CN113109533A - Water quality online intelligent monitoring, analyzing and processing method based on Internet of things and big data analysis - Google Patents
Water quality online intelligent monitoring, analyzing and processing method based on Internet of things and big data analysis Download PDFInfo
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Abstract
The invention discloses an online intelligent monitoring, analyzing and processing method for water quality of water based on Internet of things and big data analysis, which divides a sea area into depth sub-areas and obtains the biological species, the habitat density and the sea water body parameters existing in each depth sub-area, thereby counting the biological abundance pollution coefficient and the total biological species water body survival pollution coefficient corresponding to each depth sub-area, simultaneously obtaining the basic parameters of sediment at the sea area, further counting the pollution coefficient corresponding to the sediment at the sea area, obtaining the comprehensive pollution coefficient of water quality of the sea area by combining the pollution coefficients, wherein the obtained comprehensive pollution coefficient of water quality of the sea area can comprehensively reflect the comprehensive pollution condition of the sea water quality, overcomes the defect that the existing sea water quality monitoring method has too single monitoring index, and expands the range of the sea water quality monitoring index, the accuracy of the seawater quality monitoring result is improved, and the monitoring level of the seawater quality is greatly improved.
Description
Technical Field
The invention belongs to the technical field of water quality monitoring, and particularly relates to a water quality online intelligent monitoring analysis processing method based on the Internet of things and big data analysis.
Background
With the increasing development and utilization of ocean resources, the problem of seawater pollution is getting more and more serious, and in order to obtain the pollution degree of seawater, the seawater quality monitoring becomes a more and more important subject and is concerned by people in the world. However, most of the existing seawater quality monitoring methods are only used for detecting water body parameters of seawater, such as seawater temperature, chromaticity, pH value and the like, the monitoring indexes are too single, and the influence of the variety abundance of organisms in the ocean and the basic parameter change of seabed sediments on the seawater quality pollution degree is not considered, and the method is specifically embodied as follows:
1. as is known, various organisms live in the sea correspondingly inhabit in a deep sea area which is suitable for growth, when the quality of the seawater in the corresponding deep sea area is polluted, the seawater in the deep sea area can be greatly unsuitable for certain organisms to live, and then the types of the organisms living in the deep sea area can be reduced, so that the abundance of the types of the organisms in the deep sea area is reduced, and conversely, when the abundance of the types of the organisms in the deep sea area is changed, the quality of the seawater in the deep sea area can be polluted. Therefore, it can be seen that the analysis of the abundance of the biological species in the sea is very necessary to obtain the water pollution degree of the seawater;
2. generally, when the seawater is not polluted, the color chromaticity, compactness and other relevant basic parameters of the sediment at the bottom of the sea are not changed too much, but when the seawater is polluted by the pollutant, the pollutant enters the seawater, sinks to the bottom of the sea, and is mixed with the original sediment at the bottom of the sea to form new sediment at the bottom of the sea, and the basic parameters of the new sediment are likely to be changed. Therefore, it can be seen that the analysis of the change of the basic parameters of the marine sediment is also necessary to obtain the water pollution level of the seawater.
Disclosure of Invention
In view of the problems, the invention provides an online intelligent monitoring, analyzing and processing method for water quality of a water body based on the internet of things and big data analysis.
