CN113281472A - Ambient air quality sensor, monitoring device and method - Google Patents

Ambient air quality sensor, monitoring device and method Download PDF

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CN113281472A
CN113281472A CN202110841186.3A CN202110841186A CN113281472A CN 113281472 A CN113281472 A CN 113281472A CN 202110841186 A CN202110841186 A CN 202110841186A CN 113281472 A CN113281472 A CN 113281472A
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concentration
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田启明
张文仓
徐新胜
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Beijing Insights Value Technology Co ltd
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    • G01N33/0004—Gaseous mixtures, e.g. polluted air
    • G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
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Abstract

The embodiment of the disclosure provides an ambient air quality sensor, a monitoring device and a method, which belong to the technical field of signal processing, wherein the ambient air quality sensor specifically comprises: the MEMS sensor is arranged in an array and used for collecting the concentration of pollutants in the environment; the MEMS sensor used for monitoring the same pollutant comprises at least two types; the number of the MEMS sensors of each model is at least three; the pollutants include at least one of volatile organic compounds, ammonia gas, hydrogen sulfide, carbon monoxide, nitrogen dioxide, sulfur dioxide, ozone, carbon dioxide, alcohol, acetone, formaldehyde, combustible gas and smoke. By the processing scheme, the reliability of the ambient air quality monitoring device and method is improved.

Description

Ambient air quality sensor, monitoring device and method
Technical Field
The disclosure relates to the technical field of signal processing, in particular to an ambient air quality sensor, a monitoring device and a monitoring method.
Background
With the continuous development of the scientific and technological level and the continuous improvement of the economic level, people pay more and more attention to the quality of life, and pay more and more attention to the quality of the ambient air, so that the development and the progress of the ambient air monitoring equipment are caused.
In the related technology, the traditional ambient air quality monitoring equipment has the problems of short service life of a sensor, easy failure of the sensor, few monitoring types, large volume, high cost and the like.
Disclosure of Invention
In view of the above, embodiments of the present disclosure provide an ambient air quality sensor, a monitoring device and a method, which at least partially solve the problems in the prior art.
In a first aspect, an embodiment of the present disclosure provides an ambient air quality sensor, including:
the MEMS sensor is arranged in an array and used for collecting the concentration of pollutants in the environment; the MEMS sensor used for monitoring the same pollutant comprises at least two types; the number of the MEMS sensors of each model is at least three; the pollutants include at least one of volatile organic compounds, ammonia gas, hydrogen sulfide, carbon monoxide, nitrogen dioxide, sulfur dioxide, ozone, carbon dioxide, alcohol, acetone, formaldehyde, combustible gas and smoke.
In a second aspect, the disclosed embodiments provide an ambient air quality monitoring device, including the ambient air quality sensor as described above, and further including a processor;
the processor is electrically connected with the output end of the MEMS sensor and used for determining a first monitoring concentration based on the concentration output by the MEMS sensor and determining the first monitoring concentration as the monitoring result concentration of the pollutant in the environment.
Further, still include:
a gas sensor for collecting a second monitored concentration of a contaminant in the environment; the gas sensor comprises at least one of a photo-ionized PID gas sensor and an electrochemical gas sensor;
the processor is also electrically connected with the output end of the gas sensor and is used for determining the monitoring result concentration of the pollutants in the environment based on the first monitoring concentration and the second monitoring concentration output by the gas sensor.
In a third aspect, the present disclosure provides an ambient air quality monitoring method, which is applied to the ambient air quality monitoring device; the method comprises the following steps:
respectively acquiring the concentration acquired by a first type MEMS sensor and the concentration acquired by a second type MEMS sensor which are used for monitoring the same pollutant;
determining a first sensor of the at least three MEMS sensors of the first type of MEMS sensor having a smallest parallelism value and determining a second sensor of the at least three MEMS sensors of the second type of MEMS sensor having a smallest parallelism value;
determining a first monitored concentration of the contaminant based on the concentration collected by the first sensor and the concentration collected by the second sensor;
determining the first monitored concentration as a monitored result concentration of a contaminant in the environment.
