Pollution type identification method and device, electronic equipment and storage medium

Abstract

The present disclosure relates to a pollution type identification method and device, electronic equipment and storage medium, the method comprising: determining a water quality index sequence of a predetermined water area in a first time period according to water quality information of the predetermined water area, and determining a meteorological index sequence according to meteorological monitoring data; determining at least one pollution identification parameter according to the water quality index sequence and the meteorological index sequence; and determining a water quality pollution type of the predetermined water area according to the pollution identification parameter. The pollution type identification method according to the embodiments of the present disclosure can measure water quality information in situ, in real time, on line and at high frequency through, for example, a quantum dot spectrometer, determine a water quality index sequence of a predetermined water area through the water quality information, and determine a water quality pollution type in various weather environments in real time through the water quality index sequence and the meteorological index sequence, determine the influence of weather changes on water quality indexes, and improve the accuracy and application range of pollution type identification.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a method and apparatus for identifying pollution types, electronic devices, and storage media. Background Technology

[0002] Water resources are essential for human survival, and the quality of drinking water is directly related to human life and safety. With decades of rapid economic development, environmental pollution incidents have also become more frequent, and major water pollution incidents in recent years have caused serious social, economic, and environmental losses.

[0003] Currently, environmental protection management is highly valued, and water monitoring infrastructure is becoming increasingly sound, with increasingly abundant monitoring data. On the one hand, by monitoring anomalies in water quality data, sudden pollution events can be detected in a timely manner, allowing for prompt verification and handling. For example, monitoring indicators such as chemical oxygen demand (COD) can promptly capture abnormal changes in the concentration of organic pollutants in water bodies, serving as an important means of monitoring water environmental quality. On the other hand, it is also necessary to make predictions about pollution types, causes, and sources based on monitoring data. This is of paramount importance for scientifically and rationally predicting pollution development, formulating emergency response plans, and controlling pollution impacts.

[0004] However, river water quality is influenced by a combination of factors, including hydrology, meteorology, and pollutants, making it difficult to determine its patterns of change. Currently, pollution type identification based on online river water quality monitoring data, both domestically and internationally, is limited to monitoring specific pollutants, resulting in a narrow scope and weak generalization ability. Furthermore, indicators such as chemical oxygen demand (COD) typically require laboratory chemical measurements, leading to poor real-time performance.

[0005] Furthermore, water quality indicators can change accordingly with weather changes, making it difficult for relevant technologies to effectively monitor the impact of weather and to determine the pollution status of water in various weather conditions. Summary of the Invention

[0006] This disclosure presents a method and apparatus for identifying pollution types, an electronic device, and a storage medium.

[0007] According to one aspect of this disclosure, a pollution type identification method is provided, comprising: determining a water quality index sequence of a predetermined water area within a first time period based on water quality information of the predetermined water area, and determining a meteorological index sequence of the predetermined water area within the first time period based on meteorological monitoring data of the predetermined water area, wherein the water quality index sequence includes water quality indicators obtained at multiple times within the first time period, and the meteorological index sequence includes meteorological indicators obtained at multiple times within the first time period; determining at least one pollution identification parameter based on the water quality index sequence and the meteorological index sequence, wherein the pollution identification parameter includes single-sequence feature parameters and inter-sequence feature parameters; and determining the water pollution type of the predetermined water area based on the pollution identification parameter.

[0008] In one possible implementation, the water quality index sequence of the predetermined water area within a first time period is determined based on the water quality information of the predetermined water area, and the meteorological index sequence of the predetermined water area within the first time period is determined based on the meteorological monitoring data of the predetermined water area. This includes: determining the acquisition frequency and acquisition phase of the plurality of water quality indicators and the plurality of meteorological indicators; interpolating the plurality of water quality indicators according to the acquisition frequency and the acquisition phase to obtain the water quality index sequence; and / or interpolating the plurality of meteorological indicators to obtain the meteorological index sequence.

[0009] In one possible implementation, the water quality indicators include chemical oxygen demand (COD) and / or turbidity, and the meteorological indicators include wind speed and / or rainfall.

[0010] In one possible implementation, the single-sequence feature parameters include at least one of the mean, standard deviation, autocorrelation coefficient, number of peaks, and peak height of the water quality index sequence or the meteorological index sequence; the inter-sequence feature parameters include at least one of the correlation coefficient and dynamic time-warped distance between the two index sequences, wherein the two index sequences include two water quality index sequences, or one water quality index sequence and one meteorological index sequence.

[0011] In one possible implementation, the two indicator sequences include a first indicator sequence and a second indicator sequence. Determining at least one pollution identification parameter based on the water quality indicator sequence and the meteorological indicator sequence includes: determining a path normalization matrix between the two indicator sequences based on the indicators in the two indicator sequences, wherein the element in the i-th row and j-th column of the path normalization matrix is ​​the distance between the i-th indicator in the first indicator sequence and the j-th indicator in the second indicator sequence, where i and j are positive integers; determining a normalized path based on the path normalization matrix, wherein the normalized path is the path with the smallest sum of elements among the paths from the first element to the second element in the path normalization matrix, where the first element is the element in the n-th row and 1-th column of the path normalization matrix, and the second element is the element in the 1-th row and m-th column of the path normalization matrix, the first indicator sequence includes n indicators, the second indicator sequence includes m indicators, n≥i, m≥j; and determining the dynamic time normalized distance between the two indicator sequences based on the normalized path.

[0012] In one possible implementation, determining the water pollution type of the predetermined water area based on the pollution identification parameters includes: performing clustering processing on the meteorological index sequence and the water quality index sequence based on the at least one pollution identification parameter to determine the water pollution type of the predetermined water area.

[0013] In one possible implementation, the water pollution type includes water quality fluctuations under the influence of meteorological factors and water pollution under the influence of meteorological factors.

[0014] According to one aspect of this disclosure, a pollution type identification device is provided, comprising: a sequence module, configured to determine a water quality index sequence of a predetermined water area within a first time period based on water quality information of the predetermined water area, and to determine a meteorological index sequence of the predetermined water area within the first time period based on meteorological monitoring data of the predetermined water area, wherein the water quality index sequence includes water quality indicators obtained at multiple times within the first time period, and the meteorological index sequence includes meteorological indicators obtained at multiple times within the first time period; a parameter module, configured to determine at least one pollution identification parameter based on the water quality index sequence and the meteorological index sequence, wherein the pollution identification parameter includes single-sequence feature parameters and inter-sequence feature parameters; and an identification module, configured to determine the water pollution type of the predetermined water area based on the pollution identification parameter.

