CN113127808A - Water environment quality evaluation method based on reservoir dam data - Google Patents
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Abstract
The invention discloses a water environment quality evaluation method based on reservoir dam data, which comprises the steps of obtaining reservoir dam database data, and calculating river connectivity according to the obtained data; carrying out regression analysis on the river connectivity, the net primary productivity of vegetation and the water body biological diversity index, and establishing a multi-term 1ogit regression model between the river connectivity and the river connectivity; and inputting the river length and the dam quantity into the established regression model, and calculating the water ecological basin quality of the river area. The method calculates the connectivity of the river according to the database data of the global reservoir dam, then performs regression analysis on the connectivity of the river and two indexes of Net Primary Productivity (NPP) and biodiversity of vegetation to determine a regression model between the connectivity of the river and the two indexes, and finally can finish efficient, accurate and automatic diagnosis of the ecological function of the water basin only through the length and the quantity of the reservoir dam, thereby facilitating the use of data by people.
Description
Technical Field
The invention relates to the field of power grids, in particular to a water environment quality evaluation method based on reservoir dam data.
Background
Along with the continuous increase of community energy internet quantity and the continuous improvement of wind-electricity permeability, the frequent energy interaction of community energy internet and distribution network can produce a great deal of adverse effect to the safe and stable operation of distribution network. The large access of wind power can affect the active and reactive power flow distribution, the electric energy quality, the system network loss, the steady-state voltage distribution and the like of the power distribution network. The fluctuation and intermittence of wind power resources determine the fluctuation and intermittence of the output of wind power. When the access proportion of the wind power is small, the influence on the power distribution network is not obvious; and the wind power permeability that promotes gradually makes wind power access more obvious to the influence that the distribution network caused, may even make the system lose stability under the extreme condition.
Under the above conditions, if a traditional 'spontaneous self-use, surplus internet' energy transaction mechanism is still adopted between the community energy internet and the power distribution network, frequent energy interaction between the community energy internet and the power distribution network can bring adverse effects to normal operation of the power distribution network, and the community energy internet and the power distribution network are difficult to adapt to the trend of rapid development of future clean energy and power grids. The method has the advantages that wind power consumption is promoted, the wind abandon rate is reduced, unnecessary energy interaction between the community energy internet and the power distribution network is reduced, and adverse effects caused by large-scale wind power access to the safe and stable operation of the power distribution network are reduced, so that the method has important significance for effectively promoting the development of clean energy.
Disclosure of Invention
In order to solve the problems, the invention provides a water environment quality evaluation method based on reservoir dam data, which is realized by the following technical scheme:
a water environment quality evaluation method based on reservoir dam data comprises the following steps:
s1, acquiring reservoir dam database data, and calculating river connectivity according to the acquired data;
s2, carrying out regression analysis on the river connectivity, the net primary productivity of the vegetation and the water body biological diversity index, and establishing a logit regression model between the river connectivity and the river connectivity;
and S3, inputting the river length and the dam quantity into the established regression model, and calculating the water ecological basin quality of the river area.
The beneficial effect of above-mentioned scheme does: the method for detecting the environmental quality of the ecological watershed of the water area can be finished only by using river connectivity, is relatively quick in use, accurate in calculation result and provides great convenience for people to use water conservancy information.
The river connectivity calculation method comprises the following steps:
s101, acquiring all river lengths and dam quantities in an area by using remote sensing images;
and S102, dividing the river length by the dam number to obtain a connectivity value of the river.
The beneficial effects of the above further scheme are: in the past, the environmental quality of the ecological watershed of the water area needs to be collected on the spot to acquire relevant information; in addition, the conventional method for constructing the multi-index evaluation has certain subjectivity, the deep analysis of the mutual relation among indexes is lacked, the constructed indexes have no universality and cannot be widely applied, and a method which is convenient to acquire data, simple in index construction and strong in universality is urgently needed to be constructed. The method collects actual data to perform model fitting, can predict the environmental quality of the ecological watershed of the water area only by depending on one index of river connectivity according to the fitted model, greatly reduces the investment of manpower and material resources, improves the accuracy and reliability of river health evaluation, provides scientific theoretical basis and technical support for subsequent government planning and treatment schemes, and has high practical application value.