Further, before respectively acquiring the concentration collected by the first type MEMS sensor and the concentration collected by the second type MEMS sensor for monitoring the same contaminant, the method further includes:
acquiring at least twenty groups of reference data packets acquired by each MEMS sensor, wherein the same reference data packet comprises reference data acquired by at least three MEMS sensors of the same type at the same time point;
further, the determining a first sensor with a smallest parallelism value among at least three MEMS sensors of the first model of MEMS sensor includes:
determining parallelism values of each MEMS sensor in the first model MEMS sensors based on at least twenty groups of reference data packets collected by the first model MEMS sensors;
determining a first sensor of the first type of MEMS sensor, wherein the parallelism value of the first sensor is smaller than the parallelism values of other sensors of the first type of MEMS sensor.
Further, the determining the parallelism numerical value of each of the first model MEMS sensors based on at least twenty sets of reference data packets collected by the first model MEMS sensors comprises:
calculating the mean value of each reference data packet acquired by the first type MEMS sensor based on at least twenty groups of reference data packets acquired by the first type MEMS sensor;
calculating the relative standard deviation of each MEMS sensor in the first type MEMS sensors corresponding to each reference data packet based on the average value of each reference data packet acquired by the first type MEMS sensors;
and determining a parallelism index value of each MEMS sensor in the first model of MEMS sensor based on the relative standard deviation of each MEMS sensor corresponding to each reference data packet.
Further, said determining a first monitored concentration of said contaminant based on said first sensor collected concentration and said second sensor collected concentration comprises:
and carrying out mean value processing on the concentration acquired by the first sensor and the concentration acquired by the second sensor to obtain a first monitoring concentration of the pollutant.
Further, the method is applied to an ambient air quality monitoring device comprising a gas sensor; the method further comprises the following steps:
in the case that a first monitored concentration of a pollutant is determined and a second monitored concentration sent by a gas sensor is not received, taking the first monitored concentration as a monitoring result concentration of the pollutant;
under the condition that a first monitoring concentration of the pollutant is determined and a second monitoring concentration sent by a gas sensor is received, the first monitoring concentration and the second monitoring concentration are fused to determine the monitoring result concentration of the pollutant.
Further, said fusing said first monitored concentration and said second monitored concentration to determine a monitored resultant concentration of said contaminant comprises:
under the condition that the first monitoring concentration exceeds the monitoring limit value of the MEMS sensor, calculating standard deviations of the first monitoring concentration and the second monitoring concentration respectively, and determining the concentration with higher corresponding standard deviation in the first monitoring concentration and the second monitoring concentration as a third monitoring concentration;
and taking the third monitored concentration as the monitored result concentration of the pollutant.
Further, still include:
acquiring at least twenty groups of reference data packets acquired by each type of gas sensor;
the calculating standard deviations for the first monitored concentration and the second monitored concentration, respectively, comprises:
calculating a first standard deviation of a first monitored concentration corresponding to the MEMS sensor based on at least twenty groups of reference data packets corresponding to the MEMS sensor;
calculating a second standard deviation of a second monitored concentration corresponding to the gas sensor based on at least twenty sets of reference data packets corresponding to the gas sensor.
Further, said fusing said first monitored concentration and said second monitored concentration to determine a monitored resultant concentration of said contaminant comprises:
taking the second monitored concentration as the monitored result concentration of the contaminant if the first monitored concentration does not exceed the monitoring limit of the MEMS sensor and the second monitored concentration is greater than the first monitored concentration; alternatively, the first and second electrodes may be,
and taking the second monitoring concentration as the monitoring result concentration of the pollutant under the condition that the first monitoring concentration does not exceed the monitoring limit value of the MEMS sensor and the second monitoring concentration is less than or equal to the first monitoring concentration.
In a fourth aspect, embodiments of the present disclosure provide an electronic device, including a processor, a memory, and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the ambient air quality monitoring method as described above.
In a fifth aspect, embodiments of the present disclosure provide a computer program stored on the computer-readable storage medium, which when executed by a processor implements the steps of the ambient air quality monitoring method as described above.
