US20210140935A1

US20210140935A1 - Pollution-monitoring taxi roof light with airflow stabilization ability

Info

Publication number: US20210140935A1
Application number: US17/156,662
Authority: US
Inventors: Shuchun SI; Shanwen LIU; Shuaishuai JIA; Shitian KOU
Original assignee: Nova Fitness Co Ltd
Current assignee: Nova Fitness Co Ltd
Priority date: 2018-02-01
Filing date: 2021-01-25
Publication date: 2021-05-13
Also published as: WO2020020253A1; GB2581868A; GB202102643D0; GB2586709A; GB202012860D0; CN112334753B; GB202012851D0; CN112334753A; GB202012853D0; GB2580217B; WO2019149233A1; CN110383011B; US20200363384A1; WO2019149232A1; US20210148879A1; CN108195728A; US20200363385A1; GB2589810A; AU2018405991B2; WO2019149235A1

Abstract

A taxi roof light includes a gas distribution tank, a control module, and a detection module. The gas distribution tank is used to distribute detected gas to each individual sub-sensor. The gas inlet of the gas distribution tank is connected to a gas sampling head. A floc prevention net is installed at the gas inlet. The arrangement of the floc prevention net at the gas inlet and the gas outlet can prevent the unexpected matter from entering the monitoring device to damage the monitoring device.

Description

CROSS-REFERENCE TO RELAYED APPLICATIONS

This application is a continuation-in-part of International Patent Application No. PCT/CN2019/097587 with an international filing date of Jul. 25, 2019, designating the United States, now pending, and further claims foreign priority benefits to International Patent Application No. PCT/IB2018/055531 filed on Jul. 25, 2018, to International Patent Application No. PCT/IB2018/055526 filed on Jul. 25, 2018, to International Patent Application No. PCT/CN2019/074036 filed on Jan. 31, 2019, to International Patent Application No. PCT/CN2019/074037 filed on Jan. 31, 2019, to International Patent Application No. PCT/CN2019/074038 filed on Jan. 31, 2019, to International Patent Application No. PCT/CN2019/074039 filed on Jan. 31, 2019. The contents of all of the aforementioned applications, including any intervening amendments thereto, are incorporated herein by reference. Inquiries from the public to applicants or assignees concerning this document or the related applications should be directed to: Matthias Scholl P. C., Attn.: Dr. Matthias Scholl Esq., 245 First Street, 18th Floor, Cambridge, Mass. 02142.

BACKGROUND

The disclosure relates to a taxi roof light and in particular to a pollution-monitoring taxi roof light with airflow stabilization ability, belonging to the technical field of air pollution monitoring.
Atmospheric environmental monitoring is the process of measuring the types and concentrations of pollutants in the atmosphere and observing their temporal and spatial distribution and changes. The main pollutants monitored are sulfur dioxide, nitrogen oxides, ozone, carbon monoxide, PM₁, PM_2.5, PM₁₀, PM₁₀₀and VOCs (volatile organic compounds) or TVOC (total volatile organic compounds). The atmospheric environmental monitoring system can collect and process the monitored data, and timely and accurately reflect the regional ambient air quality status and changes. Environmental protection departments can use these data for environmental decision-making, environmental management, and pollution prevention; the public can take personal protection and rationally arrange their lives based on environmental data.
The current 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 (large stations) and small monitoring stations (small stations). Mobile monitoring equipment mainly comprises special atmospheric environmental monitoring vehicles, drones and handheld devices.
The large fixed monitoring site is equivalent to an independent laboratory, which monitors and analyzes the levels of multiple pollutants in the environment through expensive and sophisticated instruments. It has characterized by a variety of pollutants and high accuracy. However, the general investment of large fixed monitoring stations is in the millions to ten million level, which requires high financial support. Therefore, the number of large fixed monitoring sites is relatively small and cannot be rolled out on a large scale, and only representative and feasible location can be selected for construction. At the same time, large fixed monitoring sites also have high requirements for site selection. The site needs a large area to accommodate large equipment, and equipment needs temperature and humidity control. A large number of professional and high-quality personnel would be required to use the instrument, analyze data, and maintain the instrument. In addition, the data obtained from super stations can only be inferred at a single point, and it is difficult to find other nearby super stations to verify.
Small monitoring sites integrate grids and batches to reduce costs by integrating low-cost, miniaturized sensors. It also has the advantages of conventional power consumption (can be powered by solar power) and easy installation. However, the accuracy and consistency of monitoring data of small stations need to be improved, and sufficient operational guarantee is needed. Although a small monitoring site covers a wide area, it is still a fixed monitoring with limited flexibility.
The special atmospheric environment monitoring vehicle is a vehicle equipped with a sampling system, pollutant monitoring instruments, meteorological parameter observers, data processing devices and other auxiliary equipment. It is a mobile monitoring station and a supplement to the fixed ground monitoring stations. The atmospheric environmental monitoring vehicle can be driven to the scene of a pollution accident or a suspicious point to take measurements at any time, in order to grasp the pollution situation in time, and its use is not restricted by time, place and season. But it needs to be driven by full-time personnel, and professional personnel are required to operate related instruments. It is expensive and cannot be used on a large scale.
In the existing monitoring methods, for example, large-scale stations and special mobile monitoring vehicles use the weighing method, micro-oscillation balance method, and β-ray method to measure particulate matter. GC-FID (Gas Chromatography-Flame Ion Detection) is used for VOCs detection. Most of these precision testing instruments are large and expensive, which is not convenient for extensive spot monitoring. The detection of other pollutants such as sulfur dioxide, nitrogen oxides, ozone and carbon monoxide have similar problems. The special mobile monitoring vehicles need to park to monitor the air pollutants when they reach the designated location, which is equivalent to a fixed monitoring station and cannot be moved in real time for monitoring.
Air quality sensors are used in small monitoring sites and handheld devices to measure air pollutants. However, the air quality sensor itself may cause errors between the measured value and the actual value due to various reasons. Compared with large precision instruments or manual monitoring methods, air quality sensors have the characteristics of lower accuracy, poor stability, large errors, and frequent calibration.
The air pollution particulate matter sensors using the laser scattering method have a broad market prospect because of low cost and portability. However, the portable analysis device using the scattering method has disadvantages such as poor measurement consistency, high noise, and low measurement accuracy. The core components are easily affected by various environmental factors, and fluctuations can easily cause misjudgments.
In conclusion, the large monitoring sites and dedicated mobile monitoring vehicles in the existing monitoring technology are large in size and expensive, and are not convenient for wide-spread monitoring and cannot be moved in real time for monitoring. Small monitoring sites have the problems of low data accuracy and low reliability.
For roads and areas with high population density, the flow of traffic, especially taxis, is often dense, and such places need to be intensively monitored. The on-line monitoring system mounted on the vehicles is unstable, due to the wind speed and direction change while the vehicles are traveling, resulting in inaccurate measurement results.
When sensor data suddenly changes significantly, being able to intelligently determine whether the change is due to sensor failure or sudden pollution will greatly improve data reliability and is of great value for ensuring the quality of environmental monitoring data. When device fails, if it can be automatically repaired, the online rate of data can also be greatly increased, which is of great value for continuous monitoring required for haze control. Meanwhile, it can save manpower and material resources in device maintenance and reduce social waste.

