Method and device for determining ozone monitoring abnormal point, and computing device

Abstract

The method for determining an abnormal point of ozone monitoring provided by the embodiments of the present disclosure comprises: calculating a predicted change rate based on the ozone concentration monitoring data of similar monitoring points in a current period and a previous period; calculating ozone concentration prediction data based on the predicted change rate and the ozone concentration monitoring data of the target monitoring point in the previous period; calculating a concentration difference ratio based on the ozone concentration prediction data and the ozone concentration monitoring data of the target monitoring point in the current period; and determining that the target monitoring point is an abnormal point of ozone concentration if the concentration difference ratio is greater than a preset difference ratio. The embodiments of the present disclosure can determine whether the ozone concentration data of the target monitoring point is abnormal by using the monitoring data of similar external monitoring points, thereby identifying the systematic failure of the target monitoring point and more accurately determining whether the target monitoring point is an abnormal point.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of environmental concentration monitoring data processing, in particular to a method and device for determining abnormal ozone monitoring point and a computing device. BACKGROUND

[0002] In order to realize reasonable early ozone pollution warning and prevention, it is necessary to ensure the accuracy of ozone concentration monitoring data of each monitoring point. Accordingly, while obtaining ozone concentration monitoring data of each monitoring point, data screening of ozone concentration monitoring data is needed to exclude local inaccurate monitoring data caused by abnormal reasons. The existing ozone concentration abnormal screening method is to obtain a monitoring data sequence of a single point, and to perform correlation analysis based on the time and precursor concentration of the monitoring data sequence to screen whether accidental jump data appears in the data of the single point, and to identify the abnormal state of the monitoring point through the change of the data. However, the foregoing method cannot identify the overall abnormality of ozone concentration data caused by systematic failure (for example, caused by systematic influence of the surrounding environment), but cannot identify the problem through the monitoring data sequence. SUMMARY

[0003] In order to solve the problem of many errors in the monitoring abnormal area identified by the existing method, the embodiments of the present disclosure provide a method and device for determining abnormal ozone monitoring point and a computing device.

[0004] In a first aspect, the embodiments of the present disclosure provide a method for determining abnormal ozone monitoring point, comprising:

[0005] determining whether there is a same type monitoring point as the target monitoring point in a pre-constructed ozone change trend cluster set, wherein the ozone change trend cluster set is obtained by cluster analysis based on the historical change rate sequence of ozone concentration obtained under normal monitoring conditions of each monitoring point;

[0006] in the case that there is a same type monitoring point as the target monitoring point in the ozone change trend cluster set, taking the same type monitoring point as a similar monitoring point, and obtaining ozone concentration monitoring data of the similar monitoring point in the current period and in the previous period;

[0007] calculating a predicted change rate based on the ozone concentration monitoring data of the similar monitoring point in the current period and in the previous period, and calculating ozone concentration prediction data of the target monitoring point in the current period based on the predicted change rate and the ozone concentration monitoring data of the target monitoring point in the previous period;

[0008] calculating a concentration difference value or a concentration difference value ratio according to the ozone concentration prediction data and the ozone concentration monitoring data of the target monitoring point in the current period;

[0009] In a case that the concentration difference value is greater than the preset difference value or the concentration difference value ratio is greater than the preset difference value ratio, the target monitoring point is determined as an ozone concentration abnormal point.

[0010] Optionally, the method further comprises:

[0011] In a case that the concentration difference value of more than the preset number of ozone concentration difference values in the preset continuous monitoring period is greater than the preset difference value or the concentration difference value ratio is greater than the preset difference value ratio, the target monitoring point is determined as an ozone concentration abnormal point.

[0012] Optionally, in a case that the same type monitoring point exists in the ozone change trend cluster, meteorological feature data of the same type monitoring point is acquired;

[0013] The same type monitoring point is taken as a similar monitoring point, comprising: in a case that the same type monitoring point is a non-rain and snow weather point, the same type monitoring point is taken as the similar monitoring point.

[0014] Optionally, the method further comprises: acquiring position coordinate data of the target monitoring point and the same type monitoring point;

[0015] The spatial distance between the target monitoring point and the same type monitoring point is calculated based on the position coordinate data, or whether the target monitoring point and the same type monitoring point belong to the same terrain region is determined based on the position coordinate data.

[0016] The same type monitoring point is taken as a similar monitoring point, comprising: in a case that the spatial distance is less than a set distance or the target monitoring point and the same type monitoring point are in the same terrain region, the same type monitoring point is taken as the similar monitoring point.

[0017] Optionally, the method further comprises: acquiring precursor concentration data of the target monitoring point and the same type monitoring point, and determining whether the precursor concentration of the same type monitoring point and the target monitoring point is similar.

[0018] The same type monitoring point is taken as a similar monitoring point, comprising: in a case that the precursor concentration data of the same type monitoring point and the precursor concentration data of the target monitoring point are similar, the same type monitoring point is taken as the similar monitoring point.

