CN111650346B - Automatic checking method and device for atmospheric pollution monitoring data and electronic equipment - Google Patents
Automatic checking method and device for atmospheric pollution monitoring data and electronic equipment Download PDFInfo
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- CN111650346B CN111650346B CN202010675501.5A CN202010675501A CN111650346B CN 111650346 B CN111650346 B CN 111650346B CN 202010675501 A CN202010675501 A CN 202010675501A CN 111650346 B CN111650346 B CN 111650346B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method, e.g. intermittent, or the display, e.g. digital
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/007—Arrangements to check the analyser
Abstract
The application discloses an automatic checking method and device for atmospheric pollution monitoring data and electronic equipment. The method comprises the following steps: carrying out filtering and denoising processing on monitoring data of atmospheric pollution monitoring equipment in a period of time; sampling the monitoring data subjected to filtering and denoising processing according to the same time interval to obtain a plurality of data points; plotting corresponding first monitoring data against time for all data points; and detecting and removing abnormal data points on the curve graph to obtain a curve graph of the relationship between the second monitoring data and the time. According to the method, the relationship curve graph of the monitoring data and the time of the same monitoring device in a period of time is analyzed, the difference value and the slope of each data point are compared with the difference value and the slope of the previous data point, when the difference value and the slope exceed the preset threshold, the data measurement value of the current data point is taken as the abnormal data point and is removed, the checking of the atmospheric pollution monitoring data can be completed rapidly in an auxiliary mode, and the accuracy and the checking efficiency of the monitoring data are improved.
Description
Technical Field
The application relates to the technical field of environmental monitoring, in particular to an automatic auditing method and device for atmospheric pollution monitoring data and electronic equipment.
Background
With economic development and social progress, the atmospheric environment problem becomes more and more serious, and ecological environment bureaus in various cities carry out environmental monitoring and management work by building atmospheric super stations. A large amount of data generated in the monitoring process cannot be directly used for research and analysis due to uneven quality, and data examination and verification are required to be performed first. However, if the manual auditing is only relied on, the workload is huge, and the timeliness, accuracy and comprehensiveness of the auditing cannot be ensured, so that the analysis result of the environmental monitoring data is influenced.
In recent years, the problem of air pollution has become more severe, and the conventional air quality monitoring station has been difficult to satisfy the requirement for explaining the cause of the composite pollution. In contrast, atmospheric super stations (hereinafter referred to as super stations) are successively built by ecological environment departments and environment-related scientific research institutes in many places in the country, and components, precursors and the like of pollutants in the atmospheric environment are measured to assist in the fine source analysis of the pollutants. The number of instruments and equipment in the super station is large, the data volume is larger and larger along with the time, and the instruments and equipment cannot be directly used for research and analysis due to the fact that the data quality is uneven, so that data is required to be audited first, and invalid data is eliminated. However, if the manual auditing is simply relied on, the workload is huge, and the timeliness, accuracy and comprehensiveness of the auditing cannot be guaranteed, and an effective method for improving the auditing efficiency is urgently needed.
Currently, many software manufacturers focus on "threshold" methods for automatically reviewing the over-site monitoring data, i.e., if the concentration of contaminants exceeds a certain threshold, the over-site monitoring data is invalid. Firstly, the method obviously cannot meet the complex situation in practical application, and often more or less picks are caused by improper setting of the threshold range; secondly, this approach is not suitable for environmental monitoring around pollution sources with high pollutant concentrations. In addition, manufacturers have used global auditing methods: and performing automatic auditing based on the range of the average value and the difference value of the result of a certain point and the adjacent N points, wherein the global auditing method has higher requirement on the distance between the points and has more false alarms among the remote points.
Disclosure of Invention
The application aims to provide an automatic checking method and device for atmospheric pollution monitoring data and electronic equipment. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
According to an aspect of the embodiments of the present application, there is provided an automatic auditing method for atmospheric pollution monitoring data, including:
carrying out filtering and denoising processing on monitoring data of atmospheric pollution monitoring equipment in a period of time;
sampling the monitoring data subjected to filtering and denoising processing according to the same time interval to obtain a plurality of data points;
plotting respective first monitoring data against time for all of the data points;
and detecting and eliminating abnormal data points on the first monitoring data and time relation curve graph to obtain a second monitoring data and time relation curve graph.