In the embodiment of the disclosure, the concentration of pollutants in the environment is monitored by the MEMS sensors arranged in an array, and the environment air quality monitoring device has the advantages because the MEMS sensors have the advantages of small volume, low unit price, long service life and the like; in addition, at least two types of MEMS sensors are arranged on the same pollutant, and the number of the MEMS sensors in the same type is at least three, so that the failure of individual sensors can be avoided, the monitoring accuracy is not influenced, and the reliability of the ambient air quality monitoring device and the method is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an ambient air quality monitoring apparatus according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an ambient air quality monitoring apparatus according to another embodiment of the present disclosure;
FIG. 3 is a flow chart of a method of ambient air quality monitoring provided by an embodiment of the present disclosure;
fig. 4 is a flowchart illustrating calculation of a first monitored concentration in an ambient air quality monitoring method according to another embodiment of the present disclosure;
FIG. 5 is a flow chart of data processing of the MEMS sensor and the PID gas sensor in the ambient air quality monitoring method according to another embodiment of the disclosure;
fig. 6 is a flowchart of data fusion processing in an ambient air quality monitoring method according to another embodiment of the disclosure.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present disclosure, and the drawings only show the components related to the present disclosure rather than the number, shape and size of the components in actual implementation, and the type, amount and ratio of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
The embodiment of the present disclosure provides an ambient air quality sensor, including:
the MEMS sensor 100 is arranged in an array and used for collecting the concentration of pollutants in the environment; the MEMS sensor used for monitoring the same pollutant comprises at least two types; the number of the MEMS sensors of each model is at least three; the pollutants comprise Volatile Organic Compounds (VOC) and ammonia gas (NH)3Hydrogen sulfide H2S, CO and NO2Sulfur dioxide SO2Ozone O3Carbon dioxide CO2At least one of alcohol, acetone, formaldehyde, combustible gas and smoke.
In the embodiment of the disclosure, the concentration of pollutants in the environment is monitored by the MEMS sensors arranged in an array, and the environment air quality monitoring device has the advantages because the MEMS sensors have the advantages of small volume, low unit price, long service life and the like; in addition, at least two types of MEMS sensors are arranged on the same pollutant, and the number of the MEMS sensors in the same type is at least three, so that the failure of individual sensors can be avoided, the monitoring accuracy is not influenced, and the reliability of the ambient air quality monitoring device and the method is improved.
Compared with the traditional sensor, the MEMS sensor has the characteristics of small volume, light weight, low cost, low power consumption, high reliability, suitability for batch production, easiness in integration, realization of intellectualization and the like. According to the embodiment of the disclosure, the reliability and the service life of the ambient air quality sensor can be improved and the volume and the cost of the ambient air quality sensor are reduced by adopting the MEMS sensor.
Through the arrangement of the MEMS sensors in an array arrangement mode, the final ambient air quality sensor can form a regular shape, and meanwhile, the sensing effect of each MEMS sensor can be improved.
In the embodiment of the disclosure, the monitoring result concentration of the pollutant can be determined more comprehensively by combining the monitoring results of the at least two types of MEMS sensors on the same pollutant by setting the at least two types of MEMS sensors for monitoring the same pollutant.
In addition, the number of the MEMS sensors of the same model is at least three, so that under the condition that one MEMS sensor in the MEMS sensor 100 of the same model fails, other MEMS sensors of the same model can work, the smooth work of the ambient air quality sensor is guaranteed, and the reliability of the MEMS sensor of the same model is improved.
The ambient air quality sensor is applied to the atmospheric environment and can monitor the concentration values of various pollutants. The pollutants at least comprise Volatile Organic Compounds (VOC) and ammonia gas (NH)3Hydrogen sulfide H2S, CO and NO2Sulfur dioxide SO2Ozone O3Carbon dioxide CO2At least one of alcohol, acetone, formaldehyde, combustible gas and smoke.
The embodiment of the present disclosure further provides an ambient air quality monitoring apparatus, as shown in fig. 1, including the ambient air quality sensor described above, and further including a processor 120;
the processor 120 is electrically connected to the output of the MEMS sensor 110 for determining a first monitored concentration based on the concentration output by the MEMS sensor 110, and determining the first monitored concentration as a monitored result concentration of a contaminant in the environment.