[0015] In one possible implementation, the sequence module is further configured to: determine the acquisition frequency and acquisition phase of the plurality of water quality indicators and the plurality of meteorological indicators; perform interpolation processing on the plurality of water quality indicators according to the acquisition frequency and the acquisition phase to obtain the water quality indicator sequence, and / or perform interpolation processing on the plurality of meteorological indicators to obtain the meteorological indicator sequence.

[0016] In one possible implementation, the water quality indicators include chemical oxygen demand (COD) and / or turbidity, and the meteorological indicators include wind speed and / or rainfall.

[0017] In one possible implementation, the single-sequence feature parameters include at least one of the mean, standard deviation, autocorrelation coefficient, number of peaks, and peak height of the water quality index sequence or the meteorological index sequence; the inter-sequence feature parameters include at least one of the correlation coefficient and dynamic time-warped distance between the two index sequences, wherein the two index sequences include two water quality index sequences, or one water quality index sequence and one meteorological index sequence.

[0018] In one possible implementation, the two indicator sequences include a first indicator sequence and a second indicator sequence. The parameter module is further configured to: determine a path normalization matrix between the two indicator sequences based on the indicators in the two indicator sequences, wherein the element in the i-th row and j-th column of the path normalization matrix is ​​the distance between the i-th indicator in the first indicator sequence and the j-th indicator in the second indicator sequence, where i and j are positive integers; determine a normalized path based on the path normalization matrix, wherein the normalized path is the path with the smallest sum of path elements from the first element to the second element in the path normalization matrix, wherein the first element is the element in the n-th row and 1-th column of the path normalization matrix, and the second element is the element in the 1-th row and m-th column of the path normalization matrix, the first indicator sequence includes n indicators, the second indicator sequence includes m indicators, n≥i, m≥j; and determine the dynamic time normalized distance between the two indicator sequences based on the normalized path.

[0019] In one possible implementation, the identification module is further configured to: perform clustering processing on the meteorological index sequence and the water quality index sequence based on the at least one pollution identification parameter to determine the water pollution type of the predetermined water area.

[0020] In one possible implementation, the water pollution type includes water quality fluctuations under the influence of meteorological factors and water pollution under the influence of meteorological factors.

[0021] According to one aspect of this disclosure, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the above-described pollution type identification method.

[0022] According to one aspect of this disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the above-described pollution type identification method.

[0023] The pollution type identification method according to embodiments of this disclosure can achieve in-situ, real-time, online, and high-frequency measurement of water quality information using, for example, a quantum dot spectrometer. It then determines water quality indicators for a predetermined water area based on this information. Through interpolation, the acquisition frequency and phase of the water quality indicator sequence and the meteorological indicator sequence are made identical, improving the accuracy of the analysis. Furthermore, it can determine the type of water pollution in various weather environments in real time using the water quality indicator sequence and the meteorological indicator sequence, identifying the impact of weather changes on water quality indicators and improving the accuracy and applicability of pollution type identification.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0025] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0026] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0027] Figure 1 A flowchart illustrating a pollution type identification method according to an embodiment of the present disclosure is shown;

[0028] Figure 2A , Figure 2B , Figure 2C , Figure 2D This diagram illustrates an application of the pollution type identification method according to an embodiment of the present disclosure.

[0029] Figure 3 A block diagram of a pollution type identification device according to an embodiment of the present disclosure is shown;

[0030] Figure 4 A block diagram of an electronic device according to an embodiment of the present disclosure is shown;

[0031] Figure 5 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0032] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0033] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0034] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0035] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0036] Figure 1 A flowchart illustrating a pollution type identification method according to an embodiment of this disclosure is shown, such as... Figure 1 As shown, the method includes:

[0037] In step S11, the water quality index sequence of the predetermined water area within a first time period is determined based on the water quality information of the predetermined water area, and the meteorological index sequence of the predetermined water area within a first time period is determined based on the meteorological monitoring data of the predetermined water area. The water quality index sequence includes water quality indicators obtained at multiple times within the first time period, and the meteorological index sequence includes meteorological indicators obtained at multiple times within the first time period.

[0038] In step S12, at least one pollution identification parameter is determined based on the water quality index sequence and the meteorological index sequence, wherein the pollution identification parameter includes single-sequence feature parameters and inter-sequence feature parameters;

[0039] In step S13, the type of water pollution in the predetermined water area is determined based on the pollution identification parameters.

[0040] According to the pollution type identification method of the embodiments of this disclosure, water quality indicators can be obtained through water quality monitoring equipment. The water quality monitoring equipment is equipped with a spectral water quality sensor, and optionally other water quality sensors. The spectral water quality sensor is preferably a miniature spectral water quality sensor, such as a quantum dot spectral sensor, which can achieve in-situ, real-time, online, and high-frequency measurement of water quality information. It can determine the water quality indicator sequence of a predetermined water area based on the water quality information, and can determine the water pollution type in various weather environments in real time through the water quality indicator sequence and meteorological indicator sequence, thus determining the impact of weather changes on water quality indicators and improving the accuracy and applicability of pollution type identification.

[0041] In the example, the micro-spectral sensor can be, for example, a quantum dot spectral sensor. Based on the physical and optical properties of nanocrystals, the quantum dot spectral sensor measures incident light (e.g., light transmitted or scattered after passing through a predetermined area of ​​water sample) to obtain spectral information of the incident light, which can represent water quality information of the water body. For example, the quantum dot spectral sensor may include a nanocrystal chip made of various nanocrystals. The nanocrystal chip contains a certain arrangement of various nanocrystals (e.g., a nanocrystal array), wherein each nanocrystal has different light absorption or emission characteristics. Different types of semiconductor nanocrystals can, for example, be made of different materials and sizes, so that the nanocrystal chip can modulate the response of wavelengths over a wide wavelength range to obtain a spectrum adjusted for incident light over a wide wavelength range.