Further, the detecting and removing abnormal data points on the first monitoring data and time relation curve graph to obtain a second monitoring data and time relation curve graph includes:
according to the time sequence, sequentially judging whether each data point is an abnormal data point from the second data point;
if a certain data point is determined to be an abnormal data point, rejecting the data point, re-making a corresponding first monitoring data and time relation curve graph for the remaining data points, and then judging whether the next data point of the certain data point is an abnormal data point;
if a certain data point is not an abnormal data point, the data point is retained, and whether the next data point is an abnormal data point or not is continuously judged until the data point on the first monitoring data and time relation curve graph is detected.
Further, the step of determining whether the data point is an abnormal data point includes:
calculating the absolute value of the slope of the curve at a certain data point and calculating the absolute value of the difference value of the pollutant concentration of the data point and the previous data point;
comparing the absolute value of the slope of the curve of the data point and the absolute value of the difference value of the pollutant concentration with a slope threshold value and a difference threshold value respectively;
if the absolute value of the slope of the curve is greater than the slope threshold value and the absolute value of the difference value of the concentrations of the pollutants is greater than the difference threshold value, determining that the data point belongs to an abnormal data point; otherwise, it is determined that the data point does not belong to an outlier data point.
Further, said plotting respective first monitoring data against time for all of said data points comprises:
taking a time axis as a horizontal axis and taking the pollutant concentration value as a vertical axis to form a rectangular coordinate system;
placing all data points in a rectangular coordinate system;
performing curve fitting on all the data points to obtain a first monitoring data and time relation curve graph;
wherein the data point comprises two dimension values of a monitoring time and a pollutant concentration value detected at the monitoring time.
Further, the placing all the data points in a rectangular coordinate system includes: and enabling the monitoring time of each data point to correspond to a point on the horizontal axis, enabling the pollutant concentration value of each data point to correspond to a point on the vertical axis, and drawing each data point into a rectangular coordinate system.
Further, the method further comprises:
and comparing the real-time data point with the previous data point on the time relation curve chart of the second monitoring data, judging whether the real-time data point is abnormal data, if so, discarding the real-time data point, and otherwise, keeping the real-time data point.
Further, the comparing the real-time data point with the previous data point on the second monitoring data and time relation graph to determine whether the real-time data point is abnormal data includes:
calculating the absolute value of the slope of the curve at the data point, and calculating the absolute value of the difference value of the pollutant concentration of the data point and the previous data point;
comparing the absolute value of the slope of the curve of the data point and the absolute value of the difference value of the pollutant concentration with a slope threshold value and a difference threshold value respectively;
if the slope of the curve is greater than the slope threshold and the pollutant concentration difference is greater than the difference threshold, determining that the data point belongs to an abnormal data point; otherwise, it is determined that the data point does not belong to an outlier data point.
According to another aspect of the embodiments of the present application, there is provided an automatic auditing apparatus for atmospheric pollution monitoring data, including:
the filtering module is used for carrying out filtering and denoising processing on monitoring data of the atmospheric pollution monitoring equipment within a period of time;
the sampling module is used for sampling the monitoring data subjected to filtering and denoising processing according to the same time interval to obtain a plurality of data points;
a plotting module for plotting the corresponding first monitoring data against time for all of the data points;
and the detection module is used for detecting and eliminating abnormal data points on the first monitoring data and time relation curve graph to obtain a second monitoring data and time relation curve graph.
Further, the apparatus further comprises:
and the judging module is used for comparing the real-time data point with the previous data point on the time relation curve chart of the second monitoring data, judging whether the real-time data point is abnormal data or not, if so, discarding the real-time data point, and otherwise, keeping the real-time data point.
According to another aspect of the embodiments of the present application, there is provided an electronic device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned automatic auditing method for atmospheric pollution monitoring data.
According to another aspect of the embodiments of the present application, there is provided a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the above-mentioned method for automatically auditing air pollution monitoring data.