[0019] Optionally, before determining whether there is a same type monitoring point clustered with the target monitoring point from the pre-constructed ozone change trend cluster, the method further comprises:

[0020] Acquiring ozone concentration historical data sequences of each monitoring point acquired in non-rain and snow weather;

[0021] obtain an ozone concentration historical change rate sequence based on the ozone concentration historical data sequence of each monitoring point, wherein the ozone concentration historical change rate in the ozone concentration historical change sequence is obtained by the data after and the data before the ozone concentration historical change rate;

[0022] perform cluster analysis on the ozone concentration historical change rate sequence of each monitoring point to determine a cluster set to which each monitoring point belongs, and the cluster set includes same type monitoring points of each monitoring point.

[0023] Optionally, in the case that there is no same type monitoring point of the target monitoring point in the ozone change trend cluster set, the method further comprises:

[0024] obtain ozone concentration monitoring data of the target monitoring point in multiple continuous time periods, and calculate the standard deviation of the continuous ozone concentration monitoring data;

[0025] in the case that the standard deviation is greater than the set deviation, determine that the target monitoring point is an ozone concentration abnormal point.

[0026] Optionally, the determining whether there is a same type monitoring point clustered with the target monitoring point from the pre-constructed ozone change trend cluster set comprises:

[0027] in the case that the device state identifier of the target monitoring point is a normal identifier, determine whether there is a same type monitoring point clustered with the target monitoring point from the pre-constructed ozone change trend cluster set.

[0028] In a second aspect, the disclosure provides a device for determining an ozone monitoring abnormal point, comprising:

[0029] a same type point determination unit configured to determine whether there is a same type monitoring point clustered with the target monitoring point from a pre-constructed ozone change trend cluster set, wherein the ozone change trend cluster set is obtained by performing cluster analysis on an ozone concentration historical change rate sequence of each monitoring point obtained under normal monitoring conditions;

[0030] a data obtaining unit configured to, in the case that there is a same type monitoring point of the target monitoring point in the ozone change trend cluster set, take the same type monitoring point as a similar monitoring point, and obtain ozone concentration monitoring data of the similar monitoring point in a current time period and a previous time period;

[0031] a prediction data calculation unit configured to calculate a predicted change rate based on the ozone concentration monitoring data of the similar monitoring point in the current time period and the previous time period, and calculate ozone concentration prediction data of the target monitoring point in the current time period based on the predicted change rate and the ozone concentration monitoring data of the target monitoring point in the previous time period.

[0032] a comparison unit configured to calculate a concentration difference value according to the ozone concentration prediction data and the ozone concentration monitoring data of the target monitoring point in the current time period, or calculate a concentration difference value ratio;

[0033] an abnormal point determination unit configured to determine the target monitoring point as an abnormal point of ozone concentration in a case where the concentration difference value is greater than a preset difference value, or the concentration difference value ratio is greater than a preset ratio difference value.

[0034] In a third aspect, the embodiments of the present disclosure provide a computing device, comprising a processor and a memory, the memory being configured to store a computer program; the computer program, when loaded by the processor, causes the processor to execute the method for determining an abnormal point of ozone monitoring as described above.

[0035] By using the scheme of the embodiments of the present disclosure, the same type monitoring points in the target monitoring point are determined from the pre-constructed ozone change trend clustering set, and similar monitoring points are screened from the same type monitoring points, and the ozone concentration prediction data of the target monitoring point in the current time period is predicted based on the similar monitoring points, and whether the ozone concentration monitoring data in the current time period is abnormal is evaluated through the ozone concentration prediction data. That is, the scheme of the embodiments of the present disclosure evaluates whether the ozone concentration monitoring data of the target monitoring point is abnormal by setting the same type monitoring points independent of the target monitoring point. By using the scheme of the embodiments of the present disclosure, instead of only judging whether the target monitoring point is abnormal based on the ozone concentration data sequence of the target monitoring point, the ozone concentration data of the target monitoring point is judged to be abnormal by means of similar external monitoring point monitoring data, which realizes the identification of the systematic failure of the target monitoring point, and further more accurately determines whether the target monitoring point is an abnormal point. BRIEF DESCRIPTION OF DRAWINGS

[0036] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present disclosure and, together with the specification, serve to explain the principles of the present disclosure.

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, for those skilled in the art, other drawings can also be obtained from these drawings without creative labor, wherein:

[0038] Figure 1 is a flowchart of the method for determining an abnormal point of ozone monitoring provided by the embodiments of the present disclosure;

[0039] Figure 2 is a flowchart of the method for determining an ozone change trend clustering set provided by the embodiments of the present disclosure;

[0040] Figure 3 FIG. 1 is a structural schematic diagram of an ozone monitoring abnormal point determination device provided by an embodiment of the present disclosure.

[0041] Figure 4 FIG. 2 is a structural schematic diagram of a computing device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0042] Embodiments of the present disclosure will be described in more detail by referring to the drawings. Although certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms, and should not be construed as being limited to the embodiments set forth herein, but rather, these embodiments are provided so as to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are merely for exemplary purposes, and are not intended to limit the scope of protection of the present disclosure.