The input end of the processor 120 is electrically connected to the output end of each MEMS sensor, and is configured to obtain the concentration output by each MEMS sensor, determine a first monitoring concentration by combining the concentration output by each MEMS sensor, and determine the first monitoring concentration as the monitoring result concentration of the pollutant in the environment.
Further, as shown in fig. 2, the ambient air quality monitoring apparatus further includes:
a gas sensor 130 for collecting a second monitored concentration of a contaminant in the environment; the gas sensor 130 comprises at least one of a photo-ionized PID gas sensor and an electrochemical gas sensor;
the processor 120 is also electrically connected to the output of the gas sensor 130 for determining a monitored concentration of a contaminant in the environment based on the first monitored concentration and the second monitored concentration output by the gas sensor.
In this embodiment, the P gas sensor 130 and the MEMS sensor 110 jointly monitor the same pollutant, and the processor 120 is optimized based on a conventional algorithm, so that the second monitored concentration obtained by the gas sensor 130 and the first monitored concentration obtained by the MEMS sensor 110 can be fused to obtain the monitored result concentration of the pollutant in the environment, thereby improving the monitoring accuracy and the application range of the pollutant.
The embodiment of the present disclosure further provides an ambient air quality monitoring method, as shown in fig. 3, which is applied to the ambient air quality monitoring device described above; the method comprises the following steps:
step 310: respectively acquiring the concentration acquired by a first type MEMS sensor and the concentration acquired by a second type MEMS sensor which are used for monitoring the same pollutant;
step 320: determining a first sensor of the at least three MEMS sensors of the first type of MEMS sensor having a smallest parallelism value and determining a second sensor of the at least three MEMS sensors of the second type of MEMS sensor having a smallest parallelism value;
step 330: determining a first monitored concentration of the contaminant based on the concentration collected by the first sensor and the concentration collected by the second sensor;
step 340: determining the first monitored concentration as a monitored result concentration of a contaminant in the environment.
In the embodiment of the disclosure, the concentration of pollutants in the environment is monitored by the MEMS sensors arranged in an array, and the environment air quality monitoring device has the advantages because the MEMS sensors have the advantages of small volume, low unit price, long service life and the like; in addition, at least two types of MEMS sensors are arranged on the same pollutant, and the number of the MEMS sensors in the same type is at least three, so that the failure of individual sensors can be avoided, the monitoring accuracy is not influenced, and the reliability of the ambient air quality monitoring device and the method is improved.
In this embodiment, an example of an ambient air quality monitoring device for any pollutant is described, where the pollutant includes a volatile organic compound VOC and ammonia NH3Hydrogen sulfide H2S, CO and NO2Sulfur dioxide SO2Ozone O3Carbon dioxide CO2Any one of alcohol, acetone, formaldehyde, combustible gas, and smoke.
The first type MEMS sensor and the second type MEMS sensor are used for monitoring pollutants, wherein each MEMS sensor in each type MEMS sensor can acquire monitoring data of the pollutants. And the processor is electrically connected with each MEMS sensor so as to acquire the concentration acquired by each MEMS sensor.
And determining the sensor with the minimum parallelism in each type of MEMS sensor, wherein the parallelism refers to the parallelism between the historical monitoring data collected by each MEMS sensor. Specifically, the parallelism of each MEMS sensor can be calculated from a plurality of monitoring data previously collected by the MEMS sensor.
The method is used for determining the first sensor with the minimum parallelism value in the first type of MEMS sensor and the second sensor with the minimum parallelism value in the second type of MEMS sensor. The concentrations acquired by the first sensor and the concentrations acquired by the second sensor are then screened from the concentrations acquired in step 310.
After determining the concentration collected by the first sensor and the concentration collected by the second sensor, the first monitored concentration of the contaminant determined by the MEMS sensor can be computationally determined using the two concentrations as inputs.
Further, before respectively acquiring the concentration collected by the first type MEMS sensor and the concentration collected by the second type MEMS sensor for monitoring the same contaminant, the method further includes:
acquiring at least twenty groups of reference data packets acquired by each MEMS sensor, wherein the same reference data packet comprises reference data acquired by at least three MEMS sensors of the same type at the same time point.
