reflect the true air pollution level of the city.
  • the present invention needs to set a group of highest average missed detection rate indicators M 0 to represent the objectivity of the monitoring data. For example, a city needs to monitor PM 10 , and its missed detection rate is expressed as m (PM 10 ). If the city requires m (PM 10 ) ⁇ 20%, it means that the PM 10 pollution incident not captured by the monitoring equipment is less than 20% of total PM 10 pollution incidents.
  • the minimum delivery density index N 0 is introduced, and N 0 represents the minimum delivery density index of the monitoring equipment to be achieved by M 0 .
  • the PM 10 sensor-equipped monitoring device to be placed has a minimum delivery density of n (PM 10 ).
  • the n (PM 10 ) needs to be measured. The calculation is based on the area of the city, the number of vehicles equipped with mobile monitoring equipment, the daily mileage of the vehicle equipped with the mobile monitoring device, the driving range of the vehicle equipped with the mobile monitoring device, the type of vehicle, and the mounted Instrument accuracy and other parameters.
  • the PM 10 and SO 2 of a city are monitored.
  • the highest average miss rate index M 0 of the city is expressed as m (PM 10 ), m (SO 2 ).
  • the pollution contribution levels of different pollutants will be different, and the degree of importance of the city will be different. Therefore, the average missed detection rate of different pollutants will have corresponding requirements.
  • the city pays more attention to the monitoring of SO 2 than PM 10 .
  • PM 10 and SO 2 Monitoring of PM 10 and SO 2 in a city in which the highest average miss rate indicator M 0 of the city is expressed as m(PM 10 ), m(SO 2 ), in this example PM 10 and SO 2
  • Current monitoring equipment can also measure multiple contaminants simultaneously through a combination of internal detection modules.
  • the second delivery method is that some vehicles are equipped with monitoring equipment that can measure PM 10 and SO 2 at the same time, and the other part is equipped with vehicles that can only measure PM 10 equipment.
  • the density of the distribution can be differentiated according to the vehicle.
  • the PM 100 monitoring equipment can be more loaded on the muck, which can monitor the dust pollution more effectively, and more effectively achieve the PM 100 monitoring equipment loading density index; PM 2.5 monitoring equipment is more Assigned to small vehicles such as taxis.
  • the basic module of the air pollutant monitoring equipment includes a detection module, a main control module and a communication module.
  • the main control module is connected with the power supply of the mobile monitoring vehicle, and the main control module supplies power to the detection module and the communication module on the air pollutant monitoring device.
  • the main control module and the detection module and the communication module on the air pollutant monitoring device are also connected through a data interface, and exchange data with the detection module and the communication module.
  • the data collected by the detection module is processed by the main control module and sent to the communication module, and then transmitted back to the monitoring center by the communication module; the command sent by the monitoring center is received by the communication module and transmitted to the main control module, and the main control module adjusts and detects according to the command.
  • the main control module has the function of storing and exporting data and video data.
  • the main control module has a positioning function or a data interface with the positioning device, and uses GPS, Beidou and other positioning technologies to record the vehicle position in real time.
  • the basic module of the air pollutant monitoring equipment includes a detection module, a main control module and a communication module.
  • the detection module detects the pollutant content of the sampled gas through the mounted atmospheric pollutant sensor, and obtains the concentration data of the pollutant.
  • the detection module can be equipped with a variety of atmospheric pollutant sensors, including PM 1 sensors, PM 2.5 sensors, PM 10 sensors, PM 100 sensors, nitrogen oxide sensors, ozone sensors, sulfur dioxide sensors, VOCs sensors or TVOC sensors for contaminants. monitor.
  • PM 1 sensors PM 2.5 sensors
  • PM 10 sensors PM 100 sensors
  • nitrogen oxide sensors ozone sensors
  • sulfur dioxide sensors sulfur dioxide sensors
  • VOCs sensors VOCs sensors
  • TVOC sensors for contaminants. monitor.
  • air pollutant monitoring equipment equipped with PM 2.5 sensor and PM 10 sensor can better monitor road dust, and can timely detect road dust pollution and provide early warning.
  • the detection module can also be equipped with other types of sensors, such as wind speed sensors, wind direction sensors, temperature sensors, humidity sensors, pressure sensors, and noise sensors to provide richer monitoring information.
  • the humidity sensor can also provide moisture correction and calibration basis for the atmospheric pollutant sensor.
  • the basic module of the air pollutant monitoring equipment includes a detection module, a main control module and a communication module.
  • the communication module is used for wireless communication between the air pollutant monitoring equipment and the monitoring center, uploading monitoring data, location information, time information and monitoring video, and can also receive instructions issued by the monitoring center to adjust the operation of the air pollutant monitoring equipment.
  • the communication module communicates with the monitoring center using data transmission methods such as GPRS, 4G, 5G, Bluetooth, WIFI, LoRaWAN, and narrowband Internet of Things.
  • the communication module returns data to the monitoring center in real time, and the data return interval is in units of seconds.
  • the air pollutant monitoring equipment can also be equipped with a video acquisition module in addition to the basic module.
  • the video acquisition module is used for contaminant forensics to visualize pollution levels, facilitate post-enforcement and identify sources of pollution.
  • the video capture module is equipped with a camera that can upload the road conditions to the monitoring center.
  • a mobile monitoring vehicle equipped with an air pollutant monitoring device is a social vehicle.
  • Social vehicles include city buses, long-distance buses, taxis, mucks, municipal vehicles, official vehicles, networked vehicles, rental vehicles, shared vehicles, and vehicles with automatic driving functions. These social vehicles do not require dedicated venues and professional operators to measure the air pollution in real time. The one-time investment is lower, which reduces the energy consumption and road occupation caused by special vehicles, and ultimately reduces the public resources of the society. Occupation, reducing the cost of monitoring air pollutants.
  • the air pollutant monitoring equipment equipped with the particulate sensor is installed on the bus.
  • the bus is characterized by a relatively fixed route.
  • By using one or several buses equipped with atmospheric particulate sensor monitoring equipment it is possible to monitor the atmospheric particulate pollution along the entire bus line and reduce the monitoring cost.
  • due to the characteristics of the bus exercise it is possible to repeatedly measure a certain road section and give more reliable data for more time periods.
  • the air pollutant monitoring equipment equipped with the particulate matter sensor is installed on a large-scale social vehicle such as a muck truck, a garbage disposal vehicle, and a long-distance vehicle.
  • the routes of these large-scale social vehicles are often roads with serious dust on the roads.
  • These large-scale social vehicles can be used to monitor the key dust roads with less effort, and they can also measure the dust pollution of their own vehicles.
  • the data detected by these large-scale social vehicles include background pollution and pollution of their own vehicles. Through big data processing, the two types of data can be separated to give an evaluation of the road and its own pollution to facilitate control. Long-distance vehicles are characterized by the ability to cover blind spots between cities and achieve greater coverage.
  • Taxi is characterized by a wide distribution range and a wide time range, which can measure places that other social vehicles cannot reach.
  • social vehicles such as taxis to monitor atmospheric pollutants
  • the height of the taxi dome lamp is basically the same as the height of the personnel's mouth and nose. It is the height at which the person mainly breathes.
  • the taxi is equipped with atmospheric pollutant monitoring equipment to monitor the altitude of the atmosphere, which can effectively reflect the respiratory health of people. High air is important for the treatment of the atmospheric environment.
  • Figure 3 shows the results of taxi monitoring of atmospheric particulate matter monitoring equipment in a city in Shandong.
  • This data automatically generates a city map through the Big Data Processing Platform of the Monitoring Center. The technicians can further judge whether the pollution control of the relevant areas is in place and guide the plan for precise management.
  • the monitoring center also conducts statistical rankings for districts, counties, street offices and road sections to provide technical means for governance assessment.
  • Air pollutant monitoring equipment is installed on social vehicles.
  • the monitoring equipment has the characteristics of concealment, such as concealed installation in the interior of the taxi dome light, the lower part of the taxi dome light, and the top of the bus.
  • polluting enterprises can also find and adjust the production and discharge situation in time; for fixed monitoring stations, polluting enterprises can also make corresponding countermeasures in a targeted manner, such as changing the sewage outlets.
  • the scheme hides the monitoring equipment in the interior of the taxi dome light, the lower part of the taxi dome light, and the top of the bus, so that the monitored sewage companies and individuals cannot know that nearby equipment is testing the air pollutants around them, so that the monitoring The objectivity of the data is further improved. As shown in Figure 5, the sewage company does not know that the taxi passing by the door is testing the pollutants.
  • the invention sets a tamper-proof function on the monitoring data to ensure the reliability and accuracy of the monitoring data.
  • the monitoring data measured by the detecting module is first stored in the local storage medium of the air pollutant monitoring device, and the monitoring data measured by the detecting module is uploaded to the monitoring center by means of wireless transmission, and all raw data uploaded to the monitoring center is uploaded.
  • Set anti-modification and anti-delete features The system of the monitoring center automatically or the monitoring personnel manually remotely retrieves the sensor local original database and the monitoring center database for proofreading.
  • data transmission can also be encrypted by adding digital signatures.
  • the digital signature algorithms that can be used include RSA, ElGamal, Fiat-Shamir, Guillou-Quisquarter, Schnorr and Ong-Schnorr. -Shamir et al.
  • the license plate number and the monitoring equipment SN are bound, so that the information of the vehicle and equipment can be queried and checked through the database of the monitoring center.