[0043] The term "comprising" and variations thereof as used herein are open-ended, that is "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Related terms such as "one aspect", "another aspect", "some aspects", "one embodiment", "another embodiment", "some embodiments" mean "at least one of the aspects" and / or "at least one of the embodiments". The foregoing and other related definitions are set forth in the following description. In this document, relational terms such as "first" and "second", and the like can be used solely to distinguish one entity or action from another entity or action, without necessarily requiring or implying any actual such relationship or order between such entities or actions.

[0044] To solve the problem that the existing data screening method cannot screen out abnormal ozone monitoring points caused by systematic failure, an embodiment of the present disclosure provides a new method for determining abnormal ozone monitoring points. The method for determining abnormal ozone monitoring points provided by the present disclosure is based on the similarity of the ozone concentration change rate of similar points, uses the ozone concentration change rate determined by the similarity to calculate predicted ozone concentration data, and uses the predicted ozone concentration data to evaluate whether the monitoring data of the ozone monitoring point is abnormal, thereby realizing the screening of the monitoring point.

[0045] Figure 1 FIG. 1 is a structural schematic diagram of an ozone monitoring abnormal point determination device provided by an embodiment of the present disclosure. Figure 1 As shown in FIG. 1, the method for determining abnormal ozone monitoring points provided by the present disclosure includes S110-S150. The method of the present disclosure is executed by a computing device capable of obtaining ozone concentration data of a large number of monitoring points.

[0046] S110: Determine whether there are similar monitoring points in the target monitoring point cluster from the pre-constructed ozone change trend cluster; if yes, perform S120.

[0047] The ozone change trend cluster set is obtained by clustering analysis based on the historical change rate sequence of the ozone concentration obtained by each monitoring point under normal monitoring conditions. The ozone change trend cluster set has multiple clusters, and each cluster includes at least one monitoring point. In most cases, each cluster set includes multiple monitoring points.

[0048] How to build the ozone change trend cluster set will be described later. Here, only the premise needs to be determined is that the ozone change trend cluster set divides the monitoring points with the same ozone change trend in one cluster set and divides the monitoring points with different ozone change trends in different cluster sets.

[0049] After determining the target monitoring point, the ozone change trend cluster set is searched by the target monitoring point, so as to determine whether there is a same type monitoring point clustered with the target monitoring point and the point identifier of the same type monitoring point.

[0050] It should be noted here that considering that rain has a strong removal effect on ozone in the atmosphere, the target monitoring point in the embodiments of the present disclosure is mostly a point with non-rain and snow weather meteorological conditions, and is a point with rain and snow weather only in some cases (such as the case of determining that the ozone monitoring device itself is faulty by experience).

[0051] S120: Taking the same type monitoring point as a similar monitoring point, and obtaining ozone concentration monitoring data of the similar monitoring point in the current period and in the previous period.

[0052] In some embodiments of the present disclosure, each monitoring point is a point with the same meteorological condition under most conditions (especially for inland and less rainy areas or similar Mediterranean climate areas). After determining the same type monitoring point, the computing device can directly take the same type monitoring point as the similar monitoring point.

[0053] It should be noted here that the current period and the previous period are two time terms named for convenience of description, and do not have the absolute meanings of current and previous. Specifically, the computing device can determine the ozone concentration monitoring data in the current period and in the previous period according to the time identifier of the current period and the identifier of the similar monitoring point.

[0054] In some other embodiments, due to the distance between the monitoring sites, the similar monitoring sites may be in different weather conditions, and the similar monitoring sites may be in the rain and snow weather conditions. As analyzed above, because rain has a strong removal effect on ozone in the atmosphere, the ozone concentration change characteristics of the similar monitoring sites in the rain and snow conditions are not comparable with the ozone concentration change characteristics of the target monitoring site in the non-rain and snow weather conditions, and if the ozone concentration change characteristics obtained in the rain and snow conditions are forcibly used, prediction errors may be caused. Based on this, after determining the similar monitoring sites, it is further determined whether the similar monitoring sites are in the rain and snow weather conditions. Only in the case where the similar monitoring sites are in the non-rain and snow weather conditions, the computing device regards the similar monitoring sites as the similar monitoring sites.

[0055] In some other embodiments, when the computing device determines the ozone change trend clustering set, the data of the monitoring sites in a large area (for example, in the entire national administrative region) is processed, and the monitoring sites that are far away from each other may be clustered in one clustering set according to the foregoing method. Because the computing device determines the ozone change trend clustering set only based on the change trend in some embodiments, without considering that different factors may cause the same change trend, some monitoring sites are accidentally clustered in one clustering set. For example, the first monitoring site and the second monitoring site are accidentally clustered in one clustering set, but the main reason for the change of the ozone concentration of the first monitoring site is the dramatic change of the concentration of the atmospheric pollution precursors (for example, in the high-latitude region in winter), and the main reason for the change of the ozone concentration of the second monitoring site is the distance change of the light intensity (for example, in the low-latitude region in winter).