The purpose of the invention can be realized by the following technical scheme:
the online intelligent monitoring, analyzing and processing method for the water quality of the water body based on the Internet of things and big data analysis comprises the following steps;
s1, dividing depth sub-regions of ocean areas: acquiring a boundary profile and a seawater depth corresponding to an ocean area, counting the volume of the ocean area, dividing the acquired seawater depth according to a set depth division interval, further uniformly dividing the whole ocean area into a plurality of depth sub-areas, simultaneously acquiring the seawater level height corresponding to each depth sub-area, further numbering the divided depth sub-areas according to the sequence of the corresponding seawater level heights from low to high, and respectively marking the divided depth sub-areas as 1,2.. j.. h;
s2, counting the biological species of the depth sub-area of the ocean area: respectively counting the number of the biological species corresponding to each depth subregion, numbering the biological species corresponding to each depth subregion according to a predefined sequence, sequentially marking the biological species as 1,2j(xj1,xj2,...,xji,...,xjn)，xji represents the number of ith biological species in the jth depth subregion, and simultaneously divides the counted volume of the ocean region by the number of the divided depth subregions to obtain the volume of each depth subregion, so as to count the inhabitation density corresponding to each biological species of each depth subregion according to the number of each biological species corresponding to each depth subregion and the volume of each depth subregion;
s3, counting the biological abundance coefficients of the depth sub-region: according to the number set of the biological species in the depth subareas and the habitat density corresponding to the biological species in each depth subarea, counting biological abundance coefficients corresponding to each depth subarea;
s4, seawater sampling and water body parameter detection in a depth subregion: respectively sampling seawater in each depth subregion by using a sampling and collecting pipe to obtain seawater samples corresponding to each depth subregion, wherein the volumes of the seawater samples corresponding to each depth subregion are kept the same in the sampling process, and the seawater samples are respectively detected by a water parameter detection terminalCarrying out water body parameter detection on the seawater samples of all depth subregions, and further forming a water body parameter set Q of all depth subregions by using the detected water body parameters of the seawater samples of all depth subregionsw(qw1,qw2,...,qwj,...,qwh)，qwj is a numerical value corresponding to the water body parameter of the jth depth subregion, w is a water body parameter, and w is r1, r2, r3, r4, r5 and r6 which are respectively expressed as temperature, chroma, turbidity, pH value, dissolved oxygen and biochemical oxygen demand;
s5, counting the biological abundance pollution coefficients of the depth subareas: comparing the counted bio-abundance coefficients corresponding to the depth sub-regions with the original bio-abundance coefficients corresponding to the depth sub-regions of the ocean area so as to count the bio-abundance pollution coefficients corresponding to the depth sub-regions;
s6, counting the living pollution coefficients of the biological species water bodies in the depth subareas: according to the biological species corresponding to each depth subregion, screening out the proper water body parameters corresponding to each biological species existing in each depth subregion from the water body parameter database, comparing the water body parameter sets of each depth subregion with the proper water body parameters corresponding to each biological species in the depth subregion respectively to obtain the water body parameter comparison set corresponding to each biological species of each depth subregion
Δqw ji is expressed as the difference value between the water body parameter of the jth depth subregion and the proper water body parameter corresponding to the ith biological species in the depth subregion, and the water body survival pollution coefficients corresponding to the biological species of the depth subregions are counted according to the water body parameter comparison set corresponding to the biological species of the depth subregions, so that the water body survival pollution coefficients corresponding to the biological species of the depth subregions are superposed to obtain the total biological species water body survival pollution coefficient corresponding to the depth subregions;
s7, arranging detection points of a seabed sediment area and detecting basic parameters: extracting the boundary contour line of the corresponding seabed sediment region of the ocean region, and inputting the extracted boundary contour lineEvenly dividing the lines into equal parts to obtain equal division points corresponding to the boundary contour line of the seabed sediment, dividing the area of the seabed sediment into the equal division areas which are connected with each other according to a plane gridding division mode, further arranging a single detection point at the middle position of each equal division area, thereby obtaining a plurality of detection points distributed in the submarine sediment area, numbering the distributed detection points, marking the detection points as 1,2.. k.. m respectively, sampling the seabed sediments of the detection points, wherein the weight of the seabed sediment sampled at each detection point is kept consistent to obtain a seabed sediment sample at each detection point, thereby detecting the color chromaticity, the particle volume and the compactness of the seabed sediment sample, and forming a basic parameter set G of the seabed sediments at the detection points by using the color chromaticity, the particle volume and the compactness of the seabed sediments at the detection points.u(gu1,gu2,...,guk,...,gum)，guj is a numerical value corresponding to a basic parameter of the seabed sediment at the kth detection point, u is a basic parameter, and u is d1, d2 and d3 are respectively expressed as color chromaticity, particle volume and compactness;
s8, carrying out statistics on the pollution coefficient of the sediment at the sea in the ocean area: comparing the basic parameter set of the sediment at the detection point with the standard basic parameters corresponding to the sediment at the sea area to obtain a basic parameter comparison set delta G of the sediment at the detection pointu(Δgu1,Δgu2,...,Δguk,...,Δgum), thus counting the pollution coefficients corresponding to the submarine sediments in the marine area according to the detection point submarine sediment basic parameter comparison set;
s9, carrying out comprehensive water quality pollution coefficient statistics in the ocean area: and counting the comprehensive water quality pollution coefficient of the ocean area according to the biological abundance pollution coefficient corresponding to each depth subregion, the total biological species water body survival pollution coefficient corresponding to each depth subregion and the pollution coefficient corresponding to the ocean area seabed sediment.
