CN115994692B - Intelligent river and lake management platform based on 5G and big data - Google Patents
Intelligent river and lake management platform based on 5G and big data Download PDFInfo
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 129
- 238000012544 monitoring process Methods 0.000 claims abstract description 44
- 238000007689 inspection Methods 0.000 claims abstract description 18
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- 238000005457 optimization Methods 0.000 claims description 9
- 238000012937 correction Methods 0.000 claims description 8
- 230000036962 time dependent Effects 0.000 claims description 3
- 238000007726 management method Methods 0.000 description 16
- 241000196324 Embryophyta Species 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 6
- 241000894007 species Species 0.000 description 5
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 229910052760 oxygen Inorganic materials 0.000 description 2
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- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
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Abstract
The application relates to the technical field of river and lake management, and particularly discloses an intelligent river and lake management platform based on 5G and big data, wherein the platform comprises: the unmanned aerial vehicle inspection module is used for inspecting the river and the lake according to a preset inspection strategy to acquire surface image information of the river and the lake; the recognition module is used for receiving the surface image information of the river and the lake based on the 5G communication in real time and recognizing the surface image information of the river and the lake to acquire vegetation coverage information of the river and the lake; the water quality monitoring stations are arranged at each monitoring point of the river and the lake and are used for acquiring water quality information of each monitoring point; the background analysis module is used for analyzing according to the current river and lake vegetation coverage information, the historical vegetation coverage information and the current time information and judging vegetation coverage states; and judging the water quality risk of the river and the lake according to the vegetation coverage information and the water quality information of the river and the lake. The platform performs analysis according to the combination of the current river and lake vegetation coverage information and the historical vegetation coverage information, so that the accuracy of analysis and judgment is ensured.
Description
Technical Field
The application relates to the technical field of river and lake management, in particular to an intelligent river and lake management platform based on 5G and big data.
Background
Along with the continuous increase of the protection degree of water resources, the supervision of rivers and lakes is also higher, wherein the management content of the rivers and lakes comprises water conservancy safety management, water quality management, management of river and lake shoreline, sand production management work and the like of the river and lake water areas, and along with the rapid development of the Internet of things technology, the 5G technology and the big data technology, the work content of the management of the rivers and lakes can be used for realizing part of the management work of the rivers and lakes through intelligent equipment collocation calculation and analysis platforms.
The existing intelligent river and lake management mode is mainly characterized in that a plurality of monitoring points are arranged in a water area, water quality parameters are monitored, and then water quality of the water area is judged.
However, the existing intelligent river and lake management system mainly adopts a fixed area monitoring mode to acquire water area images, the effect of the mode on monitoring the state of the river and lake water areas is limited, and meanwhile, the water quality judging method in the prior art does not correspondingly adjust according to the specific state of the water areas, so that the judging result is poor in accuracy.
Disclosure of Invention
The application aims to provide a 5G and big data-based intelligent river and lake management platform, which solves the following technical problems:
how to improve the comprehensiveness and accuracy of monitoring the water quality and the aquatic vegetation state of the river and the lake.
The aim of the application can be achieved by the following technical scheme:
wisdom river lake management platform based on 5G and big data, the platform includes:
the unmanned aerial vehicle inspection module is used for inspecting the river and the lake according to a preset inspection strategy to acquire surface image information of the river and the lake;
the recognition module is used for receiving the surface image information of the river and the lake based on the 5G communication in real time and recognizing the surface image information of the river and the lake to acquire vegetation coverage information of the river and the lake;
the water quality monitoring stations are arranged at each monitoring point of the river and the lake and are used for acquiring water quality information of each monitoring point;
the background analysis module is used for analyzing according to the current river and lake vegetation coverage information, the historical vegetation coverage information and the current time information and judging vegetation coverage states; and judging the water quality risk of the river and the lake according to the vegetation coverage information and the water quality information of the river and the lake.