[0056] According to the analysis in the preceding paragraph, if the similar monitoring sites are directly regarded as the similar monitoring sites, the ozone concentration change trend may be used as the classification standard, and the ozone concentration change trend of the accidentally determined similar monitoring sites that are not truly similar monitoring sites may be used to predict the ozone concentration data of the target monitoring site, causing prediction errors. To avoid the foregoing problem, in some embodiments, after determining the similar monitoring sites, the position coordinate data of the target monitoring site and the similar monitoring sites are obtained, and whether the target monitoring site and the similar monitoring sites belong to the same terrain region (for example, whether they belong to the same plain terrain region or the same hilly terrain region, and accordingly the terrain type data of the target monitoring site and the similar monitoring sites need to be obtained) is determined according to the position coordinate data. In the case where the target monitoring site and the similar monitoring sites are in the same monitoring site, the similar monitoring sites are regarded as the similar monitoring sites.

[0057] In some other embodiments, after the computing device obtains the location coordinate data of the target monitoring point and similar monitoring points, it calculates the spatial distance between the target monitoring point and similar monitoring points based on the location coordinate data, and only if the aforementioned spatial distance is less than a set distance will the similar monitoring points be regarded as similar monitoring points.

[0058] As analyzed above, and considering the photochemical reaction principle of ozone formation, the concentration of precursors (carbon oxides and nitrogen oxides) directly affects the rate of ozone formation and consumption under high-altitude conditions, thus influencing the rate of ozone concentration change. Furthermore, the concentration of precursors in different regions may vary significantly due to emissions from pollution sources, resulting in substantial differences in the rate of ozone concentration change.

[0059] Based on the preceding analysis, in some embodiments, to more accurately determine ozone concentration prediction data during subsequent steps, after identifying similar monitoring points, the computing device also acquires precursor concentration data for both the similar and target monitoring points, and determines whether the precursor concentrations of the similar and target monitoring points are similar. Specifically, the computing device can determine whether the precursor concentrations of the similar and target monitoring points are within the same concentration range to ascertain their similarity. Only when the precursor concentrations of the similar and target monitoring points are determined to be similar will the computing device classify the similar monitoring points as similar monitoring points.

[0060] After similar monitoring sites are identified, the computing device triggers subsequent steps to acquire ozone concentration monitoring data for the similar monitoring sites in the current time period and in previous time periods.

[0061] S130: Calculate the predicted rate of change based on ozone concentration monitoring data of similar monitoring points in the current and previous periods; and calculate the predicted ozone concentration data of the target monitoring point in the current period based on the predicted rate of change and ozone concentration monitoring data of the target monitoring point in the previous period.

[0062] After obtaining ozone concentration monitoring data from similar monitoring points in the current and previous periods, the computing device subtracts the ozone concentration monitoring data from the current and previous periods to obtain the difference. The difference is then compared with the ozone concentration monitoring data from the previous period to obtain the predicted rate of change.

[0063] After obtaining the predicted rate of change, the computing device uses ozone concentration monitoring data from the target monitoring point in the previous period, according to c predict =c history ×(1+r predict Obtain the predicted ozone concentration data for the current time period. predict Where c historyThe ozone concentration monitoring data of the target monitoring point in the previous period is r pre di c t The aforementioned predicted change rate is

[0064] S140: Calculate the concentration difference ratio value according to the ozone concentration prediction data and the ozone concentration monitoring data of the target monitoring point in the current period.

[0065] According to the foregoing method, the computing device obtains the ozone concentration prediction data and the ozone concentration monitoring data of the target monitoring point in the current period. Then, the difference value is obtained by subtracting the foregoing data, and the concentration difference ratio value is obtained by comparing the difference value with the ozone concentration prediction data or the ozone concentration monitoring data (in the embodiment of the present disclosure, the ozone concentration monitoring data).

[0066] S150: In the case where the concentration difference ratio value is greater than the preset difference ratio value, determine that the target monitoring point is an ozone concentration abnormal point.

[0067] As analyzed above, because the target monitoring point and the similar monitoring point (or the same type of monitoring point) are in a cluster set, the two generally have the same ozone concentration change trend. Correspondingly, the ozone concentration prediction data determined based on the ozone concentration data of the similar monitoring point and the ozone concentration monitoring data of the target monitoring point should not have a large difference ratio value. If the difference ratio value is large, it is determined that the ozone concentration monitoring data of the target monitoring point is not reasonable based on the foregoing premise, and therefore the target monitoring point is determined to be an ozone concentration abnormal point.

[0068] In specific implementation, in addition to determining that the concentration difference ratio value is greater than the preset difference ratio value, in some embodiments, the computing device can also determine whether the ozone concentration monitoring data of the target monitoring point in the current period is reasonable by determining whether the concentration difference value is greater than the preset difference value, and further determine whether the target monitoring point is an ozone concentration abnormal point.

[0069] With the embodiment of the present disclosure, the computing device determines the same type of monitoring point in the target monitoring point by the pre-constructed ozone change trend clustering set, and screens the similar monitoring point from the same type of monitoring point, and predicts the ozone concentration prediction data of the target monitoring point in the current time period based on the similar monitoring point, and evaluates whether the ozone concentration monitoring data in the current time period is abnormal through the ozone concentration prediction data. That is, the embodiment of the present disclosure evaluates whether the ozone concentration monitoring data of the target monitoring point is abnormal by setting the same type of monitoring point independent of the target monitoring point. With the embodiment of the present disclosure, instead of judging whether the target monitoring point is abnormal based on the ozone concentration data sequence of the target monitoring point, the ozone concentration data of the target monitoring point is judged by means of the similar external monitoring point monitoring data, which realizes the identification of the systematic failure of the target monitoring point, and further more accurately determines whether the target monitoring point is an abnormal point.