  • Air pollutant monitoring equipment can adjust the monitoring density according to the condition of the pollutants. For example, when a social vehicle equipped with atmospheric pollutant monitoring equipment passes a certain section, the atmospheric pollutant monitoring equipment detects that the pollutant concentration exceeds the upper limit of the preset value, such as PM 2.5 value ⁇ 100 ⁇ g / m 3 (may also be 150 ⁇ g / m 3 , After 200 ⁇ g/m 3 , 250 ⁇ g/m 3 , etc., the air pollutant monitoring equipment increases the atmospheric pollutant concentration detection output frequency, for example, the output of one pollutant concentration value is calculated every 3 seconds to calculate the output of one pollutant concentration every 1 second. Value.
  • the preset value such as PM 2.5 value ⁇ 100 ⁇ g / m 3 (may also be 150 ⁇ g / m 3 , After 200 ⁇ g/m 3 , 250 ⁇ g/m 3 , etc.
  • the air pollutant monitoring equipment increases the atmospheric pollutant concentration detection output frequency, for example, the output of one pollu
  • Contaminants that trigger an increase in the detection output frequency can be other pollutants monitored (eg, nitrogen oxides, ozone, etc.).
  • the atmospheric pollutant monitoring equipment reduces the detection output frequency. For example, after PM 2.5 ⁇ 50 ⁇ g/m 3 , the detection output frequency is restored to output a pollutant concentration value every 3 seconds or longer. time interval.
  • the air pollutant monitoring equipment can adjust the return frequency of the detection value according to the pollutant condition. For example, when a social vehicle equipped with atmospheric pollutant monitoring equipment passes a certain section, the atmospheric pollutant monitoring equipment detects that the pollutant concentration exceeds the upper limit of the preset value, such as PM 2.5 value ⁇ 100 ⁇ g / m 3 (may also be 150 ⁇ g / m 3 , After 200 ⁇ g/m 3 , 250 ⁇ g/m 3 , etc., the air pollutant monitoring equipment increases the frequency of the atmospheric pollutant concentration detection value to the monitoring center, for example, the value is returned every 3 seconds, and the value is changed every 1 second.
  • the preset value such as PM 2.5 value ⁇ 100 ⁇ g / m 3 (may also be 150 ⁇ g / m 3 , After 200 ⁇ g/m 3 , 250 ⁇ g/m 3 , etc.
  • the contaminants that trigger the return frequency can be other monitored contaminants (such as nitrogen oxides, ozone, etc.).
  • the pollutant concentration is lower than the lower limit of the set value, reduce the frequency of data return of the atmospheric pollutant monitoring equipment. For example, after PM 2.5 ⁇ 50 ⁇ g/m 3 , the return frequency is restored to a time interval of 3 seconds or even longer. .
  • the air pollutant monitoring equipment can adjust the monitoring density and return frequency according to the designated area or road section.
  • the atmospheric pollutant monitoring equipment When a social vehicle equipped with atmospheric pollutant monitoring equipment enters an area or section that needs to be monitored, the atmospheric pollutant monitoring equipment increases the monitoring numerical output frequency of the corresponding atmospheric pollutants. For example, the monitoring frequency is calculated by outputting a pollutant concentration value every 3 seconds. Instead, calculate a pollutant concentration value every 1 second; when the mobile monitoring vehicle leaves an area or a road section that needs to be monitored, the atmospheric pollutant monitoring device reduces the monitoring output frequency of the corresponding atmospheric pollutant, for example, the pollutant The output frequency is restored to the level before entering the focus area or section.
  • the air pollutant monitoring device can also increase the frequency of returning the monitoring data of the corresponding air pollutant to the monitoring center, for example, by changing the value once every 3 seconds. Returning once every 1 second; when the mobile monitoring vehicle leaves the area or road section that needs to be monitored, the air pollutant monitoring device reduces the frequency of the monitoring data of the corresponding atmospheric pollutants to the monitoring center, for example, will return The transmission frequency is restored to the level before entering the key area or section.
  • Embodiment 20 is a mode of operation of the video capture module.
  • the video capture module starts working at the same time after the air pollutant monitoring device is started, and the video content is stored on the local storage medium, such as tf card, ssd, hard disk, u disk, cf card. After the storage space is exhausted, the rollback mode is used to delete the oldest video so that the video can be recorded all the time.
  • the wireless command is used to control the video collection module to upload the required video to the monitoring center by means of wireless transmission.
  • the vehicle can be monitored by the monitoring center and the video can be copied by the authorized staff on site.
  • the atmospheric pollutant monitoring equipment monitors that the concentration of a certain atmospheric pollutant exceeds the upper limit of the preset value, for example, after the PM 2.5 value is ⁇ 200 ⁇ g/m 3 (the preset value may also be other values,
  • the triggering pollutants may be other monitored pollutants such as nitrogen oxides, ozone, etc.
  • the atmospheric pollutant monitoring device automatically opens the uploading function, and uploads relevant videos to a predetermined time before and after the preset value of the pollutants to the relevant time.
  • the monitoring center such as 5 minutes before and after the trigger video upload to the monitoring center.
  • Embodiment 21 is a mode of operation of the video capture module.
  • the video capture module is turned off by default.
  • the atmospheric pollutant monitoring device finds that the concentration of an atmospheric pollutant exceeds the upper limit of the preset value, such as the PM 2.5 value ⁇ 100 ⁇ g/m 3 (the preset value may also be other values, the preset trigger
  • the pollutants can be other monitored pollutants such as nitrogen oxides, ozone, etc., and the video capture module is automatically turned on for recording. There are also three ways to collect video. Same as Example 19.
  • Embodiment 22 is a mode of operation of the video capture module.
  • Air pollutant monitoring equipment has video image recognition, video image analysis and pollution identification. By identifying and analyzing the video content obtained by the video capture module, it is possible to capture and discover the local pollution of the mobile monitoring vehicle in time. If it is determined that local air pollution exists, the relevant video content will be uploaded for a certain period of time before and after the pollution time. To the monitoring center, upload a video of 5 minutes before and after the trigger to the monitoring center. If the video uploaded to the monitoring center does have contamination after the judgment screening, the monitoring center can send the pollution data and video evidence to the relevant environmental protection department or the platform of the public security department.
  • the image recognition function can set the learning mode.
  • the data after the video forensics is manually classified by the monitoring center, and each of the classified contaminated video cases is learned by the machine for scene recognition. After the scene recognition learning of the artificial intelligence is completed, the collected video is intelligently identified by detecting the artificial intelligence of the device or the artificial intelligence of the monitoring center, and the pollution is judged.
  • Embodiment 23 is a mode of operation of the video capture module.
  • the monitoring center controls the air pollutant monitoring equipment installed on social vehicles. For example, the monitoring center needs to closely observe the pollution situation of a specific area, and can notify the vehicle to increase the detection data if it enters the area by means of instructions. Calculate the frequency, or detect the return frequency of the data. If the data is returned every 3 seconds before entering the area, the data is transmitted back every 1 second after entering the area; every 3 seconds before entering the area, a data is calculated and calculated, and the calculation is performed every 1 second after entering the area. Get a data.
  • the monitoring center can also instruct the camera of the video capture module to perform video forensics and return in real time.

