Dynamic determination method for concentration change high-value threshold value and pollutant high-value concentration threshold value

Abstract

The embodiment of the invention provides a method for dynamically determining a concentration change high-value threshold value and a pollutant high-value concentration threshold value. The concentration change high-value threshold value determination method comprises the following steps: performing difference processing of adjacent pollutant concentrations on a historical pollutant concentration sequence to obtain a difference concentration sequence, and extracting a time interval difference concentration sequence; performing mathematical statistical processing on the time interval difference concentration sequence, and determining a target time interval statistical mean value and a target time interval statistical standard deviation; determining a first concentration change high value threshold value based on the target time period statistical mean value and the target time period statistical standard deviation; a trusted concentration change high threshold for the same target period is determined based on the first concentration change high threshold. By adopting the scheme of the embodiment of the invention, the corresponding concentration change high value threshold value can be obtained for each target time period, so that the concentration change high value threshold value for the specific time period is matched with the time period characteristic and the time period characteristic for the specific time period, and the abnormal high value in the corresponding time period can be better identified.

Description

Technical Field

[0001] The present disclosure relates to the technical field of environmental monitoring data processing, and in particular to a method for dynamically determining a high-value threshold of concentration change and a high-value threshold of pollutant concentration. Background Art

[0002] Due to the influence of sampling environment, instrument status, operation and maintenance, data transmission interference and other factors, there may be abnormal data in the continuous monitoring data of ambient air quality. In order 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 is abnormally high value data.

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

[0004] However, the spatial and temporal distribution of the concentration of conventional pollutants in actual ambient air quality fluctuates greatly and is a non-stationary time series, with both diurnal variations (such as high ozone during the day and low at night), seasonality (such as PM 2.5 The concentration is higher than in summer) and long-term change characteristics (for example, as pollution control continues to deepen, the average concentration of pollutants may generally show a downward trend). At the same time, due to the influence of short-term source emissions, the use of static thresholds cannot reflect the differences in the concentration of conventional pollutants in air quality at different times. Accordingly, the high-value threshold method based on static thresholds cannot reflect the refined change characteristics of conventional pollutant concentrations, and has the disadvantages of insufficient recognition accuracy and poor pertinence when identifying invalid high values. Summary of the invention

[0005] The embodiment of the present disclosure provides a method for dynamically determining a high-value threshold of concentration change and a high-value threshold of pollutant concentration, wherein the method for determining a high-value threshold of concentration change is used to more reasonably determine the high-value threshold of concentration change in each time period.

[0006] In a first aspect, the present disclosure provides a method for determining a high-value threshold of a concentration change, comprising: 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 a time period differential concentration sequence corresponding to the same target time period on each day of the target season from the differential concentration sequence; Performing mathematical statistical processing on the differential concentrations in the time differential concentration series of each same target time period to determine the target time period statistical mean and target time period statistical standard deviation; Determine a first concentration change high value threshold based on the target period statistical mean and the target period statistical standard deviation; The trusted concentration change high value threshold for the same target time period is determined based on the first concentration change high value threshold.

[0007] 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; Determine a second concentration change high value threshold based on the seasonal statistical mean and the seasonal statistical standard deviation; 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: 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.

[0008] Optionally, determining the concentration change high value threshold for the same target time period based on the first concentration change high value threshold includes: The first concentration change high value threshold is used as the trusted concentration change high value threshold for the same target time period.

[0009] Optionally, 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 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; 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.

[0010] Optionally, determining the first digit value and the second digit value includes: Determine the 1 / 4 quantile and the 3 / 4 quantile in the sorted sequence, and use the smaller value of the 1 / 4 quantile and the 3 / 4 quantile as the first digit value, and use the larger value as the second digit value.

[0011] Optionally, before sorting the pollutant concentrations in the historical pollutant concentration sequence of the target season in order of magnitude to obtain a sorted sequence, the method further includes: Determine the meteorological conditions at each time period in the historical pollutant concentration series; When the meteorological characteristic of a certain period is a rainfall state, the corresponding pollutant concentration is deleted while maintaining the temporal order of the historical pollutant concentration sequence.