[0070] In a specific implementation, considering that single-period data has a large random error, directly using single-period data to determine the abnormal point will also have a large randomness, which will cause a large probability of identification error. To solve this problem, the ozone concentration monitoring data of the preset continuous monitoring period can be identified and judged, and in the case that the ozone concentration difference value of more than a set number is greater than a preset difference value, or the concentration difference value ratio is greater than a preset ratio difference value, the target monitoring point is determined as an ozone concentration abnormal point.

[0071] In actual implementation, the number of the same type of monitoring point of the target monitoring point is small, or it can be large. In the case that the number of the same type of monitoring point is small, the result obtained by the foregoing method has poor reliability, and in actual application, in the case that the number of the same type of monitoring point (or the similar monitoring point) is large (for example, greater than a preset statistical confidence number), the foregoing method can be used to determine whether the target monitoring point is an ozone concentration abnormal point.

[0072] In some embodiments, there can be no same type of monitoring point in the ozone change trend clustering set that is clustered with the target monitoring point, in which case the computing device can determine whether the target monitoring point is an ozone concentration abnormal point by S160-S170 as follows.

[0073] S160: Obtain the ozone concentration monitoring data of the target monitoring point in multiple continuous time periods, and calculate the standard deviation of the continuous ozone concentration monitoring data.

[0074] S170: In the case that the standard deviation is greater than a set deviation, determine that the target monitoring point is an ozone concentration abnormal point.

[0075] According to the principle of ozone photochemical reaction and diffusion mechanism, the ozone concentration will change in the daytime (typically AM 8:00-PM 5:00), but the change rate will not be too large. From a statistical point of view, the standard deviation of the ozone concentration monitoring data of the target monitoring point in multiple consecutive time periods will not be too large. Based on this, the ozone concentration monitoring data of the target monitoring point is analyzed in the case where the same monitoring point of the target monitoring point cannot be obtained, and it is judged whether the target monitoring point is an ozone concentration abnormal point.

[0076] Specifically, the computing device obtains ozone concentration monitoring data of the target monitoring point in multiple consecutive time periods, and calculates the corresponding standard deviation. If the standard deviation is greater than the preset deviation, it is determined that the premise that the ozone concentration monitoring data in multiple consecutive time periods will not change too much is violated, and therefore the target monitoring point is determined to be an ozone concentration abnormal point.

[0077] After determining the ozone abnormal point by the foregoing method, the precision and accuracy of the ozone monitoring device of the ozone abnormal point can be checked, and the surrounding environment of the ozone abnormal point can be investigated to identify the cause of the abnormal monitoring data.

[0078] The foregoing only uses the ozone change trend cluster set. Herein, how to obtain the ozone change trend cluster set is analyzed. Figure 2 is a method flowchart for determining the ozone change trend cluster set of the present disclosure. As shown in Figure 2 The method for constructing the ozone change trend cluster set includes S210-S230.

[0079] S210: Obtain ozone concentration historical data sequences of each monitoring point obtained in non-rain and snow weather.

[0080] As previously analyzed, because rain has a strong absorption effect on ozone, the monitoring data under the corresponding weather has no use significance, and therefore the ozone concentration historical data sequences of each monitoring point obtained in non-rain and snow weather are obtained for cluster analysis. In a specific implementation, considering that the ozone concentration is very small under non-illumination conditions (ozone cannot be generated under non-illumination conditions, and ozone in the atmosphere is rapidly consumed due to oxidation reaction), the obtained ozone concentration historical data sequences are data under illumination conditions, for example, data of each position corresponding to the time zone from AM 8:00 to PM 5:00.

[0081] S220: Obtain ozone concentration historical change rate sequences based on the ozone concentration historical data sequences of each monitoring point, respectively, wherein the ozone concentration historical change rate in the ozone concentration historical change sequence is obtained by the data after the ozone concentration historical change rate and the data before the ozone concentration historical change rate.

[0082] After obtaining the historical data sequence of the ozone concentration of each monitoring point, the data after the preceding data in the aforementioned sequence is subtracted from the data near the preceding data to obtain the ozone concentration change rate (the rate is relative to a statistical period), and the ozone concentration change rate of each monitoring point is sorted in time sequence to obtain the historical change rate sequence of the ozone concentration.

[0083] S230: Cluster analysis is performed on the historical change rate sequence of the ozone concentration of each monitoring point to determine the cluster set to which each monitoring point belongs, and the cluster set includes the same type monitoring points of each monitoring point.

[0084] After obtaining the historical change rate sequence of the ozone concentration, the aforementioned sequence is then subjected to cluster analysis. In a specific implementation, the K clustering method can be used to determine the number of clusters by empirical setting, and then the data is subjected to cluster analysis to determine the monitoring points included in each cluster set. In a specific implementation, in addition to using the historical change rate sequence of the ozone concentration of each monitoring point for cluster analysis, the precursor concentration data sequence of each monitoring point can also be obtained, and the cluster analysis is performed on the historical change rate sequence of the ozone concentration and the precursor concentration data sequence.

