CN114529226A - Underground water pollution monitoring method and system based on industrial Internet of things - Google Patents

Underground water pollution monitoring method and system based on industrial Internet of things Download PDF

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CN114529226A
CN114529226A CN202210432201.3A CN202210432201A CN114529226A CN 114529226 A CN114529226 A CN 114529226A CN 202210432201 A CN202210432201 A CN 202210432201A CN 114529226 A CN114529226 A CN 114529226A
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曹轩宇
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Abstract

The invention relates to the technical field of electronic digital data processing, in particular to a method and a system for monitoring underground water pollution based on an industrial Internet of things, wherein the method is a digital calculation method particularly suitable for specific functions, is used for monitoring the underground water pollution condition, and specifically comprises the following steps: according to the method, the flow conditions of the pollutants between the underground sampling wells are analyzed by obtaining the contents of the three pollutants in the flow direction of the underground water flow, the flow conditions are corrected based on the water surface height of the sampling wells, the content of deep-layer particles in the underground water is considered, the weight coefficient is calculated, and then the accurate water pollution evaluation is obtained. The invention fully considers the flowing condition of pollutants flowing along with the underground water, so that the pollution evaluation condition is more accurate. Therefore, the method is a groundwater pollution monitoring method based on industrial information and data processing, and can be used for information processing such as industrial internet of things information perception, industrial internet of things basic environment operation service and industrial data integration service.

Description

Underground water pollution monitoring method and system based on industrial Internet of things
Technical Field
The invention relates to the technical field of electronic digital data processing, in particular to a method and a system for monitoring underground water pollution based on an industrial Internet of things.
Background
With the continuous enhancement of the construction of the industrial park, the industrial park gradually becomes a high-incidence point of industrial wastewater discharge and environmental pollution accidents, and the problem of water quality type water shortage caused by pollution is more and more prominent day by day. Recently, the problem of groundwater pollution has received a great deal of attention, and the degree of pollution is still on the rise. Therefore, the development of groundwater pollution monitoring is particularly important for preventing and treating groundwater pollution.
The existing method for monitoring the pollution of the underground water cannot generally distinguish the underground water as underground dead water or flowing running water, and only monitors the water pollution by sampling and detecting the content of pollutants in the underground water. For the underground flowing running water, the pollutants can flow along with the water flow, and the condition of low pollution evaluation and large pollution evaluation error can occur when sampling is carried out in the underground flowing running water. If the phenomenon of underground fault appears, the data of the sampling well has larger deviation from the actual data, and the evaluation of underground water pollution is also influenced to a certain extent.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a groundwater pollution monitoring method based on an industrial internet of things, and the adopted technical scheme is as follows:
setting sampling wells according to a set interval according to the underground water distribution range, respectively collecting water samples in each sampling well at different moments within a certain time period, and obtaining the contents of a first pollutant, a second pollutant and a third pollutant in each water sample;
obtaining a pollutant vector based on the contents of the first pollutant, the second pollutant and the third pollutant, and calculating the association degree of any two adjacent sampling wells based on the pollutant vectors of the two adjacent sampling wells at each moment; calculating the flow coefficient of each sampling well based on the correlation degree;
respectively acquiring the water surface height of each sampling well at different moments in a period of time, and calculating the flow influence coefficient of each sampling well based on the water surface height and the flow coefficient of two adjacent sampling wells;
classifying each sampling well based on the flow influence coefficient to obtain a plurality of classes;
respectively obtaining the content of particulate matters in each water sample, and calculating the exchange coefficient of each sampling well based on the content of the particulate matters; calculating a pollutant exchange coefficient of the sampling well based on the flow influence coefficient and the exchange coefficient of the sampling well in the same category;
and calculating a category weight coefficient of each category of sampling wells based on the number of the sampling wells in each category and the pollutant exchange coefficient, performing weighted summation on the contents of the first pollutant, the second pollutant and the third pollutant in the water samples in each category by using the category weight coefficient to obtain a water pollution evaluation value, and performing water pollution monitoring according to the water pollution evaluation value and a set threshold value.