[0012] Optionally, before sorting the pollutant concentrations in the historical pollutant concentration sequence of the target season in order of magnitude to obtain a sorted sequence, the method further includes: Invalid data in the historical pollutant concentration sequence is eliminated, wherein the invalid data is zero-value data, negative-value data, and out-of-range data.

[0013] Optionally, 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.

[0014] In a second aspect, the present disclosure provides a method for dynamically determining a high-value concentration threshold of a pollutant, including: 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; 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.

[0015] 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.

[0016] 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

[0017] 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.

[0018] 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: Figure 1 is a flow chart of a method for determining a high-value threshold value of concentration change provided by the present disclosure; 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; Figure 3 It is a schematic diagram of the structure of a computing device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0019] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying 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 described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0020] The term "including" and its variations used in this document are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. In this document, relational terms such as "first" and "second" are merely 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.

[0021] The disclosed embodiment provides a method for determining a high-value threshold of concentration change, which determines the concentration change threshold of the pollutant by selecting a reasonable pollutant concentration segment, and then uses the high-value threshold of concentration change to reasonably determine the high-value concentration threshold of the pollutant.

[0022] Figure 1 : is a flow chart of the method for determining the high-value threshold value of concentration change provided by the present disclosure. Figure 1 As shown, the method for determining a high-value threshold of concentration change provided by the embodiment of the present disclosure includes S110 - S140 .

[0023] S110: Perform adjacent difference processing on the historical pollutant concentration sequence of the target season to obtain a differential concentration sequence, and extract the time period differential concentration sequence corresponding to the same time period of each day in the target season in the differential concentration sequence.

[0024] The target season is a season used to characterize the same target period. In specific implementation, the target season can be a season distinguished by seasons such as spring, summer, autumn and winter, or a partial period in each of the aforementioned seasons (such as early autumn and late autumn), or a period determined based on similar air circulation characteristics and pollutant concentration characteristics (for example, for northern China, it can be a windless season period formed when the north wind is not raging but the south wind is not strong enough). It should be noted here that the target season is a season with multiple days.

[0025] Through historical ambient air quality monitoring, the historical pollutant concentration series of a certain area (or a certain monitoring point) has been obtained.

[0026] The aforementioned historical pollutant concentration sequence may be a pollutant concentration sequence of the aforementioned target season in a year, or a pollutant concentration sequence of the aforementioned target season in multiple years, which is not specifically limited in the embodiments of the present disclosure. In specific implementation, if from the perspective of historical data, the air pollution situation in a certain area has improved significantly, and its environment is more caused by pollution sources, it is preferred to only use the pollutant concentration sequence near the target season in a year; and if the air pollution state in a certain area remains the same as in the past, or the serious air pollution in the target season is more caused by the poor atmospheric diffusion conditions, the pollutant concentration sequence of multiple years in the aforementioned target season can be used.

[0027] 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 a time accumulation characteristic), the pollutant concentration change sequence is a sequence with certain statistical characteristics. Therefore, the pollutant concentration change characteristics can be obtained by analyzing the statistical laws of the pollutant concentration change characteristics, and a reasonable pollutant concentration change characteristic can be found. Based on this, after obtaining the historical pollutant concentration sequence of the target season, the embodiment of the present disclosure performs differential processing on adjacent pollutant concentrations to obtain differential concentrations, and sorts the differential concentrations according to the corresponding time sequence to obtain a differential concentration sequence.

[0028] After obtaining the differential concentration sequence of the target season, the time period differential concentration sequence corresponding to the same target time period of each day in the target season is then determined. 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 (this 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) (for example, from 6:00 to 7:00 in the morning, particulate matter has settled overnight, and the working conditions of the enterprise are still in a state of suspension and not working, and only a small number of vehicles are on the road. During this period, the pollutant concentration is low, and from a subjective point of view, the air at this time is relatively fresh). As analyzed above, because the target season is a season that includes multiple days, the extracted time period differential concentration sequence is a multi-segment differential concentration sequence.

