Method for Dynamically Determining High-Value Threshold of Concentration Change and High-Value Concentration Threshold of Pollutant

Abstract

An embodiment of the present disclosure provides a method for dynamically determining a high-value threshold for concentration change and a high-value concentration threshold for pollutants. The method for determining the high-value threshold for concentration change includes: performing difference processing on adjacent pollutant concentrations in a historical pollutant concentration sequence to obtain a difference concentration sequence, and extracting a time-period difference concentration sequence; performing mathematical statistical processing on the time-period difference concentration sequence to determine a statistical mean of a target period and a statistical standard deviation of the target period; determining a first high-value threshold for concentration change based on the statistical mean of the target period and the statistical standard deviation of the target period; and determining an adopted high-value threshold for concentration change in the same target period based on the first high-value threshold for concentration change. By adopting the solution of the embodiment of the present disclosure, for each target period, a corresponding high-value threshold for concentration change can be obtained, so that the high-value threshold for concentration change in a specific season and a specific period matches the season characteristics and period characteristics, and thus abnormal high values in the corresponding period can be better identified.

Description

Technical Field

[0001] The present disclosure relates to the technical field of environmental monitoring data processing, and particularly to a method for dynamically determining high-value thresholds of concentration changes and high-value concentration thresholds of pollutants. Background Art

[0002] Due to the influence of factors such as sampling environment, instrument status, operation and maintenance operations, and data transmission interference, there may be abnormal data in the continuous monitoring data of ambient air quality. To provide reasonable basic data for subsequent environmental quality analysis, it is necessary to eliminate untrue and unrepresentative abnormal data in the monitoring data. In practical applications, typical abnormal data are abnormally high-value data.

[0003] Traditional quality control of ambient air quality monitoring data relies on manual review, which is costly, has low timeliness, and depends on the experience of reviewers. With the increase in the number of stations, automated data quality control relying on algorithms has become an urgent need. Among them, the high-value threshold method is a basic but effective data quality control method, which uses the set "high-value threshold" to eliminate abnormally high concentration values. The core of data quality control using the high-value threshold method is the determination of the high-value threshold. However, there is no unified standard specification for the determination method of the high-value threshold in the industry, and currently mainly static thresholds are used.

[0004] However, due to the large fluctuations in the temporal and spatial distribution of the concentrations of conventional pollutants in the actual ambient air quality, and it is a non-stationary time series, with daily variations (such as ozone being high during the day and low at night), seasonal variations (such as the PM 2.5 concentration being higher in autumn and winter than in summer), and long-term variation characteristics (such as with the continuous deepening of pollution control, the average concentration of pollutants may generally show a downward trend), and at the same time being affected by short-term source emissions, using a static threshold cannot reflect the differences in the concentrations of conventional pollutants in ambient air quality at different times. Correspondingly, the high-value threshold method based on a static threshold cannot reflect the refined variation characteristics of the concentrations of conventional pollutants, and has the disadvantages of insufficient recognition accuracy and weak pertinence in identifying invalid high values. Summary of the Invention

[0005] The embodiments of the present disclosure provide a method for dynamically determining high-value thresholds of concentration changes and high-value concentration thresholds of pollutants. Among them, the method for determining the high-value threshold of concentration changes is used to more reasonably determine the high-value thresholds of concentration changes in each time period.

[0006] In a first aspect, the embodiments of the present disclosure provide a method for determining a high-value threshold of concentration changes, including:

[0007] Performing differential processing on adjacent pollutant concentrations in the historical pollutant concentration sequence of the target season to obtain a differential concentration sequence, and extracting the time-period differential concentration sequence corresponding to the same target time period of each day in the target season from the differential concentration sequence;

[0008] Performing mathematical statistical processing on the differential concentrations in the time period differential concentration series of each same target time period to determine the target time period statistical mean and target time period statistical standard deviation;

[0009] Determine a first concentration change high value threshold based on the target period statistical mean and the target period statistical standard deviation;

[0010] The trusted concentration change high value threshold for the same target time period is determined based on the first concentration change high value threshold.