SUMMARY

Terminology

Sensor: A sensor is a detection device that can sense the concentration information of pollutants and can convert the sensed information into electrical signals or other required forms of information output in accordance with a certain rule to meet the transmission, processing, storage, display, record, and control requirements. The pollutants here mainly include particulate matter (PM₁, PM_2.5, PM₁₀, PM₁₀₀), nitrogen oxides, sulfur dioxide, ozone, VOCs/TVOC and carbon monoxide.
Sub-sensor: It is also called sub-sensor unit. Here, the sub-sensor unit includes fan, sensing element, MCU, signal conversion element and signal amplification circuit. It can independently complete the collection and calculation of pollutant data which can also be transmitted to local for storage.
Sensor module: The sensor module is a sensor device composed of multiple sub-sensors. The sub-sensors are also called cores in the sensor module. For example, a sensor module composed of four sub-sensors is called a quad-core sensor, and a sensor module composed of five sub-sensors is also called a five-core sensor.
Abnormal fluctuation of sub-sensor: It means that the discrete degree of the measurement results of the sub-sensor during continuous measurement exceeds the normal range.
Abnormal drift of sub-sensor: It means that the average value of the measurement results of the sub-sensor during continuous measurement is offset from the actual value beyond the normal range.
Abnormal correlation of sub-sensor: It means that the correlation between the measurement results of the sub-sensor and other sub-sensors during the continuous measurement is lower than the normal range.
Abnormality of sub-sensor: Abnormal fluctuation of sub-sensor, abnormal drift of sub-sensor, and abnormal correlation of sub-sensor are all abnormalities of sub-sensor. Abnormal sub-sensor: Also called a faulty sub-sensor, it is a sub-sensor in which the abnormality of sub-sensor occurs.
The suspected abnormal sub-sensor: Also called suspected faulty sub-sensor, in the sensor module, the sub-sensor with the largest fluctuation or drift that not yet triggers the isolation condition; that is, the degree of fluctuation or drift cannot make it be regarded as the abnormality of sub-sensor. The suspected abnormal sub-sensor is the sub-sensor that is closest to be abnormal among the normal sub-sensors. For example, if the measured value deviates from the normal value by 20%, it is determined to be abnormal. Assuming that the sub-sensors No. 1, 2 and 3 deviate from the normal values by 5%, 6%, 16%, then we determine the sub-sensor No. 3 is a suspected abnormal sub-sensor.
Isolation: The case where the sub-sensor does not participate in the operation of the value uploaded by the control module is called sub-sensor isolation.
Isolation condition: The isolation condition is used to determine whether the suspected abnormal sub-sensor needs to be isolated, such as the value of the degree of dispersion in the abnormal fluctuation of sub-sensor, and the offset value of the abnormal drift of sub-sensor.
Recovery condition: The recovery condition is the basis for determining whether the sub-sensors in the isolation zone will resume operating. The standard of the recovery condition should be appropriately higher than the isolation condition, and there should be a difference of at least 10% from each other to avoid the newly recovered sub-sensors from being isolated again.
Rotational rest method: It is a kind of operating method of sub-sensors, which means that the sub-sensors start and stop operating alternately at intervals.
Data deterioration: It means that the range of sub-sensor value deviates from the normal value increases.
Accuracy: It refers to the degree to which the monitoring data conforms to the actual value.
Reliability: It refers to the degree of similarity and the degree of dispersion of continuous monitoring data. The higher the degree of similarity between data, the lower the degree of dispersion, and the higher the reliability of the data.
Floc prevention net: It prevents flocs in the air from entering the sensor to protect the sensor.
Windproof filter element: Also called windproof pipe, a tubular device used to reduce the influence of external wind speed changes on the monitoring data.
Gas inlet: The inlet of air to be sampled, located on the housing of the monitoring device.
Gas outlet: An outlet used to exhaust gas; it can be a dedicated gas outlet, or a gap on the device housing, or a cooling hole, a drain hole, etc., to achieve the function of exhaust.
Buffer tank: Also called gas distribution tank, a container that plays the role of gas distribution and/or gas buffering; the buffer tank has at least one gas inlet and at least one gas outlet; and it includes an intake buffer tank and an exhaust buffer tank.
Positioning module: Provide location information at the time of detection through GPS, Beidou, GLONASS, 4G and other positioning methods.
Transmission module: Send the data, geographic location information and time stamp information detected by the detection module to the data center.
Due to various reasons, for example, the performance of the sensor itself and the influence of external interference, there is often a large error between the measured value and the actual value of the air quality sensor. When the taxi is traveling, due to the continuous change of airflow, the sensor intake is unstable and the reliability of the output monitoring data is low. In the use of taxi roof lights for air pollution monitoring, reducing errors, improving accuracy, reducing external interference, and improving data accuracy and reliability are the current efforts.
There are also many ways to improve sensor accuracy.
The first method is to use a single high-cost and high-precision sensor, but the resulting problems are also obvious. In addition to the high cost problem, it is not possible to determine whether the sensor is abnormal through the data output by the sensor itself.
The second method is to use a dual-core sensor, which independently measures and outputs the results through two sub-sensors. This method can compare the output results of the two sub-sensors according to a certain judgment standard to determine whether the sub-sensors operate abnormally, but this method cannot determine which sub-sensor has an abnormality.
The third method is to use a triple-core sensor. By comparing the output results of the three sub-sensors, it is determined which sub-sensor has a problem and then isolate the sub-sensor. However, since the sensor module runs in dual-core mode after isolating a sub-sensor, there will be a problem that the abnormal sensor cannot be determined. Therefore, once one sub-sensor of the three-core sensor is abnormal, the reliability of the whole sensor module is greatly reduced.
FIG. 1 shows the operating state of the sub-sensor. The sub-sensor 100 indicates a normal sub-sensor. The sub-sensor 101 and the sub-sensor 102 are suspected abnormal sub-sensors. The sub-sensor 104 indicates an abnormal sub-sensor. In FIG. 2, 1U indicates a one-core sensor mode. When the sensor data is abnormal, it cannot be determined whether the sensor itself is faulty or the air quality is abnormal. 2U indicates a dual-core sensor module. When the dual-core sensor module has a sub-sensor output abnormality, it cannot determine which one is abnormal, so the entire module cannot operate normally once one sub-sensor of the dual-core sensor module is abnormal. By analogy, 3U indicates a three-core sensor module.
In view of the above-mentioned shortcomings, the disclosure provides a taxi roof light capable of air pollution monitoring with airflow stabilization ability. In the disclosure, the gas pollutant online monitoring device is placed into the taxi roof light or the lower part of the roof light, and the device is equipped with a buffer tank. This solves the problem of unstable and inaccurate data caused by the airflow disturbance and unstable gas intake when the taxi is travelling. The monitoring device comprises a sensor module. The sensor module is a sensor device composed of multiple sub-sensors (at least four sub-sensor units). Such a sensor module can complement data deviations, verify each other, and improve the reliability, consistency, accuracy and life of the sensor module.
A gas inlet and a gas outlet are formed on the roof light of the taxi. The gas inlet and the gas outlet may be openings formed for the monitoring device, and may also be cooling holes, drain holes, gaps and other openings on the housing.
In the disclosure, an intake buffer tank is provided between the gas inlet and the detection module, and the buffer tank functions to stabilize the airflow. Similarly, an exhaust buffer tank may also be provided between the detection module and the gas outlet. The design of the buffer tank allows the gas to flow through the multi-channel narrow space in the inner cavity of the buffer tank, which will greatly reduce the turbulence of the gas inertia zone in the buffer tank, and as the gain effect, the airflow through the buffer tank will maintain a relatively constant value, balancing the pressure difference between the gas inlet and the gas outlet.
The arrangement of floc prevention nets at the gas inlet and the gas outlet can prevent the unexpected matter from entering the monitoring device to damage the monitoring device. The arrangement of a windproof pipe inside the floc prevention net can stabilize the airflow and gas pressure, making the detection module more accurate.
As shown in FIGS. 3 and 4, 4U indicates a quad-core sensor module. When a sub-sensor is detected to have a suspected abnormality and the suspected abnormal sub-sensor is determined as an abnormal sensor, the sub-sensor is isolated. The quad-core sensor module is downgraded to a three-core sensor module, and the three-core sensor module can still operate normally. 5U indicates a five-core sensor module. When a sub-sensor is detected to have a suspected abnormality and the suspected abnormal sub-sensor is determined as an abnormal sensor, the five-core sensor module is downgraded to a quad-core sensor module, and the quad-core sensor module can still operate normally. This is applicable to the six-core sensor module, the seven-core sensor module and the multi-core sensor module.
The multi-core sensor system is installed in the roof light of the taxi. The multi-core sensor system comprises a gas distribution tank, a control module and a detection module. The gas distribution tank is used to distribute detected gas to each individual sub-sensor. The gas inlet of the gas distribution tank is connected to a gas sampling head, and the gas outlet of the gas distribution tank is connected to the gas inlet of each sub-sensor of the detection module. The detection module is a sensor module with four or more sub-sensors built in. The detection module is configured to detect the concentration of air pollutants. The control module is configured to receive, analyze and upload the data detected by the detection module, and supply power to the detection module.
The sub-sensor types include: PM1 sensor, PM2.5 sensor, PM10 sensor, PM100 sensor, sulfur dioxide sensor, nitrogen oxide sensor, ozone sensor, carbon monoxide sensor, VOCs sensor, TVOC sensor and other sensors that can measure the concentration of environmental pollutants.
The accuracy of the sensor is related to many factors, such as the detected gas flow rate and temperature. The disclosure further improves the accuracy of the sensor module by designing in various ways.