[0029] In order to obtain a time period differential concentration data sequence of a target time period, the target time period preferably includes at least two differential concentration data. More preferably, the time period differential concentration sequence includes more differential concentrations.

[0030] S120: Performing mathematical statistical processing on the differential concentrations in the time period differential concentration sequences of each same target time period to determine the target period statistical mean and the target period statistical standard deviation.

[0031] As analyzed above, because the same target time periods in the target season have the same pollutant variation characteristics, the time period difference concentration series conforms to the normal distribution characteristics, or has characteristics similar to the normal distribution. Based on this, mathematical statistical analysis can be performed on the time period difference concentration data series of the aforementioned same target time periods to determine the target time period statistical mean and target time period statistical standard deviation.

[0032] S130: Determine a first concentration change high value threshold based on the target period statistical mean and the target period statistical standard deviation.

[0033] According to statistical principles, the aforementioned target period statistical mean and target period statistical standard deviation can be used to determine a reasonable differential concentration value range under a specific confidence level. Correspondingly, the aforementioned target period statistical mean and target period statistical standard deviation can also determine the first concentration change high value threshold. The first concentration change high value threshold is a threshold used to determine the rate of change of pollutant concentration that is obviously too high.

[0034] In specific implementation, it can be used Determine the first concentration change high value threshold ,in is the statistical mean value for the target period, is the statistical standard deviation for the target period, is the empirical coefficient. It can be set to a reasonable value according to the judgment standard. Set to 3.

[0035] S140: Determine a concentration change high value threshold for the same target time period based on the first concentration change high value threshold.

[0036] In the embodiment of the present disclosure, since there is only the first concentration change high value threshold, the first concentration change high value threshold can be directly used as the concentration change high value threshold for the same target time period.

[0037] As analyzed above, in the embodiment of the present disclosure, after obtaining the differential concentration sequence of the target season, the differential concentration sequence of the same target time period is used to perform mathematical statistical analysis, and the first concentration change high value threshold is obtained based on the mathematical statistical analysis value. Using the embodiment of the present disclosure, a corresponding concentration change high value threshold can be obtained for each target time period of the target season. Correspondingly, a corresponding concentration change high value threshold can be obtained for each target time period, so that the concentration change high value threshold for a specific season and a specific time period matches the season characteristics and time period characteristics, so as to better identify abnormal high values ​​in the corresponding time period.

[0038] In some embodiments, the concentration change high value threshold determination method may include the following S150 - S160 in addition to the aforementioned S110 - S140 .

[0039] S150: Performing mathematical statistical processing on the differential concentrations in the full differential concentration sequence to determine the seasonal statistical mean and seasonal statistical standard deviation of the target season.

[0040] S160: Performing mathematical statistical processing on the differential concentrations in the full differential concentration sequence to determine the seasonal statistical mean and seasonal statistical standard deviation of the target season.

[0041] As analyzed above, because the climate of a target season itself has stable characteristics, the differential concentration in the differential concentration sequence of this season also has certain normal distribution characteristics. Correspondingly, mathematical statistical analysis can be performed on the differential concentration of the full amount of the target period to determine the seasonal statistical mean and seasonal statistical standard deviation of the target season.

[0042] In specific implementation, it can be used Determine the first concentration change high value threshold ,in is the seasonal statistical mean, is the seasonal statistical standard deviation, is the empirical coefficient. It can be set to a reasonable value according to the judgment standard. Set to 3.

[0043] When executing the aforementioned S150 - S160 , the aforementioned S140 is specifically S141 .

[0044] S141: Select the larger value of the first concentration change high value threshold and the second concentration change high value threshold as the trusted concentration change high value threshold for the same target time period.