The technical scheme provided by one aspect of the embodiment of the application can have the following beneficial effects:
according to the automatic checking method for the atmospheric pollution monitoring data, the relation curve graph of the monitoring data and the time of the same monitoring device in a period of time is analyzed, the difference value and the slope of each data point and the previous data point are compared, when the difference value and the slope of each data point and the previous data point exceed the preset threshold value, the data measured value of the current data point is used as an abnormal data point and is removed, checking of the atmospheric pollution monitoring data is completed rapidly in an auxiliary mode, the abnormal data point is removed, and accuracy and checking efficiency of the monitoring data are improved.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the application, or may be learned by the practice of the embodiments. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 shows a flow chart of an automatic auditing method of atmospheric pollution monitoring data of an embodiment of the present application;
FIG. 2 illustrates a flow chart of plotting corresponding first monitored data against time for all data points in one embodiment of the present application;
FIG. 3 illustrates a flow diagram for determining whether a data point is an outlier data point in one embodiment of the subject application;
fig. 4 shows a block diagram of an automatic auditing device for atmospheric pollution monitoring data according to a second embodiment of the present application;
FIG. 5 shows a block diagram of the construction of a diagramming module;
fig. 6 shows a block diagram of the structure of the judgment module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is further described with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It will be understood by those within the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As shown in fig. 1, an embodiment of the present application provides an automatic auditing method for atmospheric pollution monitoring data, including:
and S00, filtering and denoising monitoring data of the atmospheric pollution monitoring equipment in a period of time.
And S10, sampling the monitoring data subjected to filtering and denoising processing according to the same time interval to obtain a plurality of data points.
For example, sulfur dioxide monitoring data is obtained for a sulfur dioxide monitoring instrument over 365 days. Each data point includes two dimensional values of a monitoring time and a contaminant concentration value detected at the monitoring time. Sampling sulfur dioxide monitoring data within 365 days at 8-hour intervals to obtain 3 × 365 ═ 1095 data points; each data point consists of two elements, the monitoring time (e.g., 16 days first) and the contaminant concentration value detected at that monitoring time (e.g., a sulfur dioxide concentration value of 0.15 mg/cubic meter).
And S20, drawing corresponding first monitoring data and time curves for all the data points.
As shown in fig. 2, in some embodiments, step S20 includes:
s201, a rectangular coordinate system is formed by taking a time axis as a horizontal axis and a pollutant concentration value as a vertical axis.
And S202, placing all data points in a rectangular coordinate system.
Specifically, the monitoring time of the data point is made to correspond to a point on the horizontal axis, the pollutant concentration value of the data point is made to correspond to a point on the vertical axis, and the data point is plotted in the rectangular coordinate system.
And S203, performing curve fitting on all the data points to fit a curve graph of the relation between the first monitoring data and the time.
Curve fitting refers to selecting an appropriate curve type to fit data to obtain a data curve graph, and analyzing the relation between two variables by using a fitted curve equation. Because the relationship between the two variables of the pollutant concentration value and the monitoring time is not a linear relationship, the curve of the relationship between the pollutant concentration value and the monitoring time can be more accurately reflected by the curve of the first monitoring data and the time relationship obtained by adopting a curve fitting method, and therefore, the technical scheme of performing curve fitting on all data points is preferred.
And S30, detecting and eliminating abnormal data points on the first monitoring data and time relation curve graph to obtain a second monitoring data and time relation curve graph.
In certain embodiments, step S30 includes:
s301, judging data points in sequence from the second data point in the time sequence according to the time sequence, and judging whether each data point is an abnormal data point in sequence;
s302, if a certain data point is determined to be an abnormal data point, rejecting the data point, re-making a corresponding first monitoring data and time relation curve graph for the remaining data points, and then judging whether the next data point of the certain data point is an abnormal data point;
s303, if a certain data point is not an abnormal data point, retaining the data point, and continuously judging whether the next data point is an abnormal data point; until the data points on the first monitoring data and time relation curve chart are detected to be finished. The first data point in the chronological order need not be detected.
In some embodiments, the step of determining whether the data point is an abnormal data point may comprise:
s3001, calculating the absolute value of the slope of the curve at a certain data point, and calculating the absolute value of the difference value of the pollutant concentration of the data point and the previous data point;
for example, if the absolute value of the slope of the curve at a data point is calculated to be 0.18, the sulfur dioxide concentration value is 0.20 mg/m, and the sulfur dioxide concentration value at a previous data point is 0.15 mg/m, the absolute value of the difference in contaminant concentrations is 0.05 mg/m.
S3002, respectively comparing the absolute value of the slope of the curve of the data point and the absolute value of the difference value of the pollutant concentration with a slope threshold value and a difference threshold value;
s3003, if the absolute value of the slope of the curve is greater than the slope threshold and the absolute value of the difference value of the concentration of the pollutants is greater than the difference threshold, determining that the data point belongs to an abnormal data point; otherwise, it is determined that the data point does not belong to an outlier data point.