The accuracy of the sensor is related to temperature. As shown in FIGS. 8A-8B, the sensor has an optimal operating temperature range. When the temperature is higher than the optimal operating temperature, the accuracy of the sensor will decrease. In the disclosure, the temperature of the sensor and the intake gas are adjusted by a temperature control device, and can be compensated by an algorithm to improve the detection accuracy.
The accuracy of the sensor is also related to the flow rate of the detected gas flowing inside the sensor. As shown in FIG. 9, the detected gas has the highest accuracy at the optimal flow rate V₀. Too high or too low flow rate will affect the detection accuracy. The internal wind resistance of the sensor or other reasons will cause the detected gas flow rate to change, as shown in FIG. 10. In the disclosure, the detected gas flow rate is controlled within the optimal flow rate range by adjusting the internal fan speed or by other flow rate adjustment methods to improve the accuracy of the sensor.
The multi-core sensor uses embedded algorithms to solve the problem of out-of-synchronization of multiple sub-sensors in detecting the sampled gas due to different lengths of intake pipes, thereby obtaining more accurate detection data.
The multi-core sensor uses multiple sensors to measure air quality at the same time, and the output value is the average value of the multiple sensors, with high data accuracy. FIG. 5 shows the output data of the quad-core sensor module, where U1, U2, U3, and U4 are the output data of the four sub-sensors, and the solid line Average is the average value of the four sensors, so the data is smoother, more stable, and more accurate.
The performance of the laser sensor is affected by light attenuation of laser. After the semiconductor laser is used for a long period of time, the problem of optical power attenuation due to semiconductor materials and production processes will occur. When the optical power attenuation reaches a certain level, the accuracy of the detection data of the sensor will be affected.
In order to understand the degree of light attenuation of a group of laser sensors after operating for a long period of time, in the disclosure, the sensor group is divided into a high-frequency group and a low frequency group. The low frequency group serves as a redundant unit to provide a calibration basis for the high-frequency group.
The disclosure discloses another multi-core sensor system, which is installed in a roof light of a taxi. The multi-core sensor system comprises a control module and a detection module. The detection module comprises a sensor module comprising at least two sub-sensor units of a same type. The sub-sensor units operate at a normal operating frequency. The detection module further comprises at least one low-frequency calibration module comprising sub-sensor units of a same type as the sensor module. The sub-sensor units in the low-frequency calibration module operate at an operating frequency much lower than the operating frequency of the sub-sensor units in the sensor module. Therefore, the low-frequency calibration module is also called a low-frequency group. For comparison, the sensor module is also called a high-frequency group.
Generally, the operating frequency of the sensor module is 10 times or more than that of the low-frequency calibration module. The ratio of the operating frequency of the high-frequency group to the operating frequency of the low-frequency group is called the high and low frequency ratio which may be set as 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1, 10:1, 15:1, or 20:1.
The operating frequency of the low-frequency group may be consistent with the rhythm of abnormal judgment. That is, when it is necessary to determine whether there is an abnormal sub-sensor in the sensor module, the low-frequency group performs the detection.
Because the laser power attenuation is slow in most of the operating life of the laser sensor, the accuracy of its data can be restored by calibration; that is, a sub-sensor that is not attenuated or that has a very low attenuation is used to calibrate the highly-attenuated sub-sensor.
During the operation of the sensor module, every certain time, for example one day, one week, or one month, the detection data of the high-frequency group is calibrated by using the detection data of the low-frequency group as a reference, and the calibration coefficient may be obtained by the ratio of the average value of the detection data of the high-frequency group to the average value of the detection data of the low-frequency group.
In addition to the light attenuation effect of laser sensors, other types of sensors may also have a tendency of unstable performance or increased data errors under long-term high-load operating conditions. By introducing a low-frequency group, it may be used as a relatively reliable reference to determine whether there is a data offset in the sensor module.
Meanwhile, since the data of the low-frequency group is generally more reliable, when determining which sub-sensor unit in the sensor module is suspected to be abnormal or is abnormal, a more reliable judgment may be made by increasing the data weight of the low-frequency group. A simple solution is that all data from the low-frequency group participates in the judgment of suspected abnormalities with twice weight.
The low-frequency group may also participate in the judgment of suspected abnormalities by adopting the following solutions to distinguish situations:
1) Single low-frequency sensor: The data weight of the low-frequency sensor is 2; and the data weight of each sub-sensor unit in the sensor module is 1;
2) Two low-frequency sensors: By using the average value of the sensor module as the reference, the data weight of a low-frequency sensor closer to the reference value is 2, and the weight of the other low-frequency sensor is 1.
3) Three or more low-frequency sensors: By using the average value of the data from the low-frequency group as the reference, the sensors that deviate farthest from the reference in the high-frequency group are suspected abnormalities.
When the control module detects a suspected abnormality in one of the sub-sensor units in the sensor module and determines that the suspected abnormal sub-sensor is an abnormal sub-sensor, the abnormal sub-sensor is isolated and classified into an isolation zone, and the multi-core sensor module continues to operate normally after it is downgraded.
The disclosure also discloses a method for identifying the operating state of the sub-sensor and isolating and recovering the sub-sensor. The method is shown in FIG. 11. The sensor module obtains a set of detection data at a moment of time, and the control module filters out suspected abnormal data from this set of data and then determines whether the corresponding sub-sensor meets the isolation condition. If the sub-sensor is determined as an abnormal sub-sensor, the abnormal sub-sensor is classified into the isolation zone; and if it is determined that the suspected abnormal sub-sensor does not meet the isolation condition, the sub-sensor continues to operate normally. It is determined whether the sub-sensor classified into the isolation zone can self-heal. If the sub-sensor can heal itself, the sub-sensor operates at a lower frequency, but the data output by the sub-sensor does not participate in the calculation of the output data of the control module. For sub-sensors that cannot self-heal, the sub-sensors stop operating, and the operator is notified to repair or replace them. For the sub-sensor that operates at a lower frequency, the control module detects its output data to determine whether it meets the recovery condition. The sub-sensor that meets the recovery condition is released from the isolation zone to resume operating. The output data participates in the calculation of sensor module data or control module data. For the abnormal sub-sensor that does not meet the recovery condition, whether it can be self-healing is determined again.
After isolating the abnormal sub-sensors in the sensor module, the average value of the remaining sub-sensor output data is used as the output result of the sensor module, and the sensor module can continue to be used normally.
The disclosure sets a rotational rest mode for the sensor module. Among the sub-sensors that operate normally, one or more sub-sensors are selected for rotational rest. This can solve the problem of reduced performance due to sensor fatigue.
With the increase of operating time, the internal state of the sensor will change to a certain extent. For example, the internal temperature will increase with the increase of operating time, and the mechanical components of the sampling device will suffer from metal fatigue. Therefore, an appropriate rest after operating for a period of time will recover the sensor to its optimal operating state.
The sensor enters the stable operating period after starting for a period of time, but after a long period of continuous work, the fatigue will increase. In order to alleviate this situation, those sub-sensors that have entered a fatigue state may be put into a rest state to reduce the data offset in the sensor fatigue stage, and try to make the sensor unit operate in a stable operating period.
For the laser sensor module, the rotational rest can also keep the light attenuation of sensors in a same group basically synchronized.
With the use of semiconductor lasers for a long period of time, there will be a problem of attenuation of the light output power due to the decrease in the efficiency of semiconductor materials. When a semiconductor laser is used as a light emitting element, the light scattering emission particulate matter sensor needs to consider the light attenuation synchronization between sub-sensors when it comprises multiple sub-sensors.
If the light attenuation between the sub-sensors is not synchronized, when the light attenuation is low, its impact on the data is relatively small. Although there will be some differences in the data of sensors, it is impossible to determine whether the sub-sensor is faulty based on these small differences. But these data will still participate in the calculation of the final detection data of the sensor and result in deviations in the final detection data.
Therefore, the control module of the multi-core sensor system should record and store the cumulative operating time of each sub-sensor, adjust the rotational rest interval of each sub-sensor according to the cumulative operating time, and keep the light attenuation of each sub-sensor basically synchronized, which is conducive to the improvement of sensor detection data accuracy.
The disclosure has low use cost. Compared with expensive precision instruments, the sensor module only adds a few sub-sensors, which does not significantly increase the overall cost of the device. However, Due to the increase in reliability and accuracy, it is also possible to apply low-precision, low-reliability, low-cost sensors to situations where only high-precision instruments can be used. The multi-core sensor module also extends the life and maintenance cycle of the entire monitoring device, thereby reducing the cost of device replacement and repair.
Sensor failure judgment may be done by the local control module, or by the data center online monitoring system. The online monitoring system is responsible for receiving, storing and processing the data, and generating visual pollution cloud maps.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a state of a sub-sensor.

FIG. 2 is a schematic diagram of a single-core sensor module, a dual-core sensor module and a triple-core sensor module, and a single sub-sensor failure in the triple-core sensor module.

FIG. 3 is a schematic diagram of determining a suspected abnormal sub-sensor module. For one-core and dual-core sensor modules, abnormal conditions cannot be determined after suspected abnormalities occur; and for sensor modules with three or more cores, sensors suspected of abnormalities can be determined.

FIG. 4 is a sensor error diagram, Do and Di are fluctuations; there is a drift between Do and the actual value.

FIG. 5 is a schematic diagram of the output data of the quad-core sensor module and its sub-sensors, Average is the average output result of the four cores, and the dotted line is the output result of each core;

FIG. 6 is a schematic diagram of an isolation method for an abnormality of a sub-sensor of a six-core sensor module.

FIG. 7 is a schematic diagram of isolation and recovery of an abnormal sub-sensor in a quad-core sensor module.

FIGS. 8A-8B are schematic diagrams of the relationship between the accuracy of the sensor and the temperature.

FIG. 9 is a schematic diagram of the relationship between the accuracy of the sensor and the detected gas flow rate.

FIG. 10 is a schematic diagram of the relationship between fan speed, wind resistance and detected gas flow rate.

FIG. 11 is a flowchart of a method for isolation and recovery of a multi-core sensor system.

FIG. 12 is a schematic structure diagram of a six-core sensor.

FIG. 13 is a schematic diagram of a quad-core sensor and its fault indicator.

FIG. 14 is a flowchart of isolation and recovery of a high- and low-frequency multi-core sensor module.

FIG. 15 is a schematic diagram of a preferred solution and related system composition.

FIG. 16 is a schematic diagram of a preferred solution for adding an exhaust buffer tank.

FIG. 17 is a schematic diagram of a monitoring roof light with a simplified exhaust buffer tank and an additional airflow stabilization device.

FIG. 18 is a schematic diagram of a roof light, with the fan arranged in the rear.

FIG. 19 is a schematic diagram of the structure composition of a taxi roof light in the form of an intake pump.

FIG. 20 is a schematic diagram of an external air pump module.

FIG. 21 is a schematic view of an improved external air pump module, added with a flow regulating valve.

FIG. 22 is a schematic diagram of a device module holder.

FIG. 23 is a schematic diagram of a buffer tank with flexible materials used in part of the tank body.

FIGS. 24A-24B are schematic diagrams of the buffer tank.

FIG. 25 is a schematic structure diagram of using a semiconductor refrigeration sheet to heat the intake air while cooling the air pump.

In the drawings: 100. Normal sensor; 101. Suspected abnormal sub-sensor (one); 102. Suspected abnormal sub-sensor (two); 104. Abnormal sub-sensor; U3. Sensor No. 3; U3. d-status indicator (red-fault); U4. d-status indicator (green-normal); 2U (3U): represents a group of three-core sensors operating in two-core mode, with one core isolated; 2. Gas inlet; 4. Gas outlet; 10. Housing of the taxi roof light; 11. Positioning module; 12. Intake buffer tank; 13. Detection module (with an airflow driving device); 15. Control module; 16. Transmission module; 17. Flow sensor; 18. Flow control valve; 19. External air pump module; 20. Data center; 30. Fixed monitoring site; 40. User; 130. Detection module (without an airflow driving device); 131. External fan; 191. Filter; 192. Air pump; 193. Muffler; 194. Air pump flow control valve; 195. Semiconductor refrigeration sheet; 1010. Bottom case of the taxi roof light; 1040. Module holder; 111. Windproof pipe; 112. Floc prevention net; and 121. A part of the buffer tank body.

DETAILED DESCRIPTION

The multi-core sensor system comprises a gas distribution tank, a control module and a detection module. The air distribution tank is used to distribute the detected gas to each individual sub-sensor. The gas inlet of the gas distribution tank is connected to a gas sampling head, and the gas outlet of the gas distribution tank is connected to the gas inlet of each sub-sensor of the detection module. The detection module comprises a sensor module with four or more sub-sensors of a same type built in. The detection module is configured to detect the concentration of air pollutants. The control module is configured to receive, analyze and upload the data detected by the detection module to the data center, and supply power to the detection module. The gas inlet of the gas distribution tank is connected to the sampling head, and the gas outlet thereof is connected to the gas inlet of the detection module. The gas distribution tank has a buffer function to relieve pressure fluctuations.
The detection module further comprises at least one low-frequency calibration module comprising sub-sensor units of a same type as the sensor module. The sub-sensor units in the low-frequency calibration module operate at an operating frequency much lower than the operating frequency of the sub-sensor units in the sensor module. In a multi-core sensor system comprising a low-frequency calibration module, the sensor module may comprise two or three sensor units.
The control module is provided with a control module data communication interface which is connected to a sensor data communication interface by wires. The sensor transmits data to the control module through the control module data communication interface connected to the sensor. The detection module is connected to the control module through a data interface. The control module can not only process the detection data of the sub-sensors, but also implement the data uploading and positioning functions. The control module can upload the data to the data center through the wireless network. The data center is responsible for receiving, storing and processing the data. The online monitoring system in the data center can manually control the secondary calibration of the abnormal sub-sensor.
In the disclosure, a sensor module comprises multiple sub-sensor units, which realizes complementary data deviations and mutual verification, and improves the reliability, consistency, accuracy and life of the sensor module. As shown in FIGS. 3 and 4, 4U indicates a quad-core sensor module. When a sub-sensor is detected to have a suspected abnormality and the suspected abnormal sub-sensor is determined as an abnormal sensor, the sub-sensor is isolated. The quad-core sensor system is downgraded to a three-core sensor system, and the three-core sensor system can still operate normally. 5U indicates a five-core sensor module. When a sub-sensor is detected to have a suspected abnormality and the suspected abnormal sub-sensor is determined as an abnormal sensor, the five-core sensor system is downgraded to a quad-core sensor system, and the quad-core sensor system can still operate normally. This is applicable to the six-core sensor module, the seven-core sensor module and the more-core sensor module.
Humidity and Temperature Adjustment
The accuracy of the sensor is related to temperature. As shown in FIGS. 8A-8B, the sensor has an optimal operating temperature range. When the temperature is higher than the optimal operating temperature, the accuracy of the sensor will decrease. In the disclosure, the intake humidity and the intake temperature of the sensor are adjusted by a humidity control device.

Example 1

The gas distribution tank may be equipped with a semiconductor refrigeration sheet made of metal and capable of heating and dehumidifying. The semiconductor refrigeration sheet comprises a hot end and a cold end. The gas distribution tank is directly heated by the hot end of the semiconductor refrigeration sheet. A humidity sensor is mounted before the gas inlet of the gas distribution tank. The system turns on the semiconductor refrigeration sheet when the humidity of the gas measured by the humidity sensor is greater than the set upper limit (the set upper limit may be 60%, 65%, 70%, etc.); and the system turns off the semiconductor refrigeration sheet when the humidity is less than the set lower limit (the set lower limit may be 40%, 50%, etc.).

Example 2

The gas distribution tank may be equipped with a semiconductor refrigeration sheet made of metal and capable of heating and dehumidifying. The semiconductor refrigeration sheet comprises a hot end and a cold end. The gas distribution tank is directly heated by the hot end of the semiconductor refrigeration sheet. The cold end of the semiconductor refrigeration sheet is connected to a heat dissipation grille. The heat energy is absorbed by the heat dissipation grille and transferred to the gas distribution tank. A humidity sensor is mounted before the gas inlet of the gas distribution tank. The system turns on the semiconductor refrigeration sheet when the humidity of the gas measured by the humidity sensor is greater than the set upper limit (the set upper limit may be 60%, 65%, 70%, etc.); and the system turns off the semiconductor refrigeration sheet when the humidity is less than the set lower limit (the set lower limit may be 40%, 50%, etc.).

Example 3

The gas distribution tank may be equipped with a semiconductor refrigeration sheet made of metal and capable of heating and dehumidifying. The semiconductor refrigeration sheet comprises a hot end and a cold end. The gas distribution tank is directly heated by the hot end of the semiconductor refrigeration sheet. The cold end of the semiconductor refrigeration sheet is connected to an air pump to cool the air pump. A humidity sensor is mounted before the gas inlet of the gas distribution tank. The system turns on the semiconductor refrigeration sheet when the humidity of the gas measured by the humidity sensor is greater than the set upper limit (the set upper limit may be 60%, 65%, 70%, etc.); and the system turns off the semiconductor refrigeration sheet when the humidity is less than the set lower limit (the set lower limit may be 40%, 50%, etc.).
Compensation of Flow Rate, Temperature, Power and Pipeline Length
The accuracy of the sensor is also related to the flow rate of the air to be measured flowing inside the sensor. As shown in FIG. 9, the detected gas has the highest accuracy in a flow rate range of V₁to V₂centered on the optimal flow rate V₀. Too high or too low flow rate will affect the accuracy. The internal air resistance of the sensor or other reasons will cause the detected gas flow rate to change, as shown in FIG. 10. In the disclosure, the detected gas flow rate is controlled within the optimal flow rate range by adjusting the internal fan speed (S₁, S₂) or by other flow rate adjustment methods to improve the accuracy of the sensor. The multi-core sensor uses embedded algorithms to solve the problem of out-of-synchronization of multiple sub-sensors in detecting the sampled gas due to different lengths of intake pipes, thereby obtaining more accurate detection data. Similarly, temperature and humidity are compensated by corresponding algorithms to improve data accuracy.

Example 4

By controlling the speed of the fan, the sampling flow is compensated. A flow rate sensor and a differential pressure sensor are used to obtain the flow rate of the sampled gas, and a fan speed control circuit is added. By controlling the fan speed by the obtained gas flow rate information, the flow rate of the sampled gas is stabilized at a value appropriate to the sensor, for example V₀in FIGS. 9 and 10. An empirical value of the optimal flow rate of the sensor is experimentally obtained.

Example 5

For the laser particulate matter sensor, a laser power detection device and a laser power control circuit are added, which are configured to compensate for the laser power. The change relationship of the particulate matter concentration corresponding to each laser power value is obtained experimentally (that is, other conditions are fixed and only the measured conditions are changed to obtain the measurement results). The attenuated data is compensated by the laser power control circuit according to the detection result of the laser power detection device.

Example 6

The temperature of the sensor is compensated. A temperature acquisition probe is installed at the sensor or for detected gas. The change relationship of the pollutant concentration values corresponding to different sampling temperature values is obtained experimentally or by temperature characteristic data of the sensor (that is, other conditions are fixed and only the temperature conditions of the detected gas are changed). When in use, the output pollutant results are compensated based on the collected temperature data.

Example 7

The humidity of the sensor is compensated. A humidity acquisition device is installed to acquire humidity of the detected gas. The change relationship of the pollutant concentration values corresponding to different sampling humidity values is obtained experimentally or by humidity characteristic data of the sensor (that is, other conditions are fixed and only the humidity conditions of the detected gas are changed). When in use, the output pollutant results are compensated based on the collected humidity data.
Output Data Calculation Method
The multi-core sensor uses multiple sensors to measure air quality at the same time, and the output value is the result of comprehensive calculation of data from the multiple sensors. The data is smoother, more stable, and more accurate. The eighth to twelfth embodiments describe data calculation methods of the sensor module. The data of abnormal sub-sensors needs to be eliminated during data calculation. For the method of determining an abnormal sub-sensor, refer to the thirteenth to seventeenth embodiments.
In the presence of both the high-frequency group and the low-frequency group, when the low-frequency group generates data, its data can be used as more reliable detection data to participate in the calculation of the output data of the sensor module.
Considering that the data of the low-frequency group is more reliable, the data of the low-frequency group can be given a double weight to participate in the calculation.

Example 8

Average value method: A method for calculating the output data of a sensor module. After eliminating data of the abnormal sub-sensor units, the average value of data of all normal sub-sensor units is used as the output result.

Example 9

Median method: A method for calculating the output data of a sensor module. After eliminating data of the abnormal sub-sensor units, the values of all normal sub-sensor units are sorted, and the median is used as the final result. If there is an even number of sub-sensor units that are sorted, and then the average value of the two sub-sensor units in the middle is used as the final result.

Example 10

Correlation coefficient method: A method for calculating the output data of a sensor module. After eliminating data of the abnormal sub-sensor units, data of all normal sub-sensor units is calculated as follows to obtain the final result.
The storage unit stores the historical detection data of each sub-sensor unit. A correlation coefficient between the values of the determined sub-sensor unit and other sub-sensor units is calculated by using the historical data within a period of time (1 minute, 10 minutes, 20 minutes, . . . 1 hour) as a time unit. The correlation coefficient is calculated as follows:
A. The correlation coefficient between the value of the determined sub-sensor within a selected historical time unit and the average value of other sub-sensor units in that period is calculated.
B. A correlation coefficient between the value of the determined sub-sensor within a selected historical time unit and the value of each sub-sensor unit is calculated. After obtaining the correlation coefficients, the average value of correlation coefficients is calculated as the final correlation coefficient. After obtaining a correlation coefficient between each normal sub-sensor unit and other sub-sensor units, the percentage of the correlation coefficients of all normal sub-senor units to the sum of the total correlation coefficients is calculated. The detection result of each normal sub-sensor unit is multiplied by this percentage and then added up to obtain the final detection result.

Example 11

Variance method: A method for calculating the output data of a sensor module. After eliminating data of the abnormal sub-sensor units, data of all normal sub-sensor units is calculated as follows to obtain the final result.
The memory stores the historical detection data of each sub-sensor unit. By using the historical data of a period of time (1 minute, 10 minutes, 20 minutes, . . . 1 hour) as the time unit, the variance Vi (or standard deviation) of each sub-sensor unit in that time unit is calculated. The variances of the sub-sensor units are added up. The difference between the sum and the variance of each sub-sensor unit is calculated. After obtaining the difference, the percentage of the difference of each sub-sensor unit to the sum is calculated. The detection result of each normal sub-sensor unit is multiplied by the percentage and then added up to obtain the final detection result.

Example 12

Percentage method: a method for calculating the output data of a sensor module. After eliminating data of the abnormal sub-sensor units, data of all normal sub-sensor units is calculated as follows to obtain the final result.
The sensor stores the historical detection data of each sub-sensor unit. By using a period of time (10 seconds, 20 seconds, . . . ) as the time unit, an average value of the detection values in the nearest time unit is calculated, and the average value is used for the calculation. The calculation method is as follows:
A. The average values of the sub-sensor units in the time unit are added up, the percentage of each sub-sensor unit to the sum is calculated, and the detection result of each normal sub-sensor unit is multiplied by the percentage to obtain the final detection result.
B. By the calculation method described in part A in Embodiment 12, the percentage of each sub-sensor unit in multiple nearest time units is calculated, the percentages of each sub-sensor unit in the multiple time units are averaged to obtain the average percentage of each sub-sensor unit in the multiple nearest time units, the detection result of each normal sub-sensor unit is multiplied by the percentage and then added up to obtain the final detection result.
Identification of Sub-Sensor Operating Status
The disclosure discloses a method for identifying the operating state of the sub-sensor and isolating and recovering the sub-sensor. This method is shown in FIG. 11.
1) Judgment of the abnormal sub-sensor: The sensor module obtains a set of detection data at a moment of time, and the control module filters out suspected abnormal data from this set of data, and then determines whether the corresponding sub-sensor meets the isolation condition.
2) Isolation of an abnormal sub-sensor: The sub-sensor was determined as an abnormal sub-sensor and then classified into the isolation zone; the sensor module continues to operate after it is downgraded. The sub-sensor classified into the isolation zone can stop operating or continue sampling and detection, but the data output by this sub-sensor does not participate in the calculation of the output data of the control module.
3) It is determined whether the sub-sensor classified into the isolation zone can self-heal. If it is determined that it can self-heal, the sub-sensor that can self-heal operates at a lower frequency. For sub-sensors that cannot self-heal, the sub-sensors stop operating, and the operator is notified to repair or replace them.
4) Recovery of abnormal sub-sensors: the data output by the sub-sensors classified into the isolation zone is monitored to determine whether they have reached the recovery conditions. If the recovery conditions are met, the sub-sensors that meet the recovery conditions are released from the isolation zone to resume operating.

Example 13

Judgment of suspected abnormal sub-sensor and abnormal sub-sensor: When the variance of the data of a certain sub-sensor exceeds the threshold, or when the drift of the data of the sub-sensor exceeds the threshold, first, it is considered as a suspected abnormal sub-sensor instead of immediately identifying the sub-sensor as abnormal. Finally, it is determined that the sub-sensor is abnormal only when multiple consecutive data are abnormal in a certain period of time.

Example 14

Comparison method of average values of sub-sensors: By taking a quad-core sensor module as an example and using the current moment of time as a reference, the data of one sub-sensor is compared with the average value of other three sub-sensors within a certain period of time (such as 5 s in average, 30 s in average, 60 s in average, etc.).

Example 15

When the sub-sensor abnormality occurs, the data output by the abnormal sub-sensor is isolated and does not participate in the calculation of the final output data of the sensor module. However, the abnormal sub-sensor still outputs data to the control module that monitors the data of the abnormal sub-sensor. The sub-sensor abnormality comprises abnormal drift of sub-sensor, abnormal fluctuation of sub-sensor and abnormal correlation of sub-sensor. The storage unit stores the historical detection data of each sub-sensor. A correlation coefficient between the values of the determined sub-sensor unit and other sub-sensor units is calculated by using the historical data within a period of time (1 minute, 10 minutes, 20 minutes, . . . 1 hour) as a time unit. If the correlation coefficient is less than a certain value, such as 0.5 (non-strong correlation), the correlation of the sensor is determined to be abnormal, and it does not participate in the calculation of the final result. The specific process of calculating the correlation coefficient is as follows:
A. The value of the determined sub-sensor within a selected historical time unit and the average value of other sub-sensor units in that period are used to calculate the correlation coefficient.
B. A correlation coefficient between the value of the determined sub-sensor within a selected historical time unit and the value of each sub-sensor unit is calculated. After obtaining the correlation coefficients, the average value of correlation coefficients is calculated as the final correlation coefficient.
The correlation method is used to determine the abnormal correlation of sub-sensor. Taking the correlation calculation of a quad-core sensor module as an example, the correlation between 100 sets of data of a sub-sensor and the average value of 100 sets of data of the other three sub-sensors is calculated. When R2 is less than or equal to 0.8, it indicates that the correlation of the sub-sensors is abnormal and the sub-sensor data is isolated. The sensor module selects the data of the other three sub-sensors to calculate and output the monitoring results.

Example 16

The sixteenth embodiment is a method for determining the abnormal fluctuation of sub-sensor. The sensor stores the historical detection data of each sub-sensor. By using the historical data of a period of time (1 minute, 10 minutes, 20 minutes, . . . 1 hour) as the time unit, the variance (or standard deviation) of each sub-sensor unit in that time unit is calculated. The variance (or standard deviation) of the determined sub-sensor is compared with the variances (or standard deviations) of other sub-sensors. The variance comparison method is as follows:
A. The variance (or standard deviation) of the determined sub-sensor unit with the average value of the variances (or standard deviations) of other sub-sensor units, respectively. If the difference between them exceeds the average value of the variances (or standard deviations) of other sub-sensor units by a certain value, such as 20%, 30%, etc., the sub-sensor unit is determined to have abnormal fluctuation.
B. The variance (or standard deviation) of the determined sub-sensor unit is compared with the variances (or standard deviations) of other sub-sensor units, respectively, and the percentage of the difference between them to the variance (or standard deviation) of the compared sub-sensor unit is calculated. The maximum value of percentage is selected. If the maximum value exceeds a certain value, such as 20%, 30%, etc., the sub-sensor is determined to have abnormal fluctuation.

Example 17

The seventeenth embodiment is a method for determining the abnormal drift of sub-sensor. The difference between the average values of the determined sub-sensor unit in the past two adjacent time units is calculated, and the percentage of the difference to the average value in the nearest time unit is calculated, and the percentage is used for judgment. The drift determination method is as follows:
A. The percentage obtained for the determined sub-sensor unit is compared with the average value of the percentages obtained for other sub-sensor units. If the difference between the percentages exceeds a certain value, such as 20%, 30%, 40%, etc., the sub-sensor unit is determined to have abnormal drift.
B. The percentage obtained for the determined sub-sensor unit is compared with the maximum value of the percentages obtained for other sub-sensor units. If the difference between the percentages exceeds a certain value, such as 20%, 30%, 40%, etc., the sub-sensor unit is determined to have abnormal drift.

Example 18

In the case of the need to isolate the abnormal sub-sensor, the data of the abnormal sub-sensor is isolated, but the fan or air pump of the abnormal sub-sensor continues running, to ensure that the wind pressure and flow are constant, and to reduce pressure fluctuation.

Example 19

As shown in FIG. 13, a status indicator is installed on the sub-sensor. After the abnormal sub-sensor is identified, the status indicator at the corresponding position on the communication port of the circuit board will change to a warning color (such as red). The status indicator corresponding to the sub-sensor in normal operating state is green.
Rotational Rest Mode
The disclosure sets a rotational rest mode for the sensor module. Among the sub-sensors that operate normally, one or more sub-sensors are selected for rotational rest. The fatigue problem of the sub-sensor is solved by actively downgrading the operation. For the laser sensor module, the rotational rest can also keep the light attenuation of sensors in a same group basically synchronized.
Common single-rotational rest conditions include:
1) the sub-sensor with the longest time to enter the fatigue state;
2) the sub-sensor closest to entering the fatigue state;
3) the sub-sensor with the longest accumulated operating time;
4) the sub-sensor with the least accumulated rotational rest;
5) when the temperature data of the sub-sensor can be obtained, the sub-sensor with the highest temperature;
6) suspected abnormal sensor.
The sub-sensors selected by using different rotational rest conditions may be inconsistent. In actual application, multiple rotational rest conditions may be given weights or priorities to quantitatively determine which sub-sensor is allowed to enter the rotational rest.
Considering that the fatigue problem is a periodic recurrence problem, ideally, each sensor should get a rest cycle before it enters the fatigue state. Assume that the average stable operating time of the sub-sensors is T, then, for the module of N sensor units, if the strategy of successive rotational rest of each sub-sensor in the sensor module is adopted, the interval between the two consecutive rotational rests should not be longer than T/N to ensure that each sensor can enter the rotational rest in time.
If T=8 hours, the sensor module comprising four sensor units can be rotated every 2 hours using the sequential rotational rest strategy, which can ensure that each sensor can enter the rotational rest state before entering the fatigue state.
A status indicator is installed on the sub-sensor unit. When an abnormal sub-sensor is identified, the status indicator at a position corresponding to the sub-sensor changes to a warning color; and the status indicator corresponding to the sub-sensor in normal operating status is continuous green. The status indicator corresponding to the sub-sensor that enters the rotational rest state is green that turns on and off alternately.

Example 20

The twentieth embodiment is a rotational rest mode of a sub-sensor. For a sensor module, rotational rest refers to turning off the sensing part of one or more sub-sensors within a specified time. For example, for a laser particulate matter sensor module using a fan, only the laser is turned off and the fan is not turned off. The off time of the sub-sensor can be a fixed time (such as 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 11 hours, 12 hours, 24 hours, 2 days, 3 days, 4 days, 5 days, 6 days, or 7 days, etc.). After the turned-off sub-sensor reaches the off time, the turned-off sub-sensor is activated, and then the next sub-sensor that meets the rotational rest condition is turned-off. The off time may also be determined according to the operating status of other sub-sensors. For example, in a quad-core sensor module with one sub-sensor in the off state, if the system determines that one of the three sub-sensors in operation has met the isolation condition and needs to be isolated, the sub-sensor in the turned-off state should be immediately enabled. The specific rotational rest conditions may be as follows:
A. The rotational rest sub-sensor is selected based on the change in temperature. Form 1: The sub-sensor with the highest temperature is selected based on the acquired sub-sensor temperature data. Form 2: The sub-sensor to be turned off is selected according to the ambient temperature. If the ambient temperature is higher than the temperature set value (for example 40 degrees Celsius), the sub-sensors will be turned off in turn according to their number.
B. The rotational rest sub-sensor is selected by detecting the change in the value. For a confirmed suspected abnormal sub-sensor, it is turned off preferentially.

Example 21

When three or more sub-sensors in the quad-core sensor module operate normally, a single-core rotational rest solution may be adopted. The operating state of the sensor is greatly affected by temperature. When the temperature is higher than 60° C., or after four hours of normal operation, the adjacent single-core cycle rest is changed, and the rest is rotated in order to reduce the operating time of the sub-sensor under high temperature and increase the operating time limit of the quad-core sensor.

Example 22

The twenty-second embodiment is shown in FIG. 15. The solution comprises a gas inlet, an intake buffer tank, a detection module, a gas outlet, a control module, and a transmission module.
The gas inlet is connected to the gas inlet of the intake buffer tank, the gas outlet of the intake buffer tank is then connected to the gas inlet of the detection module, and the gas outlet of the detection module is connected to the gas outlet. The intake buffer tank can stabilize the airflow, and can act as a gas distribution device according to the number of sensors or sensor groups to divide the gas into multiple airflows. That is to say, the number of airflow distribution outlets of the intake buffer tank matches the number of sensor units in the detection module.
The flow direction of the detected gas is that the detected gas enters the monitoring device through the gas inlet, flows through the intake buffer tank, the detection module, and the gas outlet and finally exits the monitoring device.
The detection of air pollutant concentration is performed by the detection module. The sensor unit of the detection module can comprise a PM1 sensor, a PM2.5 sensor, a PM10 sensor, a PM100 sensor, a sulfur dioxide sensor, a nitrogen oxide sensor, an ozone sensor, a carbon monoxide sensor, a TVOC sensor or a VOCs sensor. The use of detection modules may be flexibly matched according to requirements, for example using one or more sensors, one or more groups of sensors (sensor modules), one or more types of sensors, etc. A specific sensor module may be a group consisting of one PM2.5 sensor. Another specific sensor module may be a group consisting of four PM2.5 sensors. Another specific sensor module may be a group consisting of three PM2.5 sensors and one PM100 sensor. Another specific sensor module may be a group consisting of four PM2.5 sensors and one sulfur dioxide sensor. The particulate matter sensor may be a multi-channel type, that is, a particulate matter sensor can measure multiple PM values at the same time, such as PM2.5 and PM10. A sensor module using multi-channel particulate matter sensors may comprise four multi-channel particulate matter sensors (of types that can measure PM2.5 and PM10 at the same time). Another sensor module using multi-channel sensors may comprise three multi-channel sensors.
The control module is electrically connected to the detection module and the transmission module on the monitoring device, and the electrical connection can be used for both power supply and data transmission. The control module exchanges data with the detection module and the transmission module through the data interface. The detection module sends the detected data to the control module. After the control module performs further calculations, the control module sends the data to the transmission module, and the transmission module sends the data to the data center. The transmission module can receive instructions from the data center. After transmitting the instructions to the control module, the control module can adjust the operation of the detection module. The control module is equipped with data storage and local data transmission interfaces. The control module may have a positioning function or a data interface with a positioning device, to record the vehicle position in real time by GPS, Beidou and other positioning technologies.
The control module is connected to the 12V, 5V, 24V, 36V or 48V DC power supply of the taxi, and the control module supplies power to the detection module and the transmission module.
The air pollutant online monitoring device in the twenty-second embodiment may be placed in a specially designed housing, and the housing with the air pollutant online monitoring device placed therein may be hung outside the lower part of the roof light of the taxi or other parts.
FIG. 16 is a variant of the above solution. An exhaust buffer tank is added between the detection module and the gas outlet to stabilize the airflow in the exhaust part and improve the detection accuracy of the detection module.

Example 23

The twenty-third embodiment is shown in FIG. 17. The solution comprises a gas inlet, a gas outlet, a floc prevention net, a windproof pipe, an intake buffer tank, a detection module, a positioning module and a transmission module. The detection module comprises an active airflow driving device. These devices are installed inside the roof light of the taxi. In the twenty-third embodiment, the remaining space (vacant space in the inner cavity) of the taxi roof light is used as the exhaust buffer area, achieving the same function as the exhaust buffer tank.
The floc prevention net and the wind pipe are installed at the gas inlet and the gas outlet to stabilize the airflow. The gas inlet is connected to the gas inlet of the intake buffer tank, and the gas outlet of the intake buffer tank is connected to the gas inlet of the detection module. The gas outlet of the detection module is not connected to other structures. The gas exhausted from the sensor directly enters the roof light of the taxi, and the gas in the roof light is exhausted from the roof light of the taxi through the gas outlet.
The flow direction of the detected gas is that the detected gas enters the monitoring device through the gas inlet, and then the detected gas flows through the intake buffer tank, the detection module, the internal space of the taxi roof light, and the gas outlet and finally exits the monitoring device. In the twenty-third embodiment, the remaining space of the closed cavity inside the taxi roof light is used as a buffer area for the exhaust of the detection module, which simplifies the exhaust buffer tank and also stabilizes the airflow, and improves the accuracy of the sensor. The gas detected by the detection module is finally buffered in the cavity of the taxi roof light and then exhausted from the roof light of the taxi through the gas outlet.

Example 24

The twenty-fourth embodiment is an improvement of the twenty-third solution. As shown in FIG. 18, the solution comprises a gas inlet, a gas outlet, a floc prevention net, a windproof pipe, an intake buffer tank, a detection module, an external fan, a control module, a positioning module, and a transmission module. These devices are installed inside the roof light of the taxi. The detection module in the twenty-fourth embodiment does not comprise an airflow driving device. The device for driving the airflow is arranged externally, for example, an external fan is used and the external fan is placed behind the detection module.
The floc prevention net and the windproof pipe are installed at the gas inlet and the gas outlet. The gas inlet is connected to the gas inlet of the intake buffer tank, and the gas outlet of the intake buffer tank is connected to the gas inlet of the detection module. The gas outlet of the detection module is connected to the gas inlet of the external fan, and the gas outlet of the external fan is not connected to other structures.
The flow direction of the detected gas is that the detected gas enters the monitoring device through the gas inlet, and then flows through the intake buffer tank, the detection module, and the external fan. The gas exhaust from the external fan is finally buffered in the cavity of the roof light of the taxi, and then exhausted from the roof light of the taxi through the gas outlet.

Example 25

The twenty-fifth embodiment is an improvement of the twenty-second solution. This improvement can solve the problem of inaccurate data caused by airflow disturbance and unbalanced air pressure. As shown in FIG. 19, the intake buffer tank, the detection module, the external air pump module, the control module, and the transmission module are installed inside the roof light of the taxi; and the gas inlet, the gas outlet, and the floc prevention net are located on the housing of the roof light of the taxi.
Stable air flow can improve the accuracy of the sensor. The use of the air pump makes the flow more stable. In the solution of the twenty-second embodiment, the fan responsible for air intake is replaced with an external air pump module, and the exhaust buffer tank is removed, as shown in FIG. 19. The airflow flows through the gas inlet, the intake buffer tank, the detection module, and the external air pump module in turn, and finally exits the roof light of the taxi. A floc prevention net is used at the gas inlet of the roof light with an external air pump device, and the windproof pipe may not be used. Similarly, a floc prevention net is used at the gas outlet, and the windproof pipe may not be used.
The external air pump module mainly comprises a filter, an air pump and a muffler, as shown in FIG. 20. The improved external air pump module comprises a filter, an air pump flow regulating valve, an air pump and a muffler, as shown in FIG. 21.
The air pollutant online monitoring device in the twenty-fifth embodiment may be placed in a specially designed housing, and the housing with the air pollutant online monitoring device placed therein may be hung outside the lower part of the roof light of the taxi or other parts.

Example 26

The intake buffer tank can make the airflow more stable and reduce the disturbance of turbulence, as shown in FIG. 23 and FIGS. 24A-24B. Meanwhile, the intake buffer tank can be used as an airflow distribution device. The intake buffer tank has a same number of outlets as the number of sensors.
FIG. 23 and FIGS. 24A-24B show an intake buffer tank with one inlet and four outlets. FIG. 23 is an intake buffer tank for an air pump. In FIG. 23, a part 121 of the intake buffer tank body may be made of flexible materials, which can further reduce the fluctuation of the intake of the air pump. The buffer tank may be made of aluminum alloy, plastic, nylon and resin, and may be processed by machining, injection molding or casting, depending on the material and structure.
It will be obvious to those skilled in the art that changes and modifications may be made, and therefore, the aim in the appended claims is to cover all such changes and modifications.

Claims

What is claimed is:

1. A taxi roof light comprising a multi-core sensor system, wherein the multi-core sensor system comprises a control module and a detection module; the detection module comprises a sensor module comprising at least two sub-sensor units of a same type; the sub-sensor units operate at a normal operating frequency; the detection module further comprises at least one low-frequency calibration module comprising sub-sensor units of a same type as the sensor module; the sub-sensor units in the low-frequency calibration module operate at an operating frequency much lower than the operating frequency of the sub-sensor units in the sensor module; the multi-core sensor system further comprises a gas distribution tank; the air distribution tank is used to distribute a detected gas to each individual sub-sensor; the gas distribution tank comprises at least one gas inlet and a plurality of gas inlet; and a gas outlet of the detection module is connected to the gas outlet.

2. The taxi roof light of claim 1, wherein a ratio of operating frequencies between the at least two sub-sensor units of the sensor module and the sub-sensor unit of the calibration module is 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1, 10:1, 15:1, or 20:1.

3. The taxi roof light of claim 1, wherein when the control module detects one suspected abnormal sub-sensor unit in the sensor module, and judges that the suspected abnormal sub-sensor unit is an abnormal sub-sensor unit; the suspected abnormal sub-sensor unit is isolated and classified into an isolation zone, and the sensor module is degraded, and continues to operate; when the abnormal sub-sensor unit in the isolation zone self-heals, the abnormal sub-sensor unit operates at a lower frequency; the control module monitors the operation of the abnormal sub-sensor unit to judge whether a recovery condition is met; when the recovery condition is met, the abnormal sub-sensor unit is released from the isolation zone and back to the sensor module.

4. The taxi roof light of claim 3, wherein the criteria for defining abnormal behavior of the sub-sensor, comprising: 1) abnormal fluctuation occurred in the sub-sensor unit; 2) abnormal drift occurred in the sub-sensor unit; and 3) abnormal correlation existing among the sub-sensor units.

5. The taxi roof light of claim 4, wherein the gas outlet of the gas distribution tank is connected to the gas inlet of each sub-sensor of the detection module; the detection module is configured to detect the concentration of air pollutants; the control module is configured to receive, analyze and upload the data detected by the detection module.

6. The taxi roof light of claim 5, wherein the multi-core sensor uses multiple sensors to measure air quality at the same time, and the output value is the result of comprehensive calculation of data from the multiple sensors; the data of abnormal sub-sensors needs to be eliminated during data calculation; a method of determining an abnormal sub-sensor is one of the following methods: 1) Average value method; 2) Median method; 3) Correlation coefficient method; 4) Variance method; and 5) Percentage method.

7. The taxi roof light of claim 5, wherein the gas distribution tank is equipped with a semiconductor refrigeration sheet made of metal and capable of heating and dehumidifying the gas distribution tank; the semiconductor refrigeration sheet comprises a hot end and a cold end; the gas distribution tank is directly heated by the hot end of the semiconductor refrigeration sheet; the cold end of the semiconductor refrigeration sheet is connected to a heat dissipation grille; a heat energy is absorbed by the heat dissipation grille and transferred to the gas distribution tank; a humidity sensor is mounted before the gas inlet of the gas distribution tank; the system turns on the semiconductor refrigeration sheet when the humidity of the gas measured by the humidity sensor is greater than a set upper limit; and the system turns off the semiconductor refrigeration sheet when the humidity is less than the set lower limit.

8. The taxi roof light of claim 7, wherein compensation of Flow Rate, Temperature, Power and Pipeline Length using the following methods:

1) the multi-core sensor uses embedded algorithms to solve the problem of out-of-synchronization of multiple sub-sensors in detecting the sampled gas due to different lengths of intake pipes;

2) a flow rate sensor and a differential pressure sensor are used to obtain the flow rate of the sampled gas, and a fan speed control circuit is added; by controlling the fan speed by the obtained gas flow rate information, the flow rate of the sampled gas is stabilized at a value appropriate to the sensor;

3) a temperature acquisition probe is installed at the sensor or for detected gas; the change relationship of the pollutant concentration values corresponding to different sampling temperature values is obtained experimentally or by temperature characteristic data of the sensor; when in use, the output pollutant results are compensated based on the collected temperature data; and

4) a humidity acquisition device is installed to acquire humidity of the detected gas; the change relationship of the pollutant concentration values corresponding to different sampling humidity values is obtained experimentally or by humidity characteristic data of the sensor; when in use, the output pollutant results are compensated based on the collected humidity data.

9. The taxi roof light of claim 3, wherein a status indicator is installed on the sub-sensor; after the abnormal sub-sensor is identified, the status indicator at the corresponding position on the communication port of the circuit board displays a warning color; and the displays green when the sub-sensor is operating normally.

10. The taxi roof light of claim 4, wherein a status indicator is installed on the sub-sensor; after the abnormal sub-sensor is identified, the status indicator at the corresponding position on the communication port of the circuit board will change to a warning color; the status indicator light is green when the sub-sensor is operating normally.

11. The taxi roof light of claim 1, wherein the sub-sensor unit is one of the following sensors: PM1 sensor, PM2.5 sensor, PM10 sensor, PM100 sensor, sulfur dioxide sensor, nitrogen oxide sensor, ozone sensor, carbon monoxide sensor, VOCs sensor, or TVOC sensor.

12. The taxi roof light of claim 4, wherein the sub-sensor unit is one of the following sensors: PM1 sensor, PM2.5 sensor, PM10 sensor, PM100 sensor, Sulphur dioxide sensor, nitrogen oxide sensor, ozone sensor, carbon monoxide sensor, VOCs sensor, or TVOC sensor.

13. The taxi roof light of claim 1, wherein the sub-sensor unit is a laser particulate matter sensor; the multi-core sensor system improves the accuracy of the sensor module by the following method: a laser power detection device and a laser power control circuit are added, which are configured to compensate for the laser power; the change relationship of the particulate matter concentration corresponding to each laser power value is obtained experimentally; the attenuated data is compensated by the laser power control circuit according to the detection result of the laser power detection device.

14. The taxi roof light of claim 4, wherein the sub-sensor unit is a laser particulate matter sensor; the multi-core sensor system improves the accuracy of the sensor module by the following method: a laser power detection device and a laser power control circuit are added, which are configured to compensate for the laser power; the change relationship of the particulate matter concentration corresponding to each laser power value is obtained experimentally; the attenuated data is compensated by the laser power control circuit according to the detection result of the laser power detection device.