[0085] It should also be noted that in actual applications, if the ozone concentration monitoring device identifies the device state as an abnormal state through self-checking, it will generate an abnormal state identifier to prompt that the corresponding monitoring data may be incorrect data. In this case, there is no need to use the aforementioned method to determine that the monitoring point is an abnormal point. Correspondingly, only in the case where the device state identifier of the target monitoring point is a normal identifier, the aforementioned method is executed and the corresponding judgment is made.

[0086] In addition to providing the aforementioned determination method of the ozone monitoring abnormal point, the embodiments of the present disclosure also provide a determination device of the ozone monitoring abnormal point. Figure 3 is a structural schematic diagram of the determination device of the ozone monitoring abnormal point provided by the embodiments of the present disclosure. As shown in Figure 3 The determination device of the ozone monitoring abnormal point 300 includes the same type point determination unit 301, the data acquisition unit 302, the predicted data calculation unit 303, the comparison unit 304, and the abnormal point determination unit 305.

[0087] The same type point determination unit 301 is configured to determine whether there is a same type monitoring point of the target monitoring point in the pre-constructed ozone change trend cluster set, the ozone change trend cluster set is obtained by cluster analysis based on the historical change rate sequence of the ozone concentration of each monitoring point under normal monitoring, and in the case where there is a same type monitoring point of the target monitoring point in the ozone change trend cluster set, the same type monitoring point is taken as a similar monitoring point.

[0088] The data acquisition unit 302 is configured to acquire the ozone concentration monitoring data of the similar monitoring point in the current period and the previous period.

[0089] The prediction data calculation unit 303 is configured to calculate a predicted change rate based on the ozone concentration monitoring data of the similar monitoring point in the current period and the previous period, and calculate the ozone concentration prediction data of the target monitoring point in the current period based on the predicted change rate and the ozone concentration monitoring data of the target monitoring point in the previous period.

[0090] The comparison unit 304 is configured to calculate a concentration difference value or a concentration difference value ratio based on the ozone concentration prediction data and the ozone concentration monitoring data of the target monitoring point in the current period.

[0091] The abnormal point determination unit 305 is configured to determine that the target monitoring point is an abnormal ozone concentration point when the concentration difference value is greater than a preset difference value or the concentration difference value ratio is greater than a preset ratio difference value.

[0092] In some embodiments, the abnormal point determination unit 305 determines that the target monitoring point is an abnormal ozone concentration point when the ozone concentration difference value is greater than the preset difference value or the concentration difference value ratio is greater than the preset ratio difference value for more than a preset number of consecutive monitoring periods.

[0093] In some embodiments, when the ozone change trend cluster has similar monitoring points, the data acquisition unit 302 acquires meteorological feature data of the similar monitoring points; and the similar point determination unit 301 determines the similar monitoring points as the similar monitoring points when the similar monitoring points are non-rain and snow weather points.

[0094] In some embodiments, the data acquisition unit 302 acquires position coordinate data of the target monitoring point and the similar monitoring points, and calculates a spatial distance between the target monitoring point and the similar monitoring points based on the position coordinate data, or determines whether the target monitoring point and the similar monitoring points belong to the same terrain region based on the position coordinate data.

[0095] The similar unit determination unit determines the similar monitoring points as the similar monitoring points when the spatial distance is less than a set distance or the target monitoring point and the similar monitoring points are in the same terrain region.

[0096] In some embodiments, the data acquisition unit 302 acquires precursor concentration data of the target monitoring point and the similar monitoring point, and determines whether the precursor concentration of the similar monitoring point and the target monitoring point is similar; the similar unit determining unit determines the similar monitoring point as the similar monitoring point in the case that the precursor concentration data of the similar monitoring point and the precursor concentration data of the target monitoring point are similar.

[0097] In some embodiments, before determining whether there is a similar monitoring point of the target monitoring point cluster from the pre-constructed ozone change trend cluster, the data acquisition unit 302 acquires an ozone concentration historical data sequence of each monitoring point acquired under non-rain and snow weather; the clustering unit respectively calculates an ozone concentration historical change rate sequence based on the ozone concentration historical data sequence of each monitoring point, wherein the ozone concentration historical change rate in the ozone concentration historical change sequence is obtained by the data after the ozone concentration historical change rate and the data adjacent to the former; the ozone concentration historical change rate sequence of each monitoring point is clustered and analyzed to determine the cluster set to which each monitoring point belongs, and the cluster set includes the similar monitoring point of each monitoring point.

[0098] In some embodiments, in the case that there is no similar monitoring point of the target monitoring point in the ozone change trend cluster, the abnormal point determining unit 305 acquires ozone concentration monitoring data monitored by the target monitoring point in multiple continuous time periods, and calculates the standard deviation of the continuous ozone concentration monitoring data; in the case that the standard deviation is greater than the set deviation, the target monitoring point is determined as an ozone concentration abnormal point.

[0099] In some embodiments, in the case that the device state identifier of the target monitoring point is a normal identifier, the data acquisition unit 302 determines whether there is a similar monitoring point of the target monitoring point cluster from the pre-constructed ozone change trend cluster.