[0042] In one possible implementation, light transmitted or scattered through water can be affected by substances in the water (e.g., suspended solids, pollutants, etc.), thereby obtaining specific spectral information. A quantum dot spectral sensor can acquire this spectral information in real time and determine the water quality indicators represented by this spectral information. For example, by observing the absorption intensity of different wavelengths of light by a water sample, spectral information of different frequency bands of light can be obtained, and water quality indicators can be calculated from this spectral information. In this example, the water quality indicators include Chemical Oxygen Demand (COD), turbidity, permanganate index, total suspended solids, biological oxygen demand, total organic carbon, sulfate content, chloride content, dissolved iron content, dissolved manganese content, dissolved copper content, dissolved zinc content, nitrate content, nitrite content, total nitrogen content, fluoride content, selenium content, total arsenic content, total mercury content, total cadmium content, chromium content, total lead content, total cyanide, volatile phenol content, coliform bacteria content, sulfide content, etc. Water temperature can also be determined based on infrared spectra from spectral information. Alternatively, quantum dot spectrophotometers can infer water quality indicators through neural networks; for example, spectral information can be input into a neural network, which can then infer the concentrations of various substances (water quality indicators). This disclosure does not limit the method of determining water quality indicators. This disclosure does not limit the working principle of quantum dot spectrophotometers.

[0043] In this example, a quantum dot spectrometer can determine water quality indicators by analyzing the light absorption characteristics of various substances in the water. For instance, it can analyze the light intensity of light at a specific wavelength to obtain the concentration of substances corresponding to that specific wavelength range (water quality indicators). Measuring indicators using a quantum dot spectrometer allows for online, in-situ, high-frequency, and real-time measurements. When detecting water quality indicators, the quantum dot spectrometer can detect the spectral information of light passing through a predetermined water area, and then quickly calculate the water quality indicators based on this spectral information, providing highly real-time water quality indicators. Compared to the process of bringing water samples back to the laboratory for testing, detection using a quantum dot spectrometer offers better real-time performance (i.e., the detected water quality indicators are the current water quality indicators, while laboratory testing takes a long time, and the water quality indicators of the predetermined water area may have changed during the waiting period for test results). By using a quantum dot spectrometer installed in a predetermined water area to perform multiple measurements over a certain period, a water quality index sequence containing the two aforementioned water quality indicators can be obtained. Since the water quality indicators in this sequence are obtained from multiple times at the same location, they possess consistency and comparability, and can be used to observe the changing patterns of water quality indicators over a period of time to determine water pollution. For example, the measurement frequency of the quantum dot spectrometer can reach 3-60 minutes / time, preferably 5-30 minutes / time, particularly preferably 8-20 minutes / time, and most preferably 10-15 minutes / time. This measurement frequency is much higher than the frequency of bringing water samples back to the laboratory for analysis. Furthermore, the quantum dot spectrometer can be installed at a fixed location within the predetermined water area, ensuring the consistency of the water samples. Bringing the water back to the laboratory for testing makes it difficult to guarantee that the samples were taken from the exact same location for both measurements. Furthermore, due to the low measurement frequency and the long interval between measurements, even if the samples were taken from the exact same location for both measurements, the water quality at that location may have changed significantly over the long interval due to the fluidity of the water, making it difficult to guarantee the consistency of the measurements and the comparability of the results.

[0044] In one possible implementation, the meteorological indicators include wind speed and / or rainfall. Weather changes may affect the water quality indicators of a designated water area. For example, rainwater may affect the water quality indicators in the designated water area and may also cause water pollution. For instance, rainwater entering the designated water area, or wind blowing through the designated water area, may stir up silt, algae, or other organic matter at the bottom of the designated water area, causing changes in the turbidity or COD indicators of the designated water area. For instance, rainwater flowing through rooftops, gutters, sewers, etc., and then into the designated water area, may wash pollutants (e.g., dust, organic matter, etc.) from these locations into the designated water area, or wind may blow these pollutants into the designated water area, causing changes in the turbidity or COD indicators of the designated water area. Furthermore, rainy weather may cause sewage tanks or sewage pipes to overflow, allowing sewage to flow into the designated water area and causing pollution. The pollutants in the water can be industrial pollution (e.g., industrial wastewater), agricultural pollution (e.g., wastewater containing organic matter such as fertilizers or livestock manure), and domestic pollution (e.g., kitchen wastewater). This disclosure does not limit the types of pollutants.

[0045] In the example, the meteorological indicators may also include wind direction indicators (e.g., indicators that can be represented by numbers or specific identifiers), temperature indicators, humidity indicators, ultraviolet radiation intensity, etc. This disclosure does not limit the types of meteorological indicators.

[0046] In one possible implementation, in step S11, the start time for acquiring meteorological indicators may differ from the start time for acquiring water quality indicators, and the end time may also differ. Therefore, an intersection can be selected between the measurement time period of the water quality measuring device (e.g., a miniature spectrometer, specifically a quantum dot spectrometer) and the time period for acquiring meteorological data, and a first time period can be selected from this intersection. This disclosure does not limit the method of selecting the first time period.

[0047] Furthermore, in the first time period, the measurement frequency of water quality measuring equipment may differ from the acquisition frequency of meteorological monitoring data; therefore, their frequencies can be unified. For example, multiple COD indicators measured by a quantum dot spectrometer can be expressed as {(t 1,1 ,x 1,1 ),(t 1,2 ,x 1,2 ),(t 1,3 ,x 1,3 ),…,(t 1,n ,x 1,n )}, where n is a positive integer, t 1,i To obtain the i-th (i is a positive integer, and i ≤ n) COD index x 1,i The moment. For example, multiple turbidity indices measured by a quantum dot spectrometer can be expressed as {(t 2,1 ,x 2,1 ),(t2,2 ,x 2,2 ),(t 2,3 ,x 2,3 ),…,(t 2,n ,x 2,n )}, where t 2,j To obtain the j-th (j is a positive integer, and i ≤ n) turbidity index x 2,j The moment. Multiple wind speed indicators in meteorological indices can be represented as {(t...} 3,1 ,x 3,1 ),(t 3,2 ,x 3,2 ),(t 3,3 ,x 3,3 ),…,(t 3,n ,x 3,m )}, where m is a positive integer, t 3,k To obtain the k-th (k is a positive integer and k ≤ m) wind speed index x 3,k The moment. Multiple rainfall indicators in meteorological indices can be represented as {(t...} 4,1 ,x 4,1 ),(t 4,2 ,x 4,2 ),(t 4,3 ,x 4,3 ),…,(t 4,n ,x 4,m )}, where t 4,l To obtain the l-th (l is a positive integer and l≤m) wind speed index x 4,l The timing of measurements varies. Due to inconsistent frequencies, the time intervals between adjacent water quality indicator sequences may differ from the time intervals between adjacent meteorological indicators, leading to discrepancies between the number of water quality indicators and meteorological indicators measured within the first time period. For example, if the first time period is 12 hours and the water quality indicator measurement frequency is 10 minutes / measurement, then 72 water quality indicators are obtained within the first time period. If the meteorological indicator measurement frequency is 5 minutes / measurement, then 144 meteorological indicators are obtained within the first time period. This inconsistency between measurement frequency and the number of indicators makes it difficult to process multiple indicators uniformly; for example, it is difficult to obtain correlations between indicator sequences, and it is also difficult to further estimate the pollution type.