In one implementation, the volume of the ocean region is counted in S1, and the specific statistical method thereof performs the following steps:
f1, obtaining the sea level area corresponding to the sea area according to the obtained boundary contour corresponding to the sea area;
and F2, multiplying the sea level area corresponding to the ocean area by the sea depth corresponding to the ocean area to obtain the volume of the ocean area.
In one embodiment, the habitat density of each biological species in each depth sub-region is calculated by the formula
Where rhoji is the habitat density corresponding to the ith biological species in the jth depth sub-region, and v is the volume of the depth sub-region.
In an implementation manner, the calculation formula of the bio-abundance coefficient corresponding to each depth subregion is
In the formula etajExpressed as the bio-abundance coefficient corresponding to the jth depth sub-region.
In a mode that can realize, water parameter measurement terminal includes temperature meter, water colourity apparatus, turbidity apparatus, acidimeter, dissolved oxygen apparatus and biochemical oxygen demand monitor, wherein the temperature that the temperature meter is used for detecting the sea water sample, the colourity that water colourity apparatus is used for detecting the sea water sample, turbidity that turbidity apparatus is used for detecting the sea water sample, the acidimeter is used for detecting the pH valve of sea water sample, dissolved oxygen apparatus is used for detecting the dissolved oxygen volume of sea water sample, biochemical oxygen demand monitor is used for detecting the biochemical oxygen demand of sea water sample.
In an implementation manner, the calculation formula of the bio-abundance pollution coefficient corresponding to each depth subregion is
In the formula sigmajIs expressed as a bio-abundance pollution coefficient, eta 'corresponding to the jth depth subregion'jExpressed as the original bio-abundance coefficient corresponding to the jth depth sub-region.
At one kind can realizeIn the method, the calculation formula of the water body survival pollution coefficient corresponding to each biological species in each depth subregion is
In the formula
Expressed as the water body survival pollution coefficient, delta q, corresponding to the ith biological species in the jth depth sub-arear1 ji、Δqr2 ji、Δqr3 ji、Δqr4 ji、Δqr5 ji、Δqr6 ji is respectively expressed as the difference value between the temperature, the chromaticity, the turbidity, the pH value, the dissolved oxygen amount and the biochemical oxygen demand of the water body in the jth depth sub-area and the temperature, the chromaticity, the turbidity, the pH value, the dissolved oxygen amount and the biochemical oxygen demand of the water body which is suitable for the ith biological species in the depth sub-area,
respectively expressed as the temperature, the chroma, the turbidity, the pH value, the dissolved oxygen and the biochemical oxygen demand of the suitable water body corresponding to the ith biological species in the jth depth sub-area.
In one implementation, the pollution coefficient corresponding to the sediment in the sea area is calculated by the formula
Wherein λ is pollution coefficient corresponding to marine sediment, Δ gd1k、Δgd2k、Δgd2k is respectively expressed as the difference value g 'between the color chromaticity, the particle volume and the compactness of the submarine sediment at the kth detection point of the submarine sediment area and the standard color chromaticity, the particle volume and the compactness of the submarine sediment in the ocean area'd1、g′d2、g′d3Respectively expressed as a sediment of the sea bed in the sea areaThe standard color chromaticity, the particle volume and the compactness of the sediment, a1, a2 and a3 are respectively expressed as pollution weight coefficients corresponding to the color chromaticity, the particle volume and the compactness of the sediment at the bottom of the sea.
In an implementation mode, the calculation formula of the comprehensive water quality pollution coefficient of the ocean area is
In the formula
Expressed as the comprehensive water pollution coefficient, sigma, of the ocean areajExpressed as the bio-abundance pollution coefficient, xi, corresponding to the jth depth subregionjThe coefficient of the total biological species water body survival pollution corresponding to the jth depth subregion is expressed, and the lambda is the pollution coefficient corresponding to the marine area seabed sediment.