The determining a first sensor with a smallest parallelism value among at least three MEMS sensors of the first type of MEMS sensor comprises:
determining parallelism values of each MEMS sensor in the first model MEMS sensors based on at least twenty groups of reference data packets collected by the first model MEMS sensors;
determining a first sensor of the first type of MEMS sensor, wherein the parallelism value of the first sensor is smaller than the parallelism values of other sensors of the first type of MEMS sensor.
In this embodiment, the parallelism of each MEMS sensor is determined by at least twenty sets of reference data packets collected by each model MEMS sensor.
Specifically, the at least twenty groups of reference data packets may be at least twenty groups of data packets acquired after the MEMS sensor is stably preheated during startup, and the twenty groups of data packets are used for calculating the parallelism of the MEMS sensor subsequently until the MEMS sensor is started next time; it is also possible to collect at least twenty groups of data packets after every 24 hours of continuous operation, and take the twenty groups of data packets as the calculation of the parallelism of the MEMS sensors during the 24 hours until the next 24 hours to collect the data packets again.
Acquiring at least twenty groups of reference data packets acquired by each type of MEMS sensor, wherein specifically, each group of data packets are acquired by at least three MEMS sensors of the same type of MEMS sensor at the same time point; at least twenty acquisitions are made in this manner to obtain at least twenty sets of reference data packets.
And calculating the parallelism values of all the MEMS sensors in the same type of MEMS sensors based on the at least twenty groups of reference data packets through an algorithm, and then determining the MEMS sensor with the minimum parallelism data.
In an optional embodiment, the determining the parallelism value of each of the first model MEMS sensors based on at least twenty sets of reference data packets collected by the first model MEMS sensor includes:
calculating the mean value of each reference data packet acquired by the first type MEMS sensor based on at least twenty groups of reference data packets acquired by the first type MEMS sensor;
calculating the relative standard deviation of each MEMS sensor in the first type MEMS sensors corresponding to each reference data packet based on the average value of each reference data packet acquired by the first type MEMS sensors;
and determining a parallelism index value of each MEMS sensor in the first model of MEMS sensor based on the relative standard deviation of each MEMS sensor corresponding to each reference data packet.
The mean value of each reference data packet acquired by the first type of MEMS sensor is calculated according to the formula (1).
Figure 109031DEST_PATH_IMAGE001
Formula (1)
Wherein, i refers to the number of the acquisition packets; j is the sensor number; m is the number of sensors in a type of MEMS sensor group, and m is more than or equal to 3; cjiThe data value is the ith data value acquired by a sensor j to be detected; a. theiThe average value of the ith acquired data of the MEMS sensor group of one model is obtained.
After the mean value of each reference data packet acquired by the first type of MEMS sensor is obtained through the formula (1), the relative standard deviation of each MEMS sensor in the first type of MEMS sensor corresponding to each reference data packet can be calculated according to the formula (2).
Figure 665914DEST_PATH_IMAGE002
Formula (2)
Wherein, CjiThe data value is the ith data value acquired by a sensor j to be detected; a. theiThe average value of the ith acquired data of a model MEMS sensor group is obtained; djiRefers to the relative standard deviation of the ith data collected by the sensor j to be measured.
After the relative standard deviation of each MEMS sensor corresponding to each reference data packet is obtained through the formula (2), the parallelism index value of each MEMS sensor in the first model MEMS sensor can be calculated according to the formula (3).
Figure 729685DEST_PATH_IMAGE003
Formula (3)
Wherein D isjiThe standard deviation of the ith data acquired by the sensor j to be detected is referred to; n is the number of collected reference data packets for judging the parallelism of a sensor group of one model, and n is more than or equal to 20; pjWhich refers to the parallelism of the sensor j to be measured in%.
The parallelism numerical value of each MEMS sensor in the first type of MEMS sensor can be calculated through the method. In other alternative embodiments, the parallelism value of each of the first type of MEMS sensors can be calculated in other manners, which are not limited herein.
In addition, the parallelism value of each MEMS sensor in the second type MEMS sensor can also refer to the calculation mode of the parallelism value of each MEMS sensor in the first type MEMS sensor.