In one embodiment, the identifying module works as follows:
combining the river and lake surface images in the region according to the region to obtain a region combined image;
identifying a river and lake vegetation region in the region combined image, and collecting vegetation characteristic images according to preset point positions in the river and lake vegetation region;
and identifying the characteristic image based on AI to obtain the point vegetation type.
In one embodiment, the determining the vegetation cover state includes:
s1, judging whether vegetation types belong to an abnormal plant list or not in the vegetation types:
if yes, early warning is carried out, and step S2 is carried out;
if not, performing step S2;
s2, through a formulaCalculating to obtain abnormal value of vegetation cover state of ith area at t moment +.>；
wherein ,a time-varying curve of historical vegetation coverage for the ith area; />The standard vegetation coverage area corresponding to the ith area t moment; />、/>Is a preset weight coefficient, and +.>+/>=1；/>The vegetation species number at time t; />The addition amount of the active vegetation types at the moment t relative to the last inspection time point is calculated; />The vegetation species number is the last inspection time point; />Is a patrol period; />；/>Is an error allowance; />A historical vegetation coverage time-dependent curve for the jth vegetation of the ith area;
abnormal value of vegetation covering stateAnd a preset threshold->And (3) performing comparison:
if it isJudging that the vegetation coverage state of the area is abnormal;
if it isAnd judging that the vegetation coverage state of the area is normal.
In an embodiment, the determining the vegetation cover state further includes:
s3, analyzing the abnormal state of vegetation overall coverage of all areas in the same river and lake.
In one embodiment, the analysis of the abnormal vegetation overall coverage of all areas in the same river and lake is:
by the formulaCalculating to obtain the overall abnormal state value of the river and the lake；
wherein ,the influence coefficient corresponding to the jth vegetation is used; />The corresponding influence value of the river and the lake is obtained; />Is a fixed coefficient; m is the number of areas in the same river and lake, < > and the like>；
The whole abnormal state value of the river and the lakeAnd a preset threshold->And (3) performing comparison:
if it isJudging that the vegetation coverage state of the area is abnormal;
if it isJudging that the vegetation coverage state of the area is normal;
wherein ,for adjusting the coefficient, and->＞1。
In one embodiment, the process of determining the water quality risk of the river and the lake is as follows:
analyzing the water quality parameter items monitored by each monitoring point and the corresponding standard interval respectively, and judging whether the water quality of each monitoring point meets the requirement;
secondly, carrying out secondary judgment on the vegetation-related water quality parameter concentration state according to the vegetation coverage state of each area of the river and the lake;
and thirdly, judging the overall water quality risk of the river and the lake according to the water quality judgment result of each monitoring point.
In one embodiment, the process of step one includes:
respectively comparing the water quality parameter item of the ith area with the corresponding standard interval:
if any water quality parameter item is not in the corresponding standard interval, early warning is carried out;
otherwise, through the formulaCalculating to obtain the water quality state value +.>；
Water quality state valueAnd a preset threshold->And (3) performing comparison:
if it isEarly warning is carried out;
wherein P is the number of items of the monitored water quality parameters,is->Exceeding the value of the water quality parameter item corresponding to the optimization interval, wherein the water quality parameter item corresponds to the optimization interval epsilon and the parameter item corresponds to the standard interval; />And quantizing the correction coefficient for the k-th water quality parameter item numerical value.
In one embodiment, the secondary judgment process is as follows:
by the formulaCalculating the vegetation status value of the i-th area +.>；
Will vegetation status valueAnd a preset threshold->And (3) performing comparison:
if it is≥/>Judging that the water quality has risks;
wherein ,for vegetation related water quality parameter item number, +.>，/>Correspondingly comparing the quantized values for the first parameter item of the j-th vegetation type; />And the first parameter item corresponds to a correction coefficient.
In one embodiment, the process of step three includes:
by the formulaCalculating a river and lake overall water quality risk value H;
h is matched with a preset threshold valueAnd (3) performing comparison:
if it isEarly warning is carried out;
wherein M is the number of areas in the same river and lake,；/>for all areas water quality state value->Average value of (2).