The invention has the following beneficial effects:
the invention divides the sea area into depth sub-areas, obtains the biological species in each depth sub-area, the inhabitation density and the sea water body parameter corresponding to each biological species, thereby counting the biological abundance pollution coefficient corresponding to each depth sub-area, simultaneously compares the sea water body parameter of each depth sub-area with the proper water body parameter corresponding to each biological species in the depth sub-area, thereby counting the total biological species water body survival pollution coefficient corresponding to each depth sub-area, simultaneously obtains the basic parameter of the sea sediment in the sea area, further counts the pollution coefficient corresponding to the sea sediment in the sea area, and obtains the sea area water quality comprehensive pollution coefficient by combining the pollution coefficients, the obtained sea area water quality comprehensive pollution coefficient can comprehensively reflect the comprehensive pollution condition of the sea water quality, and overcomes the defect that the prior sea water quality monitoring method has too single monitoring index, the seawater quality monitoring index range is expanded, the accuracy of the seawater quality monitoring result is improved, and the monitoring level of the seawater quality is greatly improved.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a flow chart of the method steps of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the online intelligent monitoring, analyzing and processing method for water quality of a water body based on internet of things and big data analysis comprises the following steps;
s1, dividing depth sub-regions of ocean areas: obtaining a boundary contour and a sea water depth corresponding to an ocean area so as to count the volume of the ocean area, wherein the specific statistical method comprises the following steps:
f1, obtaining the sea level area corresponding to the sea area according to the obtained boundary contour corresponding to the sea area;
f2, multiplying the sea level area corresponding to the sea area by the sea depth corresponding to the sea area to obtain the volume of the sea area;
dividing the obtained seawater depth according to the set depth division intervals, further uniformly dividing the whole ocean area into a plurality of depth sub-areas, simultaneously obtaining the seawater level heights corresponding to the depth sub-areas, further numbering the divided depth sub-areas according to the sequence of the corresponding seawater level heights from low to high, and respectively marking the divided depth sub-areas as 1,2.. j.. h;
in the embodiment, the ocean area is divided into the depth sub-areas, so that a foundation is laid for carrying out the statistics of the biological abundance coefficients of the depth sub-areas;
s2. ocean regional depth subRegional biological species statistics: respectively counting the number of the biological species corresponding to each depth subregion, numbering the biological species corresponding to each depth subregion according to a predefined sequence, sequentially marking the biological species as 1,2j(xj1,xj2,...,xji,...,xjn)，xji is the number of the ith biological species in the jth depth subarea, and the statistical volume of the ocean area is divided by the number of the divided depth subareas to obtain the volume of each depth subarea, so that the inhabitation density corresponding to each biological species of each depth subarea is calculated according to the number of the biological species corresponding to each depth subarea and the volume of each depth subarea
Where rhoji is the inhabitation density corresponding to the ith biological species in the jth depth subregion, and v is the volume of the depth subregion;
the number of the biological species corresponding to each depth sub-region counted in this embodiment includes animals and plants, and the specific statistical method may adopt an image acquisition mode to acquire the biological images existing in each depth sub-region, and extract the appearance features of the organisms, such as appearance color, shape, dynamic features, and the like, from the acquired biological images, and further match the extracted biological appearance features with the appearance features corresponding to each biological species, thereby acquiring the biological species corresponding to each depth sub-region;
s3, counting the biological abundance coefficients of the depth sub-region: according to the number set of the biological species in the depth subareas and the habitat density corresponding to the biological species in each depth subarea, the biological abundance coefficient corresponding to each depth subarea is counted
In the formula etajRepresenting the biological abundance coefficient corresponding to the jth depth subregion;