For example, if the slope threshold is preset to 0.15 and the difference threshold is 0.03 mg/m, the absolute value of the slope of the curve at the data point 0.18 exceeds the slope threshold, and the absolute value of the difference of the contaminant concentrations 0.05 mg/m exceeds the difference threshold, the data point may be determined to be an abnormal data point. If, in another example, the absolute value of the contaminant concentration difference at a data point is 0.05 mg/m, the difference threshold is exceeded, and the absolute value of the slope of the curve is 0.13, the slope threshold is not exceeded, then the data point is determined not to belong to an outlier data point.
The monitoring data can be automatically checked by the method, and the obtained data measurement value corresponding to the data point in the second monitoring data and time relation curve graph is the checked monitoring data.
In some embodiments, as shown in fig. 1, the method for automatically auditing the atmospheric pollution monitoring data may further include:
and S40, comparing the real-time data point with the previous data point on the time relation curve chart of the second monitoring data, judging whether the real-time data point is abnormal data, if so, discarding the real-time data point, otherwise, keeping the real-time data point.
As shown in fig. 3, in some embodiments, the comparing the real-time data point with the previous data point on the second monitoring data and time relation graph to determine whether the real-time data point is abnormal data includes:
s400, re-fitting the real-time data points and the data points in the second monitoring data and time relation curve graph to obtain a third monitoring data and time relation curve graph.
S401, calculating the absolute value of the slope of the curve at the real-time data point, and calculating the absolute value of the difference value of the concentration of the pollutants between the real-time data point and the previous data point.
For example, if the absolute value of the slope of the curve at a data point is calculated to be 0.21, the sulfur dioxide concentration value is 0.15 mg/m, and the sulfur dioxide concentration value at a previous data point is 0.11 mg/m, then the absolute value of the difference in contaminant concentrations is 0.04 mg/m.
S402, comparing the absolute value of the slope of the curve of the real-time data point and the absolute value of the difference value of the pollutant concentration with a slope threshold value and a difference threshold value respectively;
s403, if the absolute value of the slope of the curve is larger than the slope threshold and the absolute value of the difference value of the concentration of the pollutants is larger than the difference threshold, determining that the real-time data point belongs to an abnormal data point; otherwise, determining that the real-time data point does not belong to the abnormal data point.
For example, if the slope threshold is preset to 0.15 and the difference threshold is 0.03 mg/cubic meter, the absolute value of the slope of the curve at the data point 0.21 exceeds the slope threshold, and the absolute value of the difference in contaminant concentration 0.04 mg/cubic meter exceeds the difference threshold, the data point may be determined to be an abnormal data point.
By the method, the monitoring data can be automatically audited in real time, whether the real-time data points are abnormal data points or not is quickly judged, and the obtained data measurement value corresponding to the non-abnormal data points is the audited monitoring data. .
Based on the characteristic that the emission activity of the atmospheric pollution source has burstiness and continuity, the method of the embodiment analyzes a graph of the relationship between monitoring data and time of the same monitoring equipment in a period of time, compares the difference value and the slope of the data at the current moment and the data at the previous moment, takes the data measurement value at the current moment as the moment of sudden change when the data measurement value and the slope exceed the preset threshold, gives out marks to reject, assists manpower to quickly finish the checking of the atmospheric pollution monitoring data, rejects abnormal data points, and improves the accuracy of the monitoring data.
The preset thresholds of the "difference" and the "slope" in the above determination conditions may be dynamically adjusted according to experience in different use scenarios (such as instrument brand, region, etc.).
The method of the embodiment has the following innovation points/advantages:
data points which do not meet the preset threshold value are removed through double comparison of the difference value of the monitoring data and the numerical value of the two dimensions of the slope, so that the influence of environmental meteorological factors or unstable noise of an instrument on the auditing result is avoided; the multi-angle avoids the influence of uncertain factors on the auditing result, and is suitable for atmospheric environment monitoring; data auditing can be completed quickly and auxiliarily; and sudden increase conditions caused by pollution passing, overlong acquisition period, interruption of monitoring data and the like are eliminated through slope threshold comparison, and short-time sudden increase time caused by pollution source emission is screened out.