[0011] Optionally, the method further comprises: performing mathematical statistical processing on the differential concentrations in the full differential concentration sequence to determine the season statistical mean and season statistical standard deviation of the target season;

[0012] Determine a second concentration change high value threshold based on the seasonal statistical mean and the seasonal statistical standard deviation;

[0013] The determining of the high-value threshold of the concentration change for the same target period based on the first high-value threshold of the concentration change includes:

[0014] The larger value of the first concentration change high value threshold and the second concentration change high value threshold is selected as the trusted concentration change high value threshold for the same target time period.

[0015] Optionally, determining the concentration change high value threshold for the same target time period based on the first concentration change high value threshold includes:

[0016] The first concentration change high value threshold is used as the trusted concentration change high value threshold for the same target time period.

[0017] Optionally, before performing adjacent difference processing on the historical pollutant concentration sequence of the target season, the method further includes:

[0018] Sorting the pollutant concentrations in the historical pollutant concentration sequence of the target season in order of magnitude to obtain a sorted sequence, and determining a first digit value and a second digit value therein, wherein the second digit value is greater than the first digit value;

[0019] Calculate the quantile range based on the first and second quantile values;

[0020] Determine a high concentration threshold based on the second quantile value, the quantile internal distance and a preset empirical coefficient;

[0021] On the premise of keeping the time sequence of the historical pollutant concentration sequence unchanged, the pollutant concentration values in the historical pollutant concentration sequence that are higher than the high concentration threshold are eliminated.

[0022] Optionally, determining the first order value and the second order value therein includes:

[0023] Determining the first quartile and the third quartile in the sorting sequence, and taking the smaller value of the first quartile and the third quartile as the first order value, and taking the larger value as the second order value.

[0024] Optionally, before sorting the pollutant concentrations in the historical pollutant concentration sequence of the target season in ascending order to obtain a sorting sequence, the method further includes:

[0025] Determining the meteorological states of each period in the corresponding historical pollutant concentration sequence;

[0026] When the meteorological feature in a certain period is the rainfall state, deleting the corresponding pollutant concentration on the premise of maintaining the chronological order of the historical pollutant concentration sequence.

[0027] Optionally, before sorting the pollutant concentrations in the historical pollutant concentration sequence of the target season in ascending order to obtain a sorting sequence, the method further includes:

[0028] Removing the invalid data in the historical pollutant concentration sequence, where the invalid data are zero value data, negative value data, and out-of-range data.

[0029] Optionally, before performing adjacent difference processing on the historical pollutant concentration sequence of the target season, the method further includes:

[0030] Sorting the pollutant concentrations in the historical pollutant concentration sequence of at least one full year in ascending order to obtain a sorting sequence, and determining the first order value and the second order value therein, where the second order value is greater than the first order value;

[0031] Calculating the interquartile range based on the first order value and the second order value;

[0032] Determining a high concentration threshold based on the second order value, the interquartile range, and a preset empirical coefficient;

[0033] Removing the pollutant concentration values higher than the high concentration threshold in the historical pollutant concentration sequence corresponding to the target season while ensuring the chronological order of the historical pollutant concentration sequence of the target season remains unchanged.

[0034] In a second aspect, an embodiment of the present disclosure provides a method for dynamically determining a high pollutant concentration threshold, including:

[0035] Based on the season identifier and the time period identifier of the current time period, the concentration change threshold data table is searched to obtain the high value threshold of the concentration change corresponding to the current time period; wherein the data in the concentration change threshold data table is obtained by the method for determining the high value threshold of the concentration change as described above;

[0036] The sum of the pollutant concentration in the previous time period and the corresponding high-value threshold of the concentration change in the current time period is used as the high-value pollutant concentration threshold of the current time period.

[0037] In a third aspect, the present disclosure implements a computing device comprising a processor and a memory, wherein the memory is used to store a computer program; when the computer program is loaded by the processor, the processor executes the method for determining a high-value threshold of concentration change as described above or the method for dynamically determining a high-value concentration threshold of pollutants as described above.

