Environmental air pollution monitoring method and system based on sensor technology

Abstract

The invention relates to the technical field of air quality monitoring, in particular to an ambient air pollution monitoring method and system based on a sensor technology, and the method comprises the steps: obtaining air quality data at a monitoring point at an initial sampling frequency, and constructing an air quality data curve at the monitoring point; determining a suspected pollution gas emission stage, and taking a monitoring point corresponding to a first abnormal data segment of the suspected pollution gas emission stage as a nearest neighbor monitoring point of a pollution gas emission source; determining the consistency between the environmental influence of the suspected pollution gas emission stage and the change rule of the actual air quality data; under the condition that the consistency of the change rule is greater than a first threshold value, determining the suspected pollution gas emission stage as an actual pollution gas emission stage; and determining the optimized sampling frequency of each monitoring point in the actual pollution gas emission stage, and collecting the corrected air quality data of each monitoring point by using the optimized sampling frequency. According to the invention, the monitoring efficiency, accuracy and reliability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of air quality monitoring, and in particular to a method and system for monitoring ambient air pollution based on sensor technology. Background Art

[0002] Ambient air quality refers to the proportion of various gas components in the air and the state of particulate matter and other pollutants it contains. With the acceleration of industrialization and urbanization, ambient air quality has become a global focus. Due to the complexity and variability of ambient air conditions, affected by factors such as differences in the distribution of pollution sources and emission intensity, and complex interactions of meteorological conditions, it presents a high degree of dynamics and heterogeneity at different time and spatial scales.

[0003] In some scenarios, sensors are often used to continuously monitor ambient air quality at a single fixed collection frequency. If the collection frequency is too high, a large amount of redundant data will be generated, which will increase the burden of data storage, transmission and processing, and increase equipment energy consumption and operation and maintenance costs. If the collection frequency is too low, important information may be missed, and the rapid changes and short-term fluctuations in air quality cannot be accurately captured, resulting in insufficient representativeness of the monitored ambient air quality data and low spatiotemporal resolution, making it difficult to meet business needs such as refined environmental management and air quality forecasting and warning. Therefore, the monitoring efficiency, accuracy and reliability of using sensors to monitor ambient air quality at a single fixed collection frequency are low. Summary of the invention

[0004] In order to solve the technical problems of low monitoring efficiency, accuracy and reliability of ambient air quality monitoring, the purpose of the present invention is to provide an ambient air pollution monitoring method and system based on sensor technology. The technical solutions adopted are as follows: In a first aspect, an embodiment of the present invention provides an environmental air pollution monitoring method based on sensor technology, comprising: acquiring air quality data at a monitoring point at an initial sampling frequency, and constructing an air quality data curve at the monitoring point; determining a suspected polluted gas emission stage according to the air quality data curve of each monitoring point, and taking the monitoring point corresponding to the first abnormal data segment of the suspected polluted gas emission stage as the nearest neighbor monitoring point of the polluted gas emission source; determining the consistency of the changing law of the environmental impact of the suspected polluted gas emission stage and the actual air quality data according to the minimum angle between the vector pointing from the nearest neighbor monitoring point to other monitoring points and the wind direction, the distance between other monitoring points and the nearest neighbor monitoring point, and the current wind speed, the abnormal data segments of each monitoring point in the suspected polluted gas emission stage, and the abnormal data segments of the nearest neighbor monitoring point; when the consistency of the changing law is greater than a first threshold, determining the suspected polluted gas emission stage as the actual polluted gas emission stage; determining the optimized sampling frequency of each monitoring point in the actual polluted gas emission stage according to the air quality data of each abnormal data segment in the actual polluted gas emission stage and the initial sampling frequency, and collecting the corrected air quality data of each monitoring point using the optimized sampling frequency.

[0005] Optionally, determining the suspected polluted gas emission stage based on the air quality data curve of each monitoring point includes: taking air quality data less than a passing value as abnormal data, each abnormal data constituting a plurality of abnormal data segments of the air quality data curve; screening all target abnormal data segments with a slope less than a second threshold in the abnormal data segment, and determining the area of ​​a closed figure formed by the abnormal data segment and the straight line where the passing value is located; determining the degree of environmental pollution of the target abnormal data segment based on the area, the slope of the target abnormal data segment, the time length of the target abnormal data segment and the sum of the time lengths of all target abnormal data segments; taking the abnormal data segments of multiple monitoring points where the air quality data curves of different monitoring points overlap on the time axis as the suspected polluted gas emission stage, and taking the monitoring point corresponding to the first abnormal data segment in the suspected polluted gas emission stage as the nearest neighbor monitoring point of the polluted gas emission source.

[0006] Optionally, determining the degree of environmental pollution of the target abnormal data segment according to the area, the slope of the target abnormal data segment, the time length of the target abnormal data segment, and the sum of the time lengths of all the target abnormal data segments includes: calculating a first ratio between the time length of the target abnormal data segment and the sum of the time lengths of all the target abnormal data segments; calculating a first product between the absolute value of the slope of the target abnormal data segment and the first ratio, and superimposing the first products to obtain a first superimposed value; and determining a second product between the area and the first superimposed value as the degree of environmental pollution of the target abnormal data segment.

[0007] Optionally, according to the minimum angle between the vector pointing from the nearest neighbor monitoring point to other monitoring points and the wind direction, the distance between other monitoring points and the nearest neighbor monitoring point, and the current wind speed, the abnormal data segments of each monitoring point in the suspected polluted gas emission stage and the abnormal data segments of the nearest neighbor monitoring point, the consistency of the changing law of the environmental impact of the suspected polluted gas emission stage and the actual air quality data is determined, including: according to the minimum angle between the vector pointing from the nearest neighbor monitoring point to other monitoring points and the wind direction, the distance between other monitoring points and the nearest neighbor monitoring point, and the current wind speed, the environmental impact factors of other monitoring points are determined; the abnormal data segments of each monitoring point in the suspected polluted gas emission stage and the abnormal data segments of the nearest neighbor monitoring point are determined. The Pearson correlation coefficient between the abnormal data segments of the monitoring points; determine the convergence of the pollution change rhythm of each monitoring point and the nearest neighboring monitoring point during the suspected polluted gas emission stage based on the environmental pollution degree of the abnormal data segments of each monitoring point during the suspected polluted gas emission stage and the environmental pollution degree of the abnormal data segments of the nearest neighboring monitoring point, the Pearson correlation coefficient, the starting time of the abnormal data segments of each monitoring point during the suspected polluted gas emission stage and the starting time of the abnormal data segments of the nearest neighboring monitoring point; determine the consistency of the environmental impact of the suspected polluted gas emission stage with the changing law of the actual air quality data based on the convergence of pollution change rhythm and environmental impact factors of each monitoring point during the suspected polluted gas emission stage.