As shown in fig. 4, a second embodiment of the present application provides an automatic auditing device for atmospheric pollution monitoring data, including:
the filtering module is used for carrying out filtering and denoising processing on monitoring data of the atmospheric pollution monitoring equipment within a period of time;
the sampling module is used for sampling the monitoring data subjected to filtering and denoising processing according to the same time interval to obtain a plurality of data points;
a plotting module for plotting the corresponding first monitoring data against time for all of the data points;
and the detection module is used for detecting and eliminating abnormal data points on the first monitoring data and time relation curve graph to obtain a second monitoring data and time relation curve graph.
In some embodiments, as shown in fig. 4, the apparatus for automatically auditing the atmospheric pollution monitoring data may further include:
and the judging module is used for comparing the real-time data point with the previous data point on the time relation curve chart of the second monitoring data, judging whether the real-time data point is abnormal data or not, if so, discarding the real-time data point, and otherwise, keeping the real-time data point.
As shown in fig. 5, in some embodiments, the graphing module includes:
the coordinate system module is used for making a rectangular coordinate system by taking a time axis as a horizontal axis and a pollutant concentration value as a vertical axis;
the point placement module is used for placing all the data points in a rectangular coordinate system;
the fitting module is used for performing curve fitting on all the data points to fit a first monitoring data and time relation curve graph;
wherein the data point comprises two dimension values of a monitoring time and a pollutant concentration value detected at the monitoring time.
In some embodiments, the point locating module is specifically configured to map the monitoring time of each data point to a point on the horizontal axis, map the pollutant concentration value of each data point to a point on the vertical axis, and map each data point into a rectangular coordinate system.
In certain embodiments, the detection module comprises:
the detection calculation module is used for calculating the absolute value of the slope of the curve at a certain data point and calculating the absolute value of the difference value of the pollutant concentration of the data point and the previous data point;
the detection comparison module is used for respectively comparing the absolute value of the slope of the curve of the data point and the absolute value of the difference value of the pollutant concentration with a slope threshold value and a difference threshold value; if the slope of the curve is greater than the slope threshold and the pollutant concentration difference is greater than the difference threshold, determining that the data point belongs to an abnormal data point; otherwise, it is determined that the data point does not belong to an outlier data point.
As shown in fig. 6, in some embodiments, the determining module includes:
the judgment drawing module is used for fitting the real-time data points and the data points in the second monitoring data and time relation curve graph again to make a third monitoring data and time relation curve graph;
the judgment calculation module is used for calculating the absolute value of the slope of the curve at the real-time data point and calculating the absolute value of the difference value of the pollutant concentration of the real-time data point and the previous data point;
the judgment and comparison module is used for respectively comparing the absolute value of the slope of the curve of the real-time data point and the absolute value of the difference value of the pollutant concentration with a slope threshold value and a difference threshold value; if the slope of the curve is greater than the slope threshold value and the pollutant concentration difference value is greater than the difference threshold value, determining that the real-time data point belongs to an abnormal data point; otherwise, determining that the real-time data point does not belong to the abnormal data point.
A third embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the program to implement the above-mentioned automatic auditing method for atmospheric pollution monitoring data.
A fourth embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the above-mentioned method for automatically auditing air pollution monitoring data.
It should be noted that:
the term "module" is not intended to be limited to a particular physical form. Depending on the particular application, a module may be implemented as hardware, firmware, software, and/or combinations thereof. Furthermore, different modules may share common components or even be implemented by the same component. There may or may not be clear boundaries between the various modules.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. In addition, this application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the present application.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The above-mentioned embodiments only express the embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (9)
1. An automatic auditing method for atmospheric pollution monitoring data is characterized by comprising the following steps:
carrying out filtering and denoising processing on monitoring data of atmospheric pollution monitoring equipment in a period of time;
sampling the monitoring data subjected to filtering and denoising processing according to the same time interval to obtain a plurality of data points;
plotting respective first monitoring data against time for all of the data points;
according to the time sequence, sequentially judging whether each data point is an abnormal data point from the second data point;
if a certain data point is determined to be an abnormal data point, rejecting the data point, re-making a corresponding monitoring data and time relation curve graph for the remaining data points, and then judging whether the next data point of the certain data point is an abnormal data point;
if a certain data point is not an abnormal data point, retaining the data point, and continuously judging whether the next data point is an abnormal data point or not until the data point on the first monitoring data and time relation curve graph is detected completely to obtain a second monitoring data and time relation curve graph;
the step of judging whether the data point is an abnormal data point comprises the following steps:
calculating the absolute value of the slope of the curve at a certain data point and calculating the absolute value of the difference value of the pollutant concentration of the data point and the previous data point;
comparing the absolute value of the slope of the curve of the data point and the absolute value of the difference value of the pollutant concentration with a slope threshold value and a difference threshold value respectively;
if the absolute value of the slope of the curve is greater than the slope threshold value and the absolute value of the difference value of the concentrations of the pollutants is greater than the difference threshold value, determining that the data point belongs to an abnormal data point; otherwise, it is determined that the data point does not belong to an outlier data point.