[0038] The solution provided by the embodiment of the present disclosure, after obtaining the differential concentration sequence of the target season, uses the differential concentration sequence of the same target time period to perform mathematical statistical analysis, and obtains the first concentration change high value threshold value based on the mathematical statistical analysis value. Using the solution of the embodiment of the present disclosure, a corresponding concentration change high value threshold value can be obtained for each target time period of the target season. Correspondingly, for each target time period, a corresponding concentration change high value threshold value can be obtained, so that the concentration change high value threshold value for a specific season and a specific time period matches the season characteristics and the time period characteristics, so as to better identify the abnormal high value in the corresponding time period. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art description. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work, including:

[0041] Figure 1 is a flow chart of a method for determining a high-value threshold value of concentration change provided by the present disclosure;

[0042] Figure 2 is a flow chart of a method for determining a high-value concentration threshold of a pollutant provided in some embodiments of the present disclosure;

[0043] Figure 3 It is a schematic diagram of the structure of a computing device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0044] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some 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 limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0045] As used herein, the term "including" and its variations are open-ended, that is, "including but not limited to". The term "based on" means "at least partially based 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". Relevant definitions of other terms will be given in the following description. In this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0046] Embodiments of the present disclosure provide a method for determining a high-value threshold of concentration change. By selecting a reasonable pollutant concentration segment, the concentration change threshold of the pollutant is determined, and then the high-value concentration threshold of the pollutant is reasonably determined by using the high-value threshold of concentration change.

[0047] Figure 1 is a flowchart of the method for determining the high-value threshold of concentration change provided by the embodiments of the present disclosure. As Figure 1 shown, the method for determining the high-value threshold of concentration change provided by the embodiments of the present disclosure includes S110-S140.

[0048] S110: Perform adjacent difference processing on the historical pollutant concentration sequence of the target season to obtain a difference concentration sequence, and extract the time segment difference concentration sequence corresponding to the same time segment of each day in the target season in the difference concentration sequence.

[0049] The target season is a season used to represent the same target time period. In specific implementation, the target season can be seasons such as spring, summer, autumn, and winter distinguished by seasons, or partial time periods in the foregoing seasons (such as early autumn, late autumn), or time periods determined based on similar air circulation characteristics and pollutant concentration characteristics (for example, for the northern region of China, it can be a windless season time period when the northerly wind is not raging but the southerly wind intensity is already insufficient). It should be noted here that the target season is a season with multiple days.

[0050] Through historical ambient air quality monitoring, the historical pollutant concentration sequence of a certain region (or a certain monitoring point) has been obtained.

[0051] The aforementioned historical pollutant concentration sequence may be the pollutant concentration sequence of the aforementioned target season in a year or the pollutant concentration sequence of the aforementioned target season in multiple years, and the embodiments of the present disclosure do not make specific limitations. In specific implementation, from the perspective of historical data, if the air pollution situation in a certain area has been significantly improved and its environment is more caused by pollution sources, it is preferably to use only the pollutant concentration sequence of the target season in the adjacent year; if the air pollution state in a certain area remains the same as in the past, or the severe air pollution in the target season is more affected by poor atmospheric diffusion conditions, the pollutant concentration sequence of multiple years in the aforementioned target season can be used.

[0052] Through the analysis of a large amount of environmental pollutant concentration data, it is found that although the historical pollutant concentration sequence is a non-stationary time series (which has the characteristic of time accumulation), the pollutant concentration change sequence is a sequence with certain statistical characteristics. Therefore, the pollutant concentration change characteristics can be obtained from the historical pollutant concentration sequence, the statistical law of the pollutant concentration change characteristics can be analyzed, and reasonable pollutant concentration change characteristics can be found. Based on this, after the historical pollutant concentration sequence of the target season is obtained in the embodiment of the present disclosure, the adjacent pollutant concentrations are subjected to differential processing to obtain differential concentrations, and the differential concentrations are sorted according to the corresponding time sequence to obtain a differential concentration sequence.

[0053] After obtaining the differential concentration sequence of the target season, then determine the time period differential concentration sequence corresponding to the same target time period of each day in the target season. The aforementioned target time period is a sequence with similar environmental pollution characteristics such as 6:00 - 7:00 in the morning and 4:00 - 5:00 in the afternoon (here is just an example, 6:00 - 7:00 in the morning and 4:00 - 5:00 in the afternoon are not the same target time period). As analyzed above, because the target season includes multiple days, the extracted time period differential concentration sequence is a multi-segment differential concentration sequence.