[0008] Optionally, based on the minimum angle between the vector pointing from the nearest neighbor monitoring point to other monitoring points and the wind direction, the distance between other monitoring points and the nearest neighbor monitoring point, and the current wind speed, determining the environmental impact factors of other monitoring points includes: calculating a second ratio between a first predetermined value and the minimum angle, and a third ratio between the current wind speed and the distance; and determining the third product between the second ratio and the third ratio as the environmental impact factor of the other monitoring point.

[0009] Optionally, according to the environmental pollution degree of the abnormal data segment of each monitoring point in the suspected polluted gas emission stage and the environmental pollution degree of the abnormal data segment of the nearest neighboring monitoring point, the Pearson correlation coefficient, the starting time of the abnormal data segment of each monitoring point in the suspected polluted gas emission stage and the starting time of the abnormal data segment of the nearest neighboring monitoring point, determining the convergence of the pollution change rhythm of each monitoring point and the nearest neighboring monitoring point in the suspected polluted gas emission stage includes: Calculate a first sum between the Pearson correlation coefficient and a second predetermined value, a first difference between the environmental pollution degree of the abnormal data segment of each monitoring point in the suspected polluted gas emission stage and the environmental pollution degree of the abnormal data segment of the nearest neighboring monitoring point, and a second difference between the start time of the abnormal data segment of each monitoring point in the suspected polluted gas emission stage and the start time of the abnormal data segment of the nearest neighboring monitoring point; calculate the reciprocal of the second sum between the first difference and the second difference; determine the fourth product between the first sum and the reciprocal of the second sum as the convergence of the pollution change rhythm of each monitoring point and the nearest neighboring monitoring point in the suspected polluted gas emission stage.

[0010] Optionally, determining the consistency of the environmental impact of the suspected polluted gas emission stage with the changing law of the actual air quality data based on the pollution change rhythm convergence and environmental impact factors of each monitoring point in the suspected polluted gas emission stage includes: determining a first average value of the pollution change rhythm convergence of each monitoring point in the suspected polluted gas emission stage, and a second average value of the environmental impact factors of each monitoring point in the suspected polluted gas emission stage; calculating a third difference between the pollution change rhythm convergences of adjacent monitoring points, and a fourth difference between the environmental impact factors of adjacent monitoring points; calculating a fourth ratio between the third difference and the first average value, and a fifth ratio between the fourth difference and the second average value; calculating a sixth ratio between the fourth ratio and the fifth ratio, and an absolute value of the fifth difference between the third predetermined value and the sixth ratio; superimposing the absolute values ​​of each fifth difference to obtain a second superimposed value; and performing inverse proportional normalization processing on the second superimposed value to obtain the consistency of the changing law.

[0011] Optionally, determining the optimized sampling frequency of each monitoring point in the actual pollutant gas emission stage based on the air quality data of each abnormal data segment in the actual pollutant gas emission stage and the initial sampling frequency includes: calculating the standard deviation of the air quality data of each abnormal data segment in the actual pollutant gas emission stage, and normalizing the standard deviation to obtain a normalized value; calculating the second sum between the third predetermined value and the normalized value; and determining the fifth product between the initial sampling frequency and the second sum as the optimized sampling frequency of the monitoring point in the actual pollutant gas emission stage.

[0012] Optionally, after collecting the corrected air quality data of each monitoring point using the optimized sampling frequency, the method further includes: performing abnormal monitoring on the corrected air quality data using a local outlier factor algorithm.

[0013] In a second aspect, an embodiment of the present invention provides an ambient air pollution monitoring system based on sensor technology, comprising: a processor and a memory; wherein the memory is used to store a computer program that can be run on the processor; and the processor is used to execute the program stored in the memory to implement the steps of the ambient air pollution monitoring method based on sensor technology as mentioned in the first aspect.

[0014] The present invention has the following beneficial effects: first, the air quality data at the monitoring point is acquired at an initial sampling frequency, and an air quality data curve at the monitoring point is constructed; then, the suspected polluted gas emission stage is determined according to the air quality data curve of each monitoring point, and the monitoring point corresponding to the first abnormal data segment in the suspected polluted gas emission stage is used as the nearest neighbor monitoring point of the polluted gas emission source; and according to the minimum angle between the vector pointing from the nearest neighbor monitoring point to other monitoring points and the wind direction, the distance between other monitoring points and the nearest neighbor monitoring point, and the current wind speed, the abnormal data segments of each monitoring point in the suspected polluted gas emission stage and the abnormal data segments of the nearest neighbor monitoring point, the consistency of the changing law of the environmental impact of the suspected polluted gas emission stage and the actual air quality data is determined; when the consistency of the changing law is greater than a first threshold value, the suspected polluted gas emission stage is determined to be the actual polluted gas emission stage; secondly, the optimized sampling frequency of each monitoring point in the actual polluted gas emission stage is determined according to the air quality data of each abnormal data segment in the actual polluted gas emission stage and the initial sampling frequency, and finally the corrected air quality data of each monitoring point is collected using the optimized sampling frequency.