Further, the method for acquiring the net primary productivity of the vegetation comprises the following steps:
and calculating the net primary productivity of the vegetation through the vegetation light and the effective radiation and the actual light utilization rate by using a process-based remote sensing model.
Further, the net primary productivity of vegetation is calculated in a specific manner as follows:
NPP(x,t)＝APAR(x,y)*ε(x,y)；
APAR(x,y)＝SOL(x,t)×FPAR(x，t)×0.5；
wherein SOL (x, t) represents the total solar radiation amount of the pixel x in t months, and FPAR (x, t) represents the absorption proportion of the vegetation layer to the incident photosynthetically active radiation; the constant 0.5 represents the proportion of the solar effective radiation utilized by the vegetation to the total solar radiation; epsilon (x, y) is determined by the maximum light energy utilization rate and the temperature and moisture stress factor.
The beneficial effects of the above further scheme are:
in the past, the environmental quality of the ecological watershed of the water area needs to be collected on the spot to acquire relevant information; in addition, the conventional method for constructing the multi-index evaluation has certain subjectivity, the deep analysis of the mutual relation among indexes is lacked, the constructed indexes have no universality and cannot be widely applied, and a method which is convenient to acquire data, simple in index construction and strong in universality is urgently needed to be constructed. The method collects actual data to perform model fitting, can predict the environmental quality of the ecological watershed of the water area only by depending on one index of river connectivity according to the fitted model, greatly reduces the investment of manpower and material resources, improves the accuracy and reliability of river health evaluation, provides scientific theoretical basis and technical support for subsequent government planning and treatment schemes, and has high practical application value.
Further, the method for acquiring the water body biological diversity index comprises the following steps:
s111, acquiring river bottom image data;
and S112, counting the types and the number of the fishes in the river, and calculating the biodiversity index according to the types and the number of the fishes.
Further, the specific calculation mode of the water body biological diversity index is as follows:
wherein S is the number of species, N is the total number of all species, and D is the biodiversity index.
The beneficial effects of the above further scheme are:
in the past, the environmental quality of the ecological watershed of the water area needs to be collected on the spot to acquire relevant information; in addition, the conventional method for constructing the multi-index evaluation has certain subjectivity, the deep analysis of the mutual relation among indexes is lacked, the constructed indexes have no universality and cannot be widely applied, and a method which is convenient to acquire data, simple in index construction and strong in universality is urgently needed to be constructed. The method collects actual data to perform model fitting, can predict the environmental quality of the ecological watershed of the water area only by depending on one index of river connectivity according to the fitted model, greatly reduces the investment of manpower and material resources, improves the accuracy and reliability of river health evaluation, provides scientific theoretical basis and technical support for subsequent government planning and treatment schemes, and has high practical application value.
Further, the method for establishing the river connectivity and the logic regression model between the river connectivity and the river connectivity comprises the following steps:
s31, acquiring river connectivity, NPP productivity of riparian vegetation and water biological diversity indexes as independent variables;
s32, dividing the obtained river connectivity, the NPP productivity of the riparian vegetation and the water body biological diversity index into 5 grade intervals according to the maximum value of the sample, wherein each grade interval is used as a dependent variable of a regression model;
s33, using a logit regression with one category in the dependent variables as a reference, and correspondingly generating non-redundant logit variable models with other categories;
s34, introducing the reference quantity into the generated non-redundant logic variable model, and generating a logic model value corresponding to the category reference quantity;
s35, repeating the steps S3-S4, and calculating the logit model value of the other category reference quantity;
and S36, calculating the probability corresponding to each category, wherein the category corresponding to the maximum probability value is the water environment quality grade of the river according to the probability value of each category.