[0045] In practical applications, if the number of days in the target season is too small, the number of the same target time periods may be too small. In addition, the target time period of the target season may not have strong diffusion meteorological characteristics or strong pollution processes, resulting in the data of the corresponding target time period not including some data under typical extreme conditions. The amount of historical pollutant concentration sequence data in the target season is large, and the corresponding second concentration change high value threshold obtained based on the historical pollutant concentration sequence of the target season may reflect the aforementioned typical data characteristics. Specifically, some embodiments consider the need to use the second concentration change high value threshold to determine the adopted concentration change high value threshold.

[0046] In a specific implementation, in order to retain as much high-value data as possible for subsequent analysis, in a specific implementation, the larger value of the first concentration change high-value threshold and the second concentration change high-value threshold can be used as the trusted concentration change high-value threshold corresponding to the same target time period.

[0047] In some embodiments of the present disclosure, before executing the aforementioned S110, the following S210-S240 may be executed to remove some pollutant concentrations that are obviously too large, thereby avoiding affecting the data obtained by the differential calculation and introducing excessive errors.

[0048] S210: Sort the pollutant concentrations in the historical pollutant concentration sequence of the target season in order of magnitude to obtain a sorted sequence, and determine the first-order numerical value and the second-order numerical value therein.

[0049] In a 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 score are determined. The first order and the second order in the implementation of the present disclosure are both predetermined orders. In practical applications, the first order can be the 1 / 4 order, and the second order can be the 3 / 4 order, and the corresponding first order is the 1 / 4 quantile, and the second order is the 3 / 4 quantile.

[0050] In the implementation of the present disclosure, it is determined that the second digit value is greater than the first digit value.

[0051] S220: Calculate the quantile inner distance based on the first digit value and the second digit value.

[0052] After obtaining the first and second digit values, subtract the first digit value from the second digit value to get the quantile internal distance. The quantile internal distance is a value that characterizes the overall data size variation characteristics of the sorted sequence. In the case where the first digit is the 1 / 4 quantile and the second digit is the 3 / 4 quantile, the quantile internal distance is the quartile internal distance.

[0053] S230: Determine a high concentration threshold based on the second quantile value, the quantile internal distance, and a preset empirical coefficient.

[0054] In a specific implementation, the computing device may use Calculate the high concentration threshold ,in is the second digit value, is the quantile interval, is a pre-set empirical coefficient.

[0055] S240: On the premise of keeping the time series order 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.

[0056] The term "maintaining the time series order of the pollutant concentration sequence unchanged" here means maintaining the length of the historical pollutant concentration time series sequence unchanged (thus ensuring that each moment is retained). In specific implementation, the pollutant concentration values ​​in the historical pollutant concentration sequence that are higher than the high concentration threshold are removed by replacing the pollutant concentration data that are higher than the high concentration threshold with a specific identifier (for example, none).

[0057] When executing the aforementioned S210 - S240 , in the process of executing S110 to obtain the differential concentration sequence, the differential concentration of some points cannot be calculated, and the corresponding points can also be marked with specific identifiers, and the corresponding identifiers also indicate that they will not be used for subsequent data statistics.

[0058] In the foregoing, the historical pollutant concentration sequence of the target season is used for sorting, the corresponding quantile is determined, and then the corresponding quantile internal distance and the 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 foregoing. Accordingly, after determining the high concentration threshold, the high concentration threshold can also be used as a comparison benchmark to eliminate the pollutant concentration values ​​in the historical pollutant concentration sequence corresponding to the target season that are higher than the high concentration threshold.

[0059] In some embodiments, before executing the aforementioned S210, the following S250-S260 may also be executed.

[0060] S250: Determine the meteorological conditions for each time period in the corresponding historical pollutant concentration sequence.

[0061] S260: When the meteorological characteristic of a certain period is a rainfall state, the corresponding pollutant concentration is deleted while maintaining the temporal order of the historical pollutant concentration sequence.

[0062] Because rainwater has a strong removal effect on atmospheric pollutants, the pollutant concentration is generally lower under rainfall conditions. The corresponding pollutant concentration has no statistical significance. Based on this, in the implementation of this disclosure, under the premise of the temporal order of the historical pollutant concentration sequence, if it is determined that the meteorological characteristics of a certain period are under rainfall, the pollutant concentration of the corresponding period is deleted.