Preferably, the method for acquiring the degree of association specifically includes: and obtaining the association degree of the two adjacent sampling wells at each moment according to the similarity of the pollutant vectors at the corresponding moment of the two adjacent sampling wells.
Preferably, the method for obtaining the flow coefficient specifically comprises:
Figure 273354DEST_PATH_IMAGE002
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE003
the flow coefficient of the sample well n is represented,
Figure 558842DEST_PATH_IMAGE004
indicating the degree of association of sample well n with its neighboring sample well at time i,
Figure DEST_PATH_IMAGE005
a sequence representing the degree of association of a sample well N with its neighbouring sample well at each time instant, N representing the number of sample times,
Figure 436799DEST_PATH_IMAGE006
representing a sequence of pairs
Figure 225763DEST_PATH_IMAGE005
The sequence after the median filtering process is performed,
Figure DEST_PATH_IMAGE007
to represent
Figure 921187DEST_PATH_IMAGE005
The variance of the degree of correlation in the sequence,
Figure 428392DEST_PATH_IMAGE008
to represent
Figure 93728DEST_PATH_IMAGE006
Mean of the degree of association in the sequence.
Preferably, the method for obtaining the flow influence coefficient specifically includes: and obtaining the flow influence coefficient of the sampling well according to the product of the average value of the water surface height ratio of the two adjacent sampling wells at the corresponding moment and the flow coefficient.
Preferably, the method for obtaining the exchange coefficient specifically comprises:
Figure 737199DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE011
the exchange coefficient for the sample well n is represented,
Figure 603524DEST_PATH_IMAGE012
representing the content of particulate matter in the water sample of the sampling well N at the ith moment, N representing the number of sampling moments,
Figure DEST_PATH_IMAGE013
representing a sequence of particulate matter contents in the water sample at each instant of the sample well n,
Figure 598025DEST_PATH_IMAGE014
represents the mean value of the content of particulate matter in the sequence,
Figure DEST_PATH_IMAGE015
represents the variance of the particulate matter content in the sequence,
Figure 83364DEST_PATH_IMAGE016
、
Figure DEST_PATH_IMAGE017
representing the maximum and minimum values of the particulate matter content in the sequence, respectively.
Preferably, the method for acquiring the class weight coefficient specifically includes: counting the number of the sampling wells in each category and the value of the pollutant exchange coefficient, constructing a histogram, and obtaining the category weight coefficient corresponding to each category according to the ratio of the area of the rectangle corresponding to each category on the histogram to the total area of the rectangles corresponding to all the categories.
The invention also provides a groundwater pollution monitoring system based on the Internet of things, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the computer program is executed by the processor, the steps of the groundwater pollution monitoring method based on the industrial Internet of things are realized.
The embodiment of the invention at least has the following beneficial effects:
the invention provides a method and a system for monitoring underground water pollution based on an industrial Internet of things, wherein the method is a digital calculation or data processing method particularly suitable for specific functions, and is used for monitoring the underground water pollution condition, and specifically comprises the following steps:
according to the method, the flowing condition of the pollutants between the underground sampling wells is analyzed by acquiring the contents of the three pollutants in the flowing direction of the underground water flow and based on the condition that the contents of the pollutants between two adjacent sampling wells are consistent, the flowing condition of the pollutants between the underground sampling wells is corrected based on the water surface height of the sampling wells, the content of deep-layer particles in underground water is considered, the distribution condition of the underground pollutants is subjected to hierarchical analysis, corresponding weights of the underground pollutants are obtained, and accurate water pollution evaluation is further obtained. The invention fully considers the flowing condition of the flowing pollutants along with the underground water, eliminates the detection error caused by the fault phenomenon of the underground water layer due to the geological structure problem, and determines the weight through different flowing grade categories to ensure that the pollution evaluation condition is more accurate.