Further, the non-redundant logit model is represented as:
wherein g is a logit model; pi-probability of an argument being of class i; pJ-probability of dependent variable being class J (i ≠ J); xp-divide argument, p-dummy variable index; b isi0-an intercept term; beta is ajp-a regression coefficient; p is an integer.
Further, the probability calculation formula is expressed as:
wherein, P (Y)i) Is the probability that the argument x belongs to class i; gi being the non-redundant nature of the argument x belonging to class iThe residual logit model value, gn is the non-redundant logit model value where the argument x belongs to n classes; in the formula, the values of i and n are (1, 2, 3, 4 and 5).
Further, the sum of the probabilities corresponding to each category is 1.
The beneficial effects of the above further scheme are:
in the past, the environmental quality of the ecological watershed of the water area needs to be collected on the spot to acquire relevant information; in addition, the conventional method for constructing the multi-index evaluation has certain subjectivity, the deep analysis of the mutual relation among indexes is lacked, the constructed indexes have no universality and cannot be widely applied, and a method which is convenient to acquire data, simple in index construction and strong in universality is urgently needed to be constructed. The method collects actual data to perform model fitting, can predict the environmental quality of the ecological watershed of the water area only by depending on one index of river connectivity according to the fitted model, greatly reduces the investment of manpower and material resources, improves the accuracy and reliability of river health evaluation, provides scientific theoretical basis and technical support for subsequent government planning and treatment schemes, and has high practical application value.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
fig. 1 is a schematic flow chart of a water environment quality evaluation method based on reservoir dam data.
Detailed Description
Hereinafter, the term "comprising" or "may include" used in various embodiments of the present invention indicates the presence of the invented function, operation or element, and does not limit the addition of one or more functions, operations or elements. Furthermore, as used in various embodiments of the present invention, the terms "comprises," "comprising," "includes," "including," "has," "having" and their derivatives are intended to mean that the specified features, numbers, steps, operations, elements, components, or combinations of the foregoing, are only meant to indicate that a particular feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as first excluding the existence of, or adding to the possibility of, one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.
In various embodiments of the invention, the expression "or" at least one of a or/and B "includes any or all combinations of the words listed simultaneously. For example, the expression "a or B" or "at least one of a or/and B" may include a, may include B, or may include both a and B.
Expressions (such as "first", "second", and the like) used in various embodiments of the present invention may modify various constituent elements in various embodiments, but may not limit the respective constituent elements. For example, the above description does not limit the order and/or importance of the elements described. The foregoing description is for the purpose of distinguishing one element from another. For example, the first user device and the second user device indicate different user devices, although both are user devices. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of various embodiments of the present invention.
It should be noted that: if it is described that one constituent element is "connected" to another constituent element, the first constituent element may be directly connected to the second constituent element, and a third constituent element may be "connected" between the first constituent element and the second constituent element. In contrast, when one constituent element is "directly connected" to another constituent element, it is understood that there is no third constituent element between the first constituent element and the second constituent element.
The terminology used in the various embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the various embodiments of the invention. As used herein, the singular forms are intended to include the plural forms as well, unless the context clearly indicates otherwise. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present invention belong. The terms (such as those defined in commonly used dictionaries) should be interpreted as having a meaning that is consistent with their contextual meaning in the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein in various embodiments of the present invention.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1
A water environment quality evaluation method based on reservoir dam data is shown in figure 1 and comprises the following steps:
s1, acquiring reservoir dam database data, and calculating river connectivity according to the acquired data;
the river connectivity index defined in this embodiment refers to: the length of the river is divided by the number of dams on the river. And carrying out regression analysis modeling on river connectivity, Net Primary Productivity (NPP) and biodiversity indexes according to a large amount of sample data, and finally outputting a regression analysis model. Finally, NPP and biodiversity indexes are not needed, and the water environment quality evaluation of the water body basin can be immediately calculated only by inputting the river length and the dam number into a regression model. The river connectivity calculation needs to obtain a high-resolution satellite remote sensing image of a research area, analysis is carried out according to the remote sensing image, the lengths of all rivers and the number of dams in the research area are obtained, and a numerical value obtained by dividing the lengths of the rivers by the number of the dams is the connectivity numerical value of the river.