[0015] In this way, the embodiment of the present invention can screen abnormal data segments and determine the suspected polluted gas emission stage, and then analyze the change in correlation between the environmental impact of the suspected polluted gas emission stage and the actual air quality data according to the influence of the distance and wind direction of the polluted gas emission source in the actual scene when the polluted gas is emitted, and can accurately identify the actual polluted gas emission stage in the air quality, and re-determine the optimized sampling frequency in the actual polluted gas emission stage in combination with the air quality data in the actual polluted gas emission stage and the initial sampling frequency, so that the optimized sampling frequency can adapt to the actual scene of the actual polluted gas emission stage. When the air quality data is sampled at the optimized sampling frequency, the monitoring efficiency, accuracy and reliability of monitoring the ambient air quality are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flow chart of a method for monitoring ambient air pollution based on sensor technology provided by one embodiment of the present invention; Figure 2 A schematic diagram showing the comparison between the air quality data of a monitoring point and a preset passing value provided by one embodiment of the present invention; Figure 3 A schematic structural diagram of an ambient air pollution monitoring system based on sensor technology is provided for another embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the environmental air pollution monitoring method based on sensor technology proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] A specific scheme of an ambient air pollution monitoring method based on sensor technology provided by the present invention is described in detail below in conjunction with the accompanying drawings.

[0021] Embodiment 1: See also Figure 1 , which shows a flow chart of an ambient air pollution monitoring method based on sensor technology provided by an embodiment of the present invention, including: S101, acquiring air quality data at a monitoring point at an initial sampling frequency, and constructing an air quality data curve at the monitoring point.

[0022] Specifically, the embodiment of the present invention sets a number of monitoring points in the monitoring area where air quality monitoring is required, and identifies the main air pollutants in the monitoring area according to the characteristics of the monitoring area. Including but not limited to PM2.5, PM10, sulfur dioxide, nitrogen dioxide, etc., the air quality status is evaluated by the main air pollutants in the air, as the air quality data of each monitoring point at each monitoring moment, and in order to ensure the validity and comparability of the data, the numerical range of the air quality data is determined within 0~100. Among them, 0 points represent extremely poor air quality, and 100 points represent excellent air quality. With the gradual improvement of air quality, the concentration of pollutants decreases, and the corresponding score value will gradually increase. The automatic monitoring instrument of the embodiment of the present invention performs continuous automatic monitoring to obtain monitoring results, analyzes and obtains relevant data, and the amount of data generated in the process of monitoring air quality is very large.

[0023] Furthermore, the embodiment of the present invention pre-sets a reasonable initial sampling frequency according to the air quality monitoring requirements and the probability of abnormal conditions. Monitor and obtain the air quality data of each monitoring point at the monitoring time, transmit the air quality data to the cloud platform for data analysis, build the air quality data curve corresponding to each monitoring point, and perform noise preprocessing on the collected air quality data, such as using filtering algorithms to remove high-frequency noise or outliers, so as to obtain more accurate air quality data.

[0024] S102, determining the suspected polluted gas emission stage according to the air quality data curve of each monitoring point, and taking the monitoring point corresponding to the first abnormal data segment of the suspected polluted gas emission stage as the nearest neighbor monitoring point of the polluted gas emission source.

[0025] Specifically, the embodiment of the present invention can preset the air quality data of the current monitoring area and the pass value as the basic reference for data screening based on the urban environmental air quality standards and the statistical analysis of the past monitoring data. According to the distribution of the air quality data curve of each monitoring point, the abnormal data segment is screened. Among them, the pass value can be determined according to the actual situation. In the embodiment of the present invention, the value is 70. For example, Figure 2 As shown, Figure 2 A schematic diagram of the comparison between the air quality data of a monitoring point and a preset passing value provided for an embodiment of the present invention, wherein for the air quality data of any monitoring point at a certain monitoring moment, when it is less than the passing value corresponding to the passing line, it is considered that the current monitoring point has entered an abnormal stage.

[0026] Further, as an optional embodiment of the present invention, determining the suspected polluted gas emission stage according to the air quality data curve of each monitoring point includes: taking air quality data less than a passing value as abnormal data, each abnormal data constituting a plurality of abnormal data segments of the air quality data curve; screening all target abnormal data segments with a slope less than a second threshold in the abnormal data segment, and determining the area of ​​a closed figure formed by the abnormal data segment and the straight line where the passing value is located; determining the degree of environmental pollution of the target abnormal data segment according to the area, the slope of the target abnormal data segment, the time length of the target abnormal data segment and the sum of the time lengths of all target abnormal data segments; taking the abnormal data segments of multiple monitoring points where the air quality data curves of different monitoring points overlap on the time axis as the suspected polluted gas emission stage, and taking the monitoring point corresponding to the first abnormal data segment in the suspected polluted gas emission stage as the nearest neighbor monitoring point of the polluted gas emission source.

[0027] Specifically, on the air quality data curve of each monitoring point, all air quality data less than the passing value are taken as abnormal data, thereby obtaining several abnormal data segments of the air quality data curve of each monitoring point. When there are abnormal data segments, these abnormal data segments may imply special pollution emission events, abnormal meteorological influences, or potential failures of monitoring equipment, and further analysis is required. Among them, for each abnormal data segment of each monitoring point, the longer the abnormal duration and the larger the amplitude below the passing value, it is considered that the air quality is worse, that is, the pollution degree of the environmental data of the abnormal data segment is greater. Among them, the second threshold value can be determined according to the actual situation, and the value is 0 in the embodiment of the present invention.

[0028] Further, as an optional embodiment of the present invention, determining the degree of environmental pollution of the target abnormal data segment according to the area, the slope of the target abnormal data segment, the time length of the target abnormal data segment and the sum of the time lengths of all the target abnormal data segments includes: calculating a first ratio between the time length of the target abnormal data segment and the sum of the time lengths of all the target abnormal data segments; calculating a first product between the absolute value of the slope of the target abnormal data segment and the first ratio, and superimposing the first products to obtain a first superimposed value; determining a second product between the area and the first superimposed value as the degree of environmental pollution of the target abnormal data segment.