s4, sampling seawater in a depth subregionDetecting water body parameters: the seawater in each depth subregion is respectively sampled by a sampling and collecting pipe to obtain seawater samples corresponding to each depth subregion, wherein in the sampling process, the volumes of the seawater samples corresponding to each depth subregion are kept the same, and the seawater samples of each depth subregion are respectively detected by a water parameter detection terminal, the water parameter detection terminal comprises a water thermometer, a water chromaticity determinator, a turbidity determinator, a pH meter, a dissolved oxygen determinator and a biochemical oxygen demand monitor, wherein the water thermometer is used for detecting the temperature of the seawater samples, the water chromaticity determinator is used for detecting the chromaticity of the seawater samples, the turbidity determinator is used for detecting the turbidity of the seawater samples, the pH meter is used for detecting the pH value of the seawater samples, the dissolved oxygen determinator is used for detecting the dissolved oxygen content of the seawater samples, and the biochemical oxygen demand monitor is used for detecting the biochemical oxygen demand of the seawater samples, and then the detected water body parameters of the seawater water samples of the sub-regions of each depth form a water body parameter set Q of the sub-regions of each depthw(qw1,qw2,...,qwj,...,qwh)，qwj is a numerical value corresponding to the water body parameter of the jth depth subregion, w is a water body parameter, and w is r1, r2, r3, r4, r5 and r6 which are respectively expressed as temperature, chroma, turbidity, pH value, dissolved oxygen and biochemical oxygen demand;
s5, counting the biological abundance pollution coefficients of the depth subareas: comparing the biological abundance coefficient corresponding to each depth subregion with the original biological abundance coefficient corresponding to each depth subregion in the ocean area to count the biological abundance pollution coefficient corresponding to each depth subregion
In the formula sigmajExpressed as the bio-abundance pollution coefficient, eta, corresponding to the jth depth sub-regionj' representing the original bio-abundance coefficient corresponding to the jth depth sub-region;
s6, counting the living pollution coefficients of the biological species water bodies in the depth subareas: screening out the memory of each depth subregion from the water body parameter database according to the biological species corresponding to each depth subregionThe water parameter sets of the depth sub-areas are respectively compared with the water parameter sets of the depth sub-areas corresponding to the biological species to obtain the water parameter comparison sets of the depth sub-areas corresponding to the biological species
Δqw ji is expressed as the difference value between the water body parameter of the jth depth subregion and the proper water body parameter corresponding to the ith biological species in the depth subregion, and the water body survival pollution coefficient corresponding to each biological species in each depth subregion is counted according to the water body parameter comparison set corresponding to each biological species in each depth subregion
In the formula ofj iExpressed as the water body survival pollution coefficient, delta q, corresponding to the ith biological species in the jth depth sub-arear1 ji、Δqr2 ji、Δqr3 ji、Δqr4 ji、Δqr5 ji、Δqr6 ji is respectively expressed as the difference value between the temperature, the chromaticity, the turbidity, the pH value, the dissolved oxygen amount and the biochemical oxygen demand of the water body in the jth depth sub-area and the temperature, the chromaticity, the turbidity, the pH value, the dissolved oxygen amount and the biochemical oxygen demand of the water body which is suitable for the ith biological species in the depth sub-area,
respectively expressing the temperature, the chromaticity, the turbidity, the pH value, the dissolved oxygen amount and the biochemical oxygen demand of the water body suitable for the ith biological species in the jth depth subregion, and superposing the water body survival pollution coefficients corresponding to the biological species in all the depth subregions to obtain the total biological species water body survival pollution coefficient corresponding to all the depth subregions;
s7, a submarine sediment zoneDomain detection point layout and basic parameter detection: extracting boundary contour lines of a submarine sediment area corresponding to an ocean area, uniformly dividing the extracted boundary contour lines to obtain uniform division points corresponding to the boundary contour lines of the submarine sediment, dividing the submarine sediment area into mutually connected uniform division areas according to a planar gridding division mode by the uniform division points, further arranging a single detection point at the middle position of each uniform division area to obtain a plurality of detection points distributed in the submarine sediment area, numbering the distributed detection points and respectively marking the detection points as 1,2 The particle volume and compactness form a basic parameter set G of the seabed sediments at the detection pointu(gu1,gu2,...,guk,...,gum)，guj is a numerical value corresponding to a basic parameter of the seabed sediment at the kth detection point, u is a basic parameter, and u is d1, d2 and d3 are respectively expressed as color chromaticity, particle volume and compactness;
in the embodiment, the detection points are distributed in the submarine sediment area of the ocean area, so that the submarine sediment sampling of each detection point is performed, and the influence on the reliability of the pollution coefficient corresponding to the submarine sediment of the ocean area in later period statistics due to the fact that the basic data of the submarine sediment obtained by detection is too single caused by the submarine sediment sampling of a single detection point is avoided;