The application has the beneficial effects that:
(1) The application ensures the comprehensiveness of water area state monitoring, and on the premise of analyzing and judging each monitoring point, the application also analyzes according to the combination of the current river and lake vegetation coverage information and the historical vegetation coverage information, thereby ensuring the accuracy of analysis and judgment.
(2) The method carries out secondary judgment on the vegetation-related water quality parameter concentration state according to the vegetation coverage state of each area, and carries out more accurate judgment on the related water quality parameter concentration; and finally, judging the overall water quality risk of the river and the lake according to the water quality judgment results of all the monitoring points, and realizing an accurate and comprehensive monitoring and judging process of the water quality condition.
Drawings
The application is further described below with reference to the accompanying drawings.
FIG. 1 is a schematic block diagram of a smart river/lake management platform based on 5G and big data according to the present application;
FIG. 2 is a flow chart of steps of a vegetation cover status determination process of the present application;
FIG. 3 is a flow chart of the steps of the process for judging the water quality risk of the river and the lake according to the application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, in one embodiment, a 5G and big data based smart river lake management platform is provided, the platform comprising:
the unmanned aerial vehicle inspection module is used for inspecting the river and the lake according to a preset inspection strategy to acquire surface image information of the river and the lake;
the recognition module is used for receiving the surface image information of the river and the lake based on the 5G communication in real time and recognizing the surface image information of the river and the lake to acquire vegetation coverage information of the river and the lake;
the water quality monitoring stations are arranged at each monitoring point of the river and the lake and are used for acquiring water quality information of each monitoring point;
the background analysis module is used for analyzing according to the current river and lake vegetation coverage information, the historical vegetation coverage information and the current time information and judging vegetation coverage states; and judging the water quality risk of the river and the lake according to the vegetation coverage information and the water quality information of the river and the lake.
Through the technical scheme, the method is based on the 5G communication technology, the unmanned aerial vehicle inspection module is utilized to inspect the river and the lake according to the preset inspection strategy to acquire the surface image information of the river and the lake, and meanwhile, the acquired image information is transmitted to the identification module in real time for identification, so that the requirement on the storage capacity of the unmanned aerial vehicle inspection module is low, and the comprehensiveness of water area state monitoring is ensured; meanwhile, on the premise that analysis and judgment are carried out on each monitoring point, the method also carries out analysis according to the combination of the current river and lake vegetation coverage information and the historical vegetation coverage information, and ensures the accuracy of analysis and judgment.
It should be noted that, the detection process of the water quality monitoring station for monitoring the water quality information can be implemented by the existing water quality monitor, and the monitored items include, but are not limited to, dissolved oxygen, COD, etc., which are not limited herein.
As an embodiment of the present application, the process of the identification module works is as follows:
combining the river and lake surface images in the region according to the region to obtain a region combined image;
identifying a river and lake vegetation region in the region combined image, and collecting vegetation characteristic images according to preset point positions in the river and lake vegetation region;
and identifying the characteristic image based on AI to obtain the point vegetation type.
Through the above technical scheme, the embodiment provides a working process of the identification module, firstly, the river and lake surface image combination of the area is carried out on the river and lake according to the area, and the combination mode is realized based on the position information in the image information acquisition process and the image splicing technology in the prior art, and the details are not described herein; then determining a river and lake vegetation region according to the identification and division of the vegetation coverage region and the non-vegetation coverage region in the region combined image, and collecting vegetation characteristic images of each point according to preset points, wherein the preset points are dot matrixes which are uniformly distributed transversely and longitudinally according to a set distance, so that the vegetation characteristic images of each point represent vegetation coverage conditions corresponding to the point, and then based on a big data AI identification technology, the judgment process of different vegetation types at different positions of the river and lake vegetation region can be realized; therefore, through the working process of the identification module in the embodiment, the coverage condition of river and lake vegetation can be accurately judged, and accurate data can be provided for water quality analysis.