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Abstract

一种出租车和公交车协同监测时选择公交线路的方法，所述方法以一个城市区域的交通路网为关注重点，利用市内公交车、长途车、出租车、渣土车等移动监测车辆搭载大气污染物传感器进行实时测量，来实现对该城市区域的空气质量的监测，从而提高了监测的客观性、空间与时间覆盖性。

Description

一种出租车和公交车协同监测时选择公交线路的方法 技术领域
本发明涉及一种确定移动监测漏检率以及移动监测车额定数量的方法，属于环境监测技术领域。
背景技术
经济快速发展的同时也带来了严峻的环境问题，我国大部分城市空气污染问题凸显，空气污染严重影响了城市生态景观，而且对人们的身体健康造成了严重的威胁。利用科学的环境监测技术对环境进行实时的监督和检测，可以为相关人员提供寻找解决环境问题行之有效措施和依据。大气环境监测技术可以实现空气污染现状的摸底、排查和全面分析，为治理和管控空气污染提供关键的数据和依据支持，以此达到环境保护的目的。
随着城市建设规模的不断扩大，城市功能区和产业结构布局的不断优化、调整,许多城市在城市环境、城市建设规模、人口数量及分布等方面都有了很大变化,原有的城市环境大气污染物监测设备都呈现数量上的不足或者空间分布上的不科学,不能继续满足城市环境空气监测的技术要求,从而需要增设或调整。
大气环境监测是测定大气中污染物的种类及其浓度、观察其时空分布和变化规律的过程，主要监测的污染物为大气中的二氧化硫、氮氧化物、臭氧、一氧化碳、PM 1、PM 2.5、PM 10、PM 100和VOCs(挥发性有机物)或TVOC(总挥发性有机物)。大气环境监测系统可以对监测的数据进行收集和处理，并及时准确地反映区域环境空气质量状况及变化规律。环保部门可以利用这些数据进行环境决策、环境管理、污染防治；民众可以根据环境数据采取个人防护，合理安排生活。
现在的大气环境监测设备主要有固定监测站点和移动式监测设备。目前的固定监测站点主要分为大型固定监测站点(大型站)和小型监测站点(小型站)。移动式监测设备主要有专用大气环境监测车、无人机以及手持设备等。
大型固定监测站点相当于一个独立的实验室，通过昂贵精密的仪器监测分析环境中多种污染物水平。大型固定监测站点的特点是监测污染物种类多，精度高。但是大型固定监测站点投入较大，常规投入在百万至千万级别，需要高额的财政支持，因此大型固定监测站点的数量不会很多，无法大规模铺开，因此只能选择比较有代表性和可行的位置进行建设。同时大型固定监测站点对选址也有很高的要求，站点需要有大量面积容纳大型设备，设备需要温度湿度控制，同时需要大量专业高素质人员使用仪器、分析数据和对仪器的维护。此外，从超级站获得的数据只能做单点推论，很难再找邻近的其他超级站来验证。
小型监测站点通过整合低成本、小型化传感器的方法，降低成本进行网格化、批量化的布点。小型监测站点还具有用电方便(可采用太阳能供电)、易于安装等特点。但小型站监测数据的准确性和一致性有待提高，并且需要充分的运营保障。虽然小型监测站点覆盖范围较广，但仍然属于固定式监测，灵活性有限。
专用大气环境监测车是装备有采样系统、污染物监测仪器、气象参数观测仪、数据处理装置及其他辅助设备的汽车。它是一种流动监测站，是地面固定监测站点的一种补充。大气环境监测车可以随时开到发生污染事故的现场或可疑点采样测定，以便及时掌握污染情况，其使用不受时间、地点和季节的限制。大气环境监测车需要有专职人员驾驶，并且需要专业人员操作相关仪器。其价格较为昂贵，无法大规模使用。
无人机大气污染监测是一种利用搭载小型化大气监测设备的无人机对大气环境监测的方式。无人机大气污染监测可以实现对高空垂直断面大气污染情况进行立体监测，监测范围广，监测效率高。但是高空中气流有可能紊乱，无人机螺旋桨也可能带来气流扰动，对监测结果可能造成影响。同时目前无人机的续航能力有一定问题，对连续监测也有一定阻碍。利用无人机对大气污染监测也需要专业人员操作。
现有的监测方式中，比如大型站点、专用移动监测车对颗粒物的测量大多采用称重法、微振荡天平法、β射线法；对VOCs检测使用GC-FID(气相色谱-火焰离子检测)方式。这些精密检测仪器大多体积很大，且十分昂贵，不便于广泛布点监测。其他污染物如二氧化硫、氮氧化物、臭氧和一氧化碳的检测也具有类似的问题。类似的专用移动监测车到达指定位置后需要停车监测，相当于一个固定监测站点，无法实时移动进行监测。
城市网格化的大气污染物监测测量投资巨大，目前的监测方式不能做到全面覆盖。每个监测点需要专业人员进行安装维护，每隔一段时间需要进行相应的校准；各监测点的采样口一般安装位置较高，不利于监测地面污染情况(如道路扬尘)。同时人口密度较大的道路和地区，往往车流尤其是出租车也较为密集，对这样的地点需要密集、着重监测。
采用城市社会车辆作为移动监测车辆，搭载大气污染物监测设备以及定位设备，结合无线传输技术，就能够实现大规模近地监测空气污染。
【现有技术1】CN106841525A
上述文件公开了一种大气特征污染在线监控系统构建的方法。
【现有技术2】《大气污染概论》，吴方正，农业出版社
该文献(见第147-149页)公开了城市大气监测的布点问题，根据规定的容许误差和置信度范围确定监测点的数量。
很明显，该文献给出了在一个监测区域内如何确定监测点的数量的启示；但是其只适用 于固定点监测点。对于移动车辆这种监测模式下，其面临的问题是有显著的差异的。
固定点监测特点
首先，对于固定点监测来说，其覆盖范围是固定的，监测点的数据只能代表监测点附近的污染情况，远离监测点的地方的污染情况，只能间接推算得到；特别是监测点之间的“三不管区域”，属于明显遗漏的区域。
很明显，这种以离散的监测点来覆盖一个区域，这些监测点的数量取决于每个监测点能够有效代表的污染范围。
图2显示了固定化网格布点的局限性。
移动监测特点
移动监测的显著特征在于，移动设备随着移动车辆的运行，连续变换监测位置，基本上只在交通路网沿线范围进行监测。其移动路径是交通路线；其覆盖范围是整个区域中的交通路网。
覆盖范围不同
固定监测每个监测设备的监测区域是固定的，离散的“点”；
移动监测每个监测设备的监测区域是整个路网，是连续的“线”或者“带”。
比起“点”状覆盖，“线”状覆盖具有显著的优势。
“线”状覆盖所获取的更大范围的数据分布，对于一个城市的整体空气污染的判断来说，更具有代表性。
监测数据来源不同
对于固定监测模式来说，每个被监测位置的数据来自于同一个或同一组监测设备；
对于移动监测模式来说，每个被监测位置的数据来自于很多不同的监测设备。
也就是说，路网范围内某个位置的污染情况可以被不同的移动设备(搭载在移动车辆上)在不同的时间段多次测量到。这样就可以通过不同设备的监测数据之间的关联，间接评价监测设备的数据可靠性。
发明内容
针对背景技术中监测方式的不足，以及城市大气环境污染监测的特点。本发明提供了一种在确保城市空气污染物监测数据客观性的前提下确定移动监测漏检率的方法。
本发明利用大量随机运行的社会车辆搭载隐藏式安装的监测设备作为移动监测车辆，辅助以固定的监测设备，能够实现对城市区域实时污染物分布和变化情况的监测，只要移动监测设备达到一定的数量，那么这些移动设备产生的监测数据就能够客观地反映城市 空气污染分布和污染程度的真实情况。
环境监测需要保证监测数据的客观和有效。环境污染尤其是大气环境污染与其他类型的污染相比具有随时间、空间变化大的特点。根据这些特点进行监测对于获得正确反映大气污染实况的监测结果具有重要意义。空气污染物的时空分布及其浓度与污染物排放源的分布、排放量及地形、地貌、气象等条件密切相关。污染物的类型、排放规律及污染物的性质不同，其时空分布特点也不同，同一地点的大气污染物水平是快速波动变化的。在大气污染监测中有时间分辨率的概念，要求在规定的时间内反映出污染物浓度变化。例如，有些急性危害的污染物，要求分辨率为3min；有些化学烟雾剂比如臭氧对呼吸道的刺激反应，要求分辨率为l0min。
本发明通过大量部署多种大气污染物监测设备于移动监测车辆上进行大气污染物监测，实时传输位置和监测数据，可以实现从固定点监测到全路网监测和广泛的地域覆盖，反映城市污染物的真实情况。
图1显示了本发明的系统组成，本系统包含搭载监测设备的移动监测车辆、监控中心、固定监测站点。本发明提出合理大量增加设备投放密度，实现城区机动车道路的覆盖，同时监测设备需要实时以秒级回传数据，通过多车接力可实现24小时连续监测，通过监控中心对数据的处理，最终得到可靠、客观和有效的大气环境数据。大量使用监测设备弥补了各固定监测站点点位数量的不足，为网格化监管提供了数据支持。通过安装了大气污染物在线监测设备的移动监测车辆不断在城市内移动，可实时监测到各个角落的污染物浓度、洒水和道路破损情况，使大气污染监测可以覆盖每个社区、每个路段，避免了死角和盲区。同时系统也可以接入固定监测站点的数据，使监测数据更加完整。
当搭载大气污染物在线监测设备的移动车辆数量较少时，采集的监测数据数量将会不足，样本不够，没有办法及时提供城市污染物的全貌信息。当监测车辆达到一定数量后，同时搭载多种污染物监测设备，才可以避免大量小范围短时间空气污染的漏检，大量搭载大气污染物在线监测设备的车辆能够得到更加完整的污染物空间和时间覆盖情况，提高监测数据的有效性。图6为同一地点的污染物浓度随时间的变化情况，T 0到T 6表示的是污染开始到消散的时间，T 3时刻所对应的污染物浓度最大，由于大气污染随时间变化较快，当投放的搭载大气污染物在线监测设备的车辆不足时，可能无法监测到同一地点T 0至T 6之间的数据，这样并不能得到此地真实的污染物情况，只有尽可能多地捕捉到T 0至T 6之间的数据才可以反映真正的污染全貌。本方案由于提出使用大量移动监测车辆搭载大气污染物监测设备进行数据采集，能够捕捉到大部分的小范围短时间的污染，可以对污染进行溯源，可以对大气污染的成因进行分析，可以为环保部门的执法和民众个人防护提供客观真实的大气污染信息。