2. The method of claim 1, wherein said plotting respective first monitored data versus time for all of said data points comprises:
taking a time axis as a horizontal axis and taking the pollutant concentration value as a vertical axis to form a rectangular coordinate system;
placing all the data points in a rectangular coordinate system;
performing curve fitting on all the data points to obtain a first monitoring data and time relation curve graph;
wherein the data point comprises two dimension values of a monitoring time and a pollutant concentration value detected at the monitoring time.
3. The method of claim 2, wherein said placing all of said data points in a rectangular coordinate system comprises: and enabling the monitoring time of each data point to correspond to a point on the horizontal axis, enabling the pollutant concentration value of each data point to correspond to a point on the vertical axis, and drawing each data point into a rectangular coordinate system.
4. The method according to any one of claims 1-3, further comprising:
and comparing the real-time data point with the previous data point on the time relation curve chart of the second monitoring data, judging whether the real-time data point is abnormal data, if so, discarding the real-time data point, and otherwise, keeping the real-time data point.
5. The method of claim 4, wherein comparing the real-time data point with a previous data point on the second monitoring data versus time graph to determine whether the real-time data point is abnormal data comprises:
re-fitting the real-time data points with the data points in the second monitoring data and time relation curve graph to form a third monitoring data and time relation curve graph;
calculating the absolute value of the slope of the curve at the real-time data point, and calculating the absolute value of the difference value of the pollutant concentration of the real-time data point and the previous data point;
comparing the absolute value of the slope of the curve of the real-time data point and the absolute value of the difference value of the pollutant concentration with a slope threshold value and a difference threshold value respectively;
if the slope of the curve is greater than the slope threshold value and the pollutant concentration difference value is greater than the difference threshold value, determining that the real-time data point belongs to an abnormal data point; otherwise, determining that the real-time data point does not belong to the abnormal data point.
6. An automatic auditing device of atmospheric pollution monitoring data, characterized by comprising:
the filtering module is used for carrying out filtering and denoising processing on monitoring data of the atmospheric pollution monitoring equipment within a period of time;
the sampling module is used for sampling the monitoring data subjected to filtering and denoising processing according to the same time interval to obtain a plurality of data points;
a plotting module for plotting the corresponding first monitoring data against time for all of the data points;
the detection module is used for detecting and eliminating abnormal data points on the first monitoring data and time relation curve graph to obtain a second monitoring data and time relation curve graph;
the detection module is specifically used for sequentially judging whether each data point is an abnormal data point from the second data point according to the time sequence;
if a certain data point is determined to be an abnormal data point, rejecting the data point, re-making a corresponding monitoring data and time relation curve graph for the remaining data points, and then judging whether the next data point of the certain data point is an abnormal data point;
if a certain data point is not an abnormal data point, retaining the data point, and continuously judging whether the next data point is an abnormal data point or not until the data point on the first monitoring data and time relation curve graph is detected completely to obtain a second monitoring data and time relation curve graph;
the step of determining whether the data point is an abnormal data point executed by the detection module includes:
calculating the absolute value of the slope of the curve at a certain data point and calculating the absolute value of the difference value of the pollutant concentration of the data point and the previous data point;
comparing the absolute value of the slope of the curve of the data point and the absolute value of the difference value of the pollutant concentration with a slope threshold value and a difference threshold value respectively;
if the absolute value of the slope of the curve is greater than the slope threshold value and the absolute value of the difference value of the concentrations of the pollutants is greater than the difference threshold value, determining that the data point belongs to an abnormal data point; otherwise, it is determined that the data point does not belong to an outlier data point.
7. The apparatus of claim 6, further comprising:
and the judging module is used for comparing the real-time data point with the previous data point on the time relation curve chart of the second monitoring data, judging whether the real-time data point is abnormal data or not, if so, discarding the real-time data point, and otherwise, keeping the real-time data point.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of automatically auditing atmospheric pollution monitoring data as claimed in any one of claims 1 to 5.