Therefore, the method is a groundwater pollution monitoring method based on industrial information and data processing, and can be used for information processing such as industrial internet of things information perception, industrial internet of things basic environment operation service and industrial data integration service.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flow chart of a method for monitoring groundwater pollution based on the industrial internet of things.
Detailed Description
In order to further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description, the structure, the features and the effects of the method and the system for monitoring groundwater pollution based on the internet of things of the industry according to the present invention are provided with the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the method and the system for monitoring groundwater pollution based on the industrial internet of things, which are provided by the invention, in combination with the attached drawings.
Example 1:
referring to fig. 1, a flow chart of steps of a method for monitoring groundwater pollution based on the internet of things for industry according to an embodiment of the present invention is shown, where the method includes the following steps:
firstly, setting sampling wells according to a set interval according to the distribution range of underground water, respectively collecting water samples in each sampling well at different moments within a certain time period, and obtaining the contents of a first pollutant, a second pollutant and a third pollutant in each water sample.
Specifically, the distribution range of the groundwater needs to be judged according to the address data, and the sampling wells are arranged according to the distribution range and the set distance, in this embodiment, the set distance of the sampling wells is 50m, and the implementer can select the sampling wells according to the actual situation.
In the present embodiment, the first contaminant, the second contaminant, and the third contaminant are exemplified by phosphorus, nitrogen, and heavy metal, respectively, and the implementer can select other contaminants according to the actual situation. Among them, most of the three pollutants, phosphorus, nitrogen and heavy metals, come from industrial emissions.
Gather the water sample at different moments in each sampling well respectively, carry out intensive mixing to the water sample of gathering, so that salt in the water sample is abundant and evenly distributed in the water sample, water sample after the stirring utilizes water quality total phosphorus apparatus and water quality total nitrogen apparatus respectively, water quality heavy metal content apparatus surveys, obtain the content of phosphorus, nitrogen and heavy metal, reuse mean value filter to handle the pollutant content data that obtain, the pollutant content after will handling is recorded into respectively, nitrogen content sequence A, phosphorus content sequence B, heavy metal content sequence C, specifically do:
sampling nitrogen content sequence of well n
Figure 846920DEST_PATH_IMAGE018
(wherein,
Figure DEST_PATH_IMAGE019
representing the nitrogen content of the sample well N at the ith moment, N representing the total number of the sample moments), a sequence of phosphorus contents of the sample well N
Figure 884147DEST_PATH_IMAGE020
(wherein,
Figure DEST_PATH_IMAGE021
representing the phosphorus content of the sampling well N at the ith moment, N representing the total number of the sampling moments), and the heavy metal content sequence of the sampling well N
Figure 989112DEST_PATH_IMAGE022
(wherein, the heavy metal content of the sampling well N at the ith moment is shown, and N is the total number of the sampling moments).
In this embodiment, the fixed time period is set to 20 weeks, the corresponding time in the fixed time period is set to one week, and the implementer can adjust the values corresponding to the fixed time period and the time according to the actual situation.
It should be noted that the reason for performing the mean filtering process is that the nitrogen, phosphorus and heavy metal salts of the groundwater cannot be sufficiently and uniformly distributed in the water sample by only manually stirring, so the detection data of the water sample is more reliable by adopting the data processing mode. The mean value filter performs mean value replacement on values in a window by adopting a mean value window along the sequence of data, so that the reliability of the data is higher, individual abnormal detection data are removed, detection errors are avoided, and the obtained sequence of the pollutant content is more reliable.
Secondly, obtaining a pollutant vector based on the contents of the first pollutant, the second pollutant and the third pollutant, and calculating the association degree of any two adjacent sampling wells based on the pollutant vectors of the two adjacent sampling wells at each moment; and calculating a flow coefficient for each sample well based on the degree of correlation.