In another optional embodiment, said determining a first monitored concentration of said contaminant based on said first sensor collected concentration and said second sensor collected concentration comprises:
and carrying out mean value processing on the concentration acquired by the first sensor and the concentration acquired by the second sensor to obtain a first monitoring concentration of the pollutant.
In this embodiment, the first concentration of the contaminant may be calculated by performing an averaging process by combining the concentration provided by the first sensor and the concentration provided by the second sensor according to formula (4).
Figure 663006DEST_PATH_IMAGE004
Formula (4)
Wherein K is the number of MEMS sensor models aiming at the same pollutant, and is more than or equal to 2; ctRefers to the calculation of parallelism decisionsUsing the concentration value of a certain sensor; and C is a concentration value of the MEMS sensor after mean value processing.
The first monitored concentration of the contaminant can be calculated in the above manner. In other alternative embodiments, the concentration provided by the first sensor and the concentration provided by the second sensor may be averaged in other manners to obtain the first monitored concentration of the contaminant, which is not limited herein.
As shown in fig. 4, taking as an example that the MEMS sensor X has two models Xa and Xb, each model has Xa1, Xa2, Xa3 and Xb1, Xb2 and Xb3 sensors, respectively, fig. 4 shows that the sensor Xa0 and the sensor Xb0 with the lowest parallelism value are determined from each model sensor group, and then the first monitored concentration of the contaminant is obtained by performing the averaging process on the concentrations collected by the sensor Xa0 and the sensor Xb 0.
Further, application to an ambient air quality monitoring device as shown in fig. 2; the method further comprises the following steps:
in the case that a first monitored concentration of a pollutant is determined and a second monitored concentration sent by a gas sensor is not received, taking the first monitored concentration as a monitoring result concentration of the pollutant;
under the condition that a first monitoring concentration of the pollutant is determined and a second monitoring concentration sent by a gas sensor is received, the first monitoring concentration and the second monitoring concentration are fused to determine the monitoring result concentration of the pollutant.
In the case where the processor receives the second monitored concentration sent by the gas sensor and does not receive the first monitored concentration sent by the MEMS sensor, the second monitored concentration is taken as the monitored result concentration of the contaminant, as shown in the first path of fig. 5.
In the event that the processor does not receive the second monitored concentration sent by the gas sensor and does not receive the first monitored concentration sent by the MEMS sensor, the sensor is prompted to be absent of this contaminant, as in the second path of fig. 5.
In the case where the processor receives the second monitored concentration not transmitted by the gas sensor and receives the first monitored concentration transmitted by the MEMS sensor, the first monitored concentration is taken as the monitored result concentration of the contaminant, as shown in the third path in fig. 5.
In the case where the processor receives both the second monitored concentration sent by the gas sensor and the first monitored concentration sent by the MEMS sensor, the first monitored concentration and the second monitored concentration are fused to determine the monitored concentration of the contaminant, as shown in the fourth path of fig. 5.
In an alternative embodiment, said fusing said first monitored concentration and said second monitored concentration to determine a monitored concentration of said contaminant comprises:
under the condition that the first monitoring concentration exceeds the monitoring limit value of the MEMS sensor, calculating standard deviations of the first monitoring concentration and the second monitoring concentration respectively, and determining the concentration with higher corresponding standard deviation in the first monitoring concentration and the second monitoring concentration as a third monitoring concentration;
the third monitored concentration is taken as the monitored concentration of the contaminant, as shown in the first path of fig. 6.
Specifically, each type of gas sensor in this embodiment also needs to collect at least twenty sets of reference data packets.
The calculating standard deviations for the first monitored concentration and the second monitored concentration, respectively, comprises:
calculating a first standard deviation of a first monitored concentration corresponding to the MEMS sensor based on at least twenty groups of reference data packets corresponding to the MEMS sensor;
calculating a second standard deviation of a second monitored concentration corresponding to the gas sensor based on at least twenty sets of reference data packets corresponding to the gas sensor.
In this embodiment, the first standard deviation of the MEMS sensor can be calculated by formula (5).