It should be noted that, the large data AI identification process for the type of the vegetation image is implemented based on the prior art, which is not limited in this embodiment.
As an embodiment of the present application, referring to fig. 2, the determining process of the vegetation coverage includes:
s1, judging whether vegetation types belong to an abnormal plant list or not in the vegetation types:
if yes, early warning is carried out, and step S2 is carried out;
if not, performing step S2;
s2, through a formulaCalculating to obtain abnormal value of vegetation cover state of ith area at t moment +.>；
wherein ,a time-varying curve of historical vegetation coverage for the ith area; />The standard vegetation coverage area corresponding to the ith area t moment; />、/>Is a preset weight coefficient, and +.>+/>=1；/>The vegetation species number at time t; />The addition amount of the active vegetation types at the moment t relative to the last inspection time point is calculated; />The vegetation species number is the last inspection time point; />Is a patrol period; />；/>Is an error allowance; />A historical vegetation coverage time-dependent curve for the jth vegetation of the ith area;
abnormal value of vegetation covering stateAnd a preset threshold->And (3) performing comparison:
if it isJudging that the vegetation coverage state of the area is abnormal;
if it isJudging the regionThe vegetation coverage is normal.
Through the technical scheme, the specific judging and analyzing process of the vegetation coverage state is provided, firstly, whether the plant types in the abnormal plant directory exist in the identified vegetation types is judged, wherein the abnormal plant directory is selectively set for the conditions of different areas, when the abnormal plant directory exists, the condition that the foreign plant species exist to influence the water area environment is indicated, and therefore early warning is carried out, and the river and lake related departments are reminded of processing the abnormal plant directory; judging the abnormal state of vegetation coverage after the judgment is completed; the abnormal vegetation coverage state is determined based on the change of vegetation coverage area and vegetation type, and is calculated by the formulaCalculated vegetation cover status outlier +.>The effect of judging the coverage abnormality of the vegetation state of the water area can be achieved.
Specific description of the formulas in whichPartially the out-of-tolerance condition of vegetation coverage relative to standard coverage of the ith area, wherein ∈>Planning a vegetation coverage for the area by actually monitoring the vegetation coverage +.>In comparison with this, in combination with the vegetation coverage rate +.>Whether the vegetation coverage is abnormal or not can be dynamically judged in terms of the vegetation coverage area and the growth rate thereof, namely when the vegetation coverage area is slightly low but the growth rate is higher, or when the vegetation coverage area is slightly high but the growth rate is negative,the corresponding numerical value approaches to 1, so that the dynamic intelligent judgment process of the vegetation coverage state is realized; in the formula->Part of the method is to judge whether the vegetation type in the water area is abnormal, wherein, < ->The addition amount of the dynamic vegetation type is the number of the vegetation actively introduced in the area, so that the number is not used for judging the abnormal number of the vegetation, the abnormal number of the vegetation is subtracted, and the judgment of the abnormal condition of the vegetation is realized through the change condition of the vegetation type; in the formula->The accumulated out-of-tolerance condition of different vegetation type coverage areas is partially adopted, and the abnormal condition of the variation of different vegetation can be judged through the partial formula.
The fixed amount in the formula is described, wherein the standard vegetation coverageActive vegetation type additive amount->According to the vegetation coverage area and the type of the water area planned by the corresponding water area, taking the 23 rd divided area of the Zhuxi river as an example, the area of the water area of the divided area is 0.68 hectare, and the coverage area of the planned vegetation accounts for 10.2 percent, namelyHectare; the species are not newly added relative to the previous monitoring time point, i.e. at this time +.>The method comprises the steps of carrying out a first treatment on the surface of the In addition, patrol period->Can be selected according to actual conditions, and is usuallyThe inspection can be carried out according to a cycle of one week; error allowance in the above formulaIs a fixed value, and generates error amount corresponding to the actual monitoring judgment process; preset weight coefficient->、/>According to the setting of the growth rate of the vegetation type in the area, when the growth rate of the vegetation type is faster, the growth rate is properly increased>Decrease->When the vegetation type growth rate is slower, the +.>Improvement of->The balance between the vegetation area and the vegetation growth efficiency is ensured; abnormal value of vegetation cover state +.>Comparison preset threshold +.>Fitting and obtaining according to corresponding data in an ideal state.