in the embodiment, the water sample volume of the seawater sample corresponding to each depth subregion and the weight of the submarine sediment sampled by each detection point are ensured to be the same in the process of sampling the seawater of each depth subregion and the process of sampling the submarine sediment of each detection point, so that the detection accuracy of seawater water parameters and submarine sediment basic parameters is prevented from being influenced due to different sampled water volumes and different sampled submarine sediment weights;
s8, carrying out statistics on the pollution coefficient of the sediment at the sea in the ocean area: will detectComparing the basic parameter set of the sediment at the measuring point with the standard basic parameters corresponding to the sediment at the sea area to obtain the basic parameter comparison set delta G of the sediment at the measuring pointu(Δgu1,Δgu2,...,Δguk,...,Δgum), thereby counting the pollution coefficients corresponding to the submarine sediments in the ocean area according to the basic parameter comparison set of the submarine sediments in the detection points
Wherein λ is pollution coefficient corresponding to marine sediment, Δ gd1k、Δgd2k、Δgd2k is respectively expressed as the difference value g 'between the color chromaticity, the particle volume and the compactness of the submarine sediment at the kth detection point of the submarine sediment area and the standard color chromaticity, the particle volume and the compactness of the submarine sediment in the ocean area'd1、g′d2、g′d3Respectively representing the standard color chromaticity, the particle volume and the compactness of the marine region seabed sediment, and respectively representing the pollution weight coefficients corresponding to the color chromaticity, the particle volume and the compactness of the seabed sediment by a1, a2 and a 3;
s9, carrying out comprehensive water quality pollution coefficient statistics in the ocean area: according to the biological abundance pollution coefficient corresponding to each depth subregion, the total biological species water body survival pollution coefficient corresponding to each depth subregion and the pollution coefficient corresponding to the marine area seabed sediment, the comprehensive water quality pollution coefficient of the marine area is counted
In the formula
Expressed as the comprehensive water pollution coefficient, sigma, of the ocean areajExpressed as the bio-abundance pollution coefficient, xi, corresponding to the jth depth subregionjThe coefficient of the total biological species water body survival pollution corresponding to the jth depth subregion is expressed, and the lambda is the pollution coefficient corresponding to the marine area seabed sediment.
The pollution coefficient corresponding to the marine area sediment counted by the embodiment integrates the pollution condition of the abundance of marine organisms, the living pollution condition of the biological water body and the pollution condition of the marine area sediment, can comprehensively reflect the comprehensive pollution condition of the seawater quality, overcomes the defect of single monitoring index of the conventional seawater quality monitoring method, expands the range of the seawater quality monitoring index, improves the accuracy of the seawater quality monitoring result, and further greatly improves the monitoring level of the seawater quality.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (9)
1. The online intelligent monitoring, analyzing and processing method for water quality based on the Internet of things and big data analysis is characterized by comprising the following steps: comprises the following steps;
s1, dividing depth sub-regions of ocean areas: acquiring a boundary profile and a seawater depth corresponding to an ocean area, counting the volume of the ocean area, dividing the acquired seawater depth according to a set depth division interval, further uniformly dividing the whole ocean area into a plurality of depth sub-areas, simultaneously acquiring the seawater level height corresponding to each depth sub-area, further numbering the divided depth sub-areas according to the sequence of the corresponding seawater level heights from low to high, and respectively marking the divided depth sub-areas as 1,2.. j.. h;
s2, counting the biological species of the depth sub-area of the ocean area: respectively counting the number of the biological species corresponding to each depth subregion, numbering the biological species corresponding to each depth subregion according to a predefined sequence, sequentially marking the biological species as 1,2j(xj1,xj2,...,xji,...,xjn)，xji is expressed as the ith generation in the jth depth sub-regionThe number of the species is counted, and meanwhile, the counted volume of the ocean area is divided by the number of the divided depth sub-areas to obtain the volume of each depth sub-area, so that the inhabitation density corresponding to each species of the depth sub-areas is counted according to the number of each species of the organisms corresponding to each depth sub-area and the volume of each depth sub-area;
s3, counting the biological abundance coefficients of the depth sub-region: according to the number set of the biological species in the depth subareas and the habitat density corresponding to the biological species in each depth subarea, counting biological abundance coefficients corresponding to each depth subarea;