[0048] In one possible implementation, to address the aforementioned problem, the quantity and frequency of multiple indicators can be unified. Step S11 may include: determining the acquisition frequency and acquisition phase of the multiple water quality indicators and the multiple meteorological indicators; interpolating the multiple water quality indicators according to the acquisition frequency and acquisition phase to obtain the water quality indicator sequence, and / or interpolating the multiple meteorological indicators to obtain the meteorological indicator sequence. Interpolation can be used to ensure that the acquisition frequencies of the two indicator sequences are consistent. For example, interpolation can be performed on indicators with lower acquisition frequencies. For instance, if the measurement frequency of water quality indicators is 10 minutes / time and the measurement frequency of meteorological indicators is 5 minutes / time, interpolation can be performed on the water quality indicators to obtain more water quality indicators, and to ensure that the acquisition frequencies of water quality indicators are consistent with those of meteorological indicators.

[0049] In the example, the measurement frequency of the water quality measuring equipment can be determined, which is the sampling frequency of the water quality index. Similarly, the sampling frequency of the instruments acquiring meteorological indicators can be determined; for example, the sampling frequency of the anemometer can be determined, thus determining the sampling frequency of the wind speed index, and the sampling frequency of the rain gauge can be determined, thus determining the sampling frequency of the rainfall index.

[0050] In the example, the acquisition phase can be determined based on the acquisition time. For instance, if the water quality indicator acquisition begins at 3:00 PM and the meteorological indicator acquisition begins at 3:05 PM, then the phase of the water quality indicator is 5 minutes earlier than that of the meteorological indicator.

[0051] In one possible implementation, the collection frequency and phase of water quality and meteorological indicators can be unified. For example, the later of the start time of water quality indicator collection and meteorological indicator collection can be selected as the start time of the first time period, and the earlier of the end time of water quality indicator collection and meteorological indicator collection can be selected as the end time of the first time period. Alternatively, the indicators in the phase difference can be predicted by interpolation methods (e.g., using regression analysis). As in the example above, if the phase of the water quality indicator is 5 minutes earlier than that of the meteorological indicator, the meteorological indicator can be interpolated (e.g., by using regression analysis) to predict the meteorological indicator within 5 minutes.

[0052] In one possible implementation, when the data collection frequencies are not uniform, interpolation can be performed on indicators with lower collection frequencies to predict some indicators. For example, as in the previous example, if the water quality indicators are measured at a frequency of 10 minutes / time, 72 water quality indicators are obtained in the first time period. If the meteorological indicators are measured at a frequency of 5 minutes / time, 144 meteorological indicators are obtained in the first time period. Interpolation can then be used to predict the 72 water quality indicators in the first time period, ensuring that the total number of water quality indicators reaches 144, i.e., making the acquisition frequency of meteorological indicators the same as that of water quality indicators. Conversely, if the acquisition frequency of meteorological indicators is higher than that of water quality indicators, interpolation can also be performed on the meteorological indicators. In the example, interpolation can also be performed on both water quality and meteorological indicators simultaneously. For example, if 24 water quality indicators and 36 meteorological indicators are obtained within 12 hours, interpolation can be performed on both the water quality and meteorological indicators to ensure that the total number of water quality indicators reaches 72. This disclosure does not limit the number of indicators.

[0053] In the example, after the above processing, meteorological index sequences and water quality index sequences with the same measurement frequency and start and end times can be obtained. For example, the COD index sequence {(t1,x ’ 1,1 ),(t2,x ’ 1,2 ),(t3,x ’ 1,3 ),…,(t n ,x ’ 1,n )}, Turbidity index sequence {(t1,x ’ 2,1 ),(t2,x ’ 2,2 ),(t3,x ’ 2,3 ),…,(t n ,x ’ 2,n )}, wind speed index sequence {(t1,x ’ 3,1 ),(t2,x ’ 3,2 ),(t3,x ’ 3,3 ),…,(t n ,x ’ 3,n )} and rainfall index sequence {(t1,x ’ 4,1 ),(t2,x ’ 4,2 ),(t3,x ’4,3 ),…,(t n ,x ’ 4,n )}.

[0054] In this way, interpolation and other processing can be used to make the acquisition frequency and acquisition phase of meteorological index sequences and water quality index sequences the same, which facilitates analysis and processing and improves the accuracy of identifying water pollution types.

[0055] In one possible implementation, the types of water pollution can be analyzed by combining the aforementioned indicators. In the example, each indicator sequence includes multiple data points. Based on these multiple data points, data characteristics such as peak value, rate of change, mean, and standard deviation can be obtained, as well as data characteristics such as the correlation between indicator sequences. The types of water pollution can be analyzed based on the data characteristics of two or more indicator sequences. Analyzing water pollution types using the data characteristics of two or more indicator sequences increases the variety of data features. A single water quality indicator has limited data features, can identify fewer types of water pollution, and has a narrower scope of application. Using the data characteristics of two or more indicator sequences to jointly analyze water pollution types can mitigate these problems.

[0056] In one possible implementation, multiple pollution identification parameters can be determined based on the aforementioned indicator sequences. These pollution identification parameters may include single-sequence characteristic parameters and inter-sequence characteristic parameters. Single-sequence characteristic parameters can be determined based on a single indicator sequence; for example, single-sequence characteristic parameters include at least one of the mean, standard deviation, autocorrelation coefficient, number of peaks, and peak height of the water quality indicator sequence or the meteorological indicator sequence. Inter-sequence characteristic parameters can be determined using two indicator sequences; for example, inter-sequence characteristic parameters include at least one of the correlation coefficient and dynamic time-warped distance between the two indicator sequences, wherein the two indicator sequences include two water quality indicator sequences, or one water quality indicator sequence and one meteorological indicator sequence.