S2, carrying out regression analysis on the river connectivity, the net primary productivity of the vegetation and the water body biological diversity index, and establishing a logit regression model between the river connectivity and the river connectivity;
net Primary Productivity (NPP): the method is a basis for researching the flow of substances and energy in an ecological system, the spatial distribution of the NPP is closely related to factors such as the growth condition of vegetation in an area, terrain, climate and the like, and the NPP calculation mode used in the embodiment is as follows: the method is characterized in that the CASA model is used for estimating the NPP, the CASA model is a process-based remote sensing model, the model considers that the principle is that the NPP is estimated through vegetation photosynthetic effective radiation and actual light energy utilization rate, and the calculation mode is shown as the following formula:
NPP(x，t)＝APAR(x，y)*ε(x，y)；
APAR(x，y)＝SOL(x，t)×FPAR(x，t)×0.5；
wherein SOL (x, t) represents the total solar radiation amount of the pixel x in t months, FPAR (x, t) represents the absorption proportion of the vegetation layer to incident photosynthetically active radiation, and a constant 0.5 represents the proportion of the solar active radiation utilized by the vegetation to the total solar radiation. Epsilon (x, y) is determined by the maximum light energy utilization rate and the temperature and moisture stress factor.
Calculating the biodiversity index of the fish: a camera shooting instrument is arranged at the bottom of the river to shoot for 24 hours, the shot images are analyzed in the field, the types and the number of fishes are counted, and the biodiversity index is calculated, wherein the calculation formula is shown as the following formula:
wherein S is the number of species, N is the total number of all species, and D is the biodiversity index.
And S3, inputting the river length and the dam quantity into the established regression model, and calculating the water ecological basin quality of the river area.
In this embodiment, a part of rivers selected in the global range can be counted according to the remote sensing image to obtain the river length and the dam number, the NPP and the biodiversity index are estimated by using the CASA model, and the logic regression modeling is performed by using the following method:
1) independent variable selection: three indexes of river connectivity, river bank vegetation NPP productivity and water fish biological diversity are provided;
2) the dependent variables are respectively: the quality of the river ecological basin is 5 grades including excellent, good, medium, qualified and poor, and the quality of the river ecological basin is evaluated. The grading is based on a sample interval selected by an experiment, the maximum value of the number of fishes in the sample is equally divided into 5 sections, and the 5 sections are divided into 5 sections of excellent, good, medium, qualified and poor; equally dividing the maximum value of NPP in the river sample into 5 sections, and dividing the sections into 5 sections of excellent, good, medium, qualified and poor sections; if the fish quantity index and the NPP index grade of a section of river sample are not consistent, taking the lowest grade of the two indexes as the grade of the final river;
3) when using the logit regression, the dependent variable Y has n values, one of which is taken as the reference class (baseline category),
4) other classes are compared with it to generate n-1 non-redundant logic variable models. In this embodiment, if y is 5 as a reference category, then the non-redundant logic model for y is i is shown as the following formula:
wherein g is a logit model; pi-probability of an argument being of class i; pJ-probability of dependent variable being class J (i ≠ J); xp-divide argument, p-dummy variable index; b isi0-an intercept term; beta is ajp-a regression coefficient; p is an integer.
From the above equation, 5 categories of logit model values can be calculated: g1, g2, g3, g4 and g 5.
And the probability calculation formula for 5 classes is as follows:
wherein P (Y)i) Is the probability that the argument x belongs to class i; gi is the non-redundant logic model value where the argument x belongs to class i, and gn is the non-redundant logic model value where the argument x belongs to class n.
The sum of the probabilities of the 5 classes is 1, that is:
P(1)+P(2)+P(3)+P(4)+P(5)＝1；
according to the logit regression model, the water ecological watershed texture of the river area can be calculated through the model according to the input river length and the dam number.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A water environment quality evaluation method based on reservoir dam data is characterized by comprising the following steps:
s1, acquiring reservoir dam database data, and calculating river connectivity according to the acquired data;
s2, carrying out regression analysis on the river connectivity, the net primary productivity of the vegetation and the water body biological diversity index, and establishing a logit regression model between the river connectivity and the river connectivity;
and S3, inputting the river length and the dam quantity into the established regression model, and calculating the water ecological basin quality of the river area.