[0054] In order to obtain a time period differential concentration data sequence of a target time period, the aforementioned target time period preferably includes at least two differential concentration data. More preferably, the aforementioned time period differential concentration sequence includes more differential concentrations.

[0055] S120: Perform mathematical statistical processing on the differential concentrations in the time period differential concentration sequences of each same target time period to determine the statistical mean of the target time period and the statistical standard deviation of the target time period.

[0056] As analyzed above, since the same target time period in the target season has the same pollutant change characteristics, the time period differential concentration sequence conforms to the characteristics of a normal distribution or has characteristics similar to a normal distribution. Based on this, mathematical statistical analysis can be performed on the time period differential concentration data sequences of the foregoing same target time periods to determine the statistical mean of the target time period and the statistical standard deviation of the target time period.

[0057] S130: Determine the first high threshold value of concentration change based on the statistical mean of the target time period and the statistical standard deviation of the target time period.

[0058] According to statistical principles, the foregoing statistical mean of the target time period and the statistical standard deviation of the target time period can be used to determine a reasonable differential concentration numerical interval under a specific confidence level. Correspondingly, the foregoing statistical mean of the target time period and the statistical standard deviation of the target time period can also determine the first high threshold value of concentration change. The first high threshold value of concentration change is a threshold value used to determine an obviously excessively high pollutant concentration change rate.

[0059] In specific implementation, the can be used to determine the first high threshold value of concentration change , where is the statistical mean of the target time period, is the statistical standard deviation of the target time period, is an empirical coefficient. In specific implementation can be set to a reasonable value according to the requirements of the judgment standard. In an empirical case is set to 3.

[0060] S140: Determine the high threshold value of concentration change for the same target time period based on the first high threshold value of concentration change.

[0061] In the embodiment of the present disclosure, since there is only the first high threshold value of concentration change, the first high threshold value of concentration change can be directly used as the high threshold value of concentration change for the same target time period.

[0062] As analyzed above, in the embodiment of the present disclosure, after obtaining the differential concentration sequence of the target season, mathematical statistical analysis is performed using the time period differential concentration sequences of the same target time periods therein, and the first high threshold value of concentration change is obtained according to the numerical values of the mathematical statistical analysis. By adopting the solution of the embodiment of the present disclosure, for each target time period of the target season, a corresponding high threshold value of concentration change can be obtained. Correspondingly, for each target time period, a corresponding high threshold value of concentration change can also be obtained, so that the high threshold value of concentration change for a specific time period and a specific season matches the season characteristics and time period characteristics, and thus the abnormally high values within the corresponding time period can be better identified.

[0063] In some embodiments, in addition to the aforementioned S110 - S140, the method for determining the high - value threshold of concentration change may further include the following S150 - S160.

[0064] S150: Perform mathematical statistical processing on the differential concentrations in the full - volume differential concentration sequence to determine the seasonal statistical mean and seasonal statistical standard deviation of the target season.

[0065] S160: Perform mathematical statistical processing on the differential concentrations in the full - volume differential concentration sequence to determine the seasonal statistical mean and seasonal statistical standard deviation of the target season.

[0066] As previously analyzed, because the climate of a certain target season itself has stable characteristics, the differential concentrations in the differential concentration sequence of this season also have certain normal distribution characteristics. Accordingly, mathematical statistical analysis can be performed on the differential concentrations in the full - volume differential concentration sequence of the target period to determine the seasonal statistical mean and seasonal statistical standard deviation of the target season.

[0067] In specific implementation, it can be used to determine the first high - value threshold of concentration change , where is the seasonal statistical mean, is the seasonal statistical standard deviation, is an empirical coefficient, and in specific implementation it can be set to a reasonable value according to the requirements of the judgment standard. In an empirical case it is set to 3.

[0068] When performing the aforementioned S150 - S160, the aforementioned S140 is specifically S141.

[0069] S141: Select the larger value between the first high - value threshold of concentration change and the second high - value threshold of concentration change as the adopted high - value threshold of concentration change for the same target period.