大量增加设备安装密度，实现城区机动车道路的覆盖，同时监测设备需要实时以秒级回传数据，通过多车接力可实现24小时连续监测。大量使用监测设备弥补了各监测站固 定监测点位数量的不足，为网格化监管提供了数据支持。通过大量安装了车载大气污染物在线监控系统的移动监测车辆不断在城市内移动，可实时监测到各个角落的污染物浓度、洒水和道路破损情况，使大气污染监测可以覆盖每个社区、每个路段，避免了死角和盲区。
本方案提出投放搭载大气污染物在线监测设备的移动监测车辆所需数量的模型，具体数量根据监测的平均漏检率确定。平均漏检率代表对一个监测区域的空气污染情况的检出概率。
方法一：按每平方公里的监测车辆的密度来衡量
该方法依然着眼于监测设备的数量相对于一个区域的密度。
如图7所示，平均漏检率与城市中的监测设备密度有关，监测设备密度用每平方公里的车辆数目表示。投放的车辆总数很少时，平均到城市中每平方公里的数量将会很少，对于小范围快速消散的污染物的平均漏检率会很高，投放密度越高，平均漏检率就会降低。当投放的监测设备数量达到一定值之后，如投放密度达到n 0时，平均漏检率就会降低到m 0。
为保证一个城市大气污染监测数据的客观性，需要设定一组最高平均漏检率指标M 0，与其对应的，可以测算出一组最低投放密度指标N 0。由于平均漏检率越高，监测数据的客观性越差，所以我们期望降低平均漏检率；一般来说，需要监测的大气污染物的平均漏检率应该控制在50％之下；考虑到成本问题，实际平均漏检率控制在20％-50％之间比较合理。
漏检率应该对应于某个具体的可监测数据，包括但不限于PM 1、PM 2.5、PM 10、PM 100、二氧化硫、氮氧化物、臭氧、一氧化碳、VOCs(挥发性有机物)或TVOC。
因此，平均漏检率可以采用一组指标的方式来表达如下：
m(PM 1，PM 2.5，PM 10，PM 100，二氧化硫，氮氧化物，臭氧，一氧化碳，VOCs，TVOC)
或者，
m(PM 1，PM 2.5，PM 10，PM 100，SO 2，NO X，O 3，CO，VOCs，TVOC)
例如：m(N/A，20％，20％，N/A，N/A，N/A，N/A，N/A，N/A，N/A)表示只考虑PM 2.5，PM 10的监测数据漏检率，其他气体污染物的监测暂时未纳入系统中(N/A:Not Applicable)。
同样的，投放密度也需要采用一组指标来反映具体污染监测设备的投放密度：
n(PM 1，PM 2.5，PM 10，PM 100，SO 2，NO X，O 3，CO，VOCs，TVOC)
对于m(PM 2.5)＝20％，m(PM 10)＝10％的情形，可以预见到
n(PM 2.5)<n(PM 10)
也就是说PM 10的监测设备投放密度应该大于PM 2.5的监测设备投放密度。
通过调整多传感器监测设备的传感器单元的种类配比，可以充分利用有限数量的已安装监测设备的移动监测车辆，实现多个大气污染物监测设备达到最低投放密度指标。
因此，最高平均漏检率指标M 0，及与其对应的最低投放密度指标N 0表示如下：
M 0(PM 1，PM 2.5，PM 10，PM 100，SO 2，NO X，O 3，CO，VOCs，TVOC)
N 0(PM 1，PM 2.5，PM 10，PM 100，SO 2，NO X，O 3，CO，VOCs，TVOC)
在某个投放密度指标下，通过调高某大气污染物重点污染区域的相应数据的监测输出频率，可以有效降低该大气污染物的漏检率。
同样，通过监测设备的隐藏式设计，可以有效预防污染企业的躲避行为带来的漏检。
本发明提出的确定城市大气污染物监测数据漏检率的方法如下：
1)首先在某监测地区建立由固定监测站点、监控中心和移动监测车辆组成的监测系统；所述的移动监测车辆上安装有大气污染物监测设备；确定所述监测地区的一组最高平均漏检率指标M 0；
2)依据所述一组最高平均漏检率指标M 0测算出一组最低投放密度指标N 0；
3)增加与投放密度指标N 0相关的移动监测车辆的数量，使得所述一组最高平均漏检率指标M 0得到满足；
4)若所述一组最高平均漏检率指标M 0有变化，则再次执行上述步骤2)和步骤3)。
但是，方法一存在很大的缺陷。
考虑到路网的分布与网格化的区域的对应关系的不协调；移动车辆只在路网分布的范围运行，而且存在非常明显的覆盖不均现象：特别是以出租车为载体的移动监测设备在有些路段出现和逗留的概率远远大于另外一些偏远或冷清的路段。
也就是说，移动设备在持续移动中，那么就存在移动设备在一个大的区域范围内的分布是不均衡的。
如果我们按照每个网格内是否有足够的监测数据来衡量的话，就会发现，一部分网格中存在冗余的数据(移动监测车辆扎堆出现)，而另一部分网格中缺少数据(鲜有移动监测车辆出现)。
这个现象可以如图9显示，统计每个小型网格内移动监测设备测量大气污染的次数；其中每15分钟作为一个计时单元，在一个计时单元内，同一台移动设备的多次测量按一次计算。
固定点与移动点的区别在于单点的监测区域不同，固定点只能覆盖固定大小的区域，而移动点随时间的变化覆盖区域是变动的，在一段时间内(例如一天)可以覆盖远超固定点的空间范围，在满足数据量的情况下，移动点覆盖全城所需数量与固定点差别很大，因此移动点的全城部署数量的估算是与固定点不同的问题，不能套用固定点的计算理论来解决。
目前大气污染监测国控站数据量以1小时得出一个数据为主，评价城市空气质量多以天平均数据为主，而移动监测站多以分钟级或秒级数据为主，高频数据配以移动监测可以达到多个固定监测站点的效果，车载移动监测以连续行进监测的方式进行时更是实现高覆盖率，以出租车等车辆搭载移动监测设备时，更具有出现地点灵活、随机，无法提前采取措施影响空气质量，数据更具有说服力的效果。
方法二：按交通路网上各个路段单元被监测次数的分布来衡量
考虑到移动监测的特点，我们把关注点放到路网上。
如果把一个区域(如某城市城区范围)的路网按照一定的粒度，即长度单位(比如每100米，或者200米)划分为一个个路段单元；我们把搭载移动监测设备(处于工作状态)的车辆在经过每个路段单元的次数作为重要的衡量指标。
进一步地，为排除连续多次测量带来的短期数据重复的情形，按每15分钟或30分钟作为一个计时单元，在一个计时单元内，同一台移动设备的多次经过只按一次检测计数。不同监测车辆的数据进行累计。
如果把所有的路段单元拼接在一起，形成一个长“带”。按照一天来统计的话，，我们得到图10的示意。该曲线的横坐标为各个路段单元；纵坐标为各个路段单元在一定时间内(通常选取一天)累计的检测次数(检测计数)。
由于热点路段和冷清路段的差异，以及出租车的随机性；我们基本可以判断在统计学意义上，不同路段单元上的检测次数，是按照时间不断叠加，随着数量的增加，呈现出某种半正态分布的形态。
这里需要对监测次数等术语做个定义。
检测次数：本文中，特指某个路段单元被处于监测状态的移动监测车辆经过的次数；同一辆监测车在一个连续的计时单元内的多次经过只按一次计数。
计时单元可以为15分钟、30分钟，或1个小时。
计数周期：以一个计时单元为期限，当一辆监测车辆进入某路段单元时，则触发一个新的计数周期；在计数周期内，同一辆监测车进入该路段单元都不再计入检测次数中。计数周期存续一个计时单元后结束。同一辆监测车在每个路段单元只能有最多一个计数周期；在前一个计数周期结束前，无法触发新的计数周期。
图11是依据上述方法得到的某个城市区域的24小时内监测次数(出租车)按路段单元的统计分布图。
从图中可以清晰地看到，当出租车数量只有50辆时，在24个小说内，只有大约30％的路段单元录得10次以上的检测次数。
随着安装有监测设备的出租车数量的逐步增加，越来越多的路段单元可以录得10次以上的检测次数。
如果我们以80％的路段单元在24小时内能够录得10次以上的检测次数为期望指标。那么作为示例，可以推算出安装有监测设备的出租车数量需要达到290台。
期望指标：是用来评估一个城市区域路网范围内的大气污染检测数据的采集量是否能反应整体污染状况的指标。这个指标由两个重要的参数来表征：覆盖范围、预定检测次数。
覆盖范围：达到预定检测次数的路段单元数量占路段单元总数的比例，可选的数值范围为50％～90％之间。一般选取70～80％为易。
预定检测次数：当一个路段单元在一天内录得的检测次数达到某个数值时，该路段单元被认可为得到了检测覆盖；否则，该路段单元被视为漏检路段单元。一般来说，考虑到计时单元的长度，预定检测次数可选范围为5～10次。
漏检路段单元：某一个路段单元在一天内录得的检测次数没有达到预定检测次数时，该路段单元被视为漏检路段单元。
基于路段单元的检测次数的模型下，针对某个大气污染检测对象，当我们设定一个期望指标的时候，实际上也就设定了一个平均漏检率指标m。
对于需要对多个污染物进行监测的情形，就需要对每个特定污染物设定各自的期望指标；考虑到污染危害的程度不同、治理的重点不同、时机不同，各个期望指标可能会有显著的差异。以图11为例，如果监测对象是PM2.5，其平均漏检率可以表示为：
m(PM2.5，80％，10)；其含义为：在一个城市区域内，80％的路段单元能够在24小时内录得至少10次检测(检测次数)。