As an embodiment of the present application, referring to fig. 2, the determining process of the vegetation coverage further includes:
s3, analyzing the abnormal state of vegetation overall coverage of all areas in the same river and lake.
The method is characterized by analyzing the abnormal state of vegetation overall coverage of all areas in the same river and lake as follows:
by the formulaCalculating to obtain the overall abnormal state value of the river and the lake>；
wherein ,the influence coefficient corresponding to the jth vegetation is used; />The corresponding influence value of the river and the lake is obtained; />Is a fixed coefficient; m is the number of areas in the same river and lake, < > and the like>；
The whole abnormal state value of the river and the lakeAnd a preset threshold->And (3) performing comparison:
if it isJudging that the vegetation coverage state of the area is abnormal;
if it isJudging that the vegetation coverage state of the area is normal;
wherein ,for adjusting the coefficient, and->＞1。
Through the technical scheme, the embodiment alsoJudging the abnormal state of the whole river and lake according to the vegetation coverage state of each area, and determining the abnormal state of the whole river and lake according to the formulaCalculating to obtain the overall abnormal state value of the river and the lake>, wherein ,/>The average value of the abnormal values of the vegetation coverage status of each area,then the influence state of all vegetation kind coverage of the area on the whole river and lake is represented, so by the abnormal state value of the whole river and lake +.>And a preset threshold->The abnormal state of the whole vegetation coverage of the river and the lake can be judged in the comparison process of the river and the lake.
The influence coefficient in the technical scheme is thatAccording to different adaptability of vegetation influence degree on water quality, setting the influence value corresponding to river and lake>Setting correspondingly according to the data such as the water storage capacity, the water area and the like corresponding to the river and the lake; fixed coefficient->According to the data fitting, adjusting the coefficient +.>Is a fixed value and is not described in detail herein.
As an embodiment of the present application, referring to fig. 3, the process for determining the water quality risk of a river and a lake is as follows:
analyzing the water quality parameter items monitored by each monitoring point and the corresponding standard interval respectively, and judging whether the water quality of each monitoring point meets the requirement;
secondly, carrying out secondary judgment on the vegetation-related water quality parameter concentration state according to the vegetation coverage state of each area of the river and the lake;
and thirdly, judging the overall water quality risk of the river and the lake according to the water quality judgment result of each monitoring point.
Through the technical scheme, the embodiment provides a process for judging the water quality risk of the river and the lake, firstly, the water quality parameter items monitored by each monitoring point and the corresponding standard interval are respectively analyzed to judge whether the water quality of each monitoring point meets the requirements, then, the vegetation-related water quality parameter concentration state is secondarily judged according to the vegetation coverage state of each area, and the related water quality parameter concentration is more accurately judged, such as dissolved oxygen and the like; and finally, judging the overall water quality risk of the river and the lake according to the water quality judgment results of all the monitoring points, and realizing an accurate and comprehensive monitoring and judging process of the water quality condition.
As an embodiment of the present application, the process of the first step includes:
respectively comparing the water quality parameter item of the ith area with the corresponding standard interval:
if any water quality parameter item is not in the corresponding standard interval, early warning is carried out;
otherwise, through the formulaCalculating to obtain the water quality state value +.>；
Water quality state valueAnd a preset threshold->And (3) performing comparison:
if it isEarly warning is carried out;
wherein P is the number of items of the monitored water quality parameters,is->Exceeding the value of the water quality parameter item corresponding to the optimization interval, wherein the water quality parameter item corresponds to the optimization interval epsilon and the parameter item corresponds to the standard interval; />And quantizing the correction coefficient for the k-th water quality parameter item numerical value.