[0100] The embodiments of the present disclosure also provide a computing device for implementing the foregoing method. Figure 4 FIG. 1 is a structural schematic diagram of a computing device provided by the embodiments of the present disclosure. The following will be specifically described with reference to Figure 4 FIG. 1 is a structural schematic diagram of a computing device provided by the embodiments of the present disclosure. The following will be specifically described with reference to Figure 4 The computing device shown is only an example, and should not bring any limitation to the function and use range of the embodiments of the present disclosure.

[0101] As shown in FIG. 1, the computing device 100 includes a data acquisition unit 302, a clustering unit 304, an abnormal point determining unit 305, a similar unit determining unit 306, and a device state determining unit 307. Figure 4As shown, computing device 400 can include a processing device (e.g., central processing unit, graphics processing unit, etc.) 401 that can perform various appropriate actions and processes in accordance with programs stored in read only memory (ROM) 402 or loaded into random access memory (RAM) 403 from storage device 408. Various programs and data needed in the operation of computing device 400 are also stored in RAM 403. Processing device 401, ROM 402, and RAM 403 are connected to each other by a bus 404. An input / output (I / O) interface 405 is also connected to bus 404.

[0102] Generally, the following devices can be connected to I / O interface 405: input devices 406, including, for example, a touchscreen, a touchpad, a camera, a microphone, etc.; output devices 407, including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 408, including, for example, a magnetic tape, a hard disk, etc.; and communication devices 409. Communication devices 409 can allow computing device 400 to communicate wirelessly or via wire with other devices to exchange data. Although Figure 4 Computing device 400 is shown with various devices, but it is understood that not all of the shown devices are required to be implemented or present. More or fewer devices can alternatively be implemented or present.

[0103] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication devices 409, or installed from storage devices 408, or installed from ROM 402. When the computer program is executed by processing device 401, the above-described functions defined in the methods of embodiments of the present disclosure are performed.

[0104] It is noted that the computer-readable medium of the present disclosure described above can be a computer-readable storage medium, a computer-readable signal medium, or, or any combination of the two.

[0105] A computer readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a computer readable storage medium can include, but are not limited to, the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the disclosure, a computer readable storage medium can be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0106] A computer readable signal medium can include a propagated data signal with computer readable program code embodied therein, for use by or in connection with an instruction execution system, apparatus, or device. Examples of a computer readable signal medium include but are not limited to a propagated data signal with computer readable program code embodied therein, for use by or in connection with an instruction execution system, apparatus, or device. A computer readable signal medium can be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable signal medium can be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0107] In some embodiments, the client, computing device can communicate using any known or future developed network protocols, such as HTTP (HyperText Transfer Protocol), and can be interconnected with digital data communication (e.g., a communication network) of any form or medium, such as the Internet or World Wide Web. Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet, and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any current or future developed network.

[0108] The computer readable medium described above can be included within the computing device described above; or can exist exclusively on the outside of the computing device.

[0109] Computer program code for carrying out operations of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the testee's computer, partly on the testee's computer, as a stand-alone software package, partly on the testee's computer and partly on a remote computer or entirely on the remote computer or computing device. In the latter scenario, the remote computer can be connected to the testee's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0110] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of present disclosure. In this regard, each block in the flow diagrams or block diagrams can represent a module, a procedure, or a part of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or in the reverse order, depending on the functionality involved. It is also noted that each block in the block diagrams and / or flow diagrams and combinations of blocks in the block diagrams and / or flow diagrams can be implemented by special-purpose hardware-based systems that perform the specified functions or operations, or combinations of special-purpose hardware and computer instructions.

[0111] The units described in the embodiments of the present disclosure can be implemented by software, or by hardware. In some cases, the names of the units do not constitute a limitation on the units themselves. The functions described above can be performed at least in part by one or more hardware logic components. For example, non-limiting examples of hardware logic components that can be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SOCs), complex programmable logic devices (CPLDs), etc.

[0112] The foregoing is merely illustrative of the various ways and specific embodiments in which the disclosure can be carried out. Numerous modifications can be made to these embodiments without departing from the spirit and scope of the disclosure. Therefore, the disclosure is not limited to the specific embodiments described herein, but rather the scope of the disclosure is to be accorded the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining abnormal point positions of ozone monitoring, characterized in that, The method comprises the following steps: determining whether there is a same type monitoring point in the target monitoring point cluster from a pre-constructed ozone change trend cluster set, the ozone change trend cluster set being obtained by cluster analysis based on the historical change rate sequence of the ozone concentration obtained by each monitoring point under normal monitoring conditions; in the case that there is a same type monitoring point of the target monitoring point in the ozone change trend cluster set, obtaining the meteorological feature data of the same type monitoring point; obtaining the position coordinate data of the target monitoring point and the same type monitoring point; calculating the spatial distance of the target monitoring point and the same type monitoring point based on the position coordinate data, or judging whether the target monitoring point and the same type monitoring point belong to the same terrain region based on the position coordinate data; obtaining the precursor concentration data of the target monitoring point and the same type monitoring point, and judging whether the precursor concentration of the same type monitoring point and the target monitoring point is similar; in the case that the same type monitoring point is a non-rain and snow weather point, in the case that the spatial distance is less than a set distance, or the target monitoring point and the same type monitoring point are in the same terrain region, in the case that the precursor concentration data of the same type monitoring point is similar to the precursor concentration data of the target monitoring point, the same type monitoring point is taken as a similar monitoring point, and the ozone concentration monitoring data of the similar monitoring point in the current period and in the previous period is obtained; calculating a predicted change rate based on the ozone concentration monitoring data of the similar monitoring point in the current period and in the previous period; and calculating the ozone concentration prediction data of the target monitoring point in the current period based on the predicted change rate and the ozone concentration monitoring data of the target monitoring point in the previous period; calculating a concentration difference value or a concentration difference value ratio according to the ozone concentration prediction data and the ozone concentration monitoring data of the target monitoring point in the current period; in the case that the concentration difference value is greater than a preset difference value or the concentration difference value ratio is greater than a preset difference value ratio, determining that the target monitoring point is an ozone concentration abnormal point.