[0057] In one possible implementation, the single-sequence characteristic parameters can be determined in step S12. In this example, the aforementioned index sequences may include multiple indicators, and the mean of these indicators can be determined. For example, the mean of the wind speed index sequence, the mean of the rainfall index sequence, the mean of the COD index sequence, and the mean of the turbidity index sequence can be determined.

[0058] In one possible implementation, the standard deviation of each series can be determined based on the above mean. For example, the standard deviation of the wind speed index series, the standard deviation of the rainfall index series, the standard deviation of the COD index series, and the standard deviation of the turbidity index series can be determined.

[0059] In one possible implementation, the autocorrelation coefficients of each series can be determined based on the mean and standard deviation mentioned above. For example, the autocorrelation coefficients of the wind speed index series, the rainfall index series, the COD index series, and the turbidity index series can be determined.

[0060] In one possible implementation, the peak height can be determined based on multiple indicators of each indicator sequence. For example, the maximum points in each indicator sequence can be identified, and these maximum points can be used as the peak heights. For example, the peak heights of the wind speed indicator sequence, the rainfall indicator sequence, the COD indicator sequence, and the turbidity indicator sequence can be determined.

[0061] In one possible implementation, the number of peaks can be determined based on multiple indicators in each indicator sequence. For example, the maximum points in each indicator sequence can be identified, and the number of maximum points can be determined, with the number of maximum points being defined as the number of peaks. For example, the number of peaks in a wind speed indicator sequence, a rainfall indicator sequence, a COD indicator sequence, and a turbidity indicator sequence can be determined.

[0062] In one possible implementation, in step S12, the characteristic parameters between the aforementioned sequences can be determined. In an example, the similarity between two indicator sequences can be determined. For instance, the correlation coefficient between the two indicator sequences can be determined using the aforementioned mean and variance. For example, the correlation coefficient between the rainfall indicator sequence and the COD indicator sequence, the correlation coefficient between the wind speed indicator sequence and the COD indicator sequence, the correlation coefficient between the wind speed indicator sequence and the turbidity indicator sequence, the correlation coefficient between the rainfall indicator sequence and the turbidity indicator sequence, and the correlation coefficient between the turbidity indicator sequence and the COD indicator sequence can all be determined.

[0063] In one possible implementation, the indicators in the two indicator sequences may not change simultaneously; there may be a time difference or phase difference between the changes in the two indicators. For example, the peak of the chemical oxygen demand (COD) indicator may appear earlier than the peak of the turbidity indicator. Using the correlation coefficient to determine the similarity between the two sequences may result in situations where the waveforms of the two sequences are similar, but the similarity is low due to the time difference in the indicator changes. For example, both sequences may include a peak and a trough, and the time difference between the peak and the trough in both sequences may be similar, meaning the waveforms of the two sequences are similar. However, because the changes in the indicators in the two sequences exist at different times—for example, an indicator in the first sequence changes earlier than an indicator in the second sequence—the trough in the first sequence may appear closer to the peak in the second sequence, leading to a lower correlation coefficient between the two indicator sequences, i.e., lower accuracy in determining similarity.

[0064] In one possible implementation, the inter-sequence feature parameters include the dynamic time-normalized distance between two indicator sequences. Step S13 may include: determining a path normalization matrix between the two indicator sequences based on the indicators in the two indicator sequences, wherein the element in the i-th row and j-th column of the path normalization matrix is ​​the distance between the i-th indicator in the first indicator sequence and the j-th indicator in the second indicator sequence, where i and j are positive integers; determining a normalized path based on the path normalization matrix, wherein the normalized path is the path with the smallest sum of path elements among the paths from the first element to the second element in the path normalization matrix, wherein the first element is the element in the n-th row and 1-th column of the path normalization matrix, and the second element is the element in the 1-th row and m-th column of the path normalization matrix, the first indicator sequence includes n indicators, the second indicator sequence includes m indicators, n≥i, m≥j; and determining the dynamic time-normalized distance between the two indicator sequences based on the normalized path.

[0065] In one possible implementation, the similarity between two index sequences can be determined using dynamic time-normalized distance to reduce the problem of low similarity calculation accuracy caused by time differences in the changes of indicators in the two sequences. The path normalization matrix can be determined based on the indicators in the first and second index sequences. The element in the i-th row and j-th column of the path normalization matrix is ​​the distance between the i-th chemical oxygen demand (COD) indicator in the first index sequence and the j-th turbidity indicator in the second index sequence, where i and j are positive integers. In the example, the distance can be the absolute value of the difference between corresponding indicators in the first and second index sequences. For example, when determining the normal matrix of the turbidity indicator sequence and the COD indicator sequence, if the first COD indicator is 10 and the first turbidity indicator is 15, then the element in the first row and first column of the path normalization matrix is ​​5; if the first COD indicator is 10 and the second turbidity indicator is 18, then the element in the first row and second column of the path normalization matrix is ​​8, and so on. This disclosure does not limit the values ​​of the elements in the path normalization matrix.

[0066] In one possible implementation, the regularized path from the first element to the second element (i.e., the path with the smallest sum of elements) can be determined in the path planning matrix. In the example, the first element is the element in the nth row and 1st column of the path regularization matrix (i.e., the bottom left element), and the second element is the element in the 1st row and mth column of the path regularization matrix (i.e., the top right element). The path from the first element to the second element requires traversing every row and every column of the path regularization matrix. That is, in the regularized path, each row of the path regularization matrix will have one element included in the regularized path, and each column of the path regularization matrix will also have one element included in the regularized path. That is, the path from the element in row n, column 1 to the element in row 1, column m will pass through rows n, (n-1)... row 1 (this path is monotonically decreasing in the row direction and will not skip any rows), and it will also pass through columns 1, 2... column m (this path is monotonically increasing in the column direction and will not skip any columns). Since the element in row i, column j represents the distance between the i-th chemical oxygen demand index in the first index sequence and the j-th turbidity index in the second index sequence, this regularized path traverses every index in the first index sequence and every index in the second index sequence. Furthermore, the regularized path is the path with the smallest sum of elements along the path from the first element to the second element, that is, the path with the smallest sum of distances between n indices and m indices.

[0067] In one possible implementation, the similarity between the first indicator sequence and the second indicator sequence can be determined based on the path. The normalized path is the path with the minimum sum of distances between the n indicators of the first indicator sequence and the m indicators of the second indicator sequence. The distance with the minimum sum of distances between the n indicators of the first indicator sequence and the m indicators of the second indicator sequence can be determined as the dynamic time normalized distance.

