CN115358463A - Water environment monitoring and influence evaluation method for power transmission and transformation construction engineering of ecological sensitive area - Google Patents
Water environment monitoring and influence evaluation method for power transmission and transformation construction engineering of ecological sensitive area Download PDFInfo
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Abstract
The invention relates to the field of power transmission and transformation environmental impact evaluation, in particular to a water environment monitoring and impact evaluation method for power transmission and transformation construction engineering in an ecological sensitive area; the method includes the steps of monitoring water environment indexes of the sewage receiving body, constructing a time sequence of the indexes changing along with time, simultaneously sampling hydrological indexes such as flow velocity, water temperature and water level of the sewage receiving body at corresponding time to form a multi-index vector historical time sequence, constructing a nonlinear mapping network through learning historical sample data of the time sequence, predicting a multi-index vector value at the next time according to the multi-index vector value, checking deviation degree of a multi-index vector predicted value and a water environment monitoring threshold value of an ecological sensitive area, judging whether a power transmission and transformation construction project has environmental influence on the sewage receiving body and evaluating the influence degree, and providing an effective technical means for accurate monitoring and protection of the water environment of the power transmission and transformation construction project of the ecological sensitive area.
Description
Technical Field
The invention relates to the field of power transmission and transformation environment influence evaluation, in particular to a water environment monitoring and influence evaluation method for power transmission and transformation construction engineering in an ecological sensitive area.
Background
The transmission and transformation project is a general name of transmission line construction and transformer installation project. Evaluation of the environmental impact of power transmission and transformation is an inevitable requirement for implementing national strategy and ecological environment protection planning, is an important means for synergistically promoting economic high-quality development and ecological environment high-level protection, and is the first line of defense in development. The power transmission and transformation project can be divided into an alternating current power transmission and transformation project and a direct current power transmission project, wherein the alternating current power transmission and transformation project comprises a power transmission line and a transformer substation (or a switching station and a series compensation station), and the direct current power transmission project comprises the power transmission line, a converter station and an earth electrode system. According to the regulations of the national environmental protection standard of China, namely the guide rules of environmental impact evaluation technology for power transmission and transformation projects, when power transmission and transformation construction projects enter the environment sensitive area specified in the building project environmental impact evaluation classified management directory, ecological planning conformity, environmental rationality and construction project feasibility analysis are carried out. The influence of the power transmission and transformation project on the water environment mainly comprises two aspects: firstly, construction wastewater comprises foundation construction slurry, construction site mixing wastewater, sandstone material washing and material cleaning and screening wastewater, machine maintenance oily wastewater and the like; and the second is domestic sewage. The influence of the power transmission and transformation project on the surface water environment is mainly reflected in the following indexes: one is the pH value, which is determined by the negative value of the common logarithm of the hydrogen ion concentration in the aqueous solution, namely-lg [ H + ]]Represents; secondly, chemical Oxygen Demand (COD), namely, the amount of reducing substances needing to be oxidized in the water sample is measured by a Chemical method, and the pollution degree of the reducing substances in the water is reflected; thirdly Biochemical Oxygen Demand BOD (Biochemical Oxygen Demand), that is, under certain conditions, the amount of dissolved Oxygen consumed in the Biochemical reaction process of decomposing biodegradable organic matters existing in water by microorganisms reflects the content of aerobic pollutants such as organic matters in water, and if the biological oxidation is carried out for five days, the Biochemical Oxygen Demand BOD is called as five-day Biochemical Oxygen Demand (BOD) 5 ) (ii) a IV is ammonia nitrogen NH 3 N, the ammonia nitrogen content index in water, is the direct cause of water eutrophicationFactors; and fifthly, petroleum mainly comes from pollution of waste water and domestic sewage.