[0070] In practical applications, if the number of days in the target season is too small, the number of the same target periods may be too small. In addition, there may be no strong diffusion meteorological characteristics or strong pollution processes in the target periods of the target season, resulting in the data of the corresponding target periods not including data under some typical extreme conditions. And the amount of historical pollutant concentration sequence data of the target season is relatively large, and the second high - value threshold of concentration change obtained based on the historical pollutant concentration sequence of the target season may reflect the aforementioned typical data characteristics. Specifically, in some embodiments, it is also considered necessary to use the second high - value threshold of concentration change to determine the adopted high - value threshold of concentration change.

[0071] In specific implementation, to retain high-value data for subsequent analysis as much as possible, the larger value of the first high-value threshold of concentration change and the second high-value threshold of concentration change can be used as the adopted high-value threshold of concentration change corresponding to the same target time period in specific implementation.

[0072] In some embodiments of the present disclosure, before performing the aforementioned S110, the following S210-S240 can also be performed to remove some pollutant concentrations with significantly too large values, thereby avoiding affecting the data obtained by differential calculation and introducing excessive errors.

[0073] S210: Sort the pollutant concentrations in the historical pollutant concentration sequence of the target season in ascending order to obtain a sorted sequence, and determine the first-order value and the second-order value therein.

[0074] In specific implementation, the pollutant concentrations in the historical pollutant concentration sequence of the target season can be sorted in ascending order to obtain an ascending sequence. After obtaining the ascending sequence, the first-order value and the second-order value are determined. The first order and the second order in the implementation of the present disclosure are both pre-determined orders. In practical applications, the first order can be the 1 / 4 order, and the second order can be the 3 / 4 order. Correspondingly, the first-order value is the 1 / 4 quantile, and the second-order value is the 3 / 4 quantile.

[0075] In the implementation of the present disclosure, it is determined that the second-order value is greater than the first-order value.

[0076] S220: Calculate the interquartile range based on the first-order value and the second-order value.

[0077] After obtaining the first-order value and the second-order value, subtract the first-order value from the second-order value to obtain the interquartile range. The interquartile range is a value representing the overall data size change characteristics of the sorted sequence. According to the situation where the first-order value is the 1 / 4 quantile and the second-order value is the 3 / 4 quantile, the interquartile range is the interquartile range.

[0078] S230: Determine the high-value threshold of concentration based on the second-order value, the interquartile range, and a pre-set empirical coefficient.

[0079] In specific implementation, the computing device can adopt to calculate and obtain the high-value threshold of concentration , where is the second-order value, is the interquartile range, is the pre-set empirical coefficient.

[0080] S240: Without changing the chronological order of the historical pollutant concentration sequence, remove the pollutant concentration values in the historical pollutant concentration sequence that are higher than the high concentration threshold.

[0081] When it is said that the chronological order of the pollutant concentration sequence remains unchanged here, it means that the sequence length of the historical pollutant concentration time sequence remains unchanged (thus ensuring that each moment is still retained). In specific implementation, to remove the pollutant concentration values in the historical pollutant concentration sequence that are higher than the high concentration threshold, specific identifiers (such as using "none") are used to replace the pollutant concentration data higher than the high concentration threshold.

[0082] In the case of executing the aforementioned S210 - S240, during the process of obtaining the differential concentration sequence by executing S110, for some points where the differential concentration cannot be calculated, the corresponding points can also be marked with specific identifiers, and this corresponding identifier also indicates that it will not be used for subsequent data statistics.

[0083] In the previous text, the historical pollutant concentration sequence of the target season is used for sorting to determine the corresponding quantiles, and then the corresponding inter - quantile range and high concentration threshold are determined. In other embodiments, the historical pollutant concentration sequence of the whole year can also be used for the aforementioned sorting, and the high concentration threshold is determined by the method mentioned in the previous text. Correspondingly, after determining the high concentration threshold, the pollutant concentration values in the historical pollutant concentration sequence corresponding to the target season that are higher than the high concentration threshold can also be removed using the high concentration threshold as the comparison benchmark.

[0084] In some embodiments, before executing the aforementioned S210, the following S250 - S260 can also be executed.

[0085] S250: Determine the meteorological state of each time period in the corresponding historical pollutant concentration sequence.

[0086] S260: When the meteorological feature of a certain time period is in a rainfall state, without changing the chronological order of the historical pollutant concentration sequence, delete the corresponding pollutant concentration.