本发明公开了一种确定移动监测车的额定数量的方法，所述方法以一个城市区域的交通路网为关注重点，通过给移动监测车，特别是出租车，安装空气污染检测设备，来实现对该城市区域的空气质量监测；所述方法包含以下步骤：
一)建立移动监测车检测次数曲线模型
1)针对某个城市区域，将交通路网以路段单元为单位进行分解；建立并初始化各个路段单元的数据库；该数据库包含路段单元编号、路段单元位置信息、路段单元检测记录(检测设备编号、移动监测车进入路段单元的时间、移动监测车经过该路段单元的累计经过次数(初始值为“0”))
2)选择一部分搭载有定位系统的移动监测车(例如出租车)，跟踪记录各移动监测车在不同路段单元的经过次数(在没有安装检测设备的情况下，等同于虚拟检测次数)；考虑到出租车司机一般都有区域取向，参与模型构建的出租车的数量不宜太少，一般来说应该有至少50辆。
3)持续记录至少一周；将每天的统计数据累计后计算出日平均值；
4)以时间累计换移动监测车数量的方式，形成24小时内监测次数(出租车)按路段单元的统计分布图(如图11所示)；如果参与模型建立的出租车数量为50，则2天的累计数据(日平均值乘以2)等同于C＝100的曲线；4天的累计数据等同于C＝200的曲线；依次类推。
二)确定期望指标的两个参数(覆盖范围、预定检测次数)
三)依据检测次数曲线模型，寻找出刚好满足期望指标的曲线，得到移动监测车的额定数量(C 0)。
当出租车(移动监测车)的数量超过额定数量(C 0)时，能够达到预定检测次数的路段单元的覆盖范围会进一步提高。但是这种提高的程度是有限的，原因在于某些冷清路段、偏远路段，或者由于交通管制等原因所设计的路段，被检测到的可能性几乎为零。
另一方面，某些空气污染物(如汽车尾气)的出现场景与出租车的活跃区域是存在某些正相关的；这就意味着，出租车出没比较少的地方，往往也是某些空气污染物比较稀薄的地方；因此，在选择覆盖范围时，适当放弃纠结于部分漏检区域是合理的。
方法三、两种监测车辆(出租车、公交车)共存的情形
当公交车也参与城市空气污染的监测时，覆盖范围的问题就有了显著的变化。
公交车通常路线固定，覆盖区域固定，工作时间固定，出勤率固定，因此公交车是出租车的特例在选择安装监测设备的公交线路时，选择出租车经过概率小的路线会起到补盲作用，在达到同样的漏检率指标时有助于减少总的移动监测点布设数量。
选择出租车经过概率小的公交路线安装移动监测点时，一辆公交车所监测的数据量会起到多辆出租车的效果，而计算漏检率指标时是以满足小概率路线数据量达到最低要求而得出的，所以两者结合布设会大大减少城市监控车辆总数量，节约资源。
首先，公交车通常沿着固定的线路往返运行；那么对应于基于路段单元的监测次数模型，公交线路的曲线实际上是平稳的折线。如图12下部所示，公交线路B1和B2在其各自运行路线上，在其各自经过的路段单元上，检测次数是均匀分布的。
如果对比线路B1和线路B2，就会发现，公交线路B1中包含了一部分路段(阴影部分)，刚好落在了出租车很少出没的区域。
那么，如果公交线路B1和出租车配合完成某污染物的监测工作，那么公交线路B1就可以帮助覆盖出租车难以覆盖的漏检路段。
也就是说，公交线路B1和出租车的组合可以从整体上显著增加污染检测的覆盖范围。而公交线路B2，由于其路径全部位于出租车的覆盖范围，所以，在B2上安装检测设备并不能从整体上显著增加污染检测的覆盖范围。
那么，在存在多条备选公交线路的情况下，如何确定哪些公交线路安装检测设备，就是一个很有意义的技术问题。
本发明公开了一种出租车和公交车协同监测时选择参与监测的公交线路的方法：
一)建立出租车检测次数曲线模型
1)针对某个城市区域，将交通路网以路段单元为单位进行分解；建立并初始化各个路段单元的数据库；该数据库包含路段单元编号、路段单元位置信息、路段单元检测记录(检测设备编号、进入路段的时间、累计经过次数(初始值为“0”))
2)选择部分搭载有定位系统的出租车，跟踪记录各个出租车在不同路段单元的经过次数(在没有安装检测设备的情况下，等同于虚拟检测次数)；考虑到出租车司机一般都有区域取向，参与模型构建的出租车的数量不宜太少，一般来说应该有至少50辆。
3)持续记录至少一周；将每天的统计数据累计后计算出日平均值；
4)以时间累计换出租车数量的方式，形成24小时内监测次数(出租车)按路段单元的统计分布图(如图11所示)；如果参与模型建立的出租车数量为50，则2天的累计数据(日平均值乘以2)等同于C＝100的曲线；4天的累计数据等同于C＝200的曲线；依次类推。
二)建立各条公交线路检测次数曲线模型
其横轴与出租车检测次数曲线模型的横轴一致；按照公交线路的运行计划，给相应的路段单元赋予检测次数数值。
三)将各条公交线路按照其覆盖的漏检路段单元的数量进行排序
1)先选择一个覆盖范围初始值f 0(如70％或80％)；循环变量初始值i＝0
2)i＝i+1；
3)确定覆盖的漏检路段单元最多的公交线路，排第i位；
4)计算排在第i位的公交线路所覆盖的漏检路段单元所对应的局部覆盖范围b i(百分比％)；
5)将覆盖范围f (i-1)减去局部覆盖范围b i，得到新的覆盖范围f i和新的漏检路段单元；新的漏检路段单元应当扣除排在前面的公交线路已经覆盖的漏检路段单元；
6)从剩余的公交线路中继续选择覆盖的漏检路段单元最多的公交线路，排第i+1位；
7)重复步骤2)至6)，直至排序结束。
四)按步骤三)的排序选择参与协同监测的公交线路。
出租车额定数量的调整
由于优化选择的公交线路能够部分覆盖出租车难以有效覆盖的路段单元，在维持整体覆盖范围不变的前提下，可以适当减少出租车的数量。
如图12所示，由于公交线路B1能够覆盖b 1所代表的路段单元，所以，出租车的额定数量可以从原来的290辆减少到140辆。
五)确定期望指标的两个参数(覆盖范围f、预定检测次数)；
六)对于每条选定的公交线路Bi，将其局部覆盖范围bi依次从覆盖范围f中减去，得到新的覆盖范围f’；
七)以新的覆盖范围f’和预定检测次数，从出租车检测次数曲线模型，寻找出刚好满足期望指标的曲线，得到出租车车的额定数量(C 0)。
高低频传感器
在先申请PCT/IB2018/05531中公开了大气污染检测设备，所述大气污染检测设备包含主控模块和检测模块；所述检测模块采用至少四个子传感器单元组成传感器模组；当主控模块发现其中一个子传感器单元出现疑似异常，并判断所述疑似异常子传感器为 异常子传感器后，对所述异常子传感器进行隔离，所述异常子传感器归入隔离区，多核传感器模组降级后继续正常工作。
本申请进一步公开了另一种大气污染检测设备，所述大气污染检测设备包含主控模块和检测模块；所述检测模块包含至少两个同类子传感器单元组成传感器模组；所述子传感器单元工作在正常的工作频率。所述检测模块还包含至少一个与传感器模组同类的子传感器单元组成低频校准模组；低频校准模组内的子传感器单元工作在远低于传感器模组的工作频率。因此低频校准模组也称之为低频组。作为对照，传感器模组也称之为高频组。
通常，传感器模组的工作频率是低频校准模组的10倍或以上。高频组和低频组的工作频率的比率，称为高频低频比，可以选择为：2:1，3:1，4:1，5:1，6:1，7:1，8:1，9:1，10:1，15:1，20:1。
低频组的工作频率可以与异常判断的节奏保持一致。也就是说，当需要对传感器模组中是否存在子传感器异常现象进行判断时，低频组才进行检测工作。
由于激光功率衰减在激光传感器的工作寿命内的大多数时间是缓慢进行的，是可以通过校准来恢复其数据的准确性；也就是使用未衰减或衰减程度非常低的子传感器来校准衰减程度高的子传感器。
在传感器模组运行过程中，每隔一定时间，例如1天，1周或1个月，使用低频组检测数据作参考，校准高频组检测数据，校准系数可以使用高频组传感器的检测数据平均值与低频组检测数据平均值之比得到。
除了激光传感器的光衰效应，其他类型的传感器，也存在长时间高负荷工作情况下的性能不稳定或者数据误差增大的可能倾向。通过引入一个低频组，能够作为相对可靠的基准，用来判断传感器模组是否存在数据偏移现象。
同时，由于低频组的数据通常可信度更高，在判断传感器模组中哪个子传感器单元属于疑似异常或异常时，可以通过增加低频组的数据权重，来做出更可信的判断。一种简单的方案是所有的低频组数据按两倍权重参与疑似异常判断。
隔离与恢复
在先申请PCT/IB2018/05531还公开了一套识别子传感器工作状态并对子传感器进行隔离和恢复的方法。传感器模组获得一个时刻的一组检测数据，主控模块从这一组数据中筛选出疑似异常的数据，进而判断相应的子传感器是否满足隔离条件。判断子传感器为异常子传感器后将异常子传感器归入隔离区；判断疑似异常的子传感器不满足隔离条件后，该子传感器继续正常工作。判断进入隔离区的子传感器是否可以自愈，如果判断可以自愈则对该可自愈的子传感器做降频工作处理，但是子传感器输出的数据不参与主控模块输出数据的计算。对于无法自愈的子传感器则停止工作，并通知运行维护方进行维修或者更换。对于降频后的子传感器，由主控模块检测其输出的数据，
判断其是否达到恢复条件，将达到恢复条件的子传感器调离隔离区，恢复工作，输出数据参与传感器模组数据或主控数据计算；对于不符合恢复条件的异常子传感器再次进行是否可自愈的判断。
将传感器模组中异常子传感器隔离后，剩余的子传感器输出数据平均值作为传感器模组的输出结果，传感器模组可以继续正常使用。
进一步地，根据移动监测车辆所接触大气污染物的分布特征，来选择检测模块中传感器单元的组合方式，使检测模块的选择和移动监测车辆所接触大气污染物的分布特征相匹配。
另外，通过优化检测模块中传感器单元的组合方式，能够使得在一组最高平均漏检率指标M 0得到满足的情况下，需要投放的所述移动监测车辆的数量最小。