The ecological sensitive area is used as an area which has special sensitivity to human production and living activities and is easily influenced by artificial improper development activities to generate ecological negative effects, and higher requirements are provided for the influence of the water environment of the power transmission and transformation construction engineering. According to the national environmental protection standard HJ 24-2020, the water environment influence evaluation factors of the power transmission and transformation construction engineering comprise pH value, COD and BOD 5 、NH 3 N and petroleum. In order to control the influence of power transmission and transformation construction engineering on the water environment of an ecological sensitive area, installation of sewage treatment equipment, sewage reuse facilities, a rainwater and sewage distribution pipe network, side station supervision sewage treatment facilities, side station supervision substation accident oil pools, concrete mixing wastewater treatment facilities and the like is required. However, due to the reasons that the management of the power transmission and transformation construction engineering owner is not standardized, the work of the environment supervision unit is not in place, the environmental accident is not properly treated and the like, the power transmission and transformation construction engineering, especially the engineering construction of crossing water bodies and routing nearby the water bodies, can have serious influence on the water environment of sewage containing water and can have irreversible destructive effect on the water environment of an ecological sensitive area. Therefore, the method has important significance in monitoring and influence evaluation of the water environment influence of the power transmission and transformation construction engineering in the ecological sensitive area.
Disclosure of Invention
The invention aims to provide a method for monitoring and evaluating the influence of the water environment of power transmission and transformation construction engineering on the power transmission and transformation construction engineering of an ecological sensitive area, which monitors the water environment indexes of a sewage receiving water body (the pH value, COD (chemical oxygen demand), BOD (biochemical oxygen demand) and BOD (biochemical oxygen demand) of the power transmission and transformation engineering specified by the environmental impact evaluation technical guideline of the national environmental protection standard of China) 5 、NH 3 N, petroleum and the like) to construct a time sequence of the indexes changing along with time, simultaneously sampling hydrological indexes such as flow velocity, water temperature, water level and the like of the sewage containing body at the corresponding moment to form a multi-index vector (water-containing environment index and hydrological index) historical time sequence, constructing a nonlinear mapping network through learning historical sample data of the time sequence, predicting a multi-index vector value at the next moment according to the multi-index vector value, and checking the multi-index vector predicted value and the water environment monitoring value of the ecological sensitive areaAnd measuring the deviation degree of the threshold value, judging whether the power transmission and transformation construction project has environmental influence on the sewage body and evaluating the influence degree, and providing an effective technical means for accurately monitoring and protecting the water environment of the power transmission and transformation construction project in the ecological sensitive area.
In order to achieve the above purpose, the technical solution adopted by the invention is as follows: the method for monitoring and evaluating the influence of the water environment in the power transmission and transformation construction engineering of the ecological sensitive area comprises the following steps:
s1, acquiring water environment indexes including pH value, COD (chemical oxygen demand) and BOD (biochemical oxygen demand) of sewage receiving water body of power transmission and transformation construction engineering at monitoring section and sampling point 5 、NH 3 N, petroleum and the like, and the time sequence of the water environment indexes is formed: x t ＝ {x 1，t ，x 2，t ，x 3，t ，x 4，t ，x 5，t }＝{x l，t |l＝[1，2，…，5]T is the sampling time, T is [1,2 ] \ 8230;, T]T represents the maximum sampling time, and l is the first water environment index x l，t Number of (2), x 1，t Denotes the pH value, dimensionless; x is the number of 2，t Chemical oxygen demand, COD, expressed in milligrams per liter (mg/L); x is the number of 3，t Indicating biochemical oxygen demand BOD for five days 5 In milligrams per liter (mg/L); x is the number of 4，t Represents the content of ammonia nitrogen NH 3 -N in milligrams per liter (mg/L); x is a radical of a fluorine atom 5，t Indicating petroleum in milligrams per liter (mg/L).
S2, acquiring hydrological indexes of the sewage containing body of the power transmission and transformation construction project at the monitoring section and the sampling point, wherein the hydrological indexes comprise water level, water temperature, flow velocity and the like, and a hydrological index time sequence is formed: y is t ＝{H t ，C t ，V t Where T is the sampling time, T is the [1,2, \ 8230;, T]，H t Represents the water level in meters (m); c t Temperature, in degrees Celsius (. Degree. C.); v t Representing the water flow velocity in meters per second (m/s).