[0029] Specifically, the embodiment of the present invention specifically uses the following formula to calculate the environmental pollution degree of the target abnormal data segment: In the above formula, Indicates The monitoring point The environmental pollution degree of each target abnormal data segment. Indicates that The monitoring point The target abnormal data segment and the passing line corresponding to the passing value constitute the area of ​​the closed figure. All the line segments with slopes less than zero are selected from all the abnormal data segments of the monitoring points as the target abnormal data segments. Represents the number of abnormal data segments with a slope less than zero. Indicates The slope of the abnormal data segment with a slope less than zero. Indicates The length of time for which the slope of the abnormal data is less than zero. Indicates The sum of the time lengths of all abnormal data segments whose slopes are less than zero at the monitoring points.

[0030] The longer the duration of the abnormal data segment is and the greater the difference between it and the passing value is, the lower the air quality data reflected by the current abnormal data segment is, that is, the worse the air quality at the current monitoring point is. That is, the area of ​​the closed figure formed by the abnormal data segment and the passing value is The larger the value, the greater the degree of environmental pollution in the abnormal data segment. It represents the overall decline of all air quality data decline segments in the abnormal data segment. The duration of each decline segment is used as the weight of its slope. When the duration is longer and the absolute value of the slope is larger, the downward trend is more significant, and the air quality data declines faster. That is, when The larger the product of is, the greater the degree of environmental pollution in the abnormal data segment is.

[0031] Furthermore, in the same monitoring area, due to the fluidity and diffusion of air, the air quality data reflected by each monitoring point under normal circumstances should be correlated in time series and the change trend should be relatively consistent. Therefore, on the air quality data curve constructed by all monitoring points, in theory, the abnormal data segment should occur at a similar time point, and its duration, data change amplitude and other characteristics should also show a certain correlation. Through the synchronous analysis of the air quality data curves of each monitoring point, the abnormal data segment can be identified more accurately, and then the sampling frequency of abnormal situations can be increased. In the same monitoring area, there will be multiple monitoring points. When a polluted gas emission point is set at a monitoring point in the monitoring area, the air quality data of the monitoring points around the polluted gas emission point will be significantly reduced at the monitoring time, and the polluted gas will gradually diffuse from the monitoring point to the surroundings, and affect the air quality data of the surrounding monitoring points in turn, and the air quality data of all monitoring points from the polluted gas emission point to the diffusion direction have a delay in time series. As the distance increases, the start time of the abnormal data segment of the abnormal air quality data will be later, and the environmental pollution degree of the abnormal data segment of the corresponding monitoring point will gradually decrease with the distance.

[0032] Furthermore, from a time series perspective, when the start and end times of the remaining monitoring points corresponding to the abnormal data segments of different monitoring points are usually not completely consistent, the abnormal data segments of several monitoring points where the air quality data curves of all different monitoring points overlap on the time axis are recorded as a suspected polluted gas emission stage. Ensure that the suspected polluted gas emission stage contains all abnormal data segments affecting the monitoring points. The suspected polluted gas emission stage represents that actual air anomalies may have occurred, and may also be caused by equipment abnormalities or noise data generated at a certain monitoring point. For each suspected polluted gas emission stage, the monitoring point corresponding to the first abnormal data segment at the start time is considered to be the nearest neighbor monitoring point of the polluted gas emission source. Therefore, based on the fluctuation correlation between the abnormal data segments of the remaining monitoring points and the first abnormal data segment, it can be verified whether polluted gas emissions occur around a certain monitoring point.

[0033] S103, based on the minimum angle between the vector pointing from the nearest neighbor monitoring point to other monitoring points and the wind direction, the distance between other monitoring points and the nearest neighbor monitoring point, as well as the current wind speed, the abnormal data segments of each monitoring point in the suspected polluted gas emission stage and the abnormal data segments of the nearest neighbor monitoring point, determine the consistency of the environmental impact of the suspected polluted gas emission stage with the changing pattern of the actual air quality data.

[0034] Specifically, the embodiment of the present invention obtains the current wind direction and wind speed. If there is polluted gas emission, the greater the wind speed, the faster the polluted gas will diffuse. In the current wind direction, the environmental impact factor of each monitoring point affected by pollution is determined. The environmental impact factor represents the possible degree of pollution of the monitoring point by the nearest neighboring monitoring point of the polluted gas emission source. Therefore, the environmental parameters of the current actual scene are used to determine the consistency of the environmental impact of the suspected polluted gas emission stage with the change law of the actual air quality data.

[0035] Further, as an optional embodiment of the present invention, according to the minimum angle between the vector pointing from the nearest neighbor monitoring point to other monitoring points and the wind direction, the distance between other monitoring points and the nearest neighbor monitoring point, and the current wind speed, the abnormal data segments of each monitoring point in the suspected polluted gas emission stage and the abnormal data segments of the nearest neighbor monitoring point, determining the consistency of the changing law of the environmental impact of the suspected polluted gas emission stage and the actual air quality data includes: determining the environmental impact factors of other monitoring points according to the minimum angle between the vector pointing from the nearest neighbor monitoring point to other monitoring points and the wind direction, the distance between other monitoring points and the nearest neighbor monitoring point, and the current wind speed; determining the abnormal data segments of each monitoring point in the suspected polluted gas emission stage The Pearson correlation coefficient between the data segment and the abnormal data segment of the nearest neighboring monitoring point; determine the convergence of the pollution change rhythm of each monitoring point and the nearest neighboring monitoring point during the suspected polluted gas emission stage based on the environmental pollution degree of the abnormal data segment of each monitoring point during the suspected polluted gas emission stage and the environmental pollution degree of the abnormal data segment of the nearest neighboring monitoring point, the Pearson correlation coefficient, the starting time of the abnormal data segment of each monitoring point during the suspected polluted gas emission stage and the starting time of the abnormal data segment of the nearest neighboring monitoring point; determine the consistency of the environmental impact of the suspected polluted gas emission stage with the changing law of the actual air quality data based on the convergence of pollution change rhythm and environmental impact factors of each monitoring point during the suspected polluted gas emission stage.