[0068] In the example, the dynamic time warp distance can be determined using the following formula (1):

[0069] D(e,f)=Dist(e,f)+min{D(e-1,f), D(e,f-1), D(e-1,f-1)} (1)

[0070] Where Dist(e,f) represents the distance between the e-th (e is a positive integer) indicator in the first indicator sequence and the f-th (f is a positive integer) indicator in the second indicator sequence, that is, the (e,f) element of the path normalization matrix. D(e,f) represents the dynamic time normalization distance between the first e indicators in the first indicator sequence and the first f indicators in the second indicator sequence. In the example, e = n and f = m can be made, and the dynamic time normalization distance between the first indicator sequence and the second indicator sequence can be obtained by iterating through the above formula (1).

[0071] In one possible implementation, the similarity between the first and second indicator sequences is determined by the dynamic time warp distance. For example, if the dynamic time warp distance is less than or equal to a preset distance threshold, the first and second indicator sequences are considered to have a high degree of similarity; otherwise, the first and second indicator sequences are considered to have a low degree of similarity.

[0072] In this way, by traversing all indicators in the first and second indicator sequences using the path normalization matrix and normalization path, the dynamic time normalization distance that minimizes the sum of distances between all indicators can be determined. The similarity between the first and second indicator sequences can be determined by using the dynamic time normalization distance. The distance between all indicators in the first and second indicator sequences can be referenced, which reduces the problem of low accuracy in similarity calculation caused by waveform shift due to time differences.

[0073] In one possible implementation, in step S13, the type of water pollution can be determined based on the aforementioned pollution identification parameters. Based on the aforementioned meteorological index sequence and water quality index sequence, it can be determined whether changes in water quality indicators are affected by meteorological factors. In the example, temporary changes in water quality indicators may occur due to meteorological factors. For instance, rain or wind may cause sediment to churn at the bottom of the water, increasing turbidity; however, such changes are temporary and can be considered as no water pollution. Similarly, rain or wind may wash land-based pollutants into the water, causing changes in water quality indicators; this can be considered as water pollution. Furthermore, rainwater may cause sewage overflows into normal water bodies, leading to changes in water quality indicators; this can also be considered as water pollution.

[0074] In one possible implementation, the water pollution type includes water quality fluctuations under the influence of meteorological factors and water pollution under the influence of meteorological factors. The type of water pollution can be determined using the pollution identification parameters mentioned above. Step S13 may include: clustering the meteorological index sequence and the water quality index sequence according to the at least one pollution identification parameter to determine the water pollution type of the predetermined water area.

[0075] In the example, multiple pollution identification parameters of the water quality index sequence and meteorological index sequence within the first time period can be used as clustering features to determine the type of water pollution within the first time period.

[0076] In the example, multiple pollution identification parameters within multiple sample time periods can be clustered to determine cluster centers for each category. Further, pollution identification parameters within a first time period can be clustered to determine the cluster center with the closest characteristic distance to the pollution identification parameters within the first time period, and the category described by this cluster center is determined as the water pollution type within the first time period. For example, it can be determined whether the fluctuations in water quality indicators occurring within the first time period are due to meteorological factors (i.e., temporary fluctuations, such as temporary fluctuations in turbidity caused by wind or rainfall, which are not considered water pollution) or water pollution caused by meteorological factors. Furthermore, water pollution caused by meteorological factors can be further divided into initial rainwater runoff pollution and sewage overflow pollution. Rainwater flowing through rooftops, gutters, sewers, etc., and then into a predetermined water area may wash pollutants (e.g., dust, organic matter, etc.) from these locations into the predetermined water area, causing changes in the turbidity or COD index of the predetermined water area. This situation is called initial rainwater runoff pollution. Rainfall can cause sewage tanks or pipes to overflow, allowing wastewater to flow into designated water areas and cause pollution; this is known as sewage overflow pollution. Cluster analysis can be used to determine the type of water pollution occurring within a specific time period.

[0077] The pollution type identification method according to embodiments of this disclosure can measure water quality indicators of a predetermined water area in real time and at high frequency using water quality information. Through interpolation processing, the acquisition frequency and phase of the water quality indicator sequence and the meteorological indicator sequence are made the same, improving the analysis accuracy. Furthermore, the method can determine the type of water pollution in various weather environments in real time using the water quality indicator sequence and the meteorological indicator sequence, identifying the impact of weather changes on water quality indicators, thus improving the accuracy and applicability of pollution type identification.

[0078] Figure 2A , Figure 2B , Figure 2C , Figure 2D A schematic diagram illustrating the application of the pollution type identification method according to an embodiment of the present disclosure is shown.

[0079] In one possible implementation, the designated water area can be a pool, river, lake, or other body of water. Under different meteorological environments, the designated water area may be polluted, or its water quality indicators may fluctuate due to weather changes. Pollution identification parameters can be determined by combining water quality indicator sequences with meteorological indicator sequences, and the type of water pollution can be determined using these parameters.

[0080] In one possible implementation, the first time period can be a period of change in the meteorological environment, such as a period of windy or rainy weather, and the meteorological index sequence and water quality index sequence within the first time period can be determined.

[0081] In one possible implementation, the water quality indicator sequence includes a COD indicator sequence and a turbidity indicator sequence, and the meteorological indicator sequence may include a wind speed indicator sequence and a rainfall indicator sequence. The pollution identification parameters may include single-sequence characteristic parameters such as the mean, standard deviation, autocorrelation coefficient, peak height, and number of peaks of the aforementioned indicator sequences. They may also include inter-sequence characteristic parameters such as the correlation coefficient or dynamic time-normalized distance between the rainfall indicator sequence and the COD indicator sequence, the correlation coefficient or dynamic time-normalized distance between the wind speed indicator sequence and the COD indicator sequence, the correlation coefficient or dynamic time-normalized distance between the wind speed indicator sequence and the turbidity indicator sequence, and the correlation coefficient or dynamic time-normalized distance between the rainfall indicator sequence and the turbidity indicator sequence, and the correlation coefficient or dynamic time-normalized distance between the turbidity indicator sequence and the COD indicator sequence.