s4, seawater sampling and water body parameter detection in a depth subregion: respectively sampling seawater in each depth subregion by using a sampling and collecting pipe to obtain a seawater sample corresponding to each depth subregion, wherein in the sampling process, the volume of the seawater sample corresponding to each depth subregion is kept the same, and respectively detecting the water parameters of the seawater sample of each depth subregion through a water parameter detection terminal, so that the detected water parameters of the seawater sample of each depth subregion form a water parameter set Q of each depth subregionw(qw1,qw2,...,qwj,...,qwh)，qwj is a numerical value corresponding to the water body parameter of the jth depth subregion, w is a water body parameter, and w is r1, r2, r3, r4, r5 and r6 which are respectively expressed as temperature, chroma, turbidity, pH value, dissolved oxygen and biochemical oxygen demand;
s5, counting the biological abundance pollution coefficients of the depth subareas: comparing the counted bio-abundance coefficients corresponding to the depth sub-regions with the original bio-abundance coefficients corresponding to the depth sub-regions of the ocean area so as to count the bio-abundance pollution coefficients corresponding to the depth sub-regions;
s6, counting the living pollution coefficients of the biological species water bodies in the depth subareas: according to the biological species corresponding to each depth subregion, screening out the proper water body parameters corresponding to each biological species existing in each depth subregion from the water body parameter database, comparing the water body parameter sets of each depth subregion with the proper water body parameters corresponding to each biological species in the depth subregion respectively,obtaining a water body parameter comparison set corresponding to each biological species in each depth subregion
Δqw ji is expressed as the difference value between the water body parameter of the jth depth subregion and the proper water body parameter corresponding to the ith biological species in the depth subregion, and the water body survival pollution coefficients corresponding to the biological species of the depth subregions are counted according to the water body parameter comparison set corresponding to the biological species of the depth subregions, so that the water body survival pollution coefficients corresponding to the biological species of the depth subregions are superposed to obtain the total biological species water body survival pollution coefficient corresponding to the depth subregions;
s7, arranging detection points of a seabed sediment area and detecting basic parameters: extracting boundary contour lines of a submarine sediment area corresponding to an ocean area, uniformly dividing the extracted boundary contour lines to obtain uniform division points corresponding to the boundary contour lines of the submarine sediment, dividing the submarine sediment area into mutually connected uniform division areas according to a planar gridding division mode by the uniform division points, further arranging a single detection point at the middle position of each uniform division area to obtain a plurality of detection points distributed in the submarine sediment area, numbering the distributed detection points and respectively marking the detection points as 1,2 The particle volume and compactness form a basic parameter set G of the seabed sediments at the detection pointu(gu1,gu2,...,guk,...,gum)，guj is a numerical value corresponding to a basic parameter of the seabed sediment at the kth detection point, u is a basic parameter, and u is d1, d2 and d3 are respectively expressed as color chromaticity, particle volume and compactness;
s8, carrying out statistics on the pollution coefficient of the sediment at the sea in the ocean area: collecting basic parameters of seabed sediment of detection point andcomparing standard basic parameters corresponding to the submarine sediment in the ocean area to obtain a comparison set delta G of the basic parameters of the submarine sediment at the detection pointu(Δgu1,Δgu2,...,Δguk,...,Δgum), thus counting the pollution coefficients corresponding to the submarine sediments in the marine area according to the detection point submarine sediment basic parameter comparison set;
s9, carrying out comprehensive water quality pollution coefficient statistics in the ocean area: and counting the comprehensive water quality pollution coefficient of the ocean area according to the biological abundance pollution coefficient corresponding to each depth subregion, the total biological species water body survival pollution coefficient corresponding to each depth subregion and the pollution coefficient corresponding to the ocean area seabed sediment.
2. The water quality online intelligent monitoring, analyzing and processing method based on the internet of things and big data analysis as claimed in claim 1, wherein: in the step S1, the volume of the ocean region is counted, and the specific statistical method thereof performs the following steps:
f1, obtaining the sea level area corresponding to the sea area according to the obtained boundary contour corresponding to the sea area;
and F2, multiplying the sea level area corresponding to the ocean area by the sea depth corresponding to the ocean area to obtain the volume of the ocean area.