传统监测方式，如专用监测车检测、人员现场检查的方式，监测人员都可以控制进行监测的时间、地点，使得监测没有随机性和突然性。污染企业还有可能通过各种方式从这些设备的操作人员了解到检测的时间和地点，使污染企业躲避监测有了操作的空间。本方案使用出租车等社会车辆作为移动监测车辆进行大气污染物监测，社会车辆行驶路径、时间均不是以监测为目的的，监测地点、时间均不受人为控制。同时监测设备也不受驾驶员的控制，这种方式的监测更具随机性和灵活性，在合理大量投放监测设备后，通过大数据的处理更能保证大气污染物监测数据的客观性。
同时大气环境污染监测需要具有隐蔽性的特点，传统监测方式如专用监测车以及人员进行现场监测时，污染企业也可以及时发现并调整生产排污情况；对于固定监测站点，污染企业也可以针对性地做出相应对策，如改变排污口等方式。本方案将监测设备隐蔽布置在出租车顶灯内或顶灯下、公交车顶等方式，使得被监测的排污企业和个人无法知道附近有设备正在对他们周边进行大气污染物的监测，使得监测数据的客观性进一步提高。如图5所示，排污企业并不会知道门口经过的出租车正在对其进行污染物的监测。
同时系统的设计中采用了多种防止数据篡改的方式，使得监测更具客观性。
使用社会车辆还有其他如下特点：公交车的特点是路线比较固定，利于对某一路段反复多次测量，能够给出更可靠更多时段的数据，公交车发送班次车辆比较多，间隔时间比较均匀，班次多的时候往往是交通高峰，也是颗粒物污染较严重的时段。出租车的特点是分布范围比较广，时间范围广，可以测量到公交车不能到达的地方，测量时间范围补充公交车不运营的时段。渣土车的行驶路线往往是道路扬尘污染严重的路段，让这样的测量来重点监控扬尘路段，事半功倍，还可以测量自身车辆的扬尘污染情况。多个渣土车的综合数据是道路扬尘背景数据，自身车辆的数据包含背景和自身车辆的污染，通过大数据处理，可以将两种数据分开，从而对道路和自身污染分别给出评价，以利于管控。 长途车的特点是可以覆盖城市之间的监测盲点，形成更大范围的监测。
使用出租车等社会车辆进行大气污染物监测，更能找到环境健康风险更高的地区，因为人多的地方是热点领域，也是这些社会车出现频率更高的地区。对这些地区进行多次重复地监测可以获得人流密集地区更加准确的污染信息，使得环境管理部门可以更有针对性地处理污染问题。同时出租车顶灯的高度基本与人员口鼻高度相当，是人员主要进行呼吸的高度，采用出租车搭载大气污染物监测设备对这一高度的大气进行监测，可以有效的反映影响人们呼吸健康这一高度的空气，对大气环境治理有重要意义。
环境监测尤其是网格化监测成本较高，该发明的有益之处还在于该装置利用市内公交车、长途车、出租车、渣土车等社会车辆搭载大气污染物传感器进行实时测量，不需要专用的场地、专业操作人员，对一次性投入要求较低从而降低了测量的成本。同时降低了专用车辆带来的能耗、道路占用。最终减少了对社会公共资源的占用，降低了大气污染物监测成本。
大气污染物监测设备包括检测模块、主控模块和通讯模块；检测模块包含一种或多种大气污染物传感器单元；大气污染物传感器单元为以下传感器之一：PM 1传感器、PM 2.5传感器、PM 10传感器、PM 100传感器、氮氧化物传感器、O 3传感器、SO 2传感器、VOCs传感器或TVOC传感器。
主控模块与移动监测车辆的电源进行连接，主控模块为大气污染物监测设备上的检测模块和通讯模块进行供电。主控模块与大气污染物监测设备上的检测模块和通讯模块还通过数据接口连接，与检测模块通讯模块进行数据交换。
附图说明
图1为本发明系统组成示意图；
图2是网格化固定监测站点布点模式示意图；；
图3是山东某市实例的监控平台示意图；
图4是大气污染物监测设备基本模块组成示意图；
图5示意了利用社会车辆监测的隐蔽性；
图6显示的是大气污染的特性，大气污染具有时效性；
图7显示的是平均漏检率与监测设备投放密度指标的关系；
图8是大气污染物监测设备包含视频采集模块的示意图；
图9是移动监测模式下检测数据在网格中的分布示意图；
图10是“带”状路网在基于路段单元模型下的累计检测分布示意图；
图11某个城市区域的24小时内检测次数(出租车)按路段单元的统计分布图；
图12是出租车和公交车协同监测模式下检测次数的曲线示意图；
图13为高低频多核传感器模组的隔离和恢复流程示意图。
图中，10-出租车，20-公交车，30-监控中心，40-固定监测站点，50-用户终端，70-其他社会车辆，60-大气污染物监测设备，601-检测模块，602-视频采集模块，603-通讯模块，604-主控模块；
图中，C-出租车(监测车)数量，C 0-出租车(监测车)额定数量，b 1-公交线路B1漏检路段单元所对应的局部覆盖范围(百分比)；f 0-覆盖范围初始值，f 1-新的覆盖范围(考虑b 1的情况下)。
具体实施方式
实施例1：
一种提高大气污染物监测数据客观性的方法及系统，利用社会车辆搭载大气污染物监测设备，包括大气污染物监测设备、监控中心、固定监测站点和用户终端，如图1所示。
大气污染物监测设备安装在社会车辆上，用于监测车辆所处位置的大气环境质量，大气污染物监测设备具备信息传输功能，可以将监测到的数据、位置数据和时间信息通过无线的方式回传至监控中心。大气污染物监测设备可以对道路情况进行视频录制采集，记录道路污染情况，大气污染物监测设备可以通过无线传输的方式将采集的视频回传至监控中心。大气污染物监测设备具有数据、视频资料的储存功能，将采集的数据、视频资料保存在本地。大气污染物监测设备还设有数据传输接口，可以通过本地传输的方式将保存的数据和视频资料拷贝至现场的维修或工作人员。
监控中心接收大气污染物监测设备回传的数据，监控中心对这些数据进行储存、处理。监控中心还可以收集其他类型监测设备的数据，如收集微型固定监测站点数据、收集附近国家固定监测站点的数据等。监控中心综合社会车辆的大气污染物监测设备回传的数据、收集到的微型固定监测站点数据和收集到的附近国家固定监测站点的数据，生成数据列表、数据排名和污染云图、历史回放等数据呈现方式。这些生成的数据列表、数据排名和污染云图等处理结果通过网络的方式发送至用户终端，用户可以根据需求查询和使用。监控中心还可以远程控制大气污染物监测设备的运行，如开启关闭大气污染物监测设备、开启关闭视频采集模块、调整监测频率、修正大气污染物监测设备误差等。
实施例2：
得到一个城市客观的大气污染物监测数据才可以反映这个城市真实的大气污染程度，本发明需要设定一组最高平均漏检率指标M 0代表监测数据的客观性。例如一个城市需要对PM 10进行监测，其漏检率表示为m(PM 10)，若这个城市要求m(PM 10)<20％，即表示没有被监测设备捕捉到的PM 10污染事件要小于总PM 10污染事件的20％。
为了达到m(PM 10)<20％，引入最低投放密度指标N 0，N 0表示要达到M 0所需要监测设备的最小投放密度指标。在本例子中，为了达到m(PM 10)，所需投放的搭载PM 10传感器的监测设备最低投放密度为n(PM 10)。该n(PM 10)需要进行测算，测算的依据是城市的面积、搭载移动监测设备车辆的数目、搭载移动监测设备车辆的日行驶里程、搭载移动监测设备车辆的行驶范围、车辆类型、搭载的仪器精度等参数。城市面积越大，所需要投放的监测设备越多；搭载移动监测设备的车辆日行驶里程越多，所需投放车辆越少；搭载移动监测设备的车辆行使范围越大，所需投放车辆越少。
实施例3：
对一个城市的PM 10和SO 2进行监测，在本发明中这个城市的最高平均漏检率指标M 0表示为m(PM 10)、m(SO 2)。不同污染物的污染贡献水平会有不同、城市的重视程度也不同，所以不同污染物的平均漏检率会有对应的要求。通常城市对SO 2的监测重视程度低于PM 10，则本例中PM 10和SO 2的最高平均漏检率数值设定为m(PM 10)＝20％、m(SO 2)＝30％。
当m(PM 10)＝20％、m(SO 2)＝30％，仅使用出租车搭载监测设备，每个设备仅能监测PM 10或SO 2的情况下，监测设备的最小投放密度n(PM 10)将大于n(SO 2)，即搭载PM 10监测设备的出租车投放量要多于搭载SO 2监测设备的出租车。
实施例4：
对一个城市的PM 10和SO 2进行监测，在本发明中这个城市的最高平均漏检率指标M 0表示为m(PM 10)、m(SO 2)，本例中PM 10和SO 2的最高平均漏检率数值设定为m(PM 10)＝20％、m(SO 2)＝30％。目前的监测设备还可以通过内部检测模块的组合，同时测量多种污染物。
本例中第一种投放方式是所有车辆都搭载可以同时测量PM 10和SO 2的监测设备。那么N 0最小监测设备投放密度指标只需满足指标中最严格的那个最高平均漏检率，第一种方式的n(PM 10)＝n(SO 2)，具体n(PM 10)值通过城市的面积以及实施例二中所述投放密度指标参数关系计算得出。
本例中第二种投放方式是一部分车辆都搭载可以同时测量PM 10和SO 2的监测设备,另一 部分车辆搭载仅可以测量PM 10设备的车辆。此时n(PM 10)>n(SO 2)，具体n(PM 10)和n(SO 2)需要经过测算并最终满足m(PM 10)＝20％、m(SO 2)＝30％。
实施例5：
城市中的扬尘污染主要体现在PM 100数值上，渣土车是扬尘污染的一种主要贡献。城市如果需要对PM 2.5和PM 100进行监测，最高平均漏检率指标要求为m(PM 10)＝m(PM 100)＝20％。投放密度可以根据车辆进行区分，将PM 100监测设备跟多的搭载于渣土车上，可以更有效的监测扬尘污染，更有效的达到PM 100监测设备投放密度指标；PM 2.5监测设备则更多的分配给出租车等小型车辆。