[0082] In the example, such as Figure 2A As shown, both turbidity and COD indicators exhibit strong correlations with wind speed. For instance, the correlation coefficients between turbidity and wind speed are high, and the correlation coefficients between COD and wind speed are also high. That is, when wind speed is high, the water body churns, causing both turbidity and COD to rise; conversely, when wind speed decreases, the water body returns to calm, leading to a decrease in both turbidity and COD. Therefore, the changes in water quality indicators can be considered temporary fluctuations caused by wind, and thus, it can be concluded that no water pollution has occurred under these circumstances.

[0083] In the example, such as Figure 2B As shown, during rainy weather, rainwater did not wash pollutants into the designated water area, and the COD index sequence and turbidity index sequence did not change significantly. For example, the peak height was relatively small, which can be considered as the designated water area not being polluted.

[0084] In the example, such as Figure 2C As shown, both the first and second indicator sequences exhibit significant changes; that is, the peak heights of both the COD and turbidity indicator sequences are relatively large, and the time difference between the peaks is small. Furthermore, the fluctuations occur at close intervals, meaning the COD and turbidity indicator sequences fluctuate almost simultaneously, reach their peaks almost simultaneously, and return to calm almost simultaneously. Further, the COD and turbidity indicator sequences show high similarity, meaning their dynamic time regularization distance is small. In summary, the COD and turbidity indicator sequences exhibit similar trends, changing almost simultaneously with relatively drastic fluctuations. These fluctuations are caused by rainwater washing over the ground, roofs, gutters, and sewers, simultaneously flushing various pollutants (including organic matter and dust) into the designated water area. Therefore, the pollution type identification model can classify these fluctuations as initial rainwater runoff pollution.

[0085] In the example, such as Figure 2DAs shown, both the COD and turbidity index sequences exhibit significant changes, with both sequences showing relatively high peak heights. However, the time difference between these peaks is substantial. Furthermore, the turbidity index rises rapidly (possibly due to a large influx of wastewater causing sediment turbidity in the designated water area) and declines quickly, while the COD index sequence rises and then declines more slowly, with more peaks than the turbidity index sequence (possibly because the turbidity quickly returns to calm after sediment turbidity, but the high pollutant content makes it difficult for the COD index to decrease rapidly). Further, the COD and turbidity index sequences show low similarity, meaning their trends differ, and their dynamic time regularization distance is large. In summary, both the COD and turbidity index sequences undergo drastic changes, but these changes are asynchronous. This may be due to rainwater overflowing the wastewater pond, causing a large influx of wastewater and resulting in a sudden increase in turbidity in the designated water area followed by a rapid decrease. The high pollutant content in the wastewater also leads to a prolonged period of high COD levels in the designated water area. Therefore, the pollution type identification model can identify the above fluctuations as sewage overflow pollution.

[0086] In one possible implementation, the pollution type identification method can be used to determine the relationship between changes in water quality indicators and meteorological indicators, identify the type of water pollution in different meteorological environments, and carry out targeted treatment. This disclosure does not limit the application scope of the pollution type identification method.

[0087] Figure 3 A block diagram of a pollution type identification device according to an embodiment of the present disclosure is shown, such as Figure 3 As shown, the device includes: a sequence module 11, used to determine a water quality index sequence of the predetermined water area within a first time period based on water quality information of the predetermined water area, and to determine a meteorological index sequence of the predetermined water area within the first time period based on meteorological monitoring data of the predetermined water area, wherein the water quality index sequence includes water quality indicators obtained at multiple times within the first time period, and the meteorological index sequence includes meteorological indicators obtained at multiple times within the first time period; a parameter module 12, used to determine at least one pollution identification parameter based on the water quality index sequence and the meteorological index sequence, wherein the pollution identification parameter includes single-sequence feature parameters and inter-sequence feature parameters; and an identification module 13, used to determine the water pollution type of the predetermined water area based on the pollution identification parameter.

[0088] In one possible implementation, the sequence module is further configured to: determine the acquisition frequency and acquisition phase of the plurality of water quality indicators and the plurality of meteorological indicators; perform interpolation processing on the plurality of water quality indicators according to the acquisition frequency and the acquisition phase to obtain the water quality indicator sequence, and / or perform interpolation processing on the plurality of meteorological indicators to obtain the meteorological indicator sequence.

[0089] In one possible implementation, the water quality indicators include chemical oxygen demand (COD) and / or turbidity, and the meteorological indicators include wind speed and / or rainfall.

[0090] In one possible implementation, the single-sequence feature parameters include at least one of the mean, standard deviation, autocorrelation coefficient, number of peaks, and peak height of the water quality index sequence or the meteorological index sequence; the inter-sequence feature parameters include at least one of the correlation coefficient and dynamic time-warped distance between the two index sequences, wherein the two index sequences include two water quality index sequences, or one water quality index sequence and one meteorological index sequence.

[0091] In one possible implementation, the two indicator sequences include a first indicator sequence and a second indicator sequence. The parameter module is further configured to: determine a path normalization matrix between the two indicator sequences based on the indicators in the two indicator sequences, wherein the element in the i-th row and j-th column of the path normalization matrix is ​​the distance between the i-th indicator in the first indicator sequence and the j-th indicator in the second indicator sequence, where i and j are positive integers; determine a normalized path based on the path normalization matrix, wherein the normalized path is the path with the smallest sum of path elements from the first element to the second element in the path normalization matrix, wherein the first element is the element in the n-th row and 1-th column of the path normalization matrix, and the second element is the element in the 1-th row and m-th column of the path normalization matrix, the first indicator sequence includes n indicators, the second indicator sequence includes m indicators, n≥i, m≥j; and determine the dynamic time normalized distance between the two indicator sequences based on the normalized path.

[0092] In one possible implementation, the identification module is further configured to: perform clustering processing on the meteorological index sequence and the water quality index sequence based on the at least one pollution identification parameter to determine the water pollution type of the predetermined water area.

[0093] In one possible implementation, the water pollution type includes water quality fluctuations under the influence of meteorological factors and water pollution under the influence of meteorological factors.

[0094] It is understood that the various method embodiments mentioned above in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further.

[0095] In addition, this disclosure also provides a pollution type identification device, electronic device, computer-readable storage medium, and program, all of which can be used to implement any pollution type identification method provided in this disclosure. The corresponding technical solutions and descriptions are described in the corresponding section of the method and will not be repeated here.

[0096] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0097] In some embodiments, the apparatus provided in this disclosure may have functions or include modules that can be used to perform the methods described in the above method embodiments. Specific implementations can be referred to the descriptions in the above method embodiments, and for brevity, will not be repeated here.