Figure 331885DEST_PATH_IMAGE005
Formula (5)
Wherein n refers to the judgment cycle acquisition of the same type of sensorThe number of reference packets; siThe reference data packet is the ith reference data packet in a sensor decision period;
Figure 728844DEST_PATH_IMAGE007
the average value of a plurality of reference data packets in a sensor decision period is referred to;
Figure DEST_PATH_IMAGE009
refers to the standard deviation of the sensor decision period.
Calculating a first standard deviation of the MEMS sensor by using at least twenty groups of reference data packets corresponding to the MEMS sensor and the formula (5);
the second standard deviation of the gas sensor can be calculated by using at least twenty sets of reference data packets corresponding to the gas sensor and the formula (5).
Further, said fusing said first monitored concentration and said second monitored concentration to determine a monitored resultant concentration of said contaminant comprises:
taking the second monitored concentration as the monitored result concentration of the contaminant if the first monitored concentration does not exceed the monitoring limit of the MEMS sensor and the second monitored concentration is greater than the first monitored concentration; alternatively, the first and second electrodes may be,
and taking the second monitoring concentration as the monitoring result concentration of the pollutant under the condition that the first monitoring concentration does not exceed the monitoring limit value of the MEMS sensor and the second monitoring concentration is less than or equal to the first monitoring concentration.
In the event that the first monitored concentration received by the processor does not exceed the monitoring limit of the MEMS sensor and the second monitored concentration is greater than the first monitored concentration, the first monitored concentration is taken as the monitored result concentration of the contaminant, as in the second path of fig. 6.
And in the case that the first monitoring concentration received by the processor does not exceed the monitoring limit of the MEMS sensor and the second monitoring concentration is less than or equal to the first monitoring concentration, taking the second monitoring concentration as the monitoring result concentration of the pollutant, such as a third path in FIG. 6.
The embodiment of the present disclosure further provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, where the computer program, when executed by the processor, implements each process of the above-mentioned ambient air quality monitoring method embodiment, and can achieve the same technical effect, and is not described herein again to avoid repetition.
The embodiments of the present disclosure further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements each process of the above-mentioned ambient air quality monitoring method embodiment, and can achieve the same technical effect, and in order to avoid repetition, the details are not repeated here. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present disclosure should be covered within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims (14)

1. An ambient air quality sensor, comprising:
the MEMS sensor is arranged in an array and used for collecting the concentration of pollutants in the environment; the MEMS sensor used for monitoring the same pollutant comprises at least two types; the number of the MEMS sensors of each model is at least three; the pollutants include at least one of volatile organic compounds, ammonia gas, hydrogen sulfide, carbon monoxide, nitrogen dioxide, sulfur dioxide, ozone, carbon dioxide, alcohol, acetone, formaldehyde, combustible gas and smoke.
2. An ambient air quality monitoring device comprising the ambient air quality sensor of claim 1, further comprising a processor;
the processor is electrically connected with the output end of the MEMS sensor and used for determining a first monitoring concentration based on the concentration output by the MEMS sensor and determining the first monitoring concentration as the monitoring result concentration of the pollutant in the environment.
3. The ambient air quality monitoring device of claim 2, further comprising:
a gas sensor for collecting a second monitored concentration of a contaminant in the environment; the gas sensor comprises at least one of a photo-ionized PID gas sensor and an electrochemical gas sensor;
the processor is also electrically connected with the output end of the gas sensor and is used for determining the monitoring result concentration of the pollutants in the environment based on the first monitoring concentration and the second monitoring concentration output by the gas sensor.
4. An ambient air quality monitoring method applied to the ambient air quality monitoring apparatus according to claim 2 or 3; the method comprises the following steps:
respectively acquiring the concentration acquired by a first type MEMS sensor and the concentration acquired by a second type MEMS sensor which are used for monitoring the same pollutant;
determining a first sensor of the at least three MEMS sensors of the first type of MEMS sensor having a smallest parallelism value and determining a second sensor of the at least three MEMS sensors of the second type of MEMS sensor having a smallest parallelism value;
determining a first monitored concentration of the contaminant based on the concentration collected by the first sensor and the concentration collected by the second sensor;
determining the first monitored concentration as a monitored result concentration of a contaminant in the environment.