Through the technical scheme, the embodiment passes through the formulaCalculating to obtain the water quality state value +.>, wherein ,/>Is->The value exceeding the optimization interval corresponding to the water quality parameter item, therefore, when the kth water quality parameter of the ith area +.>When the value exceeding the corresponding optimization interval is larger, the water quality state value +.>The larger the water quality state, the more the water quality state of the area is monitored and judged by the comparison process.
The number of items of water quality parametersValue quantization correction coefficientThe water quality monitoring parameter items are preset in advance according to the categories of the water quality monitoring parameter items, the water quality parameter items correspond to the optimization intervals epsilon, the parameter items correspond to the standard intervals, and the water quality monitoring parameter items are set according to experience data, and are not further described herein.
As one embodiment of the present application, the secondary judgment process is as follows:
by the formulaCalculating the vegetation status value of the i-th area +.>；
Will vegetation status valueAnd a preset threshold->And (3) performing comparison:
if it is≥/>Judging that the water quality has risks;
wherein ,for vegetation related water quality parameter item number, +.>，/>Correspondingly comparing the quantized values for the first parameter item of the j-th vegetation type; />Corresponding repair for the first item of parameter itemPositive coefficients.
Through the technical scheme, the embodiment passes through the formulaFurther judging the vegetation associated water quality parameters of the ith area, and further judging the vegetation associated water quality parameters by comparing the actual quantity with the accumulated difference value of the vegetation area reference quantity.
It should be noted that, the parameter item corresponds to the comparison quantized valueSetting corresponding correction coefficients according to the influence states of different vegetation types on different water quality monitoring parameter items>The correction coefficient set for the dimensionality removal is set, thus passing the vegetation status valueRealizing a further judging process; in addition, a preset threshold +.>The settings are selected based on empirical data and are not described in further detail herein.
As an embodiment of the present application, the process of the third step includes:
by the formulaCalculating a river and lake overall water quality risk value H;
h is matched with a preset threshold valueAnd (3) performing comparison:
if it isEarly warning is carried out;
wherein M is the number of areas in the same river and lake,；/>for all areas water quality state value->Average value of (2).
Through the technical scheme, the embodiment passes through the formulaCalculating to obtain a river and lake integral water quality risk value H, analyzing and judging according to the average state and the fluctuation state of each area, and adding the river and lake integral water quality risk value H obtained by calculation and a preset threshold value +.>Comparing, presetting threshold->And selecting and setting according to the empirical data, and judging the risk of the whole water quality of the river and the lake.
The foregoing describes one embodiment of the present application in detail, but the description is only a preferred embodiment of the present application and should not be construed as limiting the scope of the application. All equivalent changes and modifications within the scope of the present application are intended to be covered by the present application.