S3, constructing a multi-index vector (water-containing environment index and hydrological index) historical time sequence of the power transmission and transformation construction nano-sewage body through the water environment index time sequence and the hydrological index time sequence:
M＝{M t |t∈[1，2，…，T]}
wherein M is t ＝{X′ t ，Y t }，X′ t Is X t Is a subset of
X′ t Evaluation of factors pH value, COD, BOD from Water environmental impact 5 、NH 3 N and one or more indexes in petroleum and the like, wherein the specific index number is comprehensively determined according to the collection monitoring technical conditions and the environmental protection supervision requirements of the ecological sensitive area.
S4, according to the multi-index vector historical time sequence M = { M = t |t∈[1，2，…，T]Construct an input-output map. Assuming that N is the input dimension of the input-output mapping and N < T, the multi-index vector historical time series M can construct T-N input-output mappings:
(M 1 ，M 2 ，…，M N )→M N+1
(M 2 ，M 3 ，…，M N+1 )→M N+2
(M T-N ，M T-N+1 ，…，M T-1 )→M T
thus, the input-output mapping described above is as follows:
(M s ，M s+1 ，…，M s+N-1 )→M s+N ，s∈[1，2，…，T-N]
s represents the number of the s-th input-output mapping, and has values of [1,2, \8230 ], T-N]An integer in between. (M) s ，M s+1 ，…，M s+N-1 ) Representing the input of the s-th input-output mapping, M s+N Representing the expected output of the s-th input-output mapping.
And S5, constructing a power transmission and transformation construction engineering water environment index nonlinear mapping network prediction model based on a multi-index vector historical time sequence, wherein the input of the model is the input of input-output mapping, and the output of the model is a multi-index vector predicted value at the prediction moment.
The nonlinear mapping network prediction model adopts a layered structure of an input layer, a hidden layer and an output layer. At the input ofIn the layer, the input of the nonlinear mapping network prediction model is the input of the s-th input-output mapping (M) s ，M s+1 ，…，M s+N-1 ) (ii) a In the output layer, the output of the model is the multi-index vector predicted value at the s + N prediction time
In the hidden layer, the number of hidden layer nodes is equal to the input dimension N of the input-output mapping, and the nth node is marked as U n ，n∈[1，2，…，N]. For the nth node, four neurons f, i, g and o are adopted to realize the coordinated memory of the long-term trend and the short-term fluctuation of the time series, and the corresponding neuron output is as follows:
f n ＝σ(w f Z n +R f H n-1 +b f )
i n ＝σ(w i Z n +R i H n-1 +b i )
g n ＝δ(w g Z n +R g H n-1 +b g )
o n ＝σ(w o Z n +R o H n-1 +b o )
wherein f is n ，i n ，g n And o n Excitation outputs of four neurons, f, i, g and o, respectively, sigma (-) and delta (-) are sigmoid and tanh functions, respectively, b f ，b i ，b g And b o The deviation of four neurons, f, i, g and o, respectively, w f ，w i ， w g And w o Input weights, R, for four neurons, f, i, g and o, respectively f ，R i ，R g And R o The regression weights for the four neurons, f, i, g, and o, respectively. According to the model, the n-th node U n Long term memory of (C) n And short term memory H n The following are calculated respectively:
wherein H n-1 And C n-1 Respectively represent the n-1 th node U n-1 Short term memory and long term memory. When N = N, then:
this means the Nth node U N Output H of N Can be obtained by iterative calculations. At this time H N Namely the multi-index vector predicted value at the s + N prediction time
And S6, training the power transmission and transformation construction engineering water environment index nonlinear mapping network prediction model according to the T-N input-output mappings, and identifying parameters of the model. The method comprises the following specific steps:
in S4, according to the history time sequence M = { M) of multi-index vectors t |t∈[1，2，…，T]T-N input-output mappings are constructed:
(M s ，M s+1 ，…，M s+N-1 )→M s+N ，s∈[1，2，…，T-N]
s5, according to the nonlinear mapping network prediction model of the water environment indexes of the power transmission and transformation construction project, setting the input of the model as the input of input-output mapping (M) s ，M s+1 ，…，M s+N-1 ) The output of the model is a multi-index vector predicted value at the s + N prediction time
The model parameters to be identified are W:
W＝{W i ，W r ，b}
W i ＝{w f ，w i ，w g ，w o }
W r ＝{R f ，R i ，R g ，R o }
b＝{b f ，b i ，b g ，b o }
wherein, W i Input weights w representing four neurons of f, i, g and o f ，w i ，w g And w o A set of (a); w r Regression weights R representing four neurons of f, i, g and o f ，R i ，R g And R o B represents the deviation b of four neurons of f, i, g and o f ，b i ，b g And b o A collection of (a).