实施例6：
大气污染物监测设备基本模块包括检测模块、主控模块和通讯模块。其中主控模块与移动监测车辆的电源进行连接，主控模块为大气污染物监测设备上的检测模块和通讯模块进行供电。主控模块与大气污染物监测设备上的检测模块和通讯模块还通过数据接口连接，与检测模块、通讯模块进行数据交换。例如检测模块采集的数据经主控模块处理后发送至通讯模块，再由通讯模块回传至监控中心；监控中心发送的指令由通讯模块接收后传输至主控模块，主控模块根据指令调整检测模块的运行。主控模块具备数据和视频资料的储存和导出功能。主控模块具备定位功能或与定位设备的数据接口，利用GPS、北斗等定位技术实时记录车辆位置。
实施例7
大气污染物监测设备基本模块包括检测模块、主控模块和通讯模块。其中检测模块通过搭载的大气污染物传感器对采样气体的污染物含量进行检测，得到污染物的浓度数据。检测模块可以搭载多种大气污染物传感器，包括PM 1传感器、PM 2.5传感器、PM 10传感器、PM 100传感器、氮氧化物传感器、臭氧传感器、二氧化硫传感器、VOCs传感器或TVOC传感器，用于污染物的监测。例如搭载PM 2.5传感器和PM 10传感器的大气污染物监测设备可以较好的对道路扬尘进行监测，可以及时发现道路扬尘污染并进行预警。
检测模块还可以搭载其他类型的传感器，例如风速传感器、风向传感器、温度传感器、湿度传感器、压强传感器、噪音传感器，提供更丰富的监测信息。并且例如湿度传感器还可以为大气污染物传感器提供湿度修正和校准依据。
实施例8：
大气污染物监测设备基本模块包括检测模块、主控模块和通讯模块。其中通讯模块用于大气污染物监测设备与监控中心进行无线通讯，上传监测数据、位置信息、时间信息和监控的视频，还可以接受监控中心下发的调整大气污染物监测设备运行的指令。通讯模 块使用GPRS、4G、5G、蓝牙、WIFI、LoRaWAN、窄带物联网等数据传输方式与监控中心进行通讯。通讯模块实时向监控中心回传数据，每次数据回传间隔以秒级为单位。
实施例9：
大气污染物监测设备在包含基本模块之外，还可以增设视频采集模块。视频采集模块用于污染物取证，实现污染程度的可视化，方便后期执法以及确定污染源。视频采集模块内装有摄像头，可以将拍摄的道路情况上传至监控中心。
实施例10：
搭载大气污染物监测设备的移动监测车辆是社会车辆。社会车辆包括市内公交车、长途车、出租车、渣土车、市政车辆、公务车辆、网约车、租赁车辆、共享汽车，以及具有自动驾驶功能的车辆。这些社会车辆不需要专用的场地、专业操作人员便可以对大气污染的情况进行实时测量，一次性投入较低，降低了专用车辆带来的能耗、道路占用，最终减少了对社会公共资源的占用，降低了大气污染物监测成本。
实施例11：
将搭载颗粒物传感器的大气污染物监测设备安装于公交车上。公交车的特点是路线比较固定，利用一台或者几台安装了大气颗粒物传感器监测设备的公交车便可以对整条公交线路的沿线进行大气颗粒物污染监控，降低了监测成本。同时由于公交车行使的特点，可以对某一路段反复多次测量，能够给出更可靠更多时段的数据。公交车发送班次车辆比较多，间隔时间比较均匀，班次多的时候往往是交通高峰，也是颗粒物污染较严重的时段。
实施例12：
将搭载颗粒物传感器的大气污染物监测设备安装于渣土车、垃圾处理车、长途车等大型社会车辆上。这些大型社会车辆行驶的路线往往是道路扬尘严重的路段，利用这些大型社会车辆监控重点扬尘路段事半功倍，同时还可以测量自身车辆的扬尘污染情况。这些大型社会车辆所检测的数据包含背景污染情况和自身车辆的污染情况，通过大数据处理，可以将两种数据分开，从而对道路和自身污染分别给出评价，以利于管控。长途车的特点是可以覆盖城市之间的监测盲点，达成更大范围的监测。
实施例13：
将搭载颗粒物传感器的大气污染物监测设备安装于出租车上。出租车的特点是分布范围比较广，时间范围广，可以测量到其他社会车辆不能到达的地方。使用出租车等社会车辆进行大气污染物监测，更能找到环境健康风险更高的地区，因为人多的地方是热点领域，也是这些社会车辆尤其是出租车出现频率更高的地区。对这些地区进行多次重复地 监测可以获得人流密集地区更加准确的污染信息，使得环境管理部门可以更有针对性地处理污染问题。同时出租车顶灯的高度基本与人员口鼻高度相当，是人员主要进行呼吸的高度，采用出租车搭载大气污染物监测设备对这一高度的大气进行监测，可以有效的反映影响人们呼吸健康这一高度的空气，对大气环境治理有重要意义。
图3为山东某城市搭载大气颗粒物监测设备的出租车监测结果。共约100辆车，每天合计行程超过2.3万公里，可产生120万组数据。通过监控中心的大数据处理平台，这些数据可自动生成城市霾图。技术人员可进一步判断相关区域污染源监管是否到位，指导精准治理的方案。监控中心还对区县、街道办及路段进行统计排名，为治理考核提供技术手段。
实施例14：
大气污染物监测设备安装于社会车辆上。监测设备具备隐蔽性的特点，例如隐蔽安装于出租车顶灯内部、出租车顶灯下部、公交车顶部等位置。传统监测方式例如专用监测车和以及人员进行现场监测时，污染企业也可以及时发现并调整生产排污情况；对于固定监测站点，污染企业也可以针对性地做出相应对策，如改变排污口等方式。本方案将监测设备隐蔽布置在出租车顶灯内部、出租车顶灯下部、公交车顶部等方式，使得被监测的排污企业和个人无法知道附近有设备正在对他们周边进行大气污染物的检测，使得监测数据的客观性进一步提高。如图5所示，排污企业并不会知道门口经过的出租车正在对其进行污染物的检测。
实施例15：
本发明对监测数据设置了防篡改功能，保证监测数据的可靠性和准确性。实施方式为检测模块测得的监测数据先存入大气污染物监测设备的本地存储介质中，同时检测模块测得的监测数据通过无线传输的方式上传至监控中心，上传至监控中心的所有原始数据设置防修改、防删除特征。监控中心的系统自动或者监测人员手动远程调取传感器本地原始数据库与监控中心数据库进行校对。在大气污染物监测设备和监控中心的数据传输中，还可以使用添加数字签名对数据传输进行加密，可使用的数字签名算法包括RSA、ElGamal、Fiat-Shamir、Guillou-Quisquarter、Schnorr和Ong-Schnorr-Shamir等。
实施例16
对安装大气污染物监测设备的社会车辆，进行车牌号和监测设备SN(监测设备序列号)的绑定，这样可以通过监控中心的数据库查询和核对车辆与设备的信息。
实施例17：
大气污染物监测设备可以根据污染物情况调节监控密度。如当搭载大气污染物监测设备 的社会车辆路过某路段，大气污染物监测设备检测到污染物浓度超过预设值上限之后，如PM 2.5值≥100μg/m 3(也可以是150μg/m 3、200μg/m 3、250μg/m 3等)后，大气污染物监测设备提高大气污染物浓度检测输出频率，例如由每3秒计算输出一个污染物浓度数值改为每1秒计算输出一个污染物浓度数值。触发提高检测输出频率的污染物可以是监测的其他污染物(如氮氧化物、臭氧等)。当污染物浓度低于设定值下限后，大气污染物监测设备降低检测输出频率，如PM 2.5≤50μg/m 3后，检测输出频率恢复为每3秒输出一个污染物浓度数值或更长的时间间隔。
实施例18：
大气污染物监测设备可以根据污染物情况调节检测数值的回传频率。如当搭载大气污染物监测设备的社会车辆路过某路段，大气污染物监测设备检测到污染物浓度超过预设值上限之后，如PM 2.5值≥100μg/m 3(也可以是150μg/m 3、200μg/m 3、250μg/m 3等)后，大气污染物监测设备提高大气污染物浓度检测值向监控中心回传的频率，例如由每3秒回传一次个数值改为每1秒回传一次，触发提到回传频率的污染物可以是其他监控的污染物(如氮氧化物、臭氧等)。当污染物浓度低于设定值下限后，降低大气污染物监测设备的数据回传的频率，如PM 2.5≤50μg/m 3后，回传频率恢复为每3秒一次甚至更长的时间间隔。
实施例19：
大气污染物监测设备可以根据指定的区域或路段调节监控密度和回传频率。
当搭载大气污染物监测设备的社会车辆进入需要重点监控的区域或路段时，大气污染物监测设备提高相应大气污染物的监测数值输出频率，例如监测频率由每3秒计算输出一个污染物浓度数值改为每1秒计算输出一个污染物浓度数值；当所述移动监测车辆离开需要重点监控的区域或路段时，所述大气污染物监测设备降低相应大气污染物的监测输出频率，例如将污染物输出频率恢复到进入重点区域或路段之前的水平。
当移动监测车辆路过需要重点监控的区域或路段时，所述大气污染物监测设备还可以提高相应大气污染物的监测数据向监控中心回传的频率，例如由每3秒回传一次个数值改为每1秒回传一次；当所述移动监测车辆离开需要重点监控的区域或路段时，所述大气污染物监测设备降低相应大气污染物的监测数据向监控中心回传的频率，例如将回传频率恢复到进入重点区域或路段之前的水平。
实施例20：
实施例20是视频采集模块的一种工作方式。视频采集模块在大气污染物监测设备启动后便同时开始工作，视频内容存储在本地存储介质上，如tf卡、ssd、硬盘、u盘、cf卡。存储空间用尽后采取回滚方式删除时间最久的视频，使得视频可以一直进行录制。
视频取证方式：
取回视频的方式有三种。第一种，当监控中心需要某时段视频内容时，用无线指令控制视频采集模块通过无线传输的方式将所需视频上传至监控中心。第二种，可以通过监控中心定位移动监测车辆，由具备权限的工作人员现场进行视频拷贝。第三种，当车辆路过某路段，大气污染物监测设备监测到某大气污染物浓度超过预设值上限之后，如PM 2.5值≥200μg/m 3后(预设值也可以是其他值，预设触发污染物可以是其他监控的污染物如氮氧化物、臭氧等)，所述的大气污染物监测设备自动开启上传功能，将超过污染物预设值时刻前后一定时间内的相关视频上传至监控中心，如触发前后5分钟的视频上传至监控中心。