Claims (1)
1. Wisdom river lake management platform based on 5G and big data, its characterized in that, the platform includes:
the unmanned aerial vehicle inspection module is used for inspecting the river and the lake according to a preset inspection strategy to acquire surface image information of the river and the lake;
the recognition module is used for receiving the surface image information of the river and the lake based on the 5G communication in real time and recognizing the surface image information of the river and the lake to acquire vegetation coverage information of the river and the lake;
the water quality monitoring stations are arranged at each monitoring point of the river and the lake and are used for acquiring water quality information of each monitoring point;
the background analysis module is used for analyzing according to the current river and lake vegetation coverage information, the historical vegetation coverage information and the current time information and judging vegetation coverage states; judging the water quality risk of the river and the lake according to the vegetation coverage information and the water quality information of the river and the lake;
the judging process of the vegetation coverage state comprises the following steps:
s1, judging whether vegetation types belong to an abnormal plant list or not in the vegetation types:
if yes, early warning is carried out, and step S2 is carried out;
if not, performing step S2;
s2, through a formulaCalculating to obtain abnormal value of vegetation cover state of ith area at t moment +.>；
wherein ,a time-varying curve of historical vegetation coverage for the ith area; />The standard vegetation coverage area corresponding to the ith area t moment; />、/>Is a preset weight coefficient, and +.>+/>=1；/>The vegetation species number at time t;the addition amount of the active vegetation types at the moment t relative to the last inspection time point is calculated; />The vegetation species number is the last inspection time point; />Is a patrol period; j E [1, ], E>]；/>Is an error allowance; />A historical vegetation coverage time-dependent curve for the jth vegetation of the ith area;
abnormal value of vegetation covering stateAnd a preset threshold->And (3) performing comparison:
if it isJudging that the vegetation coverage state of the area is abnormal;
if it isJudging that the vegetation coverage state of the area is normal;
the working process of the identification module is as follows:
combining the river and lake surface images in the region according to the region to obtain a region combined image;
identifying a river and lake vegetation region in the region combined image, and collecting vegetation characteristic images according to preset point positions in the river and lake vegetation region;
identifying the characteristic image based on AI, and obtaining the vegetation type of the point position;
the judging process of the vegetation coverage state further comprises the following steps:
s3, analyzing the abnormal state of vegetation overall coverage of all areas in the same river and lake;
the method is characterized by analyzing the abnormal state of vegetation overall coverage of all areas in the same river and lake as follows:
by the formulaCalculating to obtain the overall abnormal state value of the river and the lake>；
wherein ,the influence coefficient corresponding to the jth vegetation is used; />The corresponding influence value of the river and the lake is obtained; />Is a fixed coefficient; m is the number of areas in the same river and lake, i is [1, M ]]；
The whole abnormal state value of the river and the lakeAnd a preset threshold->And (3) performing comparison:
if it isJudging that the vegetation coverage state of the area is abnormal;
if it isJudging that the vegetation coverage state of the area is normal;
wherein ,for adjusting the coefficient, and->＞1；
The process for judging the water quality risk of the river and the lake is as follows:
analyzing the water quality parameter items monitored by each monitoring point and the corresponding standard interval respectively, and judging whether the water quality of each monitoring point meets the requirement;
secondly, carrying out secondary judgment on the vegetation-related water quality parameter concentration state according to the vegetation coverage state of each area of the river and the lake;
judging the overall water quality risk of the river and the lake according to the water quality judgment result of each monitoring point;
the first step comprises the following steps:
respectively comparing the water quality parameter item of the ith area with the corresponding standard interval:
if any water quality parameter item is not in the corresponding standard interval, early warning is carried out;
otherwise, through the formulaCalculating to obtain the water quality state value +.>；
Water quality state valueAnd a preset threshold->And (3) performing comparison:
if it isEarly warning is carried out;
wherein P is the number of parameters of the monitored water quality, k is [1, P ]]；Is->Exceeding the value of the water quality parameter item corresponding to the optimization interval, wherein the water quality parameter item corresponds to the optimization interval epsilon and the parameter item corresponds to the standard interval; />Quantizing the correction coefficient for the kth water quality parameter item number;
the secondary judgment process comprises the following steps:
by the formulaCalculating the vegetation status value of the i-th area +.>；
Will vegetation status valueAnd a preset threshold->And (3) performing comparison:
if it is≥/>Judging that the water quality has risks;
wherein ,for vegetation related water quality parameter item number, l E [1, Q]，/>Correspondingly comparing the quantized values for the first parameter item of the j-th vegetation type; />The correction coefficient is corresponding to the first parameter item;
the process of the third step comprises the following steps:
by the formulaCalculating a river and lake overall water quality risk value H;
h is matched with a preset threshold valueAnd (3) performing comparison:
if it isEarly warning is carried out;
wherein M is the number of areas in the same river and lake, i is [1, M ]]；For all areas water quality state value->Average value of (2).
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