[0036] Specifically, when determining the environmental impact factor, first calculate the second ratio between the first predetermined value and the minimum angle, and the third ratio between the current wind speed and the distance; then determine the third product between the second ratio and the third ratio as the environmental impact factor of other monitoring points.

[0037] In this embodiment of the present invention, the first predetermined value is The embodiment of the present invention specifically uses the following formula to calculate the environmental impact factors of other monitoring points: In the above formula, Indicates Environmental impact factors of each monitoring point. The nearest neighbor monitoring point of the pollutant gas emission source points to The minimum angle between the vector of a monitoring point and the wind direction. Indicates The distance between a monitoring point and the nearest neighboring monitoring point of the pollutant gas emission source. Indicates the current wind speed.

[0038] For the above formula, when the nearest neighbor monitoring point of the pollutant gas emission source points to the The minimum angle between the vector of the monitoring point and the wind direction The larger it is, the more it is contrary to the wind direction, that is, the less likely it is to spread to the monitoring point. The larger the value, the more susceptible the monitoring point is to the spread of polluted gas. And when the wind speed is higher and the distance is closer, the polluted gas at the monitoring point spreads faster, that is, its environmental impact factor is greater.

[0039] Furthermore, in a suspected polluted gas emission stage, when the start time of the abnormal data segment of a certain monitoring point is smaller than the start time interval of the nearest neighboring monitoring point of the polluted gas emission source, it can be said that the geographical location of the monitoring point is closer to the emission point, and based on the diffusion of the polluted gas with the wind direction, when it conforms to the actual geographical location distribution, it means that the authenticity of the current suspected polluted gas emission stage is higher. Therefore, the embodiment of the present invention analyzes the fluctuation of the actual air quality data and compares the environmental impact factors of the above-mentioned monitoring points. When the difference in the changes of the two is correlated, it can be verified that the emission of polluted gas has occurred at this time, resulting in the emergence of the current suspected polluted gas emission stage.

[0040] Furthermore, the embodiment of the present invention combines the presentation of actual air quality data and determines the convergence of the pollution change rhythm of all monitoring points in the suspected polluted gas emission stage and the nearest monitoring points of the polluted gas emission source based on the fluctuation of all abnormal data segments except the first abnormal data segment in the suspected polluted gas emission stage.

[0041] Further, as an optional embodiment of the present invention, according to the environmental pollution degree of the abnormal data segment of each monitoring point in the suspected polluted gas emission stage and the environmental pollution degree of the abnormal data segment of the nearest neighboring monitoring point, the Pearson correlation coefficient, the starting time of the abnormal data segment of each monitoring point in the suspected polluted gas emission stage and the starting time of the abnormal data segment of the nearest neighboring monitoring point, determining the convergence of the pollution change rhythm of each monitoring point and the nearest neighboring monitoring point in the suspected polluted gas emission stage includes: calculating a first sum between the Pearson correlation coefficient and a second predetermined value, a first difference between the environmental pollution degree of the abnormal data segment of each monitoring point in the suspected polluted gas emission stage and the environmental pollution degree of the abnormal data segment of the nearest neighboring monitoring point, and a second difference between the starting time of the abnormal data segment of each monitoring point in the suspected polluted gas emission stage and the starting time of the abnormal data segment of the nearest neighboring monitoring point; calculating the reciprocal of the second sum between the first difference and the second difference; and determining the fourth product between the first sum and the reciprocal of the second sum as the convergence of the pollution change rhythm of each monitoring point and the nearest neighboring monitoring point in the suspected polluted gas emission stage.

[0042] Specifically, the second predetermined value may be 2. The embodiment of the present invention specifically uses the following formula to calculate the similarity of the pollution change rhythm between each monitoring point and the nearest neighboring monitoring point during the suspected polluted gas emission stage: In the above formula, Indicates The pollution change rhythm convergence of the monitoring point corresponding to the abnormal data segment and the nearest neighboring monitoring point of the pollutant gas emission source. Indicates The Pearson correlation coefficient between the abnormal data segment and the abnormal data segment of the nearest neighboring monitoring point of the pollutant gas emission source. Indicates The first difference between the environmental pollution degree of the abnormal data segment and the abnormal data segment of the nearest neighboring monitoring point of the polluted gas emission source. Indicates The second difference between the start time of the abnormal data segment and the start time of the abnormal data segment of the nearest neighboring monitoring point of the pollutant gas emission source. It should be noted that in order to ensure that the calculation result is meaningful, when performing fractional operations in the embodiment of the present invention, when the denominator is 0, it is necessary to add a parameter adjustment factor greater than 0 to the denominator to prevent the denominator from being 0. The value of the parameter adjustment factor is set by the implementer according to the actual situation, and this application does not impose any special restrictions.

[0043] Among them, in the suspected polluted gas emission stage, a characteristic value is constructed based on the difference in the degree of environmental pollution between the abnormal data segment of the nearest monitoring point of the polluted gas emission source and each of the other abnormal data segments, as well as the time interval between the start time of the abnormal data segments. The earlier the monitoring point is diffused, the larger the characteristic value is. With the increase of distance and the influence of wind, the characteristic value of the peripheral monitoring points gradually decreases. Furthermore, the embodiment of the present invention uses the Pearson correlation coefficient for measurement. The Pearson correlation coefficient is an indicator for measuring the degree of linear correlation between two variables. Its value range is [-1,1]. The closer the value is to 1, the more similar the fluctuations of the two curves are. The closer it is to -1, the more opposite the fluctuations are. The closer it is to 0, the more there is no linear correlation. Therefore Adjust it to a positive range to avoid the formula being zero and not affecting the relative similarity of the numerical value. For monitoring points where polluted gas gradually diffuses outward, as the distance increases, the Pearson correlation coefficient of the corresponding abnormal data segment will become smaller, and the time interval between the degree of environmental pollution and the start time of the abnormal data segment will become larger. Therefore, when actual abnormal gas diffusion occurs, It will show a numerical change that gradually decreases along the diffusion direction.