[0063] In addition, in some embodiments, before executing the aforementioned S250 or S210, basic data processing is first performed, specifically to eliminate invalid data in the historical pollutant concentration sequence, and the invalid data may be zero-value data, negative-value data, and out-of-range data.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, 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, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the computing device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The computing device 300 is shown with various devices, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have instead.

[0072] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. 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 contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the 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-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0073] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable storage medium, a computer-readable signal medium, or any combination of the above two.

[0074] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media may 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 thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0075] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than a computer readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0076] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0077] The computer-readable medium may be included in the computing device, or may exist independently without being incorporated into the computing device.

[0078] Computer program code for performing operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the tester's computer, partially on the tester's computer, as a separate software package, partially on the tester's computer and partially on a remote computer, or entirely on the 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 (LAN). N ) or Wide Area Network (WA N )—connects to the tester computer, or, can connect to an external computer (e.g., via the Internet using an Internet service provider).

[0079] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated system based on hardware that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0080] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware. The name of the unit does not limit the unit itself in some cases. The functions described above in this article may be at least partially performed 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 arrays (FPGAs), application specific integrated circuits (ASICs), and hardware logic components such as CPUs, CPUs, and other hardware components. I C), application-specific standard products (ASSP), system on chip (SOC), complex programmable logic devices (CPLD), etc.

[0081] The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may 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 will conform to 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: include: 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 a time period differential concentration sequence corresponding to the same target time period on each day of the target season from the differential concentration sequence; Performing mathematical statistical processing on the differential concentrations in the time differential concentration series of each same target time period to determine the target time period statistical mean and target time period statistical standard deviation; Determine a first concentration change high value threshold based on the target period statistical mean and the target period statistical standard deviation; The trusted concentration change high value threshold for the same target time period is determined based on the first concentration change high value threshold.

2. The determination method according to claim 1, characterized in that: The method further comprises: Performing mathematical statistical processing on the differential concentrations in the full differential concentration sequence to determine the seasonal statistical mean and seasonal statistical standard deviation of the target season; Determine a second concentration change high value threshold based on the seasonal statistical mean and the seasonal statistical standard deviation; 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: 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.

3. The determination method according to claim 1, characterized in that: The step of determining the concentration change high value threshold for the same target time period based on the first concentration change high value threshold comprises: The first concentration change high value threshold is used as the trusted concentration change high value threshold for the same target time period.

4. The determination method according to any one of claims 1 to 3, 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 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; 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.

5. The determination method according to claim 4, characterized in that: The determining of the first digit value and the second digit value comprises: Determine the 1 / 4 quantile and the 3 / 4 quantile in the sorted sequence, and use the smaller value of the 1 / 4 quantile and the 3 / 4 quantile as the first digit value, and use the larger value as the second digit value.

6. The determination method according to claim 4, characterized in that: Before sorting the pollutant concentrations in the historical pollutant concentration sequence of the target season in order of magnitude to obtain a sorted sequence, the method further includes: Determine the meteorological conditions at each time period in the historical pollutant concentration series; When the meteorological characteristic of a certain period is a rainfall state, the corresponding pollutant concentration is deleted while maintaining the temporal order of the historical pollutant concentration sequence.

7. The determination method according to claim 4, characterized in that: Before sorting the pollutant concentrations in the historical pollutant concentration sequence of the target season in order of magnitude to obtain a sorted sequence, the method further includes: Invalid data in the historical pollutant concentration sequence is eliminated, wherein the invalid data is zero-value data, negative-value data, and out-of-range data.

8. The determination method according to claim 4, 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 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.

9. A method for dynamically determining a high-value concentration threshold of a pollutant, 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 8; 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.

10. 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 of concentration change as described in any one of claims 1 to 8 or the method for dynamically determining a high-value concentration threshold of a pollutant as described in claim 9.

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