5. The method of claim 4, wherein before separately obtaining the concentrations collected by the first type MEMS sensor and the second type MEMS sensor for monitoring the same contaminant, further comprising:
acquiring at least twenty groups of reference data packets acquired by each MEMS sensor, wherein the same reference data packet comprises reference data acquired by at least three MEMS sensors of the same type at the same time point.
6. The method of claim 5, wherein said determining a first sensor of the at least three MEMS sensors of the first model of MEMS sensor with a least amount of parallelism comprises:
determining parallelism values of each MEMS sensor in the first model MEMS sensors based on at least twenty groups of reference data packets collected by the first model MEMS sensors;
determining a first sensor of the first type of MEMS sensor, wherein the parallelism value of the first sensor is smaller than the parallelism values of other sensors of the first type of MEMS sensor.
7. The method of claim 6, wherein determining parallelism values for each of the first model MEMS sensors based on at least twenty reference data packets collected by the first model MEMS sensor comprises:
calculating the mean value of each reference data packet acquired by the first type MEMS sensor based on at least twenty groups of reference data packets acquired by the first type MEMS sensor;
calculating the relative standard deviation of each MEMS sensor in the first type MEMS sensors corresponding to each reference data packet based on the average value of each reference data packet acquired by the first type MEMS sensors;
and determining a parallelism index value of each MEMS sensor in the first model of MEMS sensor based on the relative standard deviation of each MEMS sensor corresponding to each reference data packet.
8. The method of claim 4, wherein determining the first monitored concentration of the contaminant based on the concentrations acquired by the first sensor and the second sensor comprises:
and carrying out mean value processing on the concentration acquired by the first sensor and the concentration acquired by the second sensor to obtain a first monitoring concentration of the pollutant.
9. The method of claim 4, further comprising:
in the case that a first monitored concentration of a pollutant is determined and a second monitored concentration sent by a gas sensor is not received, taking the first monitored concentration as a monitoring result concentration of the pollutant;
under the condition that a first monitoring concentration of the pollutant is determined and a second monitoring concentration sent by a gas sensor is received, the first monitoring concentration and the second monitoring concentration are fused to determine the monitoring result concentration of the pollutant.
10. The method of claim 9, wherein said fusing said first monitored concentration and said second monitored concentration to determine a monitored resultant concentration of said contaminant comprises:
under the condition that the first monitoring concentration exceeds the monitoring limit value of the MEMS sensor, calculating standard deviations of the first monitoring concentration and the second monitoring concentration respectively, and determining the concentration with higher corresponding standard deviation in the first monitoring concentration and the second monitoring concentration as a third monitoring concentration;
and taking the third monitored concentration as the monitored result concentration of the pollutant.
11. The method of claim 10, further comprising:
acquiring at least twenty groups of reference data packets acquired by each type of gas sensor;
the calculating standard deviations for the first monitored concentration and the second monitored concentration, respectively, comprises:
calculating a first standard deviation of a first monitored concentration corresponding to the MEMS sensor based on at least twenty groups of reference data packets corresponding to the MEMS sensor;
calculating a second standard deviation of a second monitored concentration corresponding to the gas sensor based on at least twenty sets of reference data packets corresponding to the gas sensor.
12. The method of claim 9, wherein said fusing said first monitored concentration and said second monitored concentration to determine a monitored resultant concentration of said contaminant comprises:
taking the second monitored concentration as the monitored result concentration of the contaminant if the first monitored concentration does not exceed the monitoring limit of the MEMS sensor and the second monitored concentration is greater than the first monitored concentration; alternatively, the first and second electrodes may be,
and taking the second monitoring concentration as the monitoring result concentration of the pollutant under the condition that the first monitoring concentration does not exceed the monitoring limit value of the MEMS sensor and the second monitoring concentration is less than or equal to the first monitoring concentration.
13. An electronic device, comprising a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the ambient air quality monitoring method according to any one of claims 4 to 12.
14. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, carries out the steps of the ambient air quality monitoring method according to one of the claims 4 to 12.
CN202110841186.3A 2021-07-26 2021-07-26 Ambient air quality sensor, monitoring device and method Pending CN113281472A (en)

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Application publication date: 20210820