9. A computer-readable storage medium, on which a computer program is stored, which program is executable by a processor for implementing a method for automatic auditing of atmospheric pollution monitoring data according to any one of claims 1-5.
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| CN112141356A (en) * | 2020-09-23 | 2020-12-29 | 重庆领直航科技有限公司 | Oil-driven unmanned helicopter propeller pitch-accelerator curve calibration method and system |
| CN112526418A (en) * | 2020-11-24 | 2021-03-19 | 上海辰光医疗科技股份有限公司 | Data recording and processing method for magnetic field homogeneity measurement of magnetic resonance imaging |
| CN112861327B (en) * | 2021-01-21 | 2022-09-23 | 山东大学 | Atmospheric chemistry whole-process online analysis system for atmospheric super station |
| CN114221981B (en) * | 2021-12-15 | 2023-04-07 | 江苏阜民新材料有限公司 | Big data-based fire safety emergency system for energy storage power station |
| CN114580572B (en) * | 2022-04-07 | 2023-04-07 | 中科三清科技有限公司 | Abnormal value identification method and device, electronic equipment and storage medium |
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| CN116128260B (en) * | 2023-04-18 | 2023-07-11 | 山东奥斯瑞特检验检测有限公司 | Data sample-based key enterprise environment analysis method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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Family Cites Families (14)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104198661A (en) * | 2014-09-15 | 2014-12-10 | 上海市环境科学研究院 | Online monitoring method for air negative oxygen ions in ecological land |
| CN104462794A (en) * | 2014-11-26 | 2015-03-25 | 北京金水永利科技有限公司 | Algorithm for finding abnormal data of environmental monitoring based on comparative statistic analysis |
| US10338047B2 (en) * | 2015-06-16 | 2019-07-02 | International Business Machines Corporation | Air-pollution anomaly location mechanism |
| CN104915846A (en) * | 2015-06-18 | 2015-09-16 | 北京京东尚科信息技术有限公司 | Electronic commerce time sequence data anomaly detection method and system |
| CN106227640B (en) * | 2016-07-27 | 2019-03-19 | 北京市环境保护监测中心 | A kind of abnormal deviation data examination method and system based on automatic monitor |
| CN107092775B (en) * | 2017-03-17 | 2020-06-05 | 沈阳工业大学 | Taper thread data processing method based on laser measurement system |
| CN108490115B (en) * | 2018-02-08 | 2020-06-05 | 浙江大学 | Air quality abnormity detection method based on distributed online principal component analysis |
| CN108830765A (en) * | 2018-04-18 | 2018-11-16 | 中国地质大学（武汉） | A kind of checking method and system of pollution entering the water monitoring data |
| CN109063993A (en) * | 2018-07-23 | 2018-12-21 | 上海市环境监测中心 | A kind of method of atmospheric environment VOCs online monitoring data quality automatic discrimination |
| CN109241353B (en) * | 2018-09-12 | 2023-10-31 | 北京英视睿达科技股份有限公司 | Data auditing method |
| US10769920B2 (en) * | 2018-09-22 | 2020-09-08 | Fedex Corporate Services, Inc. | Systems, apparatus, and methods for detecting an environmental anomaly and initiating an enhanced automatic response using elements of a wireless node network and using sensor data from ID nodes associated with packages and environmental threshold conditions per package |
| CN109209751B (en) * | 2018-10-10 | 2020-06-09 | 贵州电网有限责任公司 | Real-time dynamically corrected water turbine unit efficiency surface fitting method |
| CN110082319B (en) * | 2019-04-22 | 2022-03-11 | 深圳市锦瑞生物科技股份有限公司 | Calibration data correction method and electronic device thereof |
| CN110737874B (en) * | 2019-09-02 | 2021-04-20 | 中国科学院地理科学与资源研究所 | Watershed water quality monitoring abnormal value detection method based on spatial relationship |
-
2020
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Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110247796A (en) * | 2019-05-30 | 2019-09-17 | 平安科技（深圳）有限公司 | Monitoring alarm method, apparatus and relevant device |
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Application publication date: 20200911 Assignee: Beijing Zhongke Sanqing Environmental Technology Co.,Ltd. Assignor: 3CLEAR TECHNOLOGY Co.,Ltd. Contract record no.: X2022980012305 Denomination of invention: Automatic review method, device and electronic equipment for air pollution monitoring data Granted publication date: 20210212 License type: Common License Record date: 20220815 |