[0087] Because rainwater has a strong removal effect on atmospheric pollutants, the pollutant concentration is generally low under rainfall conditions. Correspondingly, the pollutant concentration has no statistical significance. Based on this, in the implementation of the present disclosure, under the premise of the chronological order of the historical pollutant concentration sequence, if it is determined that the meteorological feature of a certain time period is in a rainfall state, the pollutant concentration of the corresponding time period is deleted.

[0088] In addition, in some embodiments, before executing the aforementioned S250 or S210, basic data processing is first performed, specifically to remove the invalid data in the historical pollutant concentration sequence. The invalid data can be zero - value data, negative - value data, and out - of - range data.

[0089] According to the method in the previous article, for each specific time period in a specific season, the corresponding high-value threshold of concentration change can be determined by the method in the previous article, and a time period-concentration change threshold data table can be established for subsequent determination of the high-value concentration threshold of pollutants in specific time periods.

[0090] The disclosed embodiment also provides a flow chart of a method for determining a high-value pollutant concentration threshold. Figure 2 : is a flow chart of a method for dynamically determining a high-value concentration threshold of a pollutant provided in some embodiments of the present disclosure. Figure 2 As shown, the method for determining the high-value pollutant concentration threshold includes S310-S320.

[0091] S310: Search the concentration change threshold data table based on the season identifier and the time period identifier of the current time period to obtain the high value threshold of the concentration change corresponding to the current time period.

[0092] S320: The sum of the pollutant concentration in the previous time period and the high value threshold of the concentration change corresponding to the current time period is used as the high value pollutant concentration threshold of the current time period.

[0093] As analyzed above, considering that pollutant concentrations have cumulative characteristics, we can take the pollutant concentration in the previous time period as the basis, add the pollutant concentration in the previous time period and the high value threshold of the concentration change corresponding to the current time period, and get the high value pollutant concentration threshold for the current time period.

[0094] The embodiment of the present disclosure also provides a computing device for implementing the aforementioned method. Figure 3 Schematic diagram of the structure of the computing device provided by the embodiment of the present disclosure. Figure 3 , which shows a schematic diagram of the structure of a computing device 300 suitable for implementing the method of the embodiment of the present disclosure. Figure 3 The computing device shown is only an example and should not bring any limitation to the functionality and scope of use of the embodiments of the present disclosure.

[0095] like Figure 3 As shown, the computing device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory ROM 302 or a program loaded from a storage device 308 to a random access memory RAM 303. Various programs and data required for the operation of the computing device 300 are also stored in the RAM 303. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output I / O interface 305 is also connected to the bus 304.

[0096] Typically, the following devices can be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touchpad, a camera, a microphone, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and a communication device 309. The communication device 309 can allow the computing device 300 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 3 the computing device 300 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. Instead, more or fewer devices can be implemented or had.

[0097] Specifically, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above functions defined in the methods of the embodiments of the present disclosure are executed.

[0098] It should be noted that the computer-readable medium in the present disclosure can be a computer-readable storage medium, a computer-readable signal medium, or any combination of the above two.

[0099] The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0100] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, carrying computer-readable program code. Such a propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

[0101] In some embodiments, a client, server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future developed networks.

[0102] The above computer-readable medium may be included in the above computing device; or may exist separately without being assembled into the computing device.

[0103] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages or combinations thereof, including but not limited to object-oriented programming languages such as Java, Smalltalk, C++, and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code may execute entirely on the tester's computer, partially on the tester's computer, as a stand-alone software package, partially on the tester's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the tester's computer through any type of network - including a local area network (LA N ) or wide area network (WA N ), or may be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0104] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0105] The units described in the embodiments of the present disclosure can be implemented in software or in hardware. Among them, the name of the unit does not constitute a limitation on the unit itself in some cases. The functions described above herein can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (AS I C), Application Specific Standard Product (ASSP), System on Chip (SOC), Complex Programmable Logic Device (CPLD), and so on.