Specifically, pollutant vectors are constructed according to numerical values of corresponding moments of a nitrogen content sequence A, a phosphorus content sequence B and a heavy metal content sequence C of underground water and are recorded as
Figure DEST_PATH_IMAGE023
. Obtaining the association degree of each moment of the two adjacent sampling wells according to the similarity of the pollutant vectors of the two adjacent sampling wells at the corresponding moment, and expressing the association degree as follows by using a formula:
Figure DEST_PATH_IMAGE025
wherein the content of the first and second substances,
Figure 402776DEST_PATH_IMAGE026
representing the contaminant vector for sample well n at time i,
Figure DEST_PATH_IMAGE027
representing the contaminant vector for sample well n +1 at time i.
Figure 161785DEST_PATH_IMAGE004
Represents the degree of association of the sample well n with the adjacent sample well n +1 at the ith moment, and the value range is [0,1 ]]When the value approaches to 1, the pollution state of the sampling well n and the pollution state of the adjacent sampling well n +1 are more similar or even consistent, when the value approaches to 0, the pollution state difference of the sampling well n and the adjacent sampling well n +1 is larger, the association degree between the two adjacent sampling wells can reflect the driving of water flow, and the pollutants between the two sampling wells at the current moment are consistent.
It should be noted that the correlation degree between two adjacent samples represents the condition whether the pollutant content between two sample wells is consistent, and as time goes on, the condition whether the pollutant content between two adjacent sample wells is consistent may change in the process of flowing the groundwater, so that the flowing condition of the pollutant in the sample wells can be obtained according to the change of the correlation degree between the adjacent sample wells in a period of time, and further the flow coefficient can be obtained.
Calculating the flow coefficient of the sampling well based on the correlation degree, and expressing the flow coefficient by using a formula as follows:
Figure 369912DEST_PATH_IMAGE002
wherein the content of the first and second substances,
Figure 73426DEST_PATH_IMAGE003
representing a sample well nThe flow coefficient of (a) is,
Figure 494043DEST_PATH_IMAGE004
indicating the degree of association of sample well n with its neighboring sample well at time i,
Figure 356826DEST_PATH_IMAGE005
a sequence representing the degree of association of a sample well N with its neighbouring sample well at each time instant, N representing the number of sample times,
Figure 735855DEST_PATH_IMAGE006
representing a sequence of pairs
Figure 661085DEST_PATH_IMAGE005
The sequence after the median filtering process is performed,
Figure 150972DEST_PATH_IMAGE007
to represent
Figure 478049DEST_PATH_IMAGE005
The variance of the degree of correlation in the sequence,
Figure 903345DEST_PATH_IMAGE008
to represent
Figure 315872DEST_PATH_IMAGE006
Mean of the degree of association in the sequence.
Figure 609450DEST_PATH_IMAGE028
The stability degree of the sequence formed by the association degree of the sampling well n and the adjacent sampling well at each moment is characterized, and the threshold value is [0,1 ]]The larger the value is, the pollutants in the sampling well n and the adjacent sampling well n +1 flow along with the flow of the underground water in the flowing process, and the pollution states in the sampling well n and the adjacent sampling well n +1 are consistent, so that the flowability of the sampling well n is high; the smaller the value is, the pollutants in the sampling well n and the adjacent sampling well n +1 in the process that the groundwater flows slowly or even does not flowThen the flow is slower or even no flow, and the contamination state in the sample well n is greatly different from that in the adjacent sample well n +1, the flow of the sample well n is low.
Figure 791032DEST_PATH_IMAGE003
The flow coefficient of the sample well n is expressed, and a larger value indicates a higher mobility of the contaminant in the sample well n with the flow of the groundwater flow, and a smaller value indicates a lower mobility of the contaminant in the sample well n with the flow of the groundwater flow.
Then, respectively obtaining the water surface height of each sampling well at different moments in a period of time, and calculating the flow influence coefficient of each sampling well based on the water surface height and the flow coefficient of two adjacent sampling wells; and classifying the sampling wells based on the flow influence coefficients to obtain a plurality of classes.