2. The method for evaluating the water environment quality based on the dam data of the reservoir as claimed in claim 1, wherein the method for calculating the connectivity of the river comprises:
s101, acquiring all river lengths and dam quantities in an area by using remote sensing images;
and S102, dividing the river length by the dam number to obtain a connectivity value of the river.
3. The method for evaluating the quality of the water environment based on the dam data of the reservoir as claimed in claim 2, wherein the method for acquiring the net primary productivity of the vegetation comprises the following steps:
and calculating the net primary productivity of the vegetation through the vegetation light and the effective radiation and the actual light utilization rate by using a process-based remote sensing model.
4. The method for evaluating the quality of the water environment based on the dam data of the reservoir as claimed in claim 3, wherein the specific calculation mode of the net primary productivity of vegetation is represented as follows:
NPP(x,t)＝APAR(x,y)*ε(x，y)；
APAR(x,y)＝SOL(x，t)×FPAR(x，t)×0.5；
wherein SOL (x, t) represents the total solar radiation amount of the pixel x in t months, and FPAR (x, t) represents the absorption proportion of the vegetation layer to the incident photosynthetically active radiation; the constant 0.5 represents the proportion of the solar effective radiation utilized by the vegetation to the total solar radiation; the expression epsilon (x, y) is determined by the maximum light energy utilization rate and the temperature and moisture stress factor.
5. The method for evaluating the water environment quality based on the reservoir dam data according to claim 4, wherein the method for acquiring the water biodiversity index comprises the following steps:
s111, acquiring river bottom image data;
and S112, counting the types and the number of the fishes in the river, and calculating the biodiversity index according to the types and the number of the fishes.
6. The method for evaluating the water environment quality based on the dam data of the reservoir as claimed in claim 5, wherein the specific calculation mode of the biodiversity index of the water body is as follows:
wherein S is the number of species, N is the total number of all species, and D is the biodiversity index.
7. The method for evaluating the water environment quality based on the dam data of the reservoir as claimed in claim 6, wherein the method for establishing the logistic regression model between the river connectivity and the river connectivity comprises:
s31, acquiring river connectivity, NPP productivity of riparian vegetation and water biological diversity indexes as independent variables;
s32, dividing the obtained river connectivity, the NPP productivity of the riparian vegetation and the water body biological diversity index into 5 grade intervals according to the maximum value of the sample, wherein each grade interval is used as a dependent variable of a regression model;
s33, using a logit regression with one category in the dependent variables as a reference, and correspondingly generating non-redundant logit variable models with other categories;
s34, introducing the reference quantity into the generated non-redundant logic variable model, and generating a logic model value corresponding to the category reference quantity;
s35, repeating the steps S3-S4, and calculating the logit model value of the other category reference quantity;
and S36, calculating the probability corresponding to each category, wherein the category corresponding to the maximum probability value is the water environment quality grade of the river according to the probability value of each category.
8. The method for evaluating the quality of the water environment based on the dam data of the reservoir as claimed in claim 7, wherein the non-redundant logit model is expressed as:
wherein g is a logit model; pi-probability of an argument being of class i; pJ-probability of dependent variable being class J (i ≠ J); xp-divide argument, p-dummy variable index; b isi0-an intercept term; beta is ajp-a regression coefficient; p is an integer.
9. The method for evaluating the quality of the water environment based on the dam data of the reservoir as claimed in claim 8, wherein the probability calculation formula is represented as:
wherein, P (Y)i) Is the probability that the argument x belongs to class i; gi is the non-redundant logic model value where the argument x belongs to class i, and gn is the non-redundant logic model value where the argument x belongs to class n.
10. The method as claimed in claim 8, wherein the sum of the probabilities for each category is 1.
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