3. The water quality online intelligent monitoring, analyzing and processing method based on the internet of things and big data analysis as claimed in claim 1, wherein: the calculation formula of the inhabitation density corresponding to each biological species in each depth subregion is
Where rhoji is the habitat density corresponding to the ith biological species in the jth depth sub-region, and v is the volume of the depth sub-region.
4. The water quality online intelligent monitoring, analyzing and processing method based on the internet of things and big data analysis as claimed in claim 1, wherein: each depth isThe calculation formula of the biological abundance coefficient corresponding to the subarea is as follows
In the formula etajExpressed as the bio-abundance coefficient corresponding to the jth depth sub-region.
5. The water quality online intelligent monitoring, analyzing and processing method based on the internet of things and big data analysis as claimed in claim 1, wherein: the water parameter detection terminal includes temperature meter, water colourity apparatus, turbidity apparatus, acidimeter, dissolved oxygen apparatus and biochemical oxygen demand monitor, wherein the temperature that the temperature meter is used for detecting the sea water sample, the colourity that water colourity apparatus is used for detecting the sea water sample, turbidity that turbidity apparatus is used for detecting the sea water sample, the acidimeter is used for detecting the pH valve of sea water sample, dissolved oxygen apparatus is used for detecting the dissolved oxygen volume of sea water sample, biochemical oxygen demand monitor is used for detecting the biochemical oxygen demand of sea water sample.
6. The water quality online intelligent monitoring, analyzing and processing method based on the internet of things and big data analysis as claimed in claim 1, wherein: the calculation formula of the biological abundance pollution coefficient corresponding to each depth subregion is
In the formula sigmajIs expressed as a bio-abundance pollution coefficient, eta 'corresponding to the jth depth subregion'jExpressed as the original bio-abundance coefficient corresponding to the jth depth sub-region.
7. The water quality online intelligent monitoring, analyzing and processing method based on the internet of things and big data analysis as claimed in claim 1, wherein: the calculation formula of the water body survival pollution coefficient corresponding to each biological species in each depth subregion is
In the formula ofj iExpressed as the water body survival pollution coefficient, delta q, corresponding to the ith biological species in the jth depth sub-arear1 ji、Δqr2 ji、Δqr3 ji、Δqr4 ji、Δqr5 ji、Δqr6 ji is respectively expressed as the difference value q 'between the temperature, the chromaticity, the turbidity, the pH value, the dissolved oxygen amount and the biochemical oxygen demand of the water body in the jth depth sub-area and the temperature, the chromaticity, the turbidity, the pH value, the dissolved oxygen amount and the biochemical oxygen demand of the suitable water body corresponding to the ith biological species in the depth sub-area'r1 ji、q′r2 ji、q′r3 ji、q′r4 ji、q′r5 ji、q′r6 jAnd i is respectively expressed as the temperature, the chroma, the turbidity, the pH value, the dissolved oxygen and the biochemical oxygen demand of the water body which is suitable for the ith biological species in the jth depth sub-area.
8. The water quality online intelligent monitoring, analyzing and processing method based on the internet of things and big data analysis as claimed in claim 1, wherein: the calculation formula of the pollution coefficient corresponding to the marine area seabed sediment is
Wherein λ is pollution coefficient corresponding to marine sediment, Δ gd1k、Δgd2k、Δgd2k is respectively expressed as the difference value g 'between the color chromaticity, the particle volume and the compactness of the submarine sediment at the kth detection point of the submarine sediment area and the standard color chromaticity, the particle volume and the compactness of the submarine sediment in the ocean area'd1、g′d2、g′d3Respectively representing the standard color chromaticity, the particle volume and the compactness of the marine sediment in the ocean area, and respectively representing the pollution weight coefficients corresponding to the color chromaticity, the particle volume and the compactness of the marine sediment in a1, a2 and a 3.
9. The water quality online intelligent monitoring, analyzing and processing method based on the internet of things and big data analysis as claimed in claim 1, wherein: the calculation formula of the comprehensive water quality pollution coefficient in the ocean area is
In the formula
Expressed as the comprehensive water pollution coefficient, sigma, of the ocean areajExpressed as the bio-abundance pollution coefficient, xi, corresponding to the jth depth subregionjThe coefficient of the total biological species water body survival pollution corresponding to the jth depth subregion is expressed, and the lambda is the pollution coefficient corresponding to the marine area seabed sediment.
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