It should be noted that, because the water layer structure of the groundwater is complex, a subsurface fault may occur, which may cause completely different results detected by adjacent sampling wells, or because the same water layer is separated by the subsurface fault due to geological changes in the subsurface, and eventually the adjacent water systems are exchanged slowly or not, so that the interference of the factors needs to be eliminated. And whether each sampling well is in the same water layer can be directly expressed through the water surface height in the sampling well, and then the flow coefficient of the sampling well can be corrected by utilizing the water surface height of the sampling well.
Specifically, the water surface height of each sampling well at different moments in a period of time is measured by a water level meter to obtain a water surface height sequence H which is recorded as
Figure DEST_PATH_IMAGE029
(wherein,
Figure 511864DEST_PATH_IMAGE030
representing the water level of the sample well N at time i, and N representing the total number of sample times).
Obtaining the flow influence coefficient of the sampling well according to the product of the average value of the water surface height ratio of two adjacent sampling wells at the corresponding moment and the flow coefficient, and expressing the flow influence coefficient as follows by a formula:
Figure 411686DEST_PATH_IMAGE032
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE033
the flow influence coefficient of the sample well n is shown, the more the value approaches to 1, the more similar the sample well n is to the adjacent sample well n +1, the higher the probability of being in the same flow direction,
Figure 135054DEST_PATH_IMAGE003
the flow coefficient of the sample well n is represented,
Figure 436722DEST_PATH_IMAGE030
、
Figure 62876DEST_PATH_IMAGE034
respectively representing the water surface height of the sampling well N and the adjacent sampling well N +1 at the ith moment, wherein N represents the total number of sampling moments.
And classifying each sampling well by using a k-means algorithm according to the flow influence coefficient, wherein the k value is set to be 5, so that five classification grades are obtained, and an implementer can select other clustering algorithms for classification according to specific conditions and can also select a proper k value for classification processing. Wherein the first category is marked as a first-level pollutant flow category, the second category is marked as a second-level pollutant flow category, the third category is marked as a third-level pollutant flow category, the fourth category is marked as a fourth-level pollutant flow category, and the fifth category is marked as a fifth-level pollutant flow category. The above are five classes, and the data are classified from large to small, that is, the data in the first class is the largest, and the data in the fifth class is the smallest.
Further, respectively obtaining the content of the particulate matters in each water sample, and calculating the exchange coefficient of each sampling well based on the content of the particulate matters; and calculating the pollutant exchange coefficient of the sampling well based on the flow influence coefficient and the exchange coefficient of the sampling well in the same category.
Specifically, in this embodiment, the sand content of the groundwater is taken as an example, and the sand content of the groundwater can directly express the flow direction of the deep particulate matter of the groundwater. For each sampling well, respectively collecting 1m at different moments in a period of time3And recording the corresponding sand content S.
And calculating the exchange coefficient of each sampling well based on the sand content, wherein the exchange coefficient is expressed by a formula as follows:
Figure DEST_PATH_IMAGE035
wherein the content of the first and second substances,
Figure 856520DEST_PATH_IMAGE011
the exchange coefficient for the sample well n is represented,
Figure 226321DEST_PATH_IMAGE012
representing the content of particulate matter in the water sample of the sampling well N at the ith moment, N representing the number of sampling moments,
Figure 382496DEST_PATH_IMAGE013
representing a sequence of particulate matter contents in the water sample at each instant of the sample well n,
Figure 445130DEST_PATH_IMAGE014
represents the mean value of the content of particulate matter in the sequence,
Figure 53966DEST_PATH_IMAGE015
represents the variance of the particulate matter content in the sequence,
Figure 493037DEST_PATH_IMAGE016
、
Figure 893932DEST_PATH_IMAGE017
which represent the maximum and minimum values of the particulate matter content in the series, which in this example is the sand content.
By calculating the fluctuation of sand content, the sampling can be expressedThe flow direction of deep pollutants in water in the sample well,
Figure 861888DEST_PATH_IMAGE011
the smaller the value representing the exchange coefficient of the sample well n, the worse the mobility of the deep contaminants in the water in the sample well n, and the larger the value representing the better the mobility of the deep contaminants in the water in the sample well n.
