CN115510892A - Pollution type identification method and device, electronic equipment and storage medium - Google Patents
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Abstract
The disclosure relates to a pollution type identification method and device, an electronic device and a storage medium, wherein the method comprises the following steps: determining a first index sequence, a second index sequence and a third index sequence of a predetermined water area in a first time period according to the water quality information of the predetermined water area; according to at least one index sequence including the first index sequence, the second index sequence and the third index sequence; and inputting the pollution identification parameters into a pollution type identification model, and determining the water quality pollution type of the predetermined water area. According to the pollution type identification method disclosed by the embodiment of the disclosure, the first index sequence, the second index sequence and the third index sequence of the preset water area can be measured in real time and at high frequency through the water quality information, and various pollution types can be analyzed in real time according to the two indexes, so that the accuracy and the application range of pollution type identification are improved.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for identifying a pollution type, an electronic device, and a storage medium.
Background
Water resources are resources on which human beings live, and the quality of drinking water is directly related to the life safety of human beings. With the rapid development of economy for decades, environmental pollution incidents also present a high situation, and serious water pollution incidents in recent years cause serious social, economic and environmental losses.
At present, great attention is paid to environmental protection management, water body monitoring infrastructure is increasingly healthy, monitoring data is increasingly rich, on one hand, sudden pollution events are timely found through abnormal monitoring of water quality data, and timely checking treatment is performed, for example, indexes such as Chemical Oxygen Demand (Chemical Oxygen Demand) monitoring of water quality can timely capture abnormal changes of organic pollutant concentration in water body, and the method is an important monitoring means of water environment quality; on the other hand, the pollution category, the pollution reason and the pollution source need to be judged in advance based on monitoring data, which has very important significance for scientifically and reasonably predicting pollution development, making an emergency treatment plan and controlling pollution influence.
However, the river water quality is comprehensively affected by various factors such as hydrology, meteorology and pollutants, so that the change rule is difficult to obtain, and the pollution type judgment at home and abroad based on the river water quality online monitoring data only aims at the monitoring of specific pollutants, so that the application range is limited, and the generalization capability is weak. Furthermore, indicators such as chemical oxygen demand generally need to be chemically measured in a laboratory, so that the real-time performance is poor, and multiple types of pollution are difficult to identify by a single indicator.
Disclosure of Invention
The disclosure provides a pollution type identification method and device, electronic equipment and a storage medium.
According to an aspect of the present disclosure, there is provided a contamination type identification method including: determining a first index sequence, a second index sequence and a third index sequence of a predetermined water area in a first time period according to water quality information of the predetermined water area, wherein the first index sequence comprises chemical oxygen demand indexes obtained at a plurality of moments in the first time period, the second index sequence comprises turbidity indexes obtained at a plurality of moments in the first time period, and the third index sequence comprises water temperature indexes obtained at a plurality of moments in the first time period; determining at least one pollution identification parameter from at least one of the indicator sequences comprising the first indicator sequence, the second indicator sequence and the third indicator sequence; and inputting the at least one pollution identification parameter into a pollution type identification model for processing, and determining the water quality pollution type of the predetermined water area.
In one possible implementation, the pollution identification parameter includes a peak time difference between the cod indicator and the turbidity indicator, and determining at least one pollution identification parameter according to at least one indicator sequence including the first indicator sequence, the second indicator sequence, and the third indicator sequence includes: determining a maximum value of a chemical oxygen demand indicator in the first indicator sequence and a first moment when the maximum value of the chemical oxygen demand indicator is measured; determining a maximum value of a turbidity index in the second index sequence and a second moment when the maximum value of the turbidity index is measured; and determining the peak time difference according to the first time and the second time.
In a possible implementation, the pollution identification parameter includes a first peak height of the first indicator sequence and a second peak height of the second indicator sequence, wherein determining at least one pollution identification parameter according to at least one indicator sequence including the first indicator sequence, the second indicator sequence and the third indicator sequence includes: determining the first peak height according to the maximum value of the chemical oxygen demand index and a first mean value of the chemical oxygen demand index, wherein the first mean value is a mean value of a plurality of chemical oxygen demand indexes measured in a second time period that the predetermined water area is not polluted; determining the second peak height according to the maximum value of the turbidity indicator and a second average value of the turbidity indicators, wherein the second average value is an average value of a plurality of turbidity indicators measured during a second period of time when the predetermined water area is not polluted.
In a possible implementation, the pollution identification parameter includes a first rate of change of the first indicator sequence and/or a second rate of change of a second indicator sequence, wherein determining at least one pollution identification parameter from at least one indicator sequence including the first indicator sequence, the second indicator sequence, and the third indicator sequence includes: determining a first peak start time in the first index sequence; determining a first change rate of the first index sequence according to the first time, the first peak starting time and the first peak height; determining a second peak start time in the second index sequence; and determining a second change rate of the second index sequence according to the second time, the second peak starting time and the second peak height.
In a possible implementation manner, the pollution identification parameter includes a dynamic time warping distance of the first index sequence and the second index sequence, the warping distance being used for representing a similarity between the first index sequence and the second index sequence, wherein determining at least one pollution identification parameter according to at least one of the index sequences including the first index sequence, the second index sequence and the third index sequence includes: determining a path normalized matrix of the first index sequence and the second index sequence according to a plurality of chemical oxygen demand indexes in the first index sequence and a plurality of turbidity indexes in the second index sequence, wherein the ith row and the jth column in the path normalized matrix are distances between the ith chemical oxygen demand index in the first index sequence and the jth turbidity index in the second index sequence, and i and j are positive integers; determining a normalized path according to a path normalized matrix, wherein the normalized path is a path with the minimum sum of elements of the paths in the path normalized matrix from a first element to a second element, and the first element is an n row and a 1 column element in the path normalized matrix; the second elements are elements in the 1 st row and the m th column in the path-structured matrix, the first index sequence comprises n chemical oxygen demand indexes, the second index sequence comprises m turbidity indexes, n is more than or equal to i, and m is more than or equal to j; and determining the dynamic time warping distance between the first index sequence and the second index sequence according to the warping path.
In a possible implementation, the pollution identification parameter includes a water temperature abnormal value, wherein determining at least one pollution identification parameter according to at least one of index sequences including the first index sequence, the second index sequence and the third index sequence includes: determining at least one of the maximum water temperature index value or the average water temperature index value according to the third index sequence; and determining the abnormal water temperature value according to at least one of the maximum water temperature index value or the average water temperature index value and the predicted water temperature value.
In one possible implementation, the method further includes: fitting a plurality of water temperature indexes measured in a third time period when the predetermined water area is not polluted and hydrological information of the predetermined water area to obtain a relation between the water temperature indexes and the hydrological information; and determining the predicted water temperature value according to the relation between the water temperature index and the hydrological information.
In one possible implementation, the method further includes: determining pollution identification parameters respectively corresponding to the sample time periods according to a first sample index sequence, a second sample index sequence and a third sample index sequence which are obtained in the sample time periods; inputting the pollution identification parameters into a pollution type identification model for processing, and determining a training result of the water pollution type in the sample time period; determining the model loss of a pollution type identification model according to the training result and the marking information of the water pollution type in the sample time period; and training the pollution type recognition model according to the model loss.
According to an aspect of the present disclosure, there is provided a contamination type identification apparatus including: the system comprises a sequence determination module, a first index sequence, a second index sequence and a third index sequence, wherein the first index sequence, the second index sequence and the third index sequence are used for determining a first index sequence, a second index sequence and a third index sequence of a preset water area in a first time period according to water quality information of the preset water area, the first index sequence comprises chemical oxygen demand indexes obtained at a plurality of moments in the first time period, the second index sequence comprises turbidity indexes obtained at a plurality of moments in the first time period, and the third index sequence comprises water temperature indexes obtained at a plurality of moments in the first time period; a parameter determination module for determining at least one pollution identification parameter according to at least one of the indicator sequences including the first indicator sequence, the second indicator sequence and the third indicator sequence; and the type determining module is used for inputting the at least one pollution identification parameter into a pollution type identification model for processing, and determining the water quality pollution type of the predetermined water area.
In one possible implementation, the pollution identification parameter includes a peak time difference between the chemical oxygen demand indicator and the turbidity indicator, and the parameter determination module is further configured to: determining the maximum value of the chemical oxygen demand index in the first index sequence and a first moment when the maximum value of the chemical oxygen demand index is measured; determining a maximum value of the turbidity indicator in the second indicator sequence and a second moment when the maximum value of the turbidity indicator is measured; and determining the peak time difference according to the first time and the second time.
In a possible implementation, the pollution identification parameter includes a first peak height of the first index sequence and a second peak height of the second index sequence, and the parameter determination module is further configured to: determining the first peak height according to the maximum value of the chemical oxygen demand index and a first mean value of the chemical oxygen demand index, wherein the first mean value is a mean value of a plurality of chemical oxygen demand indexes measured in a second time period that the predetermined water area is not polluted; determining the second peak height according to the maximum turbidity indicator and a second average value of the turbidity indicators, wherein the second average value is an average value of a plurality of turbidity indicators measured in a second time period in which the predetermined water area is not polluted.
In a possible implementation, the pollution identification parameter includes a first rate of change of the first index sequence and/or a second rate of change of the second index sequence, and the parameter determination module is further configured to: determining a first peak start time in the first index sequence; determining a first change rate of the first index sequence according to the first time, the first peak starting time and the first peak height; determining a second peak start time in the second index sequence; and determining a second change rate of the second index sequence according to the second time, the second peak starting time and the second peak height.
In a possible implementation manner, the pollution identification parameter includes a dynamic time warping distance of the first index sequence and the second index sequence, the warping distance is used for representing a similarity between the first index sequence and the second index sequence, and the parameter determination module is further used for: determining a path regular matrix of the first index sequence and the second index sequence according to a plurality of chemical oxygen demand indexes in the first index sequence and a plurality of turbidity indexes in the second index sequence, wherein the ith row and the jth column in the path regular matrix are distances between the ith chemical oxygen demand index in the first index sequence and the jth turbidity index in the second index sequence, and i and j are positive integers; determining a normalized path according to a path normalized matrix, wherein the normalized path is a path with the minimum sum of elements of the path in the path from a first element to a second element in the path normalized matrix, and the first element is the element in the n-th row and the 1 st column in the path normalized matrix; the second elements are elements in the 1 st row and the m th column in the path-structured matrix, the first index sequence comprises n chemical oxygen demand indexes, the second index sequence comprises m turbidity indexes, n is more than or equal to i, and m is more than or equal to j; and determining the dynamic time warping distance between the first index sequence and the second index sequence according to the warping path.
In one possible implementation, the pollution identification parameter includes a water temperature anomaly value, and the parameter determination module is further configured to: determining at least one of the maximum water temperature index value or the average water temperature index value according to the third index sequence; and determining the water temperature abnormal value according to at least one of the maximum water temperature index value or the average water temperature index value and the predicted water temperature value.
In one possible implementation, the apparatus further includes: the relation module is used for fitting a plurality of water temperature indexes measured in a third time period in which the predetermined water area is not polluted and the hydrological information of the predetermined water area to obtain the relation between the water temperature indexes and the hydrological information; and the prediction module is used for determining the predicted water temperature value according to the relation between the water temperature index and the hydrologic information.
In one possible implementation, the apparatus further includes: the training module is used for determining pollution identification parameters respectively corresponding to the sample time periods according to a first sample index sequence, a second sample index sequence and a third sample index sequence which are obtained in the sample time periods; inputting the pollution identification parameters into a pollution type identification model for processing, and determining a training result of the water pollution type in the sample time period; determining the model loss of a pollution type identification model according to the training result and the marking information of the water pollution type in the sample time period; and training the pollution type recognition model according to the model loss.
According to an aspect of the present disclosure, there is provided an electronic device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: the above contamination type identification method is performed.
According to an aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described contamination type identification method.
According to the pollution type identification method disclosed by the embodiment of the disclosure, online, in-situ, high-frequency and real-time measurement can be performed through measuring indexes by a quantum dot spectrometer, the first index sequence, the second index sequence and the third index sequence of a preset water area are obtained, the types of data characteristics can be increased according to two or more indexes, various pollution types can be analyzed in real time, and the accuracy and the application range of pollution type identification are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
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 disclosure.
FIG. 1 illustrates a flow chart of a contamination type identification method according to an embodiment of the present disclosure;
FIG. 2 illustrates a schematic diagram of an application of a contamination type identification method according to an embodiment of the present disclosure;
FIG. 3 illustrates a block diagram of a contamination type identification device, according to an embodiment of the present disclosure;
FIG. 4 shows a block diagram of an electronic device in accordance with an embodiment of the disclosure;
fig. 5 shows a block diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of a, B, and C, and may mean including any one or more elements selected from the group consisting of a, B, and C.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the subject matter of the present disclosure.
Fig. 1 shows a flow chart of a contamination type identification method according to an embodiment of the present disclosure, as shown in fig. 1, the method comprising:
in step S11, determining a first indicator sequence, a second indicator sequence and a third indicator sequence of a predetermined water area in a first time period according to water quality information of the predetermined water area, wherein the first indicator sequence comprises cod indicators obtained at a plurality of times in the first time period, the second indicator sequence comprises turbidity indicators obtained at a plurality of times in the first time period, and the third indicator sequence comprises water temperature indicators obtained at a plurality of times in the first time period;
in step S12, determining at least one pollution identification parameter according to at least one of the first indicator sequence, the second indicator sequence and the third indicator sequence;
in step S13, the at least one pollution identification parameter is input into a pollution type identification model for processing, and the water quality pollution type of the predetermined water area is determined.
According to the pollution type identification method disclosed by the embodiment of the disclosure, the first index sequence, the second index sequence and the third index sequence of the preset water area can be measured in real time and at high frequency through the water quality information, and various pollution types can be analyzed in real time according to the two indexes, so that the accuracy and the application range of pollution type identification are improved.
In an example, a micro spectrometer, such as a quantum dot spectrometer, which may include a quantum dot spectrum probe that may measure incident light (e.g., light after transmission or scattering of light through a water sample in a predetermined area) based on physical and optical properties of nanocrystals, to obtain spectral information of the incident light, which may represent water quality information of the water area, may be used as the water quality monitoring device to obtain the water quality index. For example, a quantum dot spectroscopy probe may include a nanocrystal chip made from a plurality of nanocrystals, the nanocrystal chip containing an arrangement of nanocrystals (e.g., an array of nanocrystals), wherein each nanocrystal has a different light absorption or emission characteristic, and wherein different types of semiconductor nanocrystals, for example, may be of different materials, sizes, etc., such that the nanocrystal chip may be responsive to modulation of wavelengths over a wider range of wavelengths to obtain a spectrum tailored to incident light over a wider range of wavelengths.
In one possible implementation, the light transmitted or scattered through the water may be affected by substances in the water (e.g., suspended matter, contaminants, etc.) to obtain specific spectral information. The quantum dot spectrum probe can obtain the spectrum information in real time and determine the water quality index represented by the spectrum information. For example, the spectral information of light in different frequency bands can be obtained by the absorption intensity of the water sample to light with different wavelengths, and the water quality index can be calculated through the spectral information. In an example, the water quality indicator includes a water Chemical Oxygen Demand (COD), turbidity, permanganate index, total suspended matter, biological Oxygen Demand, total organic carbon, sulfate content, chloride content, soluble iron content, soluble manganese content, soluble copper content, soluble 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 content, volatile phenol content, coliform group content, sulfide content, and the like. The water temperature can also be determined from the infrared spectrum in the spectral information. Alternatively, the spectral probe may infer a water quality indicator through a neural network, for example, spectral information may be input to the neural network, and the neural network may infer the concentration of various substances (water quality indicator). The present disclosure is not limited as to the manner in which the water quality indicator is determined. The present disclosure does not limit the working principle of micro spectrometers, such as quantum dot spectroscopic probes.
In an example, the water temperature indicator may be measured by a quantum dot spectroscopic probe, for example, the water temperature indicator may be determined by an infrared spectrum of a predetermined water area detected by the quantum dot spectroscopic probe. Alternatively, the water temperature may be detected by other means, for example, a thermometer, an infrared temperature measuring device, or the like. The water temperature index measuring mode is not limited in the present disclosure. In order to reduce the detection cost, the water temperature index, the chemical oxygen demand index and the turbidity index can be detected simultaneously by the quantum dot spectrum probe, and other temperature measuring equipment does not need to be additionally arranged.
In an example, the quantum dot spectrum probe can determine a water quality index by the absorption characteristics of various substances contained in water to light, for example, the light intensity of light with a specific wavelength can be analyzed by spectrum information, and the concentration of the substance (water quality index) corresponding to the light with the specific wavelength range can be obtained. The on-line, in-situ, high-frequency and real-time measurement can be realized by measuring the index through the quantum dot spectrum probe. When detecting the water quality index, the spectral information of the light passing through the predetermined water area can be detected through the quantum dot spectral probe, and then the water quality index can be quickly calculated based on the spectral information, so as to obtain the water quality index with stronger real-time performance. The quantum dot spectrum probe arranged in the preset water area is used for measuring for multiple times in a certain time period, so that the water quality index sequences of the two water quality indexes of the water area can be obtained, and the water quality indexes in the water quality index sequences are water quality indexes obtained at multiple moments in the same place, so that the water quality index sequences have consistency and comparability, and can be used for observing the change rule of the water quality indexes in a period of time to judge water pollution. For example, the measuring frequency of the quantum dot spectrum probe can reach 3-60 min/time, preferably 5-30 min/time, particularly preferably 8-20 min/time, and most preferably 10-15 min/time, the measuring frequency is far higher than the frequency of bringing the water body back to a laboratory test, and the quantum dot spectrum probe can be arranged at a fixed position of a preset water area, so that the consistency of the water body sample can be ensured. And bring the water body back to the laboratory to carry out the assay, then be difficult to guarantee to take a sample in the same place totally when measuring twice, and because the measuring frequency is lower, the interval time is longer between two measurements, even can guarantee to take a sample in the same place totally when measuring twice, because the mobility of water, the quality of water in this place probably has taken place great change in longer interval time, is difficult to guarantee the uniformity of measuring and the comparability of measuring result.
In one possible implementation, the first time period may be a time period after the water contamination is colored, for example, the chemical oxygen demand indicator and the turbidity indicator are started to be measured when the water contamination event is determined to occur, i.e., the first time period may start to be timed. The cod indicator and the turbidity indicator for the predetermined water area may be obtained multiple times over the time period, and the cod indicators at the multiple times may form a first indicator sequence. The turbidity indicators at a plurality of time instants may form a second indicator sequence. The water temperature indicators at the plurality of time instants may form a third indicator sequence. In an example, the chemical oxygen demand indicator obtained at a time may be expressed as: (t) c ,x 1,c ) Wherein c is any positive integer, t c Denotes the c-th time, x 1,c Representing the measured COD index at time c, the first index sequence may be expressed as { (t) 1 ,x 1,1 ),(t 2 ,x 1,2 ),(t 3 ,x 1,3 ),…,(t n ,x 1,n ) N is more than or equal to c, and n is an integer. In an example, the turbidity indicator obtained at a time can be expressed as: (t) d ,x 2,d ) Wherein d is any positive integer, t d Denotes the d-th time, x 2,d Denotes the turbidity measured at the d-th timeThe second sequence of indices may be expressed as { (t) 1 ,x 2,1 ),(t 2 ,x 2,2 ),(t 3 ,x 2,3 ),…,(t n ,x 2,n ) And n is larger than or equal to d. In an example, the water temperature indicator obtained at a time may be expressed as: (t) e ,x 2,e ) Wherein e is any positive integer, t e Denotes the e-th time, x 3,e Representing the water temperature indicator measured at the e-th moment, the third indicator sequence can be represented as { (t) 1 ,x 3,1 ),(t 2 ,x 3,2 ),(t 3 ,x 3,3 ),…,(t n ,x 3,n ) And n is more than or equal to e. In an example, the water temperature index can also be measured by a temperature measuring instrument such as a thermometer, and the disclosure does not limit the device for determining the water temperature index.
In a possible implementation manner, the first index sequence, the second index sequence and the third index sequence all record water quality indexes at multiple times, and any one of the index sequences can be used for analyzing the type of water pollution, for example, the first index sequence can be used for analyzing organic pollution, the second index sequence can be used for analyzing pollution discharge, and the water temperature index can be used for analyzing thermal pollution.
In one possible implementation, the type of water quality contamination may also be analyzed by a combination of two or more indicators. In an example, each index sequence includes a plurality of data from which peak, rate of change, mean, and the like data features can be obtained. The type of water pollution can be analyzed according to the data characteristics of two or more index sequences. The type of water pollution is analyzed through the data characteristics of two or more index sequences, and the types of the data characteristics can be increased. The data characteristics of a single water quality index are fewer, recognizable water pollution types are fewer, the application range is smaller, and the data characteristics of two or more index sequences are used for analyzing the water pollution types together, so that the problems can be reduced. In examples, the water pollution types include industrial pollution, domestic pollution, agricultural pollution, and the like, the industrial pollution may include heavy metal pollution, chemical pollution, and the like, the domestic pollution includes domestic sewage pollution, domestic garbage pollution, and the like, and the agricultural pollution may include chemical fertilizer pollution, livestock manure pollution, and the like. The present disclosure is not limited as to the type of contamination. In an example, the water pollution type can be analyzed by using the data characteristics of the first index sequence and the second index sequence, the water pollution type can be determined by obtaining the pollution identification parameter by using the data characteristics of the first index sequence and the second index sequence, and the water pollution type can be determined by determining the pollution identification parameter by using the data characteristics of the first index sequence, the second index sequence and the third index sequence. The present disclosure does not limit the index sequence used for determining the contamination identification parameter.
In a possible implementation manner, the pollution identification parameter includes a peak time difference between the chemical oxygen demand indicator and the turbidity indicator, wherein step S12 may include: determining a maximum value of a chemical oxygen demand indicator in the first indicator sequence and a first moment when the maximum value of the chemical oxygen demand indicator is measured; determining a maximum value of the turbidity indicator in the second indicator sequence and a second moment when the maximum value of the turbidity indicator is measured; and determining the peak time difference according to the first time and the second time.
In one possible implementation, the first indicator sequence and the second indicator sequence may each include a plurality of indicators, for example, the first indicator sequence may include a plurality of chemical oxygen demand indicators, and the second indicator sequence may include a plurality of turbidity indicators. A maximum value may be determined among a plurality of chemical oxygen demand indicators in a first sequence and the time at which the maximum value of the chemical oxygen demand indicator is measured, e.g., x 1,a Is the maximum value of the COD index in the first index sequence, t a The moment (first moment) of measuring the maximum value of the index of the chemical oxygen demand is shown, wherein a is a positive integer and is less than or equal to n. In an example, the first series of metrics can be traversed and a maximum value in the first series of metrics can be determined by equation (1) as follows:
x 1,a ＝max(x 1,c ) (1)
in one possible implementation, the maximum value may be determined among a plurality of turbidity indicators of the second indicator sequence, and the time at which the maximum value of turbidity is determined, e.g. x 2,b Is the turbidity in the second index sequenceMaximum value of index, t b The time (second time) when the maximum value of the turbidity index is measured is shown, wherein b is a positive integer and is less than or equal to n. In an example, the first series of metrics can be traversed by the following equation (2) and the maximum value in the first series of metrics is determined:
x 2,b ＝max(x 2,d ) (2)
in one possible implementation, the peak time difference may be determined from the first time and the second time, e.g., t may be b -t a I.e. t b -t a The absolute value of (d) is taken as the peak time difference.
In a possible implementation manner, parameters such as a trough time difference may also be determined, for example, a minimum value may be determined in the first index sequence and the second index sequence, and a time when the minimum value is detected, and the time when the minimum value is detected in the first index sequence and the second index sequence is differed to determine the trough time difference, which is not limited by the present disclosure.
In a possible implementation manner, the pollution identification parameter includes a first peak height of the first index sequence and a second peak height of the second index sequence, wherein step S12 may include: determining the first peak height according to the maximum value of the chemical oxygen demand index and a first mean value of the chemical oxygen demand index, wherein the first mean value is a mean value of a plurality of chemical oxygen demand indexes measured in a second time period that the predetermined water area is not polluted; determining the second peak height according to the maximum value of the turbidity indicator and a second average value of the turbidity indicators, wherein the second average value is an average value of a plurality of turbidity indicators measured during a second period of time when the predetermined water area is not polluted.
In one possible implementation, a mean value of the plurality of chemical oxygen demand indicators may be measured during a second period of time in which the water area is not contaminated. For example, a time period in which water quality is not polluted may be arbitrarily selected as the second time period in daily monitoring of a predetermined water area, for example, two hours before water quality pollution occurs may be selected as the second time period, for example, the first time period is a time period in which water quality pollution is present, and the second time period is a time period in which water quality pollution is not present, for example, a time period before the first time period. The selection manner of the second time period is not limited in the present disclosure.
In one possible implementation, a plurality of chemical oxygen demand indicators may be measured during the second period of time, and the chemical oxygen demand indicator obtained at a certain time during the second period of time may be expressed as: (t) 0 k ,x 0 1,k ) Wherein k is any positive integer, t 0 k Denotes the kth time, x 0 1,k Indicating the chemical oxygen demand index measured at the kth time. The plurality of cod indicators obtained during the second period of time may form a sequence of cod indicators, which may be expressed as: { (t) 0 1 ,x 0 1,1 ),(t 0 2 ,x 0 1,2 ),(t 0 3 ,x 0 1,3 ),…,(t 0 m ,x 0 1,m ) And m is more than or equal to k and is an integer. The plurality of chemical oxygen demand indexes in the sequence represent the chemical oxygen demand indexes under the condition that water pollution does not occur, and the plurality of chemical oxygen demand indexes in the sequence can be subjected to averaging operation to obtain a first average value of the chemical oxygen demand indexes. In an example, the first mean value x may be determined by the following equation (3) 0 1,mean ：
In one possible implementation, the first average is an average value of the cod indexes when no water pollution occurs, and may represent the cod under a state without water pollution. In the case of water pollution, the maximum value of the chemical oxygen demand index may represent a peak value of chemical oxygen demand when the pollution occurs, and the maximum value of the chemical oxygen demand index in the first index sequence may be subtracted from the first mean value of the chemical oxygen demand index to obtain a first peak height of the chemical oxygen demand index, that is, a chemical oxygen demand when the water pollution occursThe maximum amount of change in the quantity index. In an example, the first peak height x may be determined by the following equation (4) 1,p ：
x 1,p ＝x 1,a -x 0 1,mean (4)
In one possible implementation, a plurality of turbidity indicators may be measured during the second period of time, and the turbidity indicators obtained at a certain time during the second period of time may be represented as: (t) 0 l ,x 0 2,l ) Wherein l is any positive integer, t 0 l Denotes the l-th time, x 0 2,l Indicating the measured cod index at the first moment. The plurality of cod indicators obtained during the second period of time may constitute a sequence of cod indicators, which may be expressed, for example, as: { (t) 0 1 ,x 0 2,1 ),(t 0 2 ,x 0 2,2 ),(t 0 3 ,x 0 2,3 ),…,(t 0 m ,x 0 2,m ) And m is more than or equal to l. The plurality of chemical oxygen demand indicators in the sequence represent turbidity indicators under the condition that no water pollution occurs, and the plurality of turbidity indicators in the sequence can be subjected to averaging operation to obtain a second average value of the turbidity indicators. In an example, the first mean value x may be determined by the following equation (5) 0 2,mean ：
In a possible implementation, the second average is an average of turbidity indexes when no water pollution occurs, and may represent turbidity in a state without water pollution. In the case of water pollution, the maximum value of the turbidity may represent a peak value of the turbidity when the pollution occurs, and the maximum value of the turbidity index in the second index sequence may be subtracted from the second average value of the turbidity index to obtain a second peak height of the turbidity index, that is, a maximum variation amount of the turbidity index when the water pollution occurs. In an example, the second peak height x may be determined by the following equation (6) 2,p ：
x 2,p ＝x 2,b -x 0 2,mean (6)
In a possible implementation manner, the pollution identification parameter includes a first rate of change of the first index sequence and/or a second rate of change of the second index sequence, wherein step S12 may include: determining a first peak starting time in the first index sequence; determining a first change rate of the first index sequence according to the first time, the first peak starting time and the first peak height; determining a second peak start time in the second index sequence; and determining a second change rate of the second index sequence according to the second moment, the second peak starting moment and the second peak height.
In a possible implementation manner, the first peak start time may be a start time of a rising edge at which the value of the cod indicator in the first indicator sequence starts to increase and reaches a maximum value, so that the duration of the rising edge is a first difference between the first time and the first peak start time, and the average rate of change of the rising edge may be represented by a ratio between the first peak height and the first difference, and further, the first rate of change of the first indicator sequence may be represented by the average rate of change of the rising edge.
In a possible implementation, similarly, the second peak start time may be a start time of a rising edge at which the value of the turbidity indicator starts to increase and reaches a maximum value in the second indicator sequence, and therefore, the duration of the rising edge is a second difference between the second time and the second peak start time, and the average rate of change of the rising edge may be represented by a ratio between the second peak height and the second difference, and further, the second rate of change of the second indicator sequence may be represented by the average rate of change of the rising edge.
In a possible implementation manner, the similarity between the first index sequence and the second index sequence may also be determined, for example, a plurality of cod indexes in the first index sequence are combined into a vector, and a plurality of turbidity indexes in the second index sequence are combined into a vector, and then, the similarity such as cosine similarity of the two vectors may be determined. However, the chemical oxygen demand index and the turbidity index may not be changed at the same time, and there may be a time difference in the change of the two indexes, for example, a peak of the chemical oxygen demand index appears earlier than a peak of the turbidity index, and the like. The similarity of the two sequences is determined by the similarity of the vectors, and the waveforms of the two sequences are similar, but the similarity is low due to the index change time difference. For example, the indexes of the two sequences each include a peak and a trough, and the time difference between the peak and the trough of the two sequences is similar, that is, the waveforms of the two sequences are similar, but because there is a time difference between the changes of the indexes in the two sequences, for example, the index in the first index sequence changes earlier than the index in the second index sequence, the peaks in the first index sequence and the troughs in the second index sequence may appear at close time, and thus the similarity of the vector formed by the indexes in the two index sequences is low, that is, the accuracy in determining the similarity is low.
In a possible implementation manner, the pollution identification parameter includes a dynamic time warping distance of the first index sequence and the second index sequence, where the warping distance is used to represent a similarity between the first index sequence and the second index sequence, and step S12 may include: determining a path normalized matrix of the first index sequence and the second index sequence according to a plurality of chemical oxygen demand indexes in the first index sequence and a plurality of turbidity indexes in the second index sequence, wherein the ith row and the jth column in the path normalized matrix are distances between the ith chemical oxygen demand index in the first index sequence and the jth turbidity index in the second index sequence, and i and j are positive integers; determining a normalized path according to a path normalized matrix, wherein the normalized path is a path with the minimum sum of elements of the paths in the path normalized matrix from a first element to a second element, and the first element is an n row and a 1 column element in the path normalized matrix; the second elements are elements in the 1 st row and the m th column in the path-structured matrix, the first index sequence comprises n chemical oxygen demand indexes, the second index sequence comprises m turbidity indexes, n is more than or equal to i, and m is more than or equal to j; and determining the dynamic time warping distance between the first index sequence and the second index sequence according to the warping path.
In a possible implementation manner, the similarity of two index sequences can be determined by using the dynamic time warping distance, so that the problem of low similarity calculation precision caused by time difference existing in the change of indexes in the two sequences is reduced. The path-warping matrix may be determined based on the chemical oxygen demand indicator in the first index sequence and the turbidity indicator in the second index sequence. And in the ith row and the jth column in the path-normalized matrix, the element of the jth column is the distance between the ith COD index in the first index sequence and the jth turbidity index in the second index sequence, and i and j are positive integers. In an example, the distance may be an absolute value of a difference between corresponding indexes in the first water quality index sequence and the second water quality index sequence, the 1 st chemical oxygen demand index is 10, the 1 st turbidity index is 15, the 1 st row and the 1 st column in the path-normalized matrix have element values of 5, the 1 st chemical oxygen demand index is 10, and the 2 nd turbidity index is 18, the 1 st row and the 2 nd column in the path-normalized matrix have element values of 8 \ 8230, the path-normalized matrix is not limited by the present disclosure.
In one possible implementation, a regular path from a first element to a second element (i.e., a path with the smallest sum of elements) may be determined in the path planning matrix. In an example, the first element is the nth row, the 1 st column (i.e., the lower left corner element), the second element is the 1 st row, the mth column (i.e., the upper right corner element) of the path-warping matrix, and a path from the first element to the second element needs to traverse each row and each column of the path-warping matrix, i.e., in a warped path, one element per row of the path-warping matrix is included in the warped path, and one element per column of the path-warping matrix is included in the warped path. That is, the path from the element in the n-th row and the 1-th column to the element in the 1-st row and the m-th column goes through the (n-1) -th row and the (8230) \ 1-st row, the 1-st row (the path is monotonically decreasing in the row direction and does not skip any row), and the path also goes through the (1-nd and the 2-nd column) \8230andthe m-th column (the path is monotonically increasing in the column direction and does not skip any column). Since the element in the ith row and the jth column is the distance between the ith cod indicator in the first indicator sequence and the jth turbidity indicator in the second indicator sequence, the regular path traverses each cod indicator in the first indicator sequence and each turbidity indicator in the second indicator sequence. And the regular path is the path with the smallest sum of the elements of the path, namely the path with the smallest sum of the distances between the n chemical oxygen demand indexes and the m turbidity indexes, in the path from the first element to the second element.
In one possible implementation, the similarity between the first index sequence and the second index sequence may be determined according to the path, the regular path is a path having the smallest sum of the distances between the n cod indicators and the m turbidity indicators, and the distance having the smallest sum of the distances between the n cod indicators and the m turbidity indicators may be determined as the dynamic time-regular distance.
In an example, the dynamic time warping distance may be determined by equation (7) below:
D(e,f)＝Dist(e,f)+min{D(e-1,f)，D(e,f-1)，D(e-1,f-1)} (7)
where Dist (e, f) represents the distance between the e-th index in the first index sequence and the f-th index in the second index sequence, i.e., (e, f) elements of the path-warping matrix, and D (e, f) represents the dynamic time-warping distance between the first e-index in the first index sequence and the first f-index in the second index sequence, in an example, e = n and f = m may be set, and iteration is performed through the above equation (7) to obtain the dynamic time-warping distance between the first index sequence and the second index sequence.
In one possible implementation manner, the similarity between the first index sequence and the second index sequence is determined through the dynamic time warping distance. For example, if the dynamic time warping distance is less than or equal to the preset distance threshold, the first index sequence and the second index sequence may be considered to have higher similarity, otherwise, the first index sequence and the second index sequence may be considered to have lower similarity.
In this way, all indexes in the first index sequence and the second index sequence can be traversed through the path-warping matrix and the warping path, the dynamic time warping distance which enables the sum of the distances between the indexes to be minimum is determined, the similarity between the first index sequence and the second index sequence is determined through the dynamic time warping distance, the distances between all indexes in the first index sequence and all indexes in the second index sequence can be referred to, and the problem that the calculation accuracy of the similarity is low due to waveform deviation caused by time difference is solved.
In one possible implementation, the pollution identification parameter includes a water temperature abnormal value, wherein step S12 may include: determining at least one of a maximum value of the water temperature index or an average value of the water temperature index according to the third index sequence; and determining the water temperature abnormal value according to at least one of the maximum water temperature index value or the average water temperature index value and the predicted water temperature value.
In one possible implementation, the third sequence of indicators includes a plurality of water temperature indicators, and when the water quality is polluted, especially when direct sewage is received, the wastewater may cause the temperature of the predetermined water area to increase due to the possible high temperature of the wastewater (e.g., industrial wastewater for cooling equipment, etc.). A representative water temperature index, for example, a maximum water temperature index or an average water temperature index, may be determined from the plurality of water temperature indexes in the third index sequence, and the representative water temperature index may be any water temperature index in the third index sequence.
In one possible implementation, the temperature value of the water quality when the water quality is not polluted, that is, the predicted water temperature value, can be predicted, and then the abnormal water temperature value can be determined according to the difference between the maximum water temperature index value or the average water temperature index value and the predicted water temperature value.
In one possible implementation, the water temperature of a predetermined body of water when uncontaminated may be predicted. For example, the prediction may be made based on hydrological information of the predetermined body of water, which may include, for example, a water temperature measurement of the predetermined body of water under various climatic conditions, a water temperature measurement of the predetermined body of water at each season, a water temperature measurement of the predetermined body of water at each time period of the day, the presence or absence of influx of water into the predetermined body of water, and the like.
In one possible implementation, the method further includes: fitting a plurality of water temperature indexes measured in a third time period when the predetermined water area is not polluted and hydrological information of the predetermined water area to obtain a relation between the water temperature indexes and the hydrological information; and determining the predicted water temperature value according to the relation between the water temperature index and the hydrological information.
In one possible implementation, a plurality of water temperature indicators may be measured during a third time period in which the predetermined body of water is not contaminated. The third period of time may be a longer period of time, for example, the length of the third period of time may include a plurality of years, and the water temperature indicator may be measured in each season of the plurality of years. For another example, the third time period may include multiple weather conditions and the water temperature indicator may be measured under multiple weather conditions. For another example, the third time period may include time periods of the day during which the water temperature indicator may be measured. The present disclosure does not limit the time length of the third period.
In a possible implementation manner, after obtaining the plurality of water temperature indexes in the third time period, the plurality of hydrological information in the third time period may be fitted with the water temperature indexes. In an example, the fitting may be performed according to the following equation (8):
T(t)＝Tt(t)+Ty(t)+Td(t)+Th(t)+Tp(t)+Te(t) (8)
where T (T) is a water temperature term, tt (T) is a climate trend term (e.g., may represent climate conditions), ty (T) is an annual period term (e.g., may represent season), td (T) is a daily period term (e.g., may represent time periods of a day), th (T) is a weather condition term (e.g., may represent weather conditions), tp (T) is a water influx impact (e.g., may represent whether a predetermined water area has a water influx or the flux of influx, etc.), and Te (T) is a residual term. The above formula (8) is only an exemplary relationship between the hydrological information and the water temperature index, the water temperature index may also be influenced by other hydrological information, for example, the altitude of the water level of the predetermined water area, the flow rate of the water flow of the predetermined water area, the air temperature, etc., and the influence of the above hydrological information may also be included in the formula (8), and the disclosure does not limit the type of the hydrological information.
In one possible implementation manner, after the formula (8) is obtained by fitting a plurality of water temperature indexes and corresponding hydrologic information in the third time period, the current hydrologic information may be substituted into the formula (8) to obtain a current water temperature term, that is, a predicted water temperature value. For example, the predicted value of water temperature can be obtained by substituting the formula (8) for the current temperature of 30 ℃ in summer and fine day and the current time of 15 ℃ in afternoon. In an example, the predicted water temperature value may represent a maximum predicted water temperature value of the water area under the above condition, or may represent an average predicted water temperature value of the water area under the above condition, and the present disclosure does not limit the meaning of the predicted water temperature value.
In one possible implementation, the water temperature abnormal value may be obtained by subtracting a representative water temperature indicator (e.g., a maximum value of the water temperature indicator or an average value of the water temperature indicator) from a predicted value of the water temperature. For example, if the predicted water temperature value indicates the maximum predicted water temperature value of the water area under the above conditions, the abnormal water temperature value can be obtained by subtracting the predicted water temperature value from the maximum water temperature index value. For example, if the predicted water temperature value represents an average value of the water temperatures predicted under the above conditions, the abnormal water temperature value can be obtained by subtracting the predicted water temperature value from the average value of the water temperature index.
Alternatively, the water temperature index at the corresponding time may be differentiated from the predicted water temperature value to obtain the water temperature abnormal value, for example, the first time period may include 15 pm, and the water temperature index measured at 15 pm may be differentiated from the predicted water temperature value at 15 pm. In an example, the water temperature abnormal value may be obtained by the following equation (9):
x 3,abnormal ＝x 3,online -x 3,predict (9)
wherein x is 3,abnormal As abnormal value of water temperature, x 3,online Is an index of water temperature, x 3,predice The predicted value of the water temperature is obtained.
By the mode, the hydrologic information and the water temperature index are fitted, the water temperature predicted value is obtained, the water temperature abnormal value can be determined according to the difference between the water temperature index and the water temperature predicted value, and the water temperature abnormal value can be accurately obtained by referring to a plurality of hydrologic information.
In one possible implementation, the type of water quality pollution may be determined by a pollution type identification model. In an example, the pollution type identification model may be a neural network model, a support vector machine model, a naive bayes model, a regression model, etc., and the disclosure does not limit the type of the pollution type identification model.
In a possible implementation manner, the pollution identification parameters such as the peak time difference, the first peak height, the second peak height, the first change rate, the second change rate, the dynamic time warping distance, the water temperature abnormal value, and the like may be input into the pollution type identification model, and the pollution type identification model may process the pollution identification parameters and obtain the pollution type. For example, the type of contamination may be determined by calculating the contamination identification parameter to determine when the contamination occurs and by determining the data characteristic of the contamination identification parameter.
In one possible implementation, the pollution type recognition model may be trained prior to using the pollution type recognition model. The method further comprises the following steps: determining pollution identification parameters respectively corresponding to the sample time periods according to a first sample index sequence, a second sample index sequence and a third sample index sequence which are obtained in the sample time periods; inputting the pollution identification parameters into a pollution type identification model for processing, and determining a training result of the water pollution type in the sample time period; determining the model loss of a pollution type identification model according to the training result and the marking information of the water pollution type in the sample time period; and training the pollution type recognition model according to the model loss.
In one possible implementation, a first sample indicator sequence, a second sample indicator sequence, and a third sample indicator sequence may be obtained over a plurality of sample time periods, respectively, the first sample indicator sequence may include a plurality of chemical oxygen demand indicators, the second sample indicator sequence may include a plurality of turbidity indicators, and the third sample indicator sequence may include a plurality of water temperature indicators. Further, pollution identification parameters such as a peak time difference, a first peak height, a second peak height, a first change rate, a second change rate, a dynamic time warping distance, a water temperature abnormal value and the like of the first sample index sequence and the second sample index sequence can be obtained according to the first sample index sequence, the second sample index sequence and the third sample index sequence.
In a possible implementation manner, pollution identification parameters such as a peak time difference, a first peak height, a second peak height, a first change rate, a second change rate, a dynamic time warping distance, a water temperature abnormal value, and the like may be input into the pollution type identification model to be processed, so as to obtain a training result, that is, a training result (possibly having an error) of the water pollution type output by the pollution type identification model. Further, the type of pollution occurring in the sample time period may be marked, that is, the type of water pollution without error is marked.
In a possible implementation manner, the model loss of the pollution type identification model can be determined according to the training result of the water pollution type output by the pollution type identification model and the labeled error-free water pollution type. For example, the training result of the water quality pollution type and the error-free labeled information output by the pollution type identification model are both vector-form information, and the model loss can be determined by using the characteristic distance (e.g., euclidean distance, etc.) or the similarity (e.g., cosine similarity, etc.) between the training result and the labeled information. Alternatively, the cross-entropy loss can be determined using the class difference between the training results and the annotation information.
In one possible implementation, the pollution type identification model may be trained using model losses, for example, model losses and retrograde propagation may be performed, and parameters of the pollution type identification model may be adjusted using a gradient descent method. The training steps described above may be iteratively performed until a training condition is satisfied. The training condition may include the number of times of training, that is, when the number of iterations reaches a predetermined number, the training is completed, or the training condition may include the magnitude or the convergence property of the model loss, that is, when the model loss is less than or equal to a preset threshold value, or converges to a preset interval, the training is completed.
In one possible implementation, after the training is completed, the pollution type recognition model may be tested, for example, the pollution type recognition model may be used to determine the pollution type in some sample time periods, if the accuracy and the integrity of the pollution type determined by the pollution type recognition model satisfy the use conditions, the pollution type recognition model may be used to determine the actual use of the water pollution type, otherwise, the pollution type recognition model may be continued to be trained until the pollution type recognition model satisfies the use conditions.
According to the pollution type identification method disclosed by the embodiment of the disclosure, online, in-situ, high-frequency and real-time measurement can be performed through measuring indexes by a quantum dot spectrometer, the first index sequence, the second index sequence and the third index sequence of a preset water area are obtained, the types of data characteristics can be increased according to two or more indexes, various pollution types can be analyzed in real time, and the accuracy and the application range of pollution type identification are improved.
Fig. 2 is a schematic diagram illustrating an application of the pollution type identification method according to the embodiment of the present disclosure, and as shown in fig. 2, a quantum dot spectrometer including a quantum dot spectrum probe may be disposed in a predetermined water area, and the quantum dot spectrum probe may be used to measure a chemical oxygen demand index, a turbidity index, and a water temperature index.
In a possible implementation manner, after the water quality is polluted and the hair is dyed, a plurality of chemical oxygen demand indexes can be measured in a first time period through the quantum dot spectrum probe, and a first index sequence is obtained. Meanwhile, a plurality of turbidity indexes can be obtained through the quantum dot spectrum probe, and a second index sequence is obtained. And a plurality of water temperature indexes are obtained through the sub-point spectrum probe or the temperature measuring device, and a third index sequence is obtained.
In one possible implementation, the maximum value of the cod indicator and the time at which the maximum value of the cod indicator is measured may be determined in a first indicator sequence, and the maximum value of the turbidity indicator and the time at which the maximum value of the turbidity indicator is measured may be determined in a second indicator sequence. Further, the two moments can be differenced to obtain the peak time difference.
In one possible implementation, a plurality of chemical oxygen demand indicators may be obtained when the water is not contaminated for an average treatment to obtain a first average value. And obtaining a plurality of turbidity indexes for average processing to obtain a second average value. Further, the maximum value of the cod indicator in the first indicator sequence may be subtracted from the first average value to obtain a first peak height, and the maximum value of the turbidity indicator in the second indicator sequence may be subtracted from the second average value to obtain a second peak height.
In one possible implementation, the duration of the rising edge in the first sequence of indices and thus the first rate of change of the rising edge may be determined, and likewise the duration of the rising edge in the second sequence of indices and thus the second rate of change of the rising edge may be determined.
In one possible implementation, a similarity of the first index sequence to the second index sequence may be determined. In an example, a path-warping matrix of a first index sequence and a second index sequence may be determined, where an element in an ith row and a jth column of the path-warping matrix is a distance between an ith cod indicator in the first index sequence and a jth turbidity indicator in the second index sequence. And the regular path from the element at the lower left corner to the element at the upper right corner in the path regular matrix can be determined, namely, the path with the minimum sum of the elements of the path, namely, the path with the minimum sum of the distances between the n COD indicators and the m turbidity indicators, in the path from the element at the lower left corner to the element at the upper right corner. Further, the sum of the distances of the paths may be determined as a dynamic time warping distance representing a similarity of the first index sequence and the second index sequence.
In a possible implementation manner, a representative water temperature index such as a maximum value or an average value of the water temperature may be obtained in the third index sequence, a predicted water temperature value may be obtained according to the current hydrological information and the formula (8), and a water temperature abnormal value may be determined according to a difference between the predicted water temperature index and the predicted water temperature value.
In one possible implementation, the peak time difference, the first peak height, the second peak height, the first change rate, the second change rate, and/or the dynamic time warping distance and the water temperature abnormal value may be input into the pollution type identification model to determine the type of water pollution, for example, whether the type of water pollution is industrial pollution, agricultural pollution, or domestic pollution may be determined, and the water pollution may be treated in a targeted manner.
It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted.
In addition, the present disclosure also provides a pollution type identification apparatus, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any one of the pollution type identification methods provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the methods section are omitted for brevity.
It will be understood by those of skill in the art that in the above method of the present embodiment, the order of writing the steps does not imply a strict order of execution and does not impose any limitations on the implementation, as the order of execution of the steps should be determined by their function and possibly inherent logic.
Fig. 3 shows a block diagram of a contamination type identification device according to an embodiment of the present disclosure, as shown in fig. 3, the device comprising: the system comprises a sequence determination module, a first index sequence, a second index sequence and a third index sequence, wherein the first index sequence, the second index sequence and the third index sequence are used for determining a first index sequence, a second index sequence and a third index sequence of a preset water area in a first time period according to water quality information of the preset water area, the first index sequence comprises chemical oxygen demand indexes obtained at a plurality of moments in the first time period, the second index sequence comprises turbidity indexes obtained at a plurality of moments in the first time period, and the third index sequence comprises water temperature indexes obtained at a plurality of moments in the first time period; a parameter determination module for determining at least one pollution identification parameter according to at least one of the indicator sequences including the first indicator sequence, the second indicator sequence and the third indicator sequence; and the type determining module is used for inputting the at least one pollution identification parameter into a pollution type identification model for processing, and determining the water quality pollution type of the predetermined water area.
In one possible implementation, the pollution identification parameter includes a peak time difference between the chemical oxygen demand indicator and the turbidity indicator, and the parameter determination module is further configured to: determining a maximum value of a chemical oxygen demand indicator in the first indicator sequence and a first moment when the maximum value of the chemical oxygen demand indicator is measured; determining a maximum value of a turbidity index in the second index sequence and a second moment when the maximum value of the turbidity index is measured; and determining the peak time difference according to the first time and the second time.
In a possible implementation, the pollution identification parameter includes a first peak height of the first index sequence and a second peak height of the second index sequence, and the parameter determination module is further configured to: determining the first peak height according to the maximum value of the chemical oxygen demand index and a first mean value of the chemical oxygen demand index, wherein the first mean value is a mean value of a plurality of chemical oxygen demand indexes measured in a second time period that the predetermined water area is not polluted; determining the second peak height according to the maximum value of the turbidity indicator and a second average value of the turbidity indicators, wherein the second average value is an average value of a plurality of turbidity indicators measured during a second period of time when the predetermined water area is not polluted.
In a possible implementation, the pollution identification parameter includes a first rate of change of the first index sequence and/or a second rate of change of the second index sequence, and the parameter determination module is further configured to: determining a first peak start time in the first index sequence; determining a first change rate of the first index sequence according to the first time, the first peak starting time and the first peak height; determining a second peak start time in the second index sequence; and determining a second change rate of the second index sequence according to the second time, the second peak starting time and the second peak height.
In a possible implementation manner, the pollution identification parameter includes a dynamic time warping distance of the first index sequence and the second index sequence, the warping distance is used for representing a similarity between the first index sequence and the second index sequence, and the parameter determination module is further used for: determining a path normalized matrix of the first index sequence and the second index sequence according to a plurality of chemical oxygen demand indexes in the first index sequence and a plurality of turbidity indexes in the second index sequence, wherein the ith row and the jth column in the path normalized matrix are distances between the ith chemical oxygen demand index in the first index sequence and the jth turbidity index in the second index sequence, and i and j are positive integers; determining a normalized path according to a path normalized matrix, wherein the normalized path is a path with the minimum sum of elements of the paths in the path normalized matrix from a first element to a second element, and the first element is an n row and a 1 column element in the path normalized matrix; the second elements are elements in the 1 st row and the m th column in the path regular matrix, the first index sequence comprises n chemical oxygen demand indexes, the second index sequence comprises m turbidity indexes, n is more than or equal to i, and m is more than or equal to j; and determining the dynamic time warping distance between the first index sequence and the second index sequence according to the warping path.
In one possible implementation, the pollution identification parameter includes a water temperature anomaly value, and the parameter determination module is further configured to: determining at least one of a maximum value of the water temperature index or an average value of the water temperature index according to the third index sequence; and determining the abnormal water temperature value according to at least one of the maximum water temperature index value or the average water temperature index value and the predicted water temperature value.
In one possible implementation, the apparatus further includes: the relation module is used for fitting a plurality of water temperature indexes measured in a third time period in which the preset water area is not polluted and the hydrological information of the preset water area to obtain the relation between the water temperature indexes and the hydrological information; and the prediction module is used for determining the predicted water temperature value according to the relation between the water temperature index and the hydrological information.
In one possible implementation, the apparatus further includes: the training module is used for determining pollution identification parameters respectively corresponding to the sample time periods according to a first sample index sequence, a second sample index sequence and a third sample index sequence which are obtained in the sample time periods; inputting the pollution identification parameters into a pollution type identification model for processing, and determining a training result of the water pollution type in the sample time period; determining the model loss of a pollution type identification model according to the training result and the marking information of the water pollution type in the sample time period; and training the pollution type recognition model according to the model loss.
In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the foregoing method embodiments, and for specific implementation, reference may be made to the description of the foregoing method embodiments, and for brevity, details are not described here again
Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a non-volatile computer readable storage medium.
An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured as the above method.
The electronic device may be provided as a terminal, server, or other form of device.
Fig. 4 is a block diagram illustrating an electronic device 800 in accordance with an example embodiment. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.
Referring to fig. 4, electronic device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communications component 816.
The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices, 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 memory, flash memory, magnetic or optical disks.
The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.
The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a 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 touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a photographing mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as WiFi,2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. 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.
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, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.
Fig. 5 is a block diagram illustrating an electronic device 1900 in accordance with an example embodiment. For example, the electronic device 1900 may be provided as a server. Referring to fig. 5, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system, such as Windows Server, stored in memory 1932 TM ，Mac OS XTM ，Unix TM ,Linux TM ，FreeBSD TM Or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be interpreted as a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or an electrical signal transmitted through an electrical wire.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly 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 user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present 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 the disclosure. It will 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.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures 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 the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or improvements to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (11)
1. A contamination type identification method, characterized in that the method comprises:
determining a first index sequence, a second index sequence and a third index sequence of a predetermined water area in a first time period according to water quality information of the predetermined water area, wherein the first index sequence comprises chemical oxygen demand indexes obtained at a plurality of moments in the first time period, the second index sequence comprises turbidity indexes obtained at a plurality of moments in the first time period, and the third index sequence comprises water temperature indexes obtained at a plurality of moments in the first time period;
determining at least one pollution identification parameter from at least one of the indicator sequences comprising the first indicator sequence, the second indicator sequence and the third indicator sequence;
and inputting the at least one pollution identification parameter into a pollution type identification model for processing, and determining the water quality pollution type of the predetermined water area.
2. The method of claim 1, wherein the pollution identification parameter comprises a peak time difference between the chemical oxygen demand indicator and the turbidity indicator,
wherein determining at least one pollution identification parameter from at least one of the sequences of indicators comprising the first sequence of indicators, the second sequence of indicators, and the third sequence of indicators comprises:
determining a maximum value of a chemical oxygen demand indicator in the first indicator sequence and a first moment when the maximum value of the chemical oxygen demand indicator is measured;
determining a maximum value of the turbidity indicator in the second indicator sequence and a second moment when the maximum value of the turbidity indicator is measured;
and determining the peak time difference according to the first time and the second time.
3. The method according to claim 1 or 2, wherein the contamination identification parameter comprises a first peak height of the first index sequence and a second peak height of the second index sequence,
wherein determining at least one pollution identification parameter from at least one of the sequences of indicators comprising the first sequence of indicators, the second sequence of indicators, and the third sequence of indicators comprises:
determining the first peak height according to the maximum value of the chemical oxygen demand index and a first mean value of the chemical oxygen demand index, wherein the first mean value is a mean value of a plurality of chemical oxygen demand indexes measured in a second time period that the predetermined water area is not polluted;
determining the second peak height according to the maximum turbidity indicator and a second average value of the turbidity indicators, wherein the second average value is an average value of a plurality of turbidity indicators measured in a second time period in which the predetermined water area is not polluted.
4. The method of claim 3, wherein the contamination identification parameter comprises a first rate of change of the first sequence of indicators and/or a second rate of change of the second sequence of indicators,
wherein determining at least one pollution identification parameter from at least one of the sequences of indicators comprising the first sequence of indicators, the second sequence of indicators, and the third sequence of indicators comprises:
determining a first peak start time in the first index sequence;
determining a first change rate of the first index sequence according to the first moment, the first peak starting moment and the first peak height;
determining a second peak start time in the second index sequence;
and determining a second change rate of the second index sequence according to the second time, the second peak starting time and the second peak height.
5. The method of claim 1, wherein the pollution identification parameter comprises a dynamic time warping distance of the first index sequence and the second index sequence, the warping distance being used to represent a similarity of the first index sequence and the second index sequence,
wherein determining at least one pollution identification parameter from at least one of the sequences of indicators comprising the first sequence of indicators, the second sequence of indicators, and the third sequence of indicators comprises:
determining a path normalized matrix of the first index sequence and the second index sequence according to a plurality of chemical oxygen demand indexes in the first index sequence and a plurality of turbidity indexes in the second index sequence, wherein the ith row and the jth column in the path normalized matrix are distances between the ith chemical oxygen demand index in the first index sequence and the jth turbidity index in the second index sequence, and i and j are positive integers;
determining a normalized path according to a path normalized matrix, wherein the normalized path is a path with the minimum sum of elements of the paths in the path normalized matrix from a first element to a second element, and the first element is an n row and a 1 column element in the path normalized matrix; the second elements are elements in the 1 st row and the m th column in the path regular matrix, the first index sequence comprises n chemical oxygen demand indexes, the second index sequence comprises m turbidity indexes, n is more than or equal to i, and m is more than or equal to j;
and determining the dynamic time warping distance between the first index sequence and the second index sequence according to the warping path.
6. The method of claim 1, wherein the pollution identification parameter comprises a water temperature anomaly value,
wherein determining at least one pollution identification parameter from at least one of the sequences of indicators comprising the first sequence of indicators, the second sequence of indicators, and the third sequence of indicators comprises:
determining at least one of a maximum value of the water temperature index or an average value of the water temperature index according to the third index sequence;
and determining the water temperature abnormal value according to at least one of the maximum water temperature index value or the average water temperature index value and the predicted water temperature value.
7. The method of claim 6, further comprising:
fitting a plurality of water temperature indexes measured in a third time period when the predetermined water area is not polluted and hydrological information of the predetermined water area to obtain a relation between the water temperature indexes and the hydrological information;
and determining the predicted water temperature value according to the relation between the water temperature index and the hydrological information.
8. The method of claim 1, further comprising:
determining pollution identification parameters respectively corresponding to the sample time periods according to a first sample index sequence, a second sample index sequence and a third sample index sequence which are obtained in the sample time periods;
inputting the pollution identification parameters into a pollution type identification model for processing, and determining a training result of the water pollution type in the sample time period;
determining the model loss of a pollution type identification model according to the training result and the marking information of the water pollution type in the sample time period;
and training the pollution type recognition model according to the model loss.
9. A contamination type identification device characterized by comprising:
the system comprises a sequence determination module, a first index sequence, a second index sequence and a third index sequence, wherein the first index sequence, the second index sequence and the third index sequence are used for determining a first index sequence, a second index sequence and a third index sequence of a preset water area in a first time period according to water quality information of the preset water area, the first index sequence comprises chemical oxygen demand indexes obtained at a plurality of moments in the first time period, the second index sequence comprises turbidity indexes obtained at a plurality of moments in the first time period, and the third index sequence comprises water temperature indexes obtained at a plurality of moments in the first time period;
a parameter determination module for determining at least one pollution identification parameter according to at least one of the indicator sequences including the first indicator sequence, the second indicator sequence and the third indicator sequence;
and the type determining module is used for inputting the at least one pollution identification parameter into a pollution type identification model for processing, and determining the water quality pollution type of the preset water area.
10. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to: performing the method of any one of claims 1 to 8.
11. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any one of claims 1 to 8.
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| CN202110632748.3A CN115510892A (en) | 2021-06-07 | 2021-06-07 | Pollution type identification method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116216943A (en) * | 2023-02-14 | 2023-06-06 | 江苏博凌环境科技有限公司 | Biochemical ecological integration circulation flow-making platform equipment control system |
| CN116304913A (en) * | 2023-04-07 | 2023-06-23 | 中国长江三峡集团有限公司 | Water quality state monitoring method and device based on Bayesian model and electronic equipment |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116216943A (en) * | 2023-02-14 | 2023-06-06 | 江苏博凌环境科技有限公司 | Biochemical ecological integration circulation flow-making platform equipment control system |
| CN116216943B (en) * | 2023-02-14 | 2023-10-27 | 江苏博凌环境科技有限公司 | Biochemical ecological integration circulation flow-making platform equipment control system |
| CN116304913A (en) * | 2023-04-07 | 2023-06-23 | 中国长江三峡集团有限公司 | Water quality state monitoring method and device based on Bayesian model and electronic equipment |
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