[0044] Furthermore, when the actual Data changes and environmental impact factors of monitoring points When the changing trends show correlation, that is, when the laws of their relative changes are consistent, it can be reasonably explained that the current suspected pollutant gas emission stage is the actual situation.

[0045] Further, as an optional embodiment of the present invention, according to the pollution change rhythm convergence and environmental impact factors of each monitoring point in the suspected polluted gas emission stage, determining the consistency of the environmental impact of the suspected polluted gas emission stage with the changing law of the actual air quality data includes: determining a first average value of the pollution change rhythm convergence of each monitoring point in the suspected polluted gas emission stage, and a second average value of the environmental impact factors of each monitoring point in the suspected polluted gas emission stage; calculating a third difference between the pollution change rhythm convergences of adjacent monitoring points, and a fourth difference between the environmental impact factors of adjacent monitoring points; calculating a fourth ratio between the third difference and the first average value, and a fifth ratio between the fourth difference and the second average value; calculating a sixth ratio between the fourth ratio and the fifth ratio, and an absolute value of the fifth difference between the third predetermined value and the sixth ratio; superimposing the absolute values ​​of each fifth difference to obtain a second superimposed value; and performing inverse proportional normalization processing on the second superimposed value to obtain the consistency of the changing law.

[0046] Specifically, the third predetermined value may be 1. The embodiment of the present invention specifically uses the following formula to calculate the consistency of the change rule: In the above formula, Indicates the consistency of the changing patterns of the environmental impact during the suspected pollutant gas emission stage and the actual air quality data. Indicates the number of abnormal data segments within the suspected pollutant gas emission stage. Indicates The pollution change rhythm convergence of the monitoring point corresponding to the abnormal data segment and the nearest neighboring monitoring point of the pollutant gas emission source. Respectively represent The pollution change rhythm convergence of the monitoring point corresponding to the abnormal data segment and the nearest neighboring monitoring point of the pollutant gas emission source. Indicates The abnormal data segment corresponds to the environmental impact factor of the monitoring point. Indicates The abnormal data segment corresponds to the environmental impact factor of the monitoring point. It represents the first average value of the convergence of the pollution change rhythm of each monitoring point during the suspected pollutant gas emission stage. It represents the second average value of the environmental impact factors of each monitoring point during the suspected pollutant gas emission stage. (-) indicates the inverse normalization function, which is used to Perform inverse proportional normalization.

[0047] Among them, when two adjacent abnormal data segments The difference between The closer the difference in values ​​is, the higher the consistency of the change pattern is. Taking into account the individual differences in the change amplitudes of the two eigenvalues, we divide them by their means and then make a ratio to obtain a more reasonable presentation of the consistency of the change pattern.

[0048] S104: When the consistency of the change rule is greater than a first threshold, determining the suspected pollutant gas emission stage as the actual pollutant gas emission stage.

[0049] Specifically, the first threshold can be determined according to actual conditions, and in the embodiment of the present invention, the value is 0.7. For any suspected polluted gas emission stage, when the consistency of the change law of the environmental impact factor of the monitoring point and the actual air quality data is greater than 0.7, it is considered that the actual air quality abnormality occurs in the current suspected polluted gas emission stage, that is, the actual polluted gas emission stage, otherwise it may be caused by equipment failure or noise data.

[0050] S105, determining the optimized sampling frequency of each monitoring point in the actual pollutant gas emission stage according to the air quality data of each abnormal data segment in the actual pollutant gas emission stage and the initial sampling frequency, and collecting the corrected air quality data of each monitoring point using the optimized sampling frequency.

[0051] Specifically, if actual air quality abnormalities occur, it means that the air quality of all monitoring points during the corresponding suspected polluted gas emission stage has appeared situations that may require special attention, and the sampling frequency needs to be increased. This will help to record in more detail key information such as subtle changes in pollutant concentrations, change rates, and fluctuation amplitudes during each periodic fluctuation.

[0052] Further, as an optional embodiment of the present invention, determining the optimized sampling frequency of each monitoring point in the actual pollutant gas emission stage according to the air quality data of each abnormal data segment in the actual pollutant gas emission stage and the initial sampling frequency includes: calculating the standard deviation of the air quality data of each abnormal data segment in the actual pollutant gas emission stage, and normalizing the standard deviation to obtain a normalized value; calculating the second sum between the third predetermined value and the normalized value; and determining the fifth product between the initial sampling frequency and the second sum as the optimized sampling frequency of the monitoring point in the actual pollutant gas emission stage.

[0053] Specifically, the third predetermined value in the embodiment of the present invention can be set to 1. The embodiment of the present invention specifically uses the following formula to calculate the optimized sampling frequency of the monitoring point in the actual pollutant gas emission stage: In the above formula, Represents the optimized sampling frequency of the ith monitoring point during the actual pollutant gas emission stage. Indicates the preset initial sampling frequency. Indicates The standard deviation of the air quality data for the abnormal data segment. Represents a linear normalization function, which is used to Perform normalization.

[0054] The sampling frequency is adjusted using the standard deviation of the air quality data of the abnormal data segment corresponding to the monitoring point. The larger the standard deviation, the more significant the diffusion of the current polluted gas, and thus the sampling frequency increases accordingly.

[0055] Furthermore, according to the above steps, the optimal sampling frequency under different pollution levels in different time periods or different monitoring points is determined. During the sampling process, data is collected strictly according to this optimized sampling frequency to ensure that data details can be captured in time when the air quality changes significantly, and the number of sampling is reduced when the air quality is relatively stable to avoid unnecessary data collection. In this way, accurate monitoring can be achieved, detection efficiency can be effectively improved, and resource consumption can be reduced.

[0056] Furthermore, based on the air quality data obtained by optimizing the sampling frequency, anomaly monitoring is performed using existing anomaly detection algorithms, such as the local outlier factor algorithm. By analyzing the monitoring data using the local outlier factor algorithm, it is possible to identify abnormal air quality data that is significantly different from most data points. These data points often represent sudden and significant changes in air quality. This process can not only detect the deterioration of air quality in a timely manner, but also provide strong data support for subsequent early warning, response and governance measures. Therefore, combining the optimized sampling frequency with advanced anomaly detection algorithms, an efficient, accurate and resource-saving air quality monitoring system is constructed.