实施例21：
实施例21是视频采集模块的一种工作方式。视频采集模块默认关闭，当所述大气污染物监测设备发现某大气污染物浓度超过预设值上限之后，如PM 2.5值≥100μg/m 3后(预设值也可以是其他值，预设触发污染物可以是其他监控的污染物如氮氧化物、臭氧等)，自动开启视频采集模块进行录制。视频取证方式也有三种。同实施例19。
实施例22：
实施例22是视频采集模块的一种工作方式。大气污染物监测设备具备视频图像识别、视频图像分析和污染识别功能。通过对视频采集模块获得的视频内容进行识别分析处理，可以及时捕捉和发现移动监测车辆经过区域的局部污染情况，若判断存在局部大气污染，则将发现污染时刻前后一定时间内的相关视频内容上传至监控中心，如触发前后5分钟的视频上传至监控中心。上传至监控中心的视频如果进行判断筛查后的确有污染情况，监控中心可以将污染数据和视频证据发送给相关环保部门或者公安部门的平台。图像识别功能可以设置学习模式，视频取证后的资料由监控中心人工进行视频分类，分类后的每一个污染视频案例由机器学习，进行场景识别。当人工智能的场景案例识别学习完成后，通过检测设备本地的人工智能或者监控中心的人工智能，智能识别采集到的视频，并对污染进行判断。
实施例23：
实施例23是视频采集模块的一种工作方式。监控中心对对搭载于社会车辆的大气污染物监测设备进行控制，比如监控中心需要对某一特定区域的污染情况进行密切观察，可以通过指令的方式，通知车辆如果进入该区域则提高检测数据的计算频率，或者检测数据的回传频率。如进入该区域之前每3秒回传一次数据，改为进入区域之后每1秒回传一次数据；进入该区域之前每3秒检测计算得出一个数据，改为进入区域之后每1秒检测计算得出一个数据。监控中心也可以指令开启视频采集模块的摄像头，进行视频取证并实时回传。

Claims (13)

  1. 一种出租车和公交车协同监测时选择公交线路的方法，所述方法以一个城市区域的交通路网为关注重点，通过给部分出租车和公交车安装大气污染检测设备，来实现对该城市区域的空气质量的监测；所述方法包含如下步骤：
    1)建立出租车车检测次数曲线模型：
    a)针对所述城市区域，将交通路网以路段单元为单位进行分解；建立并初始化各个路段单元的数据库；该数据库包含路段单元编号、路段单元位置信息、路段单元检测记录；
    b)选择50辆搭载有定位系统的出租车，跟踪记录各出租车车在不同路段单元的经过次数；同一辆出租车在一个连续的计时单元内的多次经过只按一次计数；
    c)持续记录至少一周；将每天的统计数据累计后计算出日平均值；
    d)以时间累计换出租车数量的方式，形成24小时内检测次数按路段单元的统计分布图；
    2)建立各条公交线路检测次数曲线模型；所述公交线路检测次数曲线模型的横轴与出租车检测次数曲线模型的横轴一致；按照公交线路的运行计划，给相应的路段单元赋予检测次数数值；
    3)将各条公交线路按照其覆盖的漏检路段单元的数量进行排序：
    a)先选择一个覆盖范围初始值f 0；i＝0
    b)i＝i+1；
    c)确定覆盖的漏检路段单元最多的公交线路，排第i位；
    d)计算排在第i位的公交线路所覆盖的漏检路段单元所对应的局部覆盖范围bi；
    e)将覆盖范围f (i-1)减去局部覆盖范围bi，得到新的覆盖范围f i和新的漏检路段单元；新的漏检路段单元应当扣除排在前面的公交线路已经覆盖的漏检路段单元；
    f)从剩余的公交线路中继续选择覆盖的漏检路段单元最多的公交线路，排在第i+1位；
    g)重复步骤b)至f)，直至排序结束；
    4)按步骤3)的排序选择参与协同监测的公交线路。
  2. 如权利要求1所述的方法，其特征在于，所述的路段单元检测记录包含检测设备编号、移动监测车进入路段单元的时间、移动监测车经过该路段单元的累计经过次数；所述累计经过次数的初始值为“0”。
  3. 如权利要求1所述的方法，其特征在于，所述的路段单元的长度为100米或200米。
  4. 如权利要求1所述的方法，其特征在于，所述的计时单元为15分钟、30分钟，或1个小时。
  5. 如权利要求1所述的方法，其特征在于，所述的覆盖范围初始值f 0的取值为70％～80％。
  6. 如权利要求1至5之一所述的方法，其特征在于，所述的出租车还包括网约车、租赁车辆、共享汽车，以及具有自动驾驶功能的车辆。
  7. 如权利要求6所述的方法，其特征在于，所述的大气污染检测设备包括检测模块、主控模块和通讯模块；所述检测模块包含一种或多种大气污染物传感器单元；所述大气污染物传感器单元为以下传感器之一：PM 1传感器、PM 2.5传感器、PM 10传感器、PM 100传感器、氮氧化物传感器、O 3传感器、SO 2传感器、VOCs传感器或TVOC传感器。
  8. 如权利要求7所述的方法，其特征在于，所述检测模块包含至少两个同类子传感器单元组成传感器模组；所述子传感器单元工作在正常的工作频率；所述检测模块还包含至少一个与传感器模组同类的子传感器单元组成的低频校准模组；低频校准模组内的子传感器单元的工作频率远低于传感器模组内子传感器单元的工作频率。
  9. 如权利要求8所述的方法，其特征在于，所述传感器模组的工作频率与低频校准模组的工作频率的比率为：2:1，3:1，4:1，5:1，6:1，7:1，8:1，9:1，10:1，15:1，或者20:1。
  10. 如权利要求8所述的方法，其特征在于，当所述主控模块发现所述传感器模组中一个子传感器单元出现疑似异常，并判断该疑似异常子传感器为异常子传感器后，对所述异常子传感器进行隔离，所述异常子传感器归入隔离区，多核传感器模组降级后继续正常工作；进入隔离区的子传感器如无法自愈则停止工作；如可以自愈则做降频工作处理，但是子传感器输出的数据不参与 主控模块输出数据的计算；主控模块监测进入隔离区的子传感器输出的数据，判断其是否达到恢复条件；将达到恢复条件的子传感器调离隔离区，恢复工作。
  11. 如权利要求7所述的方法，其特征在于，对于出租车，所述的大气污染检测设备安装在出租车的顶灯下或者顶灯内。
  12. 如权利要求7所述的方法，其特征在于，对于公交车，所述的大气污染检测设备隐蔽安装在公交车的顶部。
  13. 如权利要求7所述的方法，其特征在于，所述的大气污染物检测设备还包括视频采集模块；所述视频采集模块默认关闭，当所述大气污染物检测设备发现某大气污染物浓度超过预设值上限之后，自动开启视频采集模块进行录制。
PCT/CN2019/074043 2017-07-29 2019-01-31 一种出租车和公交车协同监测时选择公交线路的方法 WO2019210722A1 (zh)

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CN201980003739.9A CN111033221B (zh) 2017-07-29 2019-01-31 一种出租车和公交车协同监测时选择公交线路的方法
GB2012917.7A GB2587708B (en) 2017-07-29 2019-01-31 A method for selecting bus lines for collaborative monitoring of taxis and buses
NO20210238A NO20210238A1 (en) 2017-07-29 2019-01-31 Method for Selecting Bus Lines for Collaborative Monitoring of Taxis and Buses
PCT/CN2019/097591 WO2020020257A1 (zh) 2017-07-29 2019-07-25 一种高覆盖率车载空气质量监测系统
PCT/CN2019/097592 WO2020020258A1 (zh) 2017-07-29 2019-07-25 一种利用运营车辆进行大气监测时选择线路的方法
GBGB2102430.2A GB202102430D0 (en) 2017-07-29 2019-07-25 Method for improving monitoring coverage rate when taxi is used to perform air monitoring
CN201980043012.3A CN112368563B (zh) 2017-07-29 2019-07-25 一种利用公共交通工具监测空气质量的系统
GB2102609.1A GB2599747B (en) 2017-07-29 2019-07-25 Method for selecting routes during atmospheric monitoring using operating vehicles
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CN201980042987.4A CN112703384B (zh) 2017-07-29 2019-07-25 一种利用公务车辆进行大气监测时提高监测覆盖率的方法
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