It should be noted that, because the flow influence coefficients based on the sampling wells classify the sampling wells to obtain different grade categories, the analysis is only performed on the upper-layer pollutants dissolved in water in the underground water, and the exchange of deep-layer pollutant precipitates is not considered, so that the pollutant precipitates need to be added for analysis. Specifically, the contaminant exchange coefficient of a sample well is calculated as a product of the flow impact coefficient and the exchange coefficient for each sample well.
And finally, calculating the class weight coefficients of the sampling wells in each class based on the number of the sampling wells in each class and the pollutant exchange coefficients, carrying out weighted summation on the contents of the first pollutant, the second pollutant and the third pollutant in the water samples in each class by using the class weight coefficients to obtain a water pollution evaluation value, and carrying out water pollution monitoring according to the water pollution evaluation value and a set threshold value.
It should be noted that, with the flow of groundwater, the pollutants on the water flow line are carried away and leave the groundwater layer, so the pollution condition of groundwater needs to be evaluated according to the classification of the pollutant exchange coefficient.
Taking the first category as an example, since the data in the first category is the maximum, it can be known that the flow of pollutants between a sampling well in the first category and an adjacent sampling well is large along with the flow of groundwater, the first category can be considered as a low-pollution retention area, and so on, and the data in the fifth category is the minimum, it can be known that the flow of pollutants between a sampling well in the fifth category and an adjacent sampling well is small along with the flow of groundwater, the fifth category is considered as a high-pollution retention area, and meanwhile, the corresponding pollution retention area evaluation is obtained according to other categories.
Specifically, counting the number of sampling wells in each category and the value of the pollutant exchange coefficient, constructing a histogram, and obtaining a category weight coefficient corresponding to each category according to the ratio of the area of the rectangle corresponding to each category to the total area of the rectangles corresponding to all the categories on the histogram. The horizontal axis of the histogram is the value of the pollutant exchange coefficient of the sampling wells in each category, and the vertical axis is the number of the sampling wells in each category.
Calculating a water pollution evaluation value based on the class weight coefficient, and expressing the water pollution evaluation value as follows by using a formula:
Figure DEST_PATH_IMAGE037
wherein the content of the first and second substances,
Figure 489178DEST_PATH_IMAGE038
indicating the water pollution evaluation value at the i-th time,
Figure DEST_PATH_IMAGE039
represents the class weight coefficient corresponding to the mth class, M represents the number of classes, M takes the value of 5 in this embodiment,
Figure 466361DEST_PATH_IMAGE040
、
Figure DEST_PATH_IMAGE041
、
Figure 3653DEST_PATH_IMAGE042
respectively represents the maximum values of the contents of nitrogen, phosphorus and heavy metals of the sampling well in the mth category at the ith moment, and a, b and c respectively represent standard values of the contents of nitrogen, phosphorus and heavy metals obtained according to the condition of local underground water.
And when the water pollution evaluation value is greater than the set threshold, the pollution condition of the obtained underground water is serious, and corresponding pollution treatment operation is required.
Example 2:
the embodiment provides an industrial internet of things-based underground water pollution monitoring system, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of the industrial internet of things-based underground water pollution monitoring method are realized. Since the embodiment 1 has already described a detailed description of a groundwater pollution monitoring method based on the industrial internet of things, it is not described herein too much.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (7)

1. A groundwater pollution monitoring method based on an industrial Internet of things is characterized by comprising the following steps:
setting sampling wells according to a set interval according to the underground water distribution range, respectively collecting water samples in each sampling well at different moments within a certain time period, and obtaining the contents of a first pollutant, a second pollutant and a third pollutant in each water sample;
obtaining a pollutant vector based on the contents of the first pollutant, the second pollutant and the third pollutant, and calculating the association degree of any two adjacent sampling wells based on the pollutant vectors of the two adjacent sampling wells at each moment; calculating the flow coefficient of each sampling well based on the correlation degree;
respectively acquiring the water surface height of each sampling well at different moments in a period of time, and calculating the flow influence coefficient of each sampling well based on the water surface height and the flow coefficient of two adjacent sampling wells;
classifying each sampling well based on the flow influence coefficient to obtain a plurality of classes;
respectively obtaining the content of particulate matters in each water sample, and calculating the exchange coefficient of each sampling well based on the content of the particulate matters; calculating a pollutant exchange coefficient of the sampling well based on the flow influence coefficient and the exchange coefficient of the sampling well in the same category;
and calculating the class weight coefficient of each class of sampling well based on the number of the sampling wells in each class and the pollutant exchange coefficient, performing weighted summation on the contents of the first pollutant, the second pollutant and the third pollutant in the water sample in each class by using the class weight coefficient to obtain a water pollution evaluation value, and performing water pollution monitoring according to the water pollution evaluation value and a set threshold value.