[0057] The embodiment of the present invention can screen abnormal data segments and determine the suspected polluted gas emission stage, and then analyze the change in correlation between the environmental impact of the suspected polluted gas emission stage and the actual air quality data according to the distance and wind direction of the polluted gas emission source in the actual scene. It can accurately identify the actual polluted gas emission stage in the air quality, and re-determine the optimized sampling frequency in the actual polluted gas emission stage in combination with the air quality data in the actual polluted gas emission stage and the initial sampling frequency, so that the optimized sampling frequency can adapt to the actual scene of the actual polluted gas emission stage. When the air quality data is sampled at the optimized sampling frequency, the monitoring efficiency, accuracy and reliability of the ambient air quality monitoring are improved.

[0058] Embodiment 2: Corresponding to the ambient air pollution monitoring method based on sensor technology provided in the above embodiment, based on the same technical concept, an embodiment of the present invention further provides an ambient air pollution monitoring system based on sensor technology, and the ambient air pollution monitoring system based on sensor technology is used to execute the ambient air pollution monitoring method based on sensor technology. Figure 3 A schematic diagram of the structure of an ambient air pollution monitoring system based on sensor technology is provided in another embodiment of the present invention. Figure 3 The ambient air pollution monitoring system based on sensor technology may have relatively large differences due to different configurations or performances, and may include one or more processors 301 and memory 302, the memory 302 is used to store computer programs that can be run on the processor 301, and the processor 301 is used to execute the program stored in the memory 302 to achieve the above Figure 1 The various steps in the method embodiment. Among them, the memory 302 can be a temporary storage or a permanent storage. The application stored in the memory 302 may include one or more modules (not shown in the figure), and each module may include a series of computer executable instructions in the ambient air pollution monitoring system based on sensor technology.

[0059] Furthermore, the processor 301 can be configured to communicate with the memory 302 to execute a series of computer executable instructions in the memory 302 on the ambient air pollution monitoring system based on sensor technology. The ambient air pollution monitoring system based on sensor technology can also include one or more power supplies 303, one or more wired or wireless network interfaces 304, one or more input and output interfaces 305, and one or more keyboards 306.

[0060] Specifically in this embodiment, the ambient air pollution monitoring system based on sensor technology includes a processor 301, a communication interface, a memory 302 and a communication bus; wherein the processor 301, the communication interface and the memory 302 communicate with each other through the bus; the memory 302 is used to store computer programs; the processor 301 is used to execute the program stored in the memory 302 to achieve the above Figure 1 The various steps in the method embodiment are similar to those in the method embodiment, and have the beneficial effects of the above method embodiments. To avoid repetition, the embodiments of the present invention will not be described in detail here.

[0061] It should be noted that the ambient air pollution monitoring system based on sensor technology provided in an embodiment of the present invention and the ambient air pollution monitoring method based on sensor technology provided in an embodiment of the present invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned ambient air pollution monitoring method based on sensor technology, and has the same or similar beneficial effects, and the repeated parts will not be repeated.

[0062] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0063] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for monitoring ambient air pollution based on sensor technology, characterized in that: The ambient air pollution monitoring method based on sensor technology includes: Acquire air quality data at a monitoring point at an initial sampling frequency, and construct an air quality data curve at the monitoring point; Determine the suspected polluted gas emission stage according to the air quality data curve of each of the monitoring points, and use the monitoring point corresponding to the first abnormal data segment of the suspected polluted gas emission stage as the nearest neighbor monitoring point of the polluted gas emission source; According to the minimum angle between the vector pointing from the nearest neighbor monitoring point to other monitoring points and the wind direction, the distance between the other monitoring points and the nearest neighbor monitoring point, the current wind speed, the abnormal data segments of each monitoring point in the suspected polluted gas emission stage and the abnormal data segments of the nearest neighbor monitoring point, the consistency of the change law of the environmental impact of the suspected polluted gas emission stage and the actual air quality data is determined; When the consistency of the change rule is greater than a first threshold, determining the suspected pollutant gas emission stage as the actual pollutant gas emission stage; The optimized sampling frequency of each monitoring point in the actual pollutant gas emission stage is determined according to the air quality data of each abnormal data segment in the actual pollutant gas emission stage and the initial sampling frequency, and the corrected air quality data of each monitoring point is collected using the optimized sampling frequency.

2. The method for monitoring ambient air pollution based on sensor technology according to claim 1, characterized in that: The step of determining the suspected polluted gas emission stage according to the air quality data curve of each monitoring point includes: The air quality data whose air quality data is less than the passing value is regarded as abnormal data, and each of the abnormal data constitutes a plurality of abnormal data segments of the air quality data curve; Screening all target abnormal data segments whose slopes are less than a second threshold value in the abnormal data segments, and determining the area of ​​a closed figure formed by the abnormal data segments and the straight line where the passing value is located; Determine the environmental pollution degree of the target abnormal data segment according to the area, the slope of the target abnormal data segment, the time length of the target abnormal data segment and the sum of the time lengths of all target abnormal data segments; The abnormal data segments of multiple monitoring points where the air quality data curves of different monitoring points overlap on the time axis are regarded as suspected polluted gas emission stages, and the monitoring point corresponding to the first abnormal data segment of the suspected polluted gas emission stage is regarded as the nearest neighbor monitoring point of the polluted gas emission source.

3. The method for monitoring ambient air pollution based on sensor technology according to claim 2, characterized in that: Determining the environmental pollution degree of the target abnormal data segment according to the area, the slope of the target abnormal data segment, the time length of the target abnormal data segment, and the sum of the time lengths of all target abnormal data segments includes: Calculating a first ratio between the time length of the target abnormal data segment and the sum of the time lengths of all the target abnormal data segments; Calculating a first product between the absolute value of the slope of the target abnormal data segment and the first ratio, and superimposing the first products to obtain a first superimposed value; A second product between the area and the first superposition value is determined as the environmental pollution degree of the target abnormal data segment.