The identification process of the model parameter W is as follows:
first, the training error E of the model is calculated train ：
The loss function J (W) for this model was determined as:
alpha is an adjusting factor used for optimizing the training error E in priority during iterative calculation train (the first term on the right of the J (W) expression equal sign) and the preferential optimization model parameter W (the second term on the right of the J (W) expression equal sign). Alpha is typically 0.1.
Then, the model parameter W is determined by iterative calculation using the following formula:
beta is the learning rate in the iterative calculation, and the change of W in each iterative calculation is ensured to be in a proper range. Beta typically takes a value of 0.001.
S7, according to S6, finallyAnd (3) predicting the multi-index vector predicted value at the T +1 moment by the determined model parameter W by adopting a power transmission and transformation construction engineering water environment index nonlinear mapping network prediction model. Setting the input of the model to (M) T-N+1 ，M T-N+2 ，…，M T ) The output of the model is the multi-index vector predicted value at the T +1 th prediction time
S8, calculating deviation of multi-index vector predicted values according to the water environment monitoring threshold value of the ecological sensitive area, and judging the water environment influence and evaluation influence degree of the power transmission and transformation construction project, wherein the method specifically comprises the following steps:
s8-1, setting a water environment monitoring threshold value gamma = { gamma = of the ecological sensitive area according to the quality standard of the national surface water environment and historical water environment indexes of the ecological sensitive area l L =1, \ 8230 |, 5}, wherein
Is the first water environment index x l，t The upper bound of the threshold value is,γ l is the first water environment index x l，t A lower threshold.
S8-2, calculating a multi-index vector predicted value according to the water environment monitoring threshold value of the ecological sensitive area
Degree of deviation of (e) = { e = [ (. Epsilon) l L =1, ·,5}, wherein
As the degree of deviation on the indicator of the first water environment,ε l and is the deviation degree under the first water environment index.
Andε l calculated by the following formula:
wherein the content of the first and second substances,
predicting a value for a multi-index vector
Middle water environment index x l，t Predicted value at T +1 th prediction time.
And S8-3, judging the influence and the degree of the water environment of the power transmission and transformation construction engineering according to the deviation epsilon of the multi-index vector predicted value.
(1) If it is
And is provided withε l And if the influence degree belongs to the following conditions, the influence of the power transmission and transformation construction engineering on the upper boundary of the water environment threshold value is more than or equal to 0, and the l belongs to (1., 5):
(2) If it isε l Is less than 0 and
l belongs to (1,.. 5), the electric transmission and transformation construction engineering influences the lower boundary of the water environment threshold, and the influence degree belongs to:
∈＝{maxε l |ε l ＜0∩l∈[1，2，…，5]}
(3) If it is
And isε l If the value is less than 0, l belongs to (1, 5), the electric transmission and transformation construction engineering influences the upper and lower boundaries of the water environment threshold, and the influence degree belongs to:
(4) If it is
And is
l belongs to (1.. Multidot.5), the power transmission and transformation construction engineering does not influence the water environment, and the influence degree belongs to 0.
Compared with the prior art, the invention has the beneficial effects that:
the invention relates to a method for monitoring and evaluating the water environment of power transmission and transformation construction engineering in an ecological sensitive area, which considers the law of the evolution of water environment indexes along with time, constructs a power transmission and transformation construction engineering water environment index prediction model based on a multi-index vector time sequence by a nonlinear mapping network with long-term trend and short-term fluctuation coordination memory, has stronger nonlinear mapping capability compared with the traditional zero-dimensional, one-dimensional and two-dimensional water quality models, solves the problem of parameter setting of the prediction model only depending on a multi-index vector historical time sequence rather than depending on the experience of an evaluator, and has stronger adaptability; and secondly, the method replaces the water environment absolute value judgment and evaluation of the influence and degree of the water environment of the power transmission and transformation construction project in the traditional method with the multi-index vