WO2019149232A1 - 多核传感器中子传感器的轮休方法 - Google Patents

多核传感器中子传感器的轮休方法 Download PDF

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WO2019149232A1
WO2019149232A1 PCT/CN2019/074035 CN2019074035W WO2019149232A1 WO 2019149232 A1 WO2019149232 A1 WO 2019149232A1 CN 2019074035 W CN2019074035 W CN 2019074035W WO 2019149232 A1 WO2019149232 A1 WO 2019149232A1
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sensor
sub
module
data
core
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PCT/CN2019/074035
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French (fr)
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司书春
刘善文
寇世田
许军
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山东诺方电子科技有限公司
司书春
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Priority to GB2012858.3A priority Critical patent/GB2583684B/en
Priority to CN201980001682.9A priority patent/CN110651176A/zh
Priority to GB2104144.7A priority patent/GB2588880B/en
Publication of WO2019149232A1 publication Critical patent/WO2019149232A1/zh
Priority to NO20200949A priority patent/NO20200949A1/no

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/06—Investigating concentration of particle suspensions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
    • B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
    • B60R16/023—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for transmission of signals between vehicle parts or subsystems
    • B60R16/0231—Circuits relating to the driving or the functioning of the vehicle
    • B60R16/0232—Circuits relating to the driving or the functioning of the vehicle for measuring vehicle parameters and indicating critical, abnormal or dangerous conditions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D18/00—Testing or calibrating apparatus or arrangements provided for in groups G01D1/00 - G01D15/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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    • G01N15/02—Investigating particle size or size distribution
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    • G—PHYSICS
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    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/02—Investigating particle size or size distribution
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    • G01N15/0211—Investigating a scatter or diffraction pattern
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    • G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
    • G01N33/0031—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array
    • G01N33/0032—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array using two or more different physical functioning modes
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    • G01N33/0004—Gaseous mixtures, e.g. polluted air
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    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
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    • G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
    • G08C17/00—Arrangements for transmitting signals characterised by the use of a wireless electrical link
    • G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
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    • G—PHYSICS
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    • G01N2015/0042—Investigating dispersion of solids
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    • H—ELECTRICITY
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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    • Y02A50/20—Air quality improvement or preservation, e.g. vehicle emission control or emission reduction by using catalytic converters

Definitions

  • the invention relates to a multi-core sensor system and a method thereof for isolating, recovering and rotating, especially a multi-core sensor for moving vehicles; and belongs to the field of environmental monitoring.
  • Indicators for monitoring atmospheric pollutants in environmental monitoring are sulfur dioxide, nitrogen oxides, ozone, carbon monoxide, PM 1 (particles with aerodynamic particle size less than 1 ⁇ m) and PM 2.5 (particles with aerodynamic particle size less than 2.5 ⁇ m). ), PM 10 (particles with aerodynamic particle size less than 10 microns), PM 100 (particles with aerodynamic particle size less than 100 microns) and VOCs (volatile organic compounds) or TVOC (total volatile organic compounds).
  • the atmospheric environment monitoring system can collect and process the monitored data, and timely and accurately reflect the regional ambient air quality status and changes.
  • atmospheric environmental monitoring equipment mainly has fixed monitoring stations and mobile monitoring equipment.
  • the current fixed monitoring stations are mainly divided into large fixed monitoring stations and small stations.
  • Mobile monitoring equipment mainly includes dedicated atmospheric environment monitoring vehicles, drones and handheld devices.
  • Air quality sensors are used in these small monitoring stations and handheld devices to measure atmospheric pollutants. Small sensors are characterized by low cost, miniaturization and online monitoring and can be used on a large scale. The air quality sensor itself may have errors in the measured values that are inconsistent with the true values for various reasons. Compared with large precision instruments or manual monitoring methods, air quality sensors are characterized by lower accuracy, poor stability, large errors, and frequent calibration.
  • the airborne particulate matter sensor of the laser scattering method has a broad market prospect because of its low cost and portability.
  • the portable analysis device using the scattering method has disadvantages such as poor measurement consistency, high noise, and low measurement accuracy.
  • the core device is susceptible to various environmental factors, and the fluctuation is likely to cause misjudgment.
  • the sensor data suddenly changes greatly, it can intelligently determine whether the cause of the change is sensor failure or sudden pollution, which will greatly improve the reliability of the data, and is of great value for ensuring the quality of environmental monitoring data.
  • the equipment fails, if the automatic repair can be done, the online rate of the data can be greatly improved, which is of great value for the continuous monitoring required for the treatment work. At the same time, it can save manpower and material resources in equipment maintenance and reduce social waste.
  • Patent CN 105823856 A discloses an air quality monitoring method based on multi-sensor fusion, which combines multiple sets of measurement data of multiple sensors to optimize the pseudo-random error problem introduced by the volatility of light.
  • the data fusion method can select an existing fusion algorithm according to the needs.
  • the paper discloses that when the scattering method measures the pollutants in the air, the emitted laser light ranges from several hundred nanometers to more than one thousand nanometers, and the particle size of the pollutant to be tested is 2500 nanometers for PM 2.5 .
  • the particle diameter is below 10000 nm, and the visible laser wavelength is equivalent to the particle size of the pollutant to be tested.
  • the laser exhibits both volatility and particle properties at the same wavelength, and the light scattering method utilizes The scattering effect can only be measured by the particle properties of light, so a single measurement cannot fully accurately represent the number of particles in the space to be measured.
  • Patent CN 101763053 A discloses a detection system with real-time self-diagnosis function capable of recognizing sensor failure, signal anomaly, subsystem function failure or system abnormality. In the event of a fault, the system can immediately upload the fault message and activate the alarm; it also isolates the fault sensor.
  • Patent CN 102480783A discloses a wireless sensor system, which can delay the redundant nodes in the network by a duty-keeping scheduling mechanism to prolong the life.
  • the sensor is a detection device that can sense the pollutant concentration information and can transform the sensed information into an electrical signal or other required form of information output to meet the information transmission, processing, storage, Requirements for display, recording, and control.
  • the pollutants herein mainly include particulate matter (PM 1 , PM 2.5 , PM 10 , PM 100 ), nitrogen oxides, sulfur dioxide, ozone, VOCs/TVOC and carbon monoxide.
  • Sub-sensor Also known as sub-sensor unit, the sub-sensor unit in this paper includes a fan, a sensing element, an MCU, a signal conversion component and a signal amplifying circuit, which can independently collect and calculate the pollutant data, and can also transmit the local stored data.
  • the sensor module is a sensor device composed of a plurality of sub-sensors, which are also referred to as cores in the sensor module.
  • a sensor module consisting of four sub-sensors is also called a quad-core sensor, and a sensor module consisting of five sub-sensors is also called a five-core sensor.
  • Sub-sensor abnormal fluctuation It means that the measurement result is more discrete than the normal range when the sensor is continuously measured.
  • the sub-sensor is abnormally drifted: it means that the average value of the measurement result of the sensor during continuous measurement is out of the normal range.
  • Sub-sensor correlation anomaly Indicates that the sensor's correlation with other sensors is lower than the normal range when continuously measuring.
  • Sub-sensor anomalies sub-sensor anomalies, sub-sensor anomalies, and sub-sensor correlation anomalies are sub-sensor anomalies.
  • Abnormal sub-sensor Also known as a fault sub-sensor, it is a sub-sensor with sub-sensor anomalies.
  • Suspected abnormal sub-sensor also known as a suspected sub-sensor; in the sensor module, the sub-sensor with the largest fluctuation or the largest drift, but the fluctuation or offset has not triggered the isolation condition; that is, the degree of fluctuation or offset is not enough It is considered as a sub sensor abnormality.
  • the suspected abnormal sub-sensor is the sub-sensor closest to the abnormality in the normal sub-sensor. For example, the measured value deviates from the normal value by 20% and is judged to be abnormal. It is assumed that the sub-sensors of No. 1, No. 2, and No. 3 deviate from the normal value by 5% and 6%, respectively. 16%, then we judge the No. 3 sub-sensor as a suspected fault sub-sensor.
  • Isolation The case where a sub-sensor does not participate in the operation of the control module to upload a value is called sub-sensor isolation.
  • the isolation condition is a condition for judging whether to isolate a suspected abnormal sub-sensor. Such as the degree of dispersion in the abnormal fluctuation of the sub-sensor, the offset value in the abnormal drift of the sub-sensor, and the like.
  • Recovery condition The recovery condition is the basis for judging whether to restore the sub-sensor in the isolation area.
  • the criteria for recovery conditions should be appropriately higher than the isolation conditions, and there should be at least a 10% difference between them to avoid the sub-sensors that have just been restored from being isolated.
  • Wheel break It is a working mode of the sub-sensor, which means that the sub-sensors are alternately started and stopped.
  • the first method is to use a single high-cost and high-precision sensor, but the problem is obvious. In addition to the high cost problem, it is impossible to judge whether the sensor is abnormal by the data output by the sensor itself.
  • the second method is a dual-core sensor that measures and outputs the results independently by two sensors. In this way, the output of the two sub-sensors can be compared according to a certain judgment standard to judge whether the sub-sensor is abnormal, but this method cannot determine which sub-sensor is abnormal.
  • the third type is a three-core sensor. By comparing the output results of the three sub-sensors, it is determined which sub-sensor has a problem, and then isolates the sub-sensor of the problem; however, since the sensor module is operated in dual-core mode after isolation There is a problem that the abnormal sub-sensor cannot be judged, so once the sub-sensor of the three-core sensor has an abnormality, the reliability of the entire sensor module is greatly degraded.
  • 1 shows the operational state of the sub-sensors
  • sub-sensor 100 represents a normal sub-sensor
  • sub-sensor 101 and sub-sensor 102 are both suspected abnormal sub-sensors
  • sub-sensor 104 represents an abnormal sub-sensor.
  • 1U indicates a nuclear sensor mode.
  • 2U indicates a dual-core sensor module.
  • the dual-core sensor module has a sub-sensor output abnormality, it cannot be judged. Which one is abnormal, so the dual-core sensor module has a sub-sensor abnormality, and the entire module cannot work normally.
  • 3U represents a three-core sensor module.
  • the present invention provides a multi-core sensor system and a method for the same, isolation, recovery and rotation.
  • the invention adopts at least four sub-sensor units to form a sensor module, which realizes complementary data deviation and mutual verification, and improves reliability, consistency, precision and life of the sensor module.
  • 4U represents a quad-core sensor module.
  • a sub-sensor When a sub-sensor is found to have a suspected abnormality and the suspected abnormal sub-sensor is an abnormal sub-sensor, it can be isolated, and the quad-core sensor module can be isolated. Downgraded to a three-core sensor module, the three-core sensor module can still work normally; 5U represents a five-core sensor module, when a sub-sensor is found to have a suspected abnormality, and the suspected abnormal sub-sensor is an abnormal sub-sensor, the five-core The sensor module is downgraded to a quad-core sensor module, and the quad-core sensor module can still work normally; such as a six-core sensor module, a seven-core sensor module, and more nuclear sensor modules.
  • the multi-core sensor system includes a gas distribution box, a main control module, and a detection module.
  • a gas distribution box is used to distribute the gas to be measured to each individual sub-sensor.
  • the gas inlet of the gas distribution box is connected to the gas sampling head, and the gas outlet of the gas distribution box is connected with the inlet of each sub sensor of the detection module.
  • the detection module is a sensor module with four or more sub-sensors built in, and the detection module is used to detect the concentration of atmospheric pollutants.
  • the main control module is configured to receive, analyze, and upload data detected by the detection module, and supply power to the detection module.
  • the types of sub-sensors include PM 1 sensor, PM 2.5 sensor, PM 10 sensor, PM 100 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. .
  • Sensor accuracy is related to a variety of factors, such as the measured gas flow rate, temperature, and so on.
  • the invention 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 Figure 8, the sensor has an optimal operating temperature range. When the temperature is higher than the optimal operating temperature, the accuracy will decrease.
  • the invention adjusts the sensor temperature and the intake air temperature through the temperature control device, and can also compensate the temperature by an algorithm to improve the detection precision.
  • the accuracy of the sensor is also related to the flow rate of the gas being measured flowing through the sensor.
  • the measured gas has the highest accuracy at the optimum flow rate V 0 , and the measured gas flow rate is too fast or too slow to affect the accuracy.
  • the internal wind resistance of the sensor or other reasons may cause the flow rate of the measured gas to change.
  • the present invention controls the flow rate of the measured gas within the optimal flow rate range by adjusting the internal fan speed or other flow rate adjustment, thereby improving the sensor. Accuracy.
  • the multi-core sensor solves the problem that the sampling sensors are out of synchronization due to the multiple sub-sensors caused by the different lengths of the intake pipeline, thereby obtaining more accurate detection data.
  • the multi-core sensor simultaneously measures the air quality by using a plurality of sensors, and the output value is an average value of a plurality of sensors, and the data accuracy is high.
  • Figure 5 shows the output data of the quad-core sensor module.
  • U1, U2, U3 and U4 are the output data of the four sub-sensors respectively.
  • the solid line Average is the average of the four sensors. The data is smoother, more stable and more accurate. high.
  • the performance of a laser sensor is affected by the light decay of the laser.
  • Semiconductor lasers will have a problem of optical power attenuation caused by semiconductor materials and production processes due to the prolonged use time. When the optical power attenuation reaches a certain level, the accuracy of the sensor detection data will be affected.
  • the present invention provides a calibration basis for the high frequency group by using the sensor components as a high frequency group and a low frequency group as a redundant unit.
  • the invention discloses another multi-core sensor system, which comprises a main control module and a detection module; the detection module comprises at least two homogeneous sub-sensor units to form a sensor module; the sub-sensor unit works in a normal state working 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 frequencies of the high frequency group and the low frequency group 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 low frequency group participates in the suspected abnormality judgment and can also adopt the following schemes for different situations:
  • a single low frequency sensor the data weight of the low frequency sensor is 2; the data weight of each sensor unit in the sensor module is 1;
  • Two low frequency sensors based on the average value of the sensor module, a low frequency sensor closer to the reference value has a data weight of 2 and another weight of 1;
  • Three or more low-frequency sensors Based on the average of the data of the low-frequency group, the farthest deviation from the reference in the high-frequency group is a suspected abnormality.
  • the main control module finds that a sub-sensor unit in the sensor module has a suspected abnormality, and determines that the suspected abnormal sub-sensor is an abnormal sub-sensor, the abnormal sub-sensor is isolated, and the abnormal sub-sensor is classified into the isolation area. After the multi-core sensor module is downgraded, it will continue to work normally.
  • the invention also discloses a method for identifying the working state of a 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 time, and the main control module filters out the data of the suspected abnormality from the data of the group, and further determines whether the corresponding sub-sensor satisfies the isolation condition.
  • 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, determines whether it has reached the recovery condition, and transfers the sub-sensor that has reached the recovery condition away from the isolation area, resumes the work, and outputs data to participate in the sensor module data or main control. Data calculation; for the abnormal sub-sensor that does not meet the recovery condition, it is judged whether it can be self-healing.
  • 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 invention provides a working mode of the wheel-resting for the sensor module, and selecting one or more of the sub-sensors that work normally can solve the problem of the performance degradation caused by the fatigue of the sensor.
  • the internal state will change. For example, the internal temperature will increase with the increase of working time.
  • the mechanical components of the sampling device will have metal fatigue. Therefore, proper rest after working for a period of time will restore the sensor. Good working condition.
  • the sensor After the sensor starts working for a period of time, it enters a stable working period, and after a long period of continuous work, the fatigue rises.
  • the fatigue stage of the sensor select those sub-sensors that enter the fatigue state, put them into a rest state, and try to make the sensor unit work during stable working hours.
  • the wheel rest can also keep the light decay of the same group of sensors substantially synchronized.
  • the light-scattering particle sensor using the semiconductor laser as the light-emitting element needs to consider the light-fading synchronization problem between the sensors when the plurality of sub-sensors are included.
  • the effect on the data is relatively small when the light decay is light, so that there is some difference in the data of each sensor, but according to the difference of these lightness, it is impossible to determine whether the sub-sensor is faulty. , will still participate in the calculation of the sensor's final test data, resulting in deviations in the final test data.
  • the main control module of the multi-core sensor system should record and save the accumulated working time of each sub-sensor, adjust the rotation interval of each sub-sensor according to the accumulated working time, so that the light decay of each sub-sensor remains basically synchronized, which helps the sensor to detect data accuracy. Improvement.
  • the invention is low in cost of use. Compared with expensive precision instruments, the sensor module only adds a few sub-sensors, and there is no significant cost increase for the whole device. However, due to the increased reliability and precision, this method can also be used in low-precision, low-reliability but low-cost sensors in scenarios where high-precision instruments can only be used.
  • the multi-core sensor module also extends the life of the entire monitoring device, maintenance cycle, and reduces the cost of equipment replacement and maintenance.
  • the judgment of the sensor failure can be completed by the local master control, and can also be completed by the data center online monitoring system.
  • the online monitoring system is responsible for receiving data, storage, data processing, and generating a visualized pollution cloud map.
  • Figure 1 is a schematic diagram of the state of the sub-sensor
  • FIG. 2 is a schematic diagram of a single-core sensor module, a dual-core sensor module, a three-core sensor module, and a three-core sensor module having a sub-sensor failure;
  • Figure 3 is a schematic diagram of the judgment of the suspected abnormal sub-sensor module.
  • a nuclear and dual-core sensor module cannot determine an abnormal situation after a suspected abnormality; a sensor module with more than three cores can determine a sensor that is suspected to be abnormal.
  • Figure 4 is a schematic diagram of the sensor error, between D 0 and D 1 is a fluctuation; D 0 and the actual value is a drift;
  • Figure 5 is a schematic diagram of the output data of the quad-core sensor module and its sub-sensor output data. Average is the quad-core average output result, and the dotted line is the output result of each core;
  • FIG. 6 is a schematic diagram of an isolation manner after a sub-sensor of a six-core sensor module is abnormal
  • FIG. 7 is a schematic diagram of isolating and restoring an abnormal sub-sensor in a quad-core sensor module
  • Figure 8 is a schematic diagram showing the relationship between sensor accuracy and temperature
  • Figure 9 is a schematic diagram showing the relationship between sensor accuracy and measured gas flow rate
  • Figure 10 is a schematic diagram showing the relationship between the fan speed, the wind resistance and the measured gas flow rate
  • Figure 11 is a schematic diagram showing the flow of the isolation and recovery method of the multi-core sensor module
  • Figure 12 is a schematic diagram of the structure of a six-core sensor
  • Figure 13 is a schematic diagram of a quad-core sensor and its fault indicator light
  • Figure 14 is a schematic diagram of the isolation and recovery process of the high and low frequency multi-core sensor module.
  • 100-normal sensor 101-suspected abnormal sub-sensor (one), 102-suspected abnormal sub-sensor (2), 104-abnormal sub-sensor, U3-3 sensor, U3-d-status indicator ( Red-fault), U4-d-status indicator (green-normal);
  • the multi-core sensor system includes a gas distribution box, a main control module, and a detection module.
  • the gas distribution box is used to distribute the gas to be measured to each individual sub-sensor.
  • the gas inlet of the gas distribution box is connected to the gas sampling head, and the gas outlet of the gas distribution box is connected with the inlet of each sub sensor of the detection module.
  • the detection module includes a sensor module with four or more sub-sensors built in, and the detection module is used to detect the concentration of atmospheric pollutants.
  • the main control module is configured to receive, analyze, and upload data detected by the detection module to the data center, and supply power to the detection module.
  • the air inlet of the air distribution box is connected with the sampling head, and the air outlet is connected with the air inlet of the detection module.
  • the gas distribution box has a buffering effect to relieve pressure fluctuations.
  • the detection module may further comprise at least one sub-sensor unit similar to the sensor module to form a low-frequency calibration module; the sensor unit in the low-frequency calibration module works far below the operating frequency of the sensor module.
  • the sensor module can be reduced to two or three sensor units.
  • the main control module is installed with a control module data communication interface, and the control module data interface and the sensor data communication interface are connected by wires.
  • the sensor transmits the data to the control module via the control module data communication interface to which it is connected.
  • the detection module is connected to the main control module through a data interface, and the main control module can process the detection data of the sub sensor, and has a data upload function and a positioning function.
  • the master module can upload data to the data center over a wireless network.
  • the data center is responsible for receiving data, storage, and data processing.
  • the data center online monitoring system can manually control the secondary calibration of the abnormal sub-sensor.
  • the invention adopts a plurality of sub-sensor units to form a sensor module, realizes data deviation complementary and mutual verification, and improves reliability, consistency, precision and life of the sensor module.
  • 4U represents a quad-core sensor module.
  • a sub-sensor When a sub-sensor is found to have a suspected abnormality and the suspected abnormal sub-sensor is an abnormal sub-sensor, it can be isolated and the quad-core sensor system is degraded.
  • the three-core sensor system the three-core sensor system can still work normally; 5U represents the five-core sensor module.
  • the quad-core sensor system the quad-core sensor system can still work normally; and so on, the six-core sensor module, the seven-core sensor module and more nuclear sensor modules.
  • the accuracy of the sensor is related to temperature. As shown in Figure 8, the sensor has an optimal operating temperature range. When the temperature is higher than the optimal operating temperature, the accuracy will decrease.
  • the present invention adjusts sensor intake air humidity and intake air temperature by a humidity control device.
  • the gas distribution box may be provided with a semiconductor refrigeration sheet having a heating dehumidification function, the selected material is metal, and the semiconductor refrigeration sheet includes a hot end and a cold end.
  • the heat exchanger is directly heated by the hot end of the semiconductor refrigerating piece, and the humidity sensor is installed before the air inlet of the air distribution box.
  • the humidity of the gas measured by the humidity sensor is greater than the upper limit of the set value (the upper limit of the set value may be 60%, 65%) 70%, etc.)
  • the system turns on the heating and dehumidifying function of the semiconductor cooling sheet; when the humidity is less than the lower limit of the set value (the lower limit of the set value can be 40%, 50%, etc.), the semiconductor cooling sheet is turned off.
  • the gas distribution box may be provided with a semiconductor refrigeration sheet having a heating dehumidification function, the selected material is metal, and the semiconductor refrigeration sheet includes a hot end and a cold end.
  • the heat transfer port of the semiconductor refrigerating sheet is directly heated to the gas distribution box, and the cold end of the semiconductor refrigerating piece is connected with the heat dissipating grid, and the heat is radiated to the gas distribution box through the heat dissipating grid.
  • the dehumidification function is heated; when the humidity is less than the lower limit of the set value (the lower limit of the set value can be 40%, 50%, etc.), the semiconductor refrigerating sheet is turned off.
  • the gas distribution box may be provided with a semiconductor refrigeration sheet having a heating dehumidification function, the selected material is metal, and the semiconductor refrigeration sheet includes a hot end and a cold end.
  • the heat exchanger of the semiconductor refrigeration fin is used to heat the gas distribution box, and the cold end of the semiconductor refrigeration chip is connected to the air pump to dissipate heat for the air pump.
  • the system turns on the heating and dehumidifying function;
  • the humidity is less than the lower limit of the set value (the lower limit of the set value can be 40%, 50%, etc.)
  • the semiconductor refrigerating sheet is turned off.
  • the accuracy of the sensor is also related to the flow rate of the gas being measured flowing through the sensor.
  • the measured gas is in the range of the optimum flow velocity V 0 from the center V 1 to V 2 , and the accuracy is optimal, and the measured gas flow rate is too fast or too slow to affect the accuracy.
  • the internal wind resistance of the sensor or other reasons may cause the flow rate of the measured gas to change.
  • the present invention controls the flow rate of the measured gas at the most by adjusting the internal fan speed (S 1 , S 2 ) or other flow rate adjustment. Improve sensor accuracy within the best flow rate range.
  • the multi-core sensor compensates for the problem of multiple sub-sensor detection sampling out-of-synchronization caused by different lengths of the intake pipeline through the embedding algorithm, thereby obtaining more accurate detection data. Similar temperature and humidity are also compensated by corresponding algorithms to improve data accuracy.
  • the sampling flow is compensated by controlling the fan speed.
  • the flow rate of the sampled gas is obtained by using a flow meter and a differential pressure sensor, and a fan speed control circuit is added at the same time.
  • the fan speed is controlled by the obtained gas flow rate information so that the sampling gas flow rate is stabilized near the flow rate suitable for the sensor, as shown by V 0 of FIGS. 9 and 10.
  • the optimal flow rate of the sensor is empirically obtained based on experimental methods.
  • a laser power detecting device and a laser power control circuit are added to compensate the laser power.
  • the experimental method is used to obtain the change relationship of the particle concentration values corresponding to the respective laser power values (that is, the other conditions are fixed, and only the measured conditions are obtained to obtain the measurement results).
  • the attenuated data is compensated by the laser power control circuit according to the detection result of the power detector.
  • the multi-core sensor uses a plurality of sensors to simultaneously measure the air quality, and the output value is a result of comprehensive calculation of data of a plurality of sensors, and the data is smoother, more stable, and has higher precision.
  • the eighth embodiment to the twelfth embodiment are the data calculation methods of the sensor module.
  • the data of the abnormal sub-sensor needs to be eliminated during the data calculation.
  • the data of the low frequency group can be used as more reliable detection data to participate in the calculation of the output data of the sensor module when generating data.
  • the data of the low frequency group can be given a weight of 2 times to be added to the calculation.
  • Mean method A sensor module output data calculation method; after rejecting the abnormal sub-sensor unit data, the average value of all normal sub-sensor unit data is taken as the output result.
  • Median method a sensor module output data calculation method; after culling the abnormal sub-sensor unit data, sorting the values of all normal sub-sensor units, taking the intermediate value of the sort as the final result, if the sub-sensor unit participating in the sorting If the number is even, the average of the two sub-sensor units in the middle is taken as the final result.
  • Correlation coefficient method a sensor module output data calculation method; after rejecting the abnormal sub-sensor unit data, the data of all normal sub-sensor units is calculated as follows to obtain the final result.
  • the storage unit stores historical detection data of each sub-sensor unit, and calculates the values of the judging sub-sensor unit and other sub-sensor units by using time history data (1 minute, 10 minutes, 20 minutes, ... 1 hour) as time units.
  • time history data (1 minute, 10 minutes, 20 minutes, ... 1 hour
  • Variance method A method for calculating the output data of a sensor module; after rejecting the data of the abnormal sub-sensor unit, the data of all the normal sub-sensor units is calculated as follows to obtain the final result.
  • the memory stores historical detection data of each sub-sensor unit, and calculates the variance Vi of each sub-sensor unit in the time unit by using time history data (1 minute, 10 minutes, 20 minutes, ... 1 hour) as time units. Or standard deviation), sum the variance of each sub-sensor unit, calculate the difference between the sum and the variance of each sub-sensor unit, and obtain the difference, then calculate the percentage of the sum of the differences of each sub-sensor unit, and each will be normal.
  • the detection result of the sub sensor unit is multiplied by the percentage and summed to obtain the final detection result.
  • Percentage method A method for calculating the output data of a sensor module. After the data of the abnormal sub-sensor unit is removed, the data of all the 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, and calculates the average value of the detected values in the time unit closest to the current time in a time unit (10 seconds, 20 seconds, etc.), and uses the average value to calculate, the above calculation method :
  • Embodiment Twelf A Using the calculation method described in Embodiment Twelf A., calculating the percentage of each sub-sensor in a plurality of time units from the nearest time, and averaging the percentage of each sub-sensor unit in a plurality of time units, The average percentage of each sub-sensor unit in multiple time units that are closest to the current time,
  • the detection result of each normal sub-sensor unit is multiplied by the percentage and summed to obtain the final detection result.
  • the program invents a method for identifying the working state of the sub-sensor and isolating and recovering the sub-sensor. This method is shown in Figure 11.
  • the sensor module obtains a set of detection data at a time, and the main control module selects data of the suspected abnormality from the data of the group, and further determines whether the corresponding sub-sensor satisfies the isolation condition.
  • Isolation of the abnormal sub-sensor After determining that the sub-sensor is an abnormal sub-sensor, the abnormal sub-sensor is classified into the isolation area; the sensor module continues to work after being degraded.
  • the sub-sensor entering the isolation zone can stop working, and can continue sampling and detection, but the data output by the sub-sensor does not participate in the calculation of the output data of the main control module.
  • Recovery of abnormal sub-sensor monitor the data outputted by the sub-sensor entering the isolation area to determine whether it has reached the recovery condition, and adjust the sub-sensor that has reached the recovery condition to the isolation area to resume operation.
  • Judgment of suspected sub-sensor anomaly and sub-sensor anomaly When the variance of the data of a sub-sensor exceeds the threshold, or when the drift of the data of the sub-sensor exceeds the threshold, the sub-sensor is not immediately identified as abnormal, first listed as suspect Abnormal subsensor. Finally, it is determined that the sub-sensor is abnormal according to whether a plurality of consecutive data are abnormal in a certain period of time.
  • Sub-sensor average comparison method Take the quad-core sensor module as an example, compare the data of one sub-sensor with the average of the other three sub-sensors for a certain period of time (such as 5s mean, 30s mean, 60s mean, etc.) based on the current time. ).
  • Sub-sensor anomalies include abnormal drift of sub-sensors, abnormal fluctuations of sub-sensors, and sub-sensor correlation anomalies.
  • the storage unit stores historical detection data of each sub-sensor unit, and calculates the values of the determined sub-sensor unit and other sub-sensor units in units of time with historical data (1 minute, 10 minutes, 20 minutes, ... 1 hour) for a period of time.
  • Correlation coefficient if the correlation coefficient is less than a certain value, such as 0.5 (non-strong correlation), it is judged that the sensor correlation is abnormal and does not participate in the calculation of the final result.
  • the correlation method judges the sub-sensor correlation anomaly. Taking the correlation calculation of a quad-core sensor module as an example, the correlation between the 100 sets of data of the sub-sensor and the average of the 100 sets of data of the other three sub-sensings is performed. If the R 2 ⁇ 0.8, the sub-sensor sub-sensor correlation is abnormal, and the sensor module selects the data of the other three sub-sensors to calculate and output the monitoring result.
  • Embodiment 16 is a sub-sensor fluctuation abnormality determination method.
  • the sensor stores historical detection data of each sub-sensor unit, and calculates the variance of each sub-sensor unit in the time unit by using time history data (1 minute, 10 minutes, 20 minutes, ... 1 hour) as time units. Standard deviation), by comparing the variance (or standard deviation) of the sub-sensor unit to the variance (or standard deviation) of other sub-sensor units, the above variance comparison method:
  • a certain value of the average value such as 20%, 30%, etc., determines that the sub-sensor unit is abnormally undulated.
  • Embodiment 17 is a sub-sensor drift abnormality determining method.
  • the average value of the two adjacent time units of the past sub-sensor unit is judged to be a difference, and the percentage of the difference with respect to the average value in the latest time unit is calculated, and the percentage is used for the judgment.
  • the above drift determination method :
  • the data of the abnormal sub-sensor is isolated, but the fan or the air pump of the abnormal sub-sensor continues to operate, ensuring that the wind pressure and the flow rate are constant, and the pressure fluctuation is reduced.
  • the status indicator is installed on the sub-sensor. After the abnormal sub-sensor is recognized, the status indicator at the corresponding position of the communication port of the circuit board changes color to a warning color (such as red); The status indicator corresponding to the sub sensor is green.
  • the invention provides a working mode of the wheel-offset for the sensor module, and one or more of the sub-sensors that work normally are selected to perform the wheel-off, that is, the method of the active de-leveling operation solves the fatigue problem of the sensor.
  • the wheel rest can also keep the light decay of the same group of laser sensors substantially synchronized.
  • Sub-sensors screened by different rotation conditions may be inconsistent; in practical applications, multiple rotation conditions may be given weights or priorities to quantitatively determine which sub-sensors to enter the rotation.
  • each sensor should get a rotation before it enters the fatigue state.
  • the average stable working time of the sub-sensor unit is T; then, for the modules of the N sensor units, when the sequential rotation strategy is selected, that is, the sub-sensors in the sensor module are sequentially rotated, and the interval between the two rotations should not be Greater than T/N to ensure that each sensor can enter the shift in time.
  • T 8 hours, for the sensor module composed of 4 sensor units, adopting the sequential wheel-off strategy, then every 2 hours, it can ensure that each sensor can enter the wheel rest before entering the fatigue state.
  • a status indicator is installed on the sub-sensor unit.
  • the status indicator color of the corresponding position changes to a warning color;
  • the status indicator corresponding to the sub-sensor of the normal working state is continuous green.
  • the status indicator corresponding to the sub-sensor entering the rotation state is alternately bright green.
  • Embodiment 20 is a wheel-off mode of a sub-sensor.
  • the wheeled finger turns off the sensing portion of one or more sub-sensors within a specified time. For example, using a laser particle sensor module of a fan, only the laser is turned off, and the fan is not turned off.
  • the closing time of the sub-sensor may be a fixed time (eg, 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 closed sub-sensor reaches the off time, activate the closed sub-sensor, and then close the next sub-sensor that reaches the rotation condition;
  • the time can also be determined according to the working status of other sub-sensors. For example, in the quad-core sensor module, one sub-sensor is in the off state. At this time, the system judges that one of the three sub-sensors that are running reaches the isolation condition and needs to be isolated. , then the sub-sensor in the off state is immediately enabled.
  • the specific rotation conditions can be:
  • Form 1 Select the sub-sensor with the highest temperature by the acquired sub-sensor temperature data
  • Form 2 Select the closed sub-sensor according to the ambient temperature. If the ambient temperature is higher than the temperature set value (such as 40 degrees Celsius), the number is in order. Turn off the sub-sensors in turn;
  • a single-core rotation plan can be adopted.
  • the operating state of the sensor is greatly affected by temperature.
  • the temperature is higher than 60 °C, the single-core wheel rest, the rest of the normal work after four hours, change the adjacent single-core wheel break, and then rotate in turn, reduce the working time of the sub-sensor under high temperature, and improve the working time limit of the quad-core sensor.

Abstract

一种多核传感器系统中子传感器(100)的轮休方法，在传感器数据突然大幅变化时，能够智能判断出变化原因是传感器故障还是突发污染，提高数据可靠性。当设备发生故障时，如果能够通过自动判断修复，提高数据的在线率，对于治霾工作所需的连续监测具有重要价值。同时又可以节省设备维护保养方面的人力物力，减少社会浪费。

Description

多核传感器中子传感器的轮休方法 技术领域
本发明涉及一种多核传感器系统及其隔离、恢复和轮休的方法，特别是用于移动车辆的多核传感器；属于环境监测领域。
背景技术
环境监测中大气污染物监测指标为大气中的二氧化硫、氮氧化物、臭氧、一氧化碳、PM 1(空气动力学粒径小于1微米的粒子)、PM 2.5(空气动力学粒径小于2.5微米的粒子)、PM 10(空气动力学粒径小于10微米的粒子)、PM 100(空气动力学粒径小于100微米的粒子)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。
现在的大气环境监测设备主要有固定式监测站和移动式监测设备。目前的固定式监测站主要分为大型固定监测站点和小型站点。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。上述小型监测站点、手持设备都用到了空气质量传感器来测量大气中的污染物。小型传感器具有低成本、小型化和在线监测的特点，可以大规模使用。空气质量传感器本身会由于各种原因造成测得值与真实值不一致而存在误差。与大型精密仪器或者手工监测方式相比，空气质量传感器还有精确度更低、稳定性差、误差大、需要经常校准的特点。
激光散射法的大气污染颗粒物传感器，因为低成本和便携性，有着宽广的市场前景。然而采用散射法的便携式分析装置就会存在测量一致性差、噪声大、测量精度低等缺点，核心器件容易受到各种环境因素影响，而波动容易引起误判。
当传感器数据突然大幅变化时，能够智能判断出变化原因是传感器故障还是突发污染，将会极大提高数据可靠性，对于保证环保监测数据质量具有重要价值。当设备发生故障时，如果能够通过自动修复，也可以大幅提高数据的在线率，对于治霾工作所需的连续监测具有重要价值。同时又可以节省设备维护保养方面的人力物力，减少社会浪费。
专利CN 105823856 A公开了一种基于多传感器融合的空气质量监测方法，将多个传感器的多组测量数据做融合处理，优化了光的波动性引入的伪随机误差问题。数据的融合方法可根据需求选择已有的融合算法。
该文披露：散射法测量空气中的污染物时，发射的激光在几百纳米到一千多纳米范围内，而待测污染物的粒径尺寸对于PM 2.5来说，为粒径在2500纳米以下，对于PM 10来说，为粒径在10000纳米以下，可见激光波长和待测污染物的粒径尺寸相当，激光在波长相当的情况下同时呈现波动性和粒子性，而光散射法利用的散射效应仅能利用光的粒子性进行测量，因此单次测量无法完全准确呈现待测空间范围内的粒子数。
专利CN 101763053A公开了一种检测系统，具有实时自诊断功能，能够识别传感器失效、信号异常、子系统功能失效或系统异常的。出现故障时，系统能立即上传故障信息并激活报警；同时隔离故障传感器。
专利CN 102480783A公开了一宗无线传感器系统，可以通过值守调度机制使网络中的冗余节点不定期地轮休以延长寿命。
发明内容
术语解释
传感器：传感器是一种检测装置，能感受到污染物浓度信息，并能将感受到的信息按一定规律变换成为电信号或其他所需形式的信息输出，以满足信息的传输、处理、存储、显示、记录和控制等要求。本文中的污染物主要包括颗粒物(PM 1、PM 2.5、PM 10、PM 100)、氮氧化物、二氧化硫、臭氧、VOCs/TVOC和一氧化碳。
子传感器：也称为子传感器单元，本文中子传感器单元包括风扇、传感元件、MCU、信号转换元件和信号放大电路，可以独立完成污染物数据的采集和计算，也可以传输本地储存数据。
传感器模组：传感器模组是由多个子传感器构成的传感器装置，子传感器在传感器模组中也称为核。如由四个子传感器组成的传感器模组也称为四核传感器，五个子传感器组成的传感器模组也称为五核传感器。
子传感器异常波动：表示传感器在连续测量时测量结果的离散程度超过正常范围。
子传感器异常飘移：表示传感器在连续测量时测量结果的平均值偏移真实值超出正常范围。
子传感器相关性异常：表示传感器在连续测量时测量结果与其他传感器的相关性低于正常范围。
子传感器异常：子传感器异常波动、子传感器异常飘移和子传感器相关性异常都属于子传感器异常。
异常子传感器：也称为故障子传感器，是出现子传感器异常现象的子传感器。
疑似异常子传感器：也称为疑似故障子传感器；在传感器模组中，波动最大或者飘移最大的子传感器，但波动或者偏移还未触发隔离条件；也就是说波动或者偏移的程度还不足以被认定为子传感器异常。疑似异常子传感器是正常子传感器里最接近异常的子传感器，例如测量数值偏离正常值20％判断为异常，假设1号，2号，3号子传感器偏离正常值分别为5％，6％，16％，那么我们判断3号子传感器为疑似故障子传感器。
隔离：子传感器不参与控制模块上传数值的运算的情况被称为子传感器隔离。
隔离条件：隔离条件是判断是否将疑似异常子传感器进行隔离的条件。如子传感器异常波动中的离散程度值，子传感器异常飘移中的偏移值等。
恢复条件：恢复条件是判断是否将处于隔离区的子传感器恢复工作的依据。恢复条件的标准应当适当高于隔离条件，相互之间应该有至少10％的差异，以避免刚刚恢复的子传感器又被隔离。
轮休：是子传感器的一种工作方式，表示子传感器间隔轮流启停工作。
数据变差：表示传感器数值偏离正常值范围加大。
由于各种原因，例如，传感器本身性能原因、外界干扰影响，空气质量传感器的测得值与真实值之间往往存在不小的误差。降低误差、提高精度是传感器领域努力的方向。
目前也出现了多种提高传感器精度的方式。
第一种方式为采用单个高成本高精度的传感器，但带来的问题也显而易见，除了成本高的问题外，还无法通过传感器本身输出的数据判断传感器是否异常。
第二种方式为双核传感器，通过两个传感器独立采样测量并输出结果。该种方式可以按照一定的判断标准比对两个子传感器的输出结果，判断子传感器是否工作异常，但此方式无法判断具体是哪一个子传感器出现了异常。
第三种为三核传感器，通过比对三个子传感器的输出结果，进而判断是哪一个子传感器出现了问题，进而隔离出问题的子传感器；但由于此时隔离后传感器模组以双核模式运行，又会出现无法判断异常子传感器的问题，因此一旦三核传感器有一个子传感器出现异常后，整个传感器模组可靠性大大下降。
图1表示子传感器的工作状态，子传感器100表示正常的子传感器，子传感器101和子传感器102均为疑似异常子传感器，子传感器104表示异常子传感器。图2中，1U表示一核传感器模式，当传感器数据异常时，无法判断是传感器本身故障还是空气质量异常；2U表示双核传感器模组，当双核传感器模组有一个子传感器输出异常时，无法判断是哪一个出现异常，因此双核传感器模组有一个子传感器出现异常，整个模组便无法正常工作。以此类推，3U表示三核传感器模组。
针对上述不足，本发明提供了一种多核传感器系统及其隔离、恢复和轮休的方法。本发明采用至少四个子传感器单元组成传感器模组，实现数据偏差互补，相互校验，提高传感器模组的可靠性、一致性、精度以及寿命。
如图3和图4所示，4U表示四核传感器模组，当发现有一个子传感器出现疑似异常，并判断疑似异常子传感器为异常子传感器后，可以对其进行隔离，四核传感器模组降级为三核传感器模组，三核传感器模组仍然可以正常工作；5U表示五核传感器模组，当发现有一个子传感器出现疑似异常，并判断疑似异常子传感器为异常子传感器后，五核传感器模组降级为四核传感器模组，四核传感器模组仍然可以正常工作；以此类推六核传感器模组、七核传感器模组及更多核的传感器模组。
多核传感器系统包括分气箱、主控模块和检测模块。分气箱用来将被测气体分配给每个单独子传感器。分气箱的进气口连接气体采样头，分气箱出气口与检测模块的每个子传感器进气口连接。检测模块为传感器模组，内置四个或者四个以上子传感器，检测模块用于检测大气污染物浓度。主控模块用于接收、分析和上传检测模块检测的数据，并且为检测模块供电。
子传感器的类型包括PM 1传感器、PM 2.5传感器、PM 10传感器、PM 100传感器、二氧化硫传感器、氮氧化物传感器、臭氧传感器、一氧化碳传感器、VOCs传感器、TVOC传感器 和其他可以测量环境污染物浓度的传感器。
传感器准确度与多种因素有关，如被测气体流速、温度等。本发明通过多种方式的设计进一步提高传感器模组的准确度。
传感器准确度与温度有关，如图8所示，传感器有最佳工作温度范围，温度高于最佳工作温度情况下，准确度会下降。本发明通过温度控制装置调节传感器温度和进气温度，还可以通过算法对温度进行补偿，提高检测精度。
传感器的准确度也与流过传感器内部的被测气体流速有关。如图9所示，被测气体在最佳流速V 0情况下，准确度最高，被测气体流速过快或者过慢都会影响准确度。传感器内部风阻或者其他原因会使得被测气体流速有变化，如图10所示，本发明通过调节内部风扇转速或者其他流速调节的方式，将被测气体流速控制在最佳流速范围内，提高传感器准确度。
多核传感器通过嵌入算法，解决由于不同长度进气管路造成的多个子传感器检测采样气体不同步问题，从而得到更精确的检测数据。
多核传感器通过利用多个传感器同时测量空气质量，输出数值为多个传感器平均值，数据准确性高。如图5所示为四核传感器模组的输出数据，U1、U2、U3和U4为四个子传感器分别的输出数据，实线Average为四个传感器的平均值，数据更平滑、稳定，精度更高。
激光传感器的性能受激光器光衰的影响。半导体激光器随使用时间加长会出现因为半导体材料和生产工艺导致的光功率衰减的问题，光功率衰减达到一定程度时会影响传感器检测数据的准确性。
为了解一组激光传感器在长时间工作后其光衰的程度，本发明通过把传感器组分为高频组和低频组，低频组作为冗余单元为高频组提供校准依据。
本发明公开了另一种多核传感器系统，所述多核传感器系统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。
低频组参与疑似异常判断还可以采用如下区别情形的方案：
1)单个低频传感器：低频传感器的数据权重为2；传感器模组中每个传感器单元的数据权重为1；
2)两个低频传感器：以传感器模组的平均值为基准，离基准值更近的一个低频传感器的数据权重为2，另一个权重为1；
3)三个及三个以上低频传感器：以低频组的数据平均值为基准，高频组中与基准偏离最远的为疑似异常。
当主控模块发现传感器模组中某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。
本发明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。该方法如图11所示，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。
本发明对传感器模组设置了轮休的工作模式，在工作正常的子传感器中，选择一个或者多个进行轮休，可以解决传感器疲劳带来的工作性能下降的问题。
传感器随工作时间的增加，内部状态会有一定的变化，例如内部温度随工作时间增加而升高，采样装置的机械元件会有金属疲劳的问题，因此工作一段时间后适当休息会使传感器恢复最佳工作状态。
传感器启动工作一段时间后，进入稳定工作时段，经过长时间的连续工作后会出现疲劳度上升的情况。为了缓解这种情况，减少传感器疲劳阶段的数据偏移，选择那些进入疲劳状态的子传感器，使其进入休息状态，尽量使传感器单元在稳定工作时段工作。
对于激光传感器模组而言，轮休还可以使得同组传感器的光衰保持基本同步。
半导体激光器随使用时间加长会出现因为半导体材料效率的降低导致出光功率衰减的问题，使用半导体激光器作为发光元件的光散射发颗粒物传感器在包含多个子传感器时需要考虑传感器之间的光衰同步问题。
如果子传感器之间的光衰不同步，在光衰较轻时，其对数据的影响相对小一些，使得各传感器数据会有一些差异，但是根据这些较轻程度差异无法判定该子传感器是否故障，仍然会参与传感器最终检测数据的计算，导致最终检测数据出现偏差。
因此，多核传感器系统的主控模块应记录保存各子传感器的累计工作时间，根据累计工作时间调整各子传感器轮休间隔，使各子传感器的光衰保持基本同步，有助于传感器检测数据准确度的提高。
本发明使用成本低。与昂贵的精密仪器相比，传感器模组仅增加数个子传感器，对设备整体并没有大幅度成本提升。但是由于可靠性和精度的增加，运用此方式还可以将低精度、低可靠性但低成本的传感器应用于原本只能使用高精密仪器的场景下。多核传感器模组还延长了整个监测设备的寿命、维护周期，减少设备更换和维修带来的成本。
传感器故障的判断可以通过本地主控完成，还可以通过数据中心在线监测系统完成。在线监测系统负责接收数据、存储、数据处理、生成可视化的污染云图。
附图说明
图1为子传感器状态示意图；
图2单核传感器模组、双核传感器模组、三核传感器模组以及三核传感器模组有一个子传感器故障的示意图；
图3疑似异常子传感器模组判断示意图。一核、双核传感器模组，出现疑似异常后无法判断异常情况；三核以上的传感器模组才可以判断疑似异常的传感器。
图4传感器误差示意图，D 0和D 1之间为波动；D 0和实际数值之间为飘移；
图5为四核传感器模组输出数据及其子传感器输出数据示意图，Average为四核平均输出结果，虚线为各个核的输出结果；
图6为对六核传感器模组的子传感器出现异常后隔离方式的示意图；
图7为隔离和恢复四核传感器模组中异常子传感器的示意图；
图8传感器准确度与温度的关系示意图；
图9传感器准确度与被测气体流速的关系示意图；
图10风扇转速、风阻与被测气体流速关系的示意图；
图11多核传感器模组隔离和恢复方法流程的示意图；
图12六核传感器结构示意图；
图13四核传感器及其故障指示灯示意图；
图14为高低频多核传感器模组的隔离和恢复流程示意图
图中：100-正常传感器，101-疑似异常子传感器(之一)，102-疑似异常子传感器(之二)，104-异常子传感器，U3-3号传感器，U3-d-状态指示灯(红色-故障)，U4-d-状态指示灯(绿色-正常)；2U(3U)-代表一组三核传感器按两核模式运行，其中一核被隔离；
具体实施方式
多核传感器系统包括分气箱、主控模块和检测模块。分气箱用途为将被测气体分配给每个单独子传感器。分气箱的进气口连接气体采样头，分气箱出气口与检测模块的每个子传感器进气口连接。检测模块包含传感器模组，内置四个或者四个以上同类子传感器，检测模块用于检测大气污染物浓度。主控模块用于接收、分析和上传检测模块检测的数据至数据中心，并且为检测模块供电。分气箱进气口与采样头连接，出气口与检测模块的进气口连接。分气箱具有缓冲作用，缓解压力波动。
检测模块还可以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；低频校准模组内的传感器单元工作在远低于传感器模组的工作频率。在包含低频校准模组的多核传感器系统中，传感器模组可以减少为两个或三个传感器单元。
主控模块安装有控制模块数据通信接口，控制模块数据接口与传感器数据通信接口通过导线连接。传感器将数据通过与其连接的控制模块数据通信接口传输给控制模块。检测模块通过数据接口与主控模块相连，主控模块可以处理子传感器的检测数据，还具有数据上传功能和定位功能。主控模块可以通过无线网络将数据上传至数据中心。数据中心负责接收数据、存储和数据处理。数据中心在线监测系统可以人工控制对异常子传感器进行二次校准。
本发明采用多个子传感器单元组成传感器模组，实现数据偏差互补、相互校验，提高传感器模组的可靠性、一致性、精度以及寿命。如图3和图4所示，4U表示四核传感器模组，当发现有一个子传感器出现疑似异常，并判断疑似异常子传感器为异常子传感器后，可以对其进行隔离，四核传感器系统降级为三核传感器系统，三核传感器系统仍然可以正常工作；5U表示五核传感器模组，当发现有一个子传感器出现疑似异常，并判 断疑似异常子传感器为异常子传感器后，五核传感器系统降级为四核传感器系统，四核传感器系统仍然可以正常工作；以此类推六核传感器模组、七核传感器模组及更多核的传感器模组。
湿度、温度调节
传感器准确度与温度有关，如图8所示，传感器有最佳工作温度范围，温度高于最佳工作温度情况下，准确度会下降。本发明通过湿度控制装置调节传感器进气湿度和进气温度。
实施例一
分气箱可加装具有加热除湿功能的半导体制冷片，所选材料为金属，半导体制冷片包括热端和冷端。利用半导体制冷片热端直接加热分气箱，在分气箱进气口之前安装湿度传感器，当湿度传感器测得的气体湿度大于设定值上限时(设定值上限可以为60％、65％、70％等)，系统开启半导体制冷片的加热除湿功能；当湿度小于设定值下限(设定值下限可以为40％、50％等)，则关闭半导体制冷片。
实施例二
分气箱可加装具有加热除湿功能的半导体制冷片，所选材料为金属，半导体制冷片包括热端和冷端。利用半导体制冷片热端直接加热分气箱，半导体制冷片冷端与散热格栅连接，通过散热格栅吸收热量传导给分气箱。在分气箱进气口之前安装湿度传感器，当湿度传感器测得的气体湿度大于设定值上限时(设定值上限可以为60％、65％、70％等)，系统开启半导体制冷片的加热除湿功能；当湿度小于设定值下限(设定值下限可以为40％、50％等)，则关闭半导体制冷片。
实施例三
分气箱可加装具有加热除湿功能的半导体制冷片，所选材料为金属，半导体制冷片包括热端和冷端。利用半导体制冷片热端加热分气箱，半导体制冷片冷端连接气泵，为气泵散热。在分气箱进气口之前安装湿度传感器，当湿度传感器测得的气体湿度大于设定值上限时(设定值上限可以为60％、65％、70％等)，系统开启加热除湿功能；当湿度小于设定值下限(设定值下限可以为40％、50％等)，则关闭半导体制冷片。
流速、温度、功率、管路长度补偿
传感器的准确度也与流过传感器内部的被测气体流速有关。如图9所示，被测气体在最佳流速V 0为中心V 1～V 2范围内，准确度最优，被测气体流速过快或者过慢都会影响准确度。传感器内部风阻或者其他原因会使得被测气体流速有变化，如图10所示，本发明通过调节内部风扇转速(S 1，S 2)或者其他流速调节的方式，将被测气体流速控制在最佳流速范围内，提高传感器准确度。多核传感器通过嵌入算法，补偿由于不同长度进气管路造成的多个子传感器检测采样不同步问题，从而得到更精确的检测数据。类似的对温度和湿度也通过相应算法进行补偿，提高数据准确度。
实施例四
通过控制风扇转速的方式，对采样流量进行补偿。利用流量计、压差传感器方式获取采样气体流速，同时添加风扇转速控制电路。通过获得的气体流速信息控制风扇转速，使得采样气体流速稳定在传感器适合的流速附近，如图9和图10的V 0。传感器最佳流速根据实验方式获得经验值。
实施例五
对于激光颗粒物传感器，添加激光功率检测装置和激光功率控制电路，对激光功率进行补偿。采用实验法获取各激光功率值对应的颗粒物浓度值的变化关系(即固定其他条件不变，只改变被测条件获取测量结果)。根据功率检测器的检测结果通过激光功率控制电路对衰减后的数据做补偿。
实施例六
对传感器采取温度补偿措施。对传感器或者被测气体安装温度采集探头。先通过采用实验法或者传感器温度特性数据，获得不同采样温度值对应的污染物浓度值的变化关系(即固定其他条件不变，只改变被测温度条件)。使用中根据采集的温度数据，对输出污染物结果进行补偿。
实施例七
对传感器采取湿度补偿措施。安装湿度采集设备采集被测气体湿度数据。先通过采用实验法或者传感器的湿度特性数据，获得不同采样湿度值对应的污染物浓度值的变化关系(即固定其他条件不变，只改变被测气体湿度条件)。使用中根据采集的湿度数据，对输出的污染物结果进行补偿。
输出数据计算方法
多核传感器通过利用多个传感器同时测量空气质量，输出数值为多个传感器的数据经过综合计算得出的结果，数据更平滑、稳定，精度更高。实施例八至实施例十二是传感器模组数据计算方法，数据计算时需要剔除异常子传感器的数据，异常子传感器判断方式可以参考实施例十三至实施例十七。
在存在高频组和低频组的情况下，低频组在产生数据的时候，其数据可以作为更可信的检测数据用来参与传感器模组的输出数据的计算。
考虑到低频组的数据可信度更高，可以将低频组的数据赋予2倍权重来加入计算。
实施例八
均值法：一种传感器模组输出数据计算方法；在剔除异常子传感器单元数据后，取所有正常子传感器单元数据的平均值作为输出结果。
实施例九
中值法：一种传感器模组输出数据计算方法；在剔除异常子传感器单元数据后，对所有正常子传感器单元的数值进行排序，取排序的中间值作为最终结果，若参与排序的子传感器单元个数为偶数，则取中间两个子传感器单元的平均值为最终结果。
实施例十
相关系数法：一种传感器模组输出数据计算方法；在剔除异常子传感器单元数据后，对所有正常子传感器单元数据如下计算来获取最终结果。
存储单元储存各子传感器单元的历史检测数据，以一段时间的历史数据(1分钟，10分钟，20分钟，……1小时)为时间单位分别计算被判断子传感器单元与其他子传感器单元的数值相关系数，上述相关系数的计算方法：
A.使用被判断子传感器的所选取历史时间单位的数值与其他子传感器单元该时段的平均值做相关系数的计算。
B.使用被判断子传感器的所选取历史时间单位的数值分别与其他各个子传感器单元做相关系数的计算，获得结果后计算出各个相关系数的平均值作为最终相关 系数，获取各个正常子传感器单元与其他子传感器单元的相关系数后，计算所有正常子传感器单元的相关系数占总相关系数之和的百分比，将每个正常子传感器单元的检测结果乘以该百分比后加和得到最终检测结果。
实施例十一
方差法：一种传感器模组输出数据计算方法；在剔除异常子传感器单元数据后，对所有正常子传感器单元数据如下计算来获取最终结果。
存储器储存各子传感器单元的历史检测数据，以一段时间的历史数据(1分钟，10分钟，20分钟，……1小时)为时间单位分别计算各个子传感器单元在该时间单位内的方差Vi(或标准差)，将各个子传感器单元方差进行加和，计算该加和与各个子传感器单元方差的差值，获得差值后计算各个子传感器单元差值占加和的百分比，将每个正常子传感器单元的检测结果乘以该百分比后加和得到最终检测结果。
实施例十二
百分比法：一种传感器模组输出数据计算方法，剔除异常子传感器单元数据后，对所有正常子传感器单元数据如下计算来获取最终结果。
传感器存储各子传感器单元的历史检测数据，以一段时间(10秒，20秒等)为时间单位，计算距离目前最近的时间单位内检测数值的平均值，利用该平均值进行计算，上述计算方法：
A.将各个子传感器单元在该时间单位的平均值加和，计算各个子传感器单元在加和中所占的百分比，将每个正常子传感器单元的检测结果乘以该百分比后加和得到最终检测结果。
B.利用实施例十二A.中所描述的计算方法，计算距离现在最近多个时间单位内每个子传感器所占百分比，将每个子传感器单元在多个时间单位内的百分比做平均值，得到每个子传感器单元在距离现在最近的多个时间单位内的平均百分比，
将每个正常子传感器单元的检测结果乘以该百分比后加和得到最终检测结果。
识别子传感器工作状态
本方案发明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。该方法如图11所示。
1)子传感器异常的判定：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。
2)异常子传感器的隔离：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；传感器模组降级后继续工作。进入隔离区的子传感器可以停止工作，也可以继续采样和检测，但是该子传感器输出的数据不参与主控模块输出数据的计算。
3)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，对于无法自愈的子传感器则通知运行维护方进行维修或者更换。
4)异常子传感器的恢复：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，对达到恢复条件的子传感器调离隔离区，恢复工作。
实施例十三：
疑似子传感器异常与子传感器异常的判定：当某个子传感器的数据的方差超过阈值时，或者子传感器的数据的飘移超过阈值时，并不立即认定该子传感器为异常，首先将其列为疑似异常子传感器。最后根据一定时长内连续多个数据都表现为异常时，才判定该子传感器异常。
实施例十四
子传感器平均值对比方式：以四核传感器模组为例，以当前时刻为基准，以一台子传感器的数据对比另外三台子传感器一定时间内的平均值(如5s均值、30s均值、60s均值等)。
实施例十五
子传感器出现异常时，对异常子传感器数据加以隔离，异常子传感器的数据不参与传感器模组最终输出数据的计算，但异常子传感器仍然输出数据给控制模块，控制模块对异常子传感器的数据进行监控。子传感器异常包括子传感器出现异常飘移、子传感器出现异常波动和子传感器相关性异常。
存储单元存储各子传感器单元的历史检测数据，以一段时间的历史数据(1分钟，10分钟，20分钟，……1小时)为时间单位分别计算被判断子传感器单元与其他子传感器单元的数值相关系数，若相关系数小于一定值，如0.5(非强相关)，则判断该传感器相关性异常，不参与最终结果的计算。上述相关系数的计算方法：
A.使用被判断子传感器的所选取历史时间单位的数值与其他子传感器单元该时段的平均值做相关系数的计算。
B.使用被判断子传感器的所选取历史时间单位的数值分别与其他各个子传感器单元做相关系数的计算，获得结果后计算出各个相关系数的平均值作为最终相关系数。
相关性法判断子传感器相关性异常，以一台四核传感器模组的相关性计算为例，取子传感器的100组数据与另外三个子传感的100组数据的均值进行相关性计算，相关性R 2≤0.8，则隔离子传感器数据子传感器相关性异常，传感器模组选取另外三台子传感器的数据进行计算并输出监测结果。
实施例十六
实施例十六为子传感器波动异常判定方法。传感器存储各子传感器单元的历史检测数据，以一段时间的历史数据(1分钟，10分钟，20分钟，……1小时)为时间单位分别计算各个子传感器单元在该时间单位内的方差(或标准差)，通过将被判断子传感器单元的方差(或标准差)与其他子传感器单元的方差(或标准差)进行比较，上述方差比较方法：
A.将被判断子传感器单元的方差(或标准差)与其他子传感器单元的方差(或标准差)的平均值进行比较，若两者差值超过其他子传感器单元的方差(或标准差)的平均值的一定值，如20％，30％等，则判断该子传感器单元波动异常。
B.将被判断子传感器单元的方差(或标准差)与其他子传感器单元的方差(或标准差)分别进行比较，并计算两者差值相对于被比较子传感器单元方差(或标准差)的百分比，选择比较的百分比的最大值，若该最大值超过一定值，如20％，30％等，则判断该子传感器波动异常。
实施例十七
实施例十七为子传感器飘移异常判定方法。将被判断子传感器单元过去的相邻的两个时间单位内平均值做差，计算该差值相对于最近时间单位内平均值的百分比，利用该百分比进行判断。上述飘移判断方法：
A.将被判断子传感器单元所获取的百分比与其他子传感器单元所获取百分比的平均值进行比较，若百分比的差值超过一定值，如20％，30％，40％等，则判断该子 传感器单元飘移异常。
B.将被判断子传感器单元所获取的百分比与其他子传感器单元所获取百分比的最大值进行比较，若百分比的差值超过一定值，如20％，30％，40％等，则判断该子传感器单元飘移异常。
实施例十八
在出现需要隔离异常子传感器的情况下，隔离异常子传感器的数据，但异常子传感器的风扇或气泵继续保持运转，保证风压、流量不变，减少压力波动。
实施例十九
如图13所示，在子传感器上安装状态指示灯，异常子传感器被识别出后，在其电路板通讯口处对应位置的状态指示灯改变颜色为警示色(如红色)；正常工作状态的子传感器对应的状态指示灯则为绿色。
轮休模式
本发明对传感器模组设置了轮休的工作模式，在工作正常的子传感器中，选择一个或者多个进行轮休，即通过主动降级运行的方式，解决传感器的疲劳问题。对于激光传感器模组而言，轮休还可以使同组激光传感器的光衰保持基本同步。
常用的单一轮休条件包括：
1)进入疲劳状态时间最长的子传感器；
2)离进入疲劳状态最近的子传感器；
3)累计工作时间最长的子传感器；
4)累计轮休次数最少的子传感器；
5)在可以获取子传感器温度数据的情况下，温度最高的子传感器；
6)疑似异常子传感器。
由于采用不同的轮休条件筛选出来的子传感器可能不一致；在实际应用时，可以将多个轮休条件赋予权重或优先级，来定量判断让哪个子传感器进入轮休。
考虑到疲劳问题是个周期性复发的问题，理想情况下，每个传感器应当在其进入到疲劳状态前得到轮休。假设子传感器单元的平均稳定工作时长为T；那么对于N个传感器单元的模组，选择依次轮休策略时，也就是传感器模组中的各个子传感器依次轮休，前后 两个轮休的间隔时长应当不大于T/N，以保证每个传感器能及时进入轮休。
如果T＝8小时，对于4个传感器单元组成的传感器模组，采用依次轮休策略，那么每隔2个小时轮换一次，就可以保证每个传感器都可以在进入疲劳状态前进入轮休。
在所述子传感器单元上安装状态指示灯，当异常子传感器被识别出后，与其对应位置的状态指示灯颜色改变为警示色；正常工作状态的子传感器对应的状态指示灯则为持续的绿色；进入轮休状态的子传感器对应的状态指示灯则为交替明灭的绿色。
实施例二十
实施例二十是一种子传感器的轮休方式。对于传感器模组，轮休指在规定时间内关闭一台或多台子传感器的传感部分。例如使用风扇的激光颗粒物传感器模组，仅关闭激光器，风扇不关闭。子传感器的关闭时间可以为固定时间(如1小时、2小时、3小时、4小时、5小时、6小时、7小时、8小时、9小时、10小时、11小时、12小时、24小时、2天、3天、4天、5天、6天，或7天等)，在关闭的子传感器到达关闭时间后，激活关闭的子传感器，然后关闭下一个达到轮休条件的子传感器；关闭的时间也可以根据其他子传感器工作状态来确定，如四核传感器模组中，已有一个子传感器处于关闭状态，此时系统判断正在运行的三个子传感器中有一个子传感器达到隔离条件需要进行隔离，那么此时立即启用处于关闭状态的子传感器。具体的轮休条件可以为：
A.通过温度的变化选定轮休子传感器。形式一：通过获取的子传感器温度数据，选择温度最高的子传感器轮休；形式二：根据环境温度选择关闭的子传感器，如环境温度高于温度设定值(如40摄氏度)，则按编号顺序轮流关闭子传感器；
B.通过检测数值的变化选定轮休子传感器。对于确认的疑似异常子传感器，优先关闭。
实施例二十一
四核传感器模组中三核或三核以上子传感器工作正常时，可以采取单核轮休方案。传感器的工作状态受温度影响较大。温度高于60℃时，单核轮休，其余的正常工作四小时后换相邻单核轮休，依次轮休，降低高温下子传感器工作时间，提高四核传感器工作时限。

Claims (12)

  1. 一种多核传感器系统中子传感器的轮休方法，所述多核传感器系统包含主控模块和检测模块；所述检测模块包含至少四个同类子传感器单元组成的传感器模组；其特征在于，在确保传感器模组中至少维持三个正常工作的子传感器单元的前提下，选择一个或多个子传感器单元进行轮休，所述多核传感器系统主动降级工作。
  2. 一种多核传感器系统中子传感器的轮休方法，所述多核传感器系统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的传感器模组；所述检测模块还包含至少一个同类子传感器单元组成的低频校准模组；其特征在于，在确保传感器模组中至少维持一个正常工作的子传感器单元的前提下，从传感器模组中选择一个或多个子传感器单元进行轮休，所述多核传感器系统主动降级工作。
  3. 如权利要求2所述的轮休方法，其特征在于，所述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的工作频率。
  4. 如权利要求3所述的轮休方法，其特征在于，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。
  5. 如权利要求2所述的轮休方法，其特征在于，所述低频校准模组的数据按两倍权重并入到传感器模组的数据中；用于从传感器模组中筛选出疑似异常子传感器。
  6. 如权利要求5所述的轮休方法，其特征在于，所述疑似异常子传感器，是在传感器模组中，波动最大或者飘移最大的子传感器，但波动或者偏移的程度还不足以被认定为子传感器异常；所述子传感器异常的判定标准为下列几种异常之一：
    1)子传感器异常波动；
    2)子传感器异常飘移；
    3)子传感器相关性异常。
  7. 如权利要求1至6之一所述的轮休方法，其特征在于，在所述子传感器单元上安装状态指示灯，当异常子传感器被识别出后，与其对应位置的状态指示灯颜色改变为警示色；正常工作状态的子传感器对应的状态指示灯则为持续的绿色；进入轮休状态的子传感器对应的状态指示灯则为交替明灭的绿色。
  8. 如权利要求1至6之一所述的轮休方法，其特征在于，所述检测模块用于检测大气污染物浓度；所述主控模块用于接收、分析和上传检测模块的检测数据；所述主控 模块在接收传感器模组同时获得的一组检测数据后，对该组检测数据进行分析和综合计算得出的结果作为输出数值；计算时剔除异常子传感器的数据；所述综合计算为如下计算方法之一：1)均值法；2)中值法；3)相关系数法；4)方差法；5)百分比法。
  9. 如权利要求1至6之一所述的轮休方法，其特征在于，所述传感器模组中的各个子传感器依次轮休；前后两个轮休的间隔时长不大于T/N，其中：N为传感器模组中子传感器单元的数量，T为子传感器单元的平均稳定工作时长。
  10. 如权利要求1至6之一所述的轮休方法，其特征在于，从所述传感器模组中选择一个达到轮休条件的子传感器单元进行轮休；所述轮休条件为如下条件之一：
    1)进入疲劳状态时间最长的子传感器；
    2)离进入疲劳状态最近的子传感器；
    3)累计工作时间最长的子传感器；
    4)累计轮休次数最少的子传感器；
    5)在可以获取子传感器温度数据的情况下，温度最高的子传感器；
    6)疑似异常子传感器。
  11. 如权利要求1至6之一所述的轮休方法，其特征在于，所述子传感器单元为下列传感器之一：PM 1传感器、PM 2.5传感器、PM 10传感器、PM 100传感器、二氧化硫传感器、氮氧化物传感器、臭氧传感器、一氧化碳传感器、VOCs传感器或TVOC传感器。
  12. 如权利要求1至6之一所述的轮休方法，所述子传感器单元为激光颗粒物传感器；其特征在于，所述传感器系统通过下述方法来提高传感器模组检测数据的准确度：添加激光功率检测装置和激光功率控制电路，对激光功率进行补偿；采用实验法获取各激光功率值对应的颗粒物浓度值的变化关系；根据功率检测器的检测结果通过激光功率控制电路对衰减后的检测数据做补偿。
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