[0098] This disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the above-described method. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0099] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured as described above.

[0100] Electronic devices can be provided as terminals, servers, or other forms of devices.

[0101] Figure 4 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, or other terminal.

[0102] Reference Figure 4 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0103] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0104] Memory 804 is configured to store various types of data to support the operation of electronic device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0105] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0106] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0107] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0108] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0109] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 can detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0110] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0111] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0112] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions that can be executed by a processor 820 of an electronic device 800 to perform the above-described method.

[0113] Figure 5 This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. For example, the electronic device 1900 may be provided as a server. (Refer to...) Figure 5 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0114] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output (I / O) interface 1958. Electronic device 1900 can operate on an operating system, such as Windows Server, stored in memory 1932. TM Mac OS XTM Unix TM Linux TM FreeBSD TM Or similar.

[0115] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of an electronic device 1900 to perform the above-described method.

[0116] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0117] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0118] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0119] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0120] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0121] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0122] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0123] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0124] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for identifying pollution types, characterized in that, The method includes: Based on the water quality information of the predetermined water area, a water quality index sequence for the predetermined water area within a first time period is determined, and based on the meteorological monitoring data of the predetermined water area, a meteorological index sequence for the predetermined water area within the first time period is determined. The water quality index sequence includes water quality indicators obtained at multiple times within the first time period, and the meteorological index sequence includes meteorological indicators obtained at multiple times within the first time period. Based on the water quality index sequence and the meteorological index sequence, at least one pollution identification parameter is determined, wherein the pollution identification parameter includes single-sequence characteristic parameters and inter-sequence characteristic parameters, and the inter-sequence characteristic parameters are determined by two index sequences, including a water quality index sequence and a meteorological index sequence. Based on the pollution identification parameters, the type of water pollution in the predetermined water area is determined; Based on the pollution identification parameters, the type of water pollution in the predetermined water area is determined, including: Based on the at least one pollution identification parameter, the meteorological index sequence and the water quality index sequence are clustered to determine the water pollution type of the predetermined water area. The water pollution type includes water quality fluctuations under the influence of meteorological factors and water pollution under the influence of meteorological factors. The two indicator sequences include a first indicator sequence and a second indicator sequence. Specifically, based on the water quality index sequence and the meteorological index sequence, at least one pollution identification parameter is determined, including: Based on the indicators in the two indicator sequences, a path normalization matrix is ​​determined between the two indicator sequences, wherein the element in the i-th row and j-th column of the path normalization matrix is ​​the distance between the i-th indicator in the first indicator sequence and the j-th indicator in the second indicator sequence, where i and j are positive integers. Based on the path regularization matrix, a regularized path is determined, wherein the regularized path is the path with the smallest sum of elements among the paths from the first element to the second element in the path regularization matrix, wherein the first element is the element in the nth row and 1st column of the path regularization matrix, the second element is the element in the 1st row and mth column of the path regularization matrix, the first indicator sequence includes n indicators, the second indicator sequence includes m indicators, n≥i, m≥j; Based on the regularization path, the dynamic time regularization distance between the two indicator sequences is determined.

2. The method according to claim 1, characterized in that, Based on the water quality information of the predetermined water area, a sequence of water quality indicators for the predetermined water area within a first time period is determined, and based on the meteorological monitoring data of the predetermined water area, a sequence of meteorological indicators for the predetermined water area within the first time period is determined, including: Determine the sampling frequency and sampling phase for multiple water quality indicators and multiple meteorological indicators; Based on the acquisition frequency and the acquisition phase, interpolation processing is performed on the multiple water quality indicators to obtain the water quality indicator sequence, and / or interpolation processing is performed on the multiple meteorological indicators to obtain the meteorological indicator sequence.

3. The method according to claim 1, characterized in that, The water quality indicators include chemical oxygen demand and / or turbidity, and the meteorological indicators include wind speed and / or rainfall.

4. The method according to claim 1, characterized in that, The single-sequence characteristic parameters include at least one of the following: mean, standard deviation, autocorrelation coefficient, number of peaks, and peak height of the water quality index sequence or the meteorological index sequence. The inter-sequence characteristic parameters include at least one of the correlation coefficient and dynamic time-normalized distance between the two index sequences, wherein the two index sequences include two water quality index sequences, or one water quality index sequence and one meteorological index sequence.

5. A pollution type identification device, characterized in that, include: The sequence module is used to determine the water quality index sequence of the predetermined water area within a first time period based on the water quality information of the predetermined water area, and to determine the meteorological index sequence of the predetermined water area within a first time period based on the meteorological monitoring data of the predetermined water area. The water quality index sequence includes water quality indicators obtained at multiple times within the first time period, and the meteorological index sequence includes meteorological indicators obtained at multiple times within the first time period. The parameter module is used to determine at least one pollution identification parameter based on the water quality index sequence and the meteorological index sequence, wherein the pollution identification parameter includes single-sequence feature parameters and inter-sequence feature parameters, and the inter-sequence feature parameters are determined by two index sequences, including a water quality index sequence and a meteorological index sequence. An identification module is used to determine the type of water pollution in the predetermined water area based on the pollution identification parameters. The identification module is further used for: Based on the at least one pollution identification parameter, the meteorological index sequence and the water quality index sequence are clustered to determine the water pollution type of the predetermined water area. The water pollution type includes water quality fluctuations under the influence of meteorological factors and water pollution under the influence of meteorological factors. The two indicator sequences include a first indicator sequence and a second indicator sequence. The parameter module is further used for: Based on the indicators in the two indicator sequences, a path normalization matrix is ​​determined between the two indicator sequences, wherein the element in the i-th row and j-th column of the path normalization matrix is ​​the distance between the i-th indicator in the first indicator sequence and the j-th indicator in the second indicator sequence, where i and j are positive integers. Based on the path regularization matrix, a regularized path is determined, wherein the regularized path is the path with the smallest sum of elements among the paths from the first element to the second element in the path regularization matrix, wherein the first element is the element in the nth row and 1st column of the path regularization matrix, the second element is the element in the 1st row and mth column of the path regularization matrix, the first indicator sequence includes n indicators, the second indicator sequence includes m indicators, n≥i, m≥j; Based on the regularization path, the dynamic time regularization distance between the two indicator sequences is determined.

6. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to execute the method described in any one of claims 1 to 4.

7. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 4.

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