[0106] The above are only specific embodiments of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but rather will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining a high-value threshold of concentration change, characterized in that, Including: Performing difference processing on adjacent pollutant concentrations in the historical pollutant concentration sequence of the target season to obtain a difference concentration sequence, and extracting the period difference concentration sequences corresponding to the same target period of each day in the target season from the difference concentration sequence; Performing mathematical statistical processing on the difference concentrations in the period difference concentration sequences of each same target period to determine the statistical mean of the target period and the statistical standard deviation of the target period; Determining a first high threshold of concentration change based on the statistical mean of the target period and the statistical standard deviation of the target period, and using the first high threshold of concentration change as the adopted high threshold of concentration change for the same target period.

2. The determination method according to claim 1, characterized in that The method further includes: Performing mathematical statistical processing on the difference concentrations in the full difference concentration sequence to determine the seasonal statistical mean and the seasonal statistical standard deviation of the target season; Determining a second high threshold of concentration change based on the seasonal statistical mean and the seasonal statistical standard deviation; Using the first high threshold of concentration change as the adopted high threshold of concentration change for the same target period includes: Selecting the larger value between the first high threshold of concentration change and the second high threshold of concentration change as the adopted high threshold of concentration change for the same target period.

3. The determination method according to any one of claims 1-2, characterized in that, Before performing adjacent difference processing on the historical pollutant concentration sequence of the target season, the method further includes: Sorting the pollutant concentrations in the historical pollutant concentration sequence of the target season in ascending order to obtain a sorted sequence, and determining the first order value and the second order value therein, where the second order value is greater than the first order value; Calculating the interquartile range based on the first order value and the second order value; Determining a high threshold of concentration based on the second order value, the interquartile range, and a preset empirical coefficient; Excluding the pollutant concentration values higher than the high threshold of concentration in the historical pollutant concentration sequence while keeping the time sequence order of the historical pollutant concentration sequence unchanged.

4. The determination method according to claim 3, characterized in that: The determining of the first order value and the second order value therein includes: Determining the 1 / 4 quantile and the 3 / 4 quantile in the sorted sequence, and using the smaller value between the 1 / 4 quantile and the 3 / 4 quantile as the first order value, and the larger value as the second order value.

5. The determination method according to claim 3, wherein Before sorting the pollutant concentrations in the historical pollutant concentration sequence of the target season in ascending order to obtain a sorted sequence, the method further includes: Determining the meteorological state of each period corresponding to the historical pollutant concentration sequence; In the case where the meteorological characteristic of a certain period is a rainfall state, deleting the corresponding pollutant concentration while keeping the time sequence order of the historical pollutant concentration sequence.

6. The determination method according to claim 3, wherein Before sorting the pollutant concentrations in the historical pollutant concentration sequence of the target season in ascending order to obtain a sorted sequence, the method further includes: Excluding the invalid data in the historical pollutant concentration sequence, where the invalid data are zero value data, negative value data, and over-range data.

7. The determination method according to claim 3, wherein Before performing adjacent difference processing on the historical pollutant concentration sequence of the target season, the method further includes: Sorting the pollutant concentrations in at least one full year of historical pollutant concentration sequence in order of magnitude to obtain a sorted sequence, and determining a first digit value and a second digit value therein, wherein the second digit value is greater than the first digit value; Calculate the quantile range based on the first and second quantile values; Determine a high concentration threshold based on the second quantile value, the quantile internal distance and a preset empirical coefficient; While ensuring that the temporal order of the historical pollutant concentration sequence of the target season remains unchanged, the pollutant concentration values in the historical pollutant concentration sequence corresponding to the target season that are higher than the high concentration threshold are eliminated.

8. A method for dynamically determining the high-value concentration threshold of pollutants, characterized in that, include: Based on the season identifier and the time period identifier of the current time period, the concentration change threshold data table is searched to obtain the high value threshold of the concentration change corresponding to the current time period; wherein the data in the concentration change threshold data table is obtained by using the method for determining the high value threshold of the concentration change according to any one of claims 1 to 7; The sum of the pollutant concentration in the previous time period and the corresponding high-value threshold of the concentration change in the current time period is used as the high-value pollutant concentration threshold of the current time period.

9. A computing device, characterized in that, It includes a processor and a memory, wherein the memory is used to store a computer program; when the computer program is loaded by the processor, the processor executes the method for determining a high-value threshold value of concentration change as claimed in any one of claims 1 to 7 or the method for dynamically determining a high-value concentration threshold value of pollutants as claimed in claim 8.

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