2. The determination method according to claim 1, characterized in that, The method for determining that the target monitoring point is an ozone concentration abnormal point comprises: in the case that more than a preset number of ozone concentration difference values are greater than a preset difference value or the concentration difference value ratio is greater than a preset ratio difference value in a preset continuous monitoring period, determining that the target monitoring point is an ozone concentration abnormal point.

3. The determination method according to any one of claims 1-2, characterized in that, Before determining whether there is a same type monitoring point in the target monitoring point cluster from a pre-constructed ozone change trend cluster set, the method further comprises: obtaining a historical ozone concentration data sequence of each monitoring point under non-rain and snow weather; obtaining an ozone concentration historical change rate sequence based on the historical ozone concentration data sequence of each monitoring point, wherein the ozone concentration historical change rate in the ozone concentration historical change sequence is obtained by the data after the ozone concentration historical change rate and the data near the previous data; performing cluster analysis on the ozone concentration historical change rate sequence of each monitoring point to determine the cluster set to which each monitoring point belongs, and the cluster set comprises the same type monitoring point of each monitoring point.

4. The determination method according to any one of claims 1-2, characterized in that, The method further comprises: In the case that there is no same type monitoring point in the ozone change trend cluster set, the method further comprises: obtaining ozone concentration monitoring data of the target monitoring point in multiple continuous time periods, and calculating the standard deviation of the continuous ozone concentration monitoring data; in the case that the standard deviation is greater than the set deviation, determining that the target monitoring point is an abnormal ozone concentration point.

5. The determination method according to any one of claims 1-2, characterized in that, The method further comprises: in the case that the device state identifier of the target monitoring point is a normal identifier, determining whether there is a same type monitoring point clustered with the target monitoring point from the pre-constructed ozone change trend cluster set.

6. An apparatus for determining abnormal point of ozone monitoring, characterized in that, The method further comprises: a same type point determination unit, configured to determine whether there is a same type monitoring point clustered with the target monitoring point from the pre-constructed ozone change trend cluster set, the ozone change trend cluster set being obtained based on clustering analysis of historical ozone concentration change rate sequences of each monitoring point obtained under normal monitoring conditions; a data obtaining unit, configured to, in the case that there is a same type monitoring point of the target monitoring point in the ozone change trend cluster set, obtain meteorological feature data of the same type monitoring point; obtain position coordinate data of the target monitoring point and the same type monitoring point; calculate the spatial distance between the target monitoring point and the same type monitoring point based on the position coordinate data, or determine whether the target monitoring point and the same type monitoring point belong to the same terrain region based on the position coordinate data; obtain precursor concentration data of the target monitoring point and the same type monitoring point, and determine whether the precursor concentration of the same type monitoring point is similar to that of the target monitoring point; in the case that the same type monitoring point is a non-rain and snow weather point, in the case that the spatial distance is less than a set distance, or the target monitoring point and the same type monitoring point are in the same terrain region, in the case that the precursor concentration data of the same type monitoring point is similar to the precursor concentration data of the target monitoring point, taking the same type monitoring point as a similar monitoring point, and obtaining ozone concentration monitoring data of the similar monitoring point in the current time period and in the previous time period; a prediction data calculation unit, configured to calculate a predicted change rate based on the ozone concentration monitoring data of the similar monitoring point in the current time period and in the previous time period, and calculate ozone concentration prediction data of the target monitoring point in the current time period based on the predicted change rate and the ozone concentration monitoring data of the target monitoring point in the previous time period; a comparison unit, configured to calculate a concentration difference value or a concentration difference value ratio according to the ozone concentration prediction data and the ozone concentration monitoring data of the target monitoring point in the current time period; an abnormal point determination unit, configured to, in the case that the concentration difference value is greater than a preset difference value, or the concentration difference value ratio is a preset ratio difference value, determine that the target monitoring point is an abnormal ozone concentration point.

7. A computing device, comprising: The method comprises a processor and a memory for storing a computer program; the computer program, when loaded by the processor, enables the processor to execute the method for determining abnormal ozone monitoring points as claimed in any one of claims 1-5.

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