4. The method for monitoring ambient air pollution based on sensor technology according to claim 1, characterized in that: The determination of consistency between the environmental impact of the suspected pollutant gas emission stage and the change rule of the actual air quality data based on the minimum angle between the vector pointing from the nearest neighbor monitoring point to other monitoring points and the wind direction, the distance between the other monitoring points and the nearest neighbor monitoring point, the current wind speed, the abnormal data segments of each monitoring point in the suspected pollutant gas emission stage and the abnormal data segments of the nearest neighbor monitoring point includes: Determine the environmental impact factor of the other monitoring points according to the minimum angle between the vector pointing from the nearest neighbor monitoring point to the other monitoring points and the wind direction, the distance between the other monitoring points and the nearest neighbor monitoring point, and the current wind speed; Determine the Pearson correlation coefficient between the abnormal data segments of each monitoring point in the suspected polluted gas emission stage and the abnormal data segments of the nearest neighboring monitoring point; Determine the similarity of the pollution change rhythm of each monitoring point in the suspected polluted gas emission stage and the nearest neighbor monitoring point according to the environmental pollution degree of the abnormal data segment of each monitoring point in the suspected polluted gas emission stage and the environmental pollution degree of the abnormal data segment of the nearest neighbor monitoring point, the Pearson correlation coefficient, the starting time of the abnormal data segment of each monitoring point in the suspected polluted gas emission stage and the starting time of the abnormal data segment of the nearest neighbor monitoring point; Based on the similarity of the pollution change rhythm and the environmental impact factors of each monitoring point during the suspected polluted gas emission stage, the consistency of the environmental impact of the suspected polluted gas emission stage with the change law of the actual air quality data is determined.

5. The method for monitoring ambient air pollution based on sensor technology according to claim 4, characterized in that: Determining the environmental impact factor of the other monitoring point according to the minimum angle between the vector pointing from the nearest neighbor monitoring point to the other monitoring point and the wind direction, the distance between the other monitoring point and the nearest neighbor monitoring point, and the current wind speed includes: Calculating a second ratio between the first predetermined value and the minimum angle, and a third ratio between the current wind speed and the distance; A third product between the second ratio and the third ratio is determined as the environmental impact factor of the other monitoring point.

6. The method for monitoring ambient air pollution based on sensor technology according to claim 4, characterized in that: The determining of the similarity of the pollution change rhythm between each monitoring point and the nearest neighbor monitoring point during the suspected polluted gas emission stage according to the environmental pollution degree of the abnormal data segment of each monitoring point during the suspected polluted gas emission stage and the environmental pollution degree of the abnormal data segment of the nearest neighbor monitoring point, the Pearson correlation coefficient, the starting time of the abnormal data segment of each monitoring point during the suspected polluted gas emission stage and the starting time of the abnormal data segment of the nearest neighbor monitoring point comprises: Calculate a first sum value between the Pearson correlation coefficient and a second predetermined value, a first difference value between the environmental pollution degree of the abnormal data segment of each monitoring point in the suspected polluted gas emission stage and the environmental pollution degree of the abnormal data segment of the nearest neighboring monitoring point, and a second difference value between the start time of the abnormal data segment of each monitoring point in the suspected polluted gas emission stage and the start time of the abnormal data segment of the nearest neighboring monitoring point; calculating a reciprocal of a second sum between the first difference and the second difference; The fourth product between the first sum and the reciprocal of the second sum is determined as the convergence of the pollution change rhythm between each monitoring point and the nearest neighbor monitoring point during the suspected polluted gas emission stage.

7. The method for monitoring ambient air pollution based on sensor technology according to claim 4, characterized in that: Determining the consistency of the environmental impact of the suspected polluted gas emission stage with the change law of the actual air quality data according to the similarity of the pollution change rhythm and the environmental impact factor of each monitoring point during the suspected polluted gas emission stage includes: Determine a first average value of the pollution change rhythm convergence of each monitoring point during the suspected polluted gas emission stage, and a second average value of the environmental impact factor of each monitoring point during the suspected polluted gas emission stage; Calculate the third difference between the pollution change rhythm convergence of adjacent monitoring points, and the fourth difference between the environmental impact factors of adjacent monitoring points; calculating a fourth ratio between the third difference and the first average value, and a fifth ratio between the fourth difference and the second average value; calculating a sixth ratio between the fourth ratio and the fifth ratio, and an absolute value of a fifth difference between a third predetermined value and the sixth ratio; superimposing the absolute values ​​of the fifth difference values ​​to obtain a second superimposed value; The second superposition value is subjected to inverse proportional normalization processing to obtain the consistency of the variation rule.

8. The method for monitoring ambient air pollution based on sensor technology according to claim 1, characterized in that: The step of determining the optimized sampling frequency of each monitoring point in the actual pollutant gas emission stage according to the air quality data of each abnormal data segment in the actual pollutant gas emission stage and the initial sampling frequency includes: Calculating the standard deviation of the air quality data of each abnormal data segment during the actual pollutant gas emission stage, and normalizing the standard deviation to obtain a normalized value; Calculating a second sum between a third predetermined value and the normalized value; A fifth product between the initial sampling frequency and the second sum is determined as the optimized sampling frequency of the monitoring point in the actual pollutant gas emission stage.

9. The method for monitoring ambient air pollution based on sensor technology according to claim 1, characterized in that: After collecting the corrected air quality data of each monitoring point using the optimized sampling frequency, the method further includes: The corrected air quality data is monitored for anomalies using a local outlier factor algorithm.

10. An ambient air pollution monitoring system based on sensor technology, characterized in that: include: A processor and a memory; wherein the memory is used to store a computer program that can be run on the processor; A processor is used to execute the program stored in the memory to implement the steps of the ambient air pollution monitoring method based on sensor technology as described in any one of claims 1 to 9.

Images