2. The method for monitoring groundwater pollution based on the industrial internet of things according to claim 1, wherein the method for acquiring the correlation degree specifically comprises the following steps: and obtaining the association degree of the two adjacent sampling wells at each moment according to the similarity of the pollutant vectors at the corresponding moment of the two adjacent sampling wells.
3. The method for monitoring groundwater pollution based on the industrial Internet of things according to claim 1, wherein the method for acquiring the flow coefficient specifically comprises the following steps:
Figure DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE002
the flow coefficient of the sample well n is represented,
Figure 726925DEST_PATH_IMAGE003
indicating the degree of association of sample well n with its neighboring sample well at time i,
Figure DEST_PATH_IMAGE004
a sequence representing the degree of association of a sample well N with its neighbouring sample well at each time instant, N representing the number of sample times,
Figure 175224DEST_PATH_IMAGE005
representing a sequence of pairs
Figure 95907DEST_PATH_IMAGE004
The sequence after the median filtering process is performed,
Figure DEST_PATH_IMAGE006
to represent
Figure 756695DEST_PATH_IMAGE004
The variance of the degree of correlation in the sequence,
Figure 836646DEST_PATH_IMAGE007
to represent
Figure 659109DEST_PATH_IMAGE005
Mean of the degree of association in the sequence.
4. The underground water pollution monitoring method based on the industrial internet of things according to claim 1, wherein the flow influence coefficient obtaining method specifically comprises the following steps:
and obtaining the flow influence coefficient of the sampling well according to the product of the average value of the water surface height ratio of the two adjacent sampling wells at the corresponding moment and the flow coefficient.
5. The method for monitoring groundwater pollution based on the industrial internet of things according to claim 1, wherein the method for obtaining the exchange coefficient specifically comprises the following steps:
Figure DEST_PATH_IMAGE008
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE009
the exchange coefficient for the sample well n is represented,
Figure DEST_PATH_IMAGE010
representing the content of particulate matter in the water sample of the sampling well N at the ith moment, N representing the number of sampling moments,
Figure 346049DEST_PATH_IMAGE011
representing a sequence of particulate matter contents in the water sample at each instant of the sample well n,
Figure DEST_PATH_IMAGE012
represents the mean value of the content of particulate matter in the sequence,
Figure 76108DEST_PATH_IMAGE013
represents the variance of the particulate matter content in the sequence,
Figure DEST_PATH_IMAGE014
、
Figure 885932DEST_PATH_IMAGE015
representing the maximum and minimum values of the particulate matter content in the sequence, respectively.
6. The method for monitoring groundwater pollution based on the industrial internet of things according to claim 1, wherein the method for obtaining the category weight coefficient specifically comprises the following steps:
counting the number of the sampling wells in each category and the value of the pollutant exchange coefficient, constructing a histogram, and obtaining the category weight coefficient corresponding to each category according to the ratio of the area of the rectangle corresponding to each category on the histogram to the total area of the rectangles corresponding to all the categories.
7. An industrial internet of things-based underground pollution monitoring system, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program is used for realizing the steps of the industrial internet of things-based underground water pollution monitoring method according to any one of claims 1 to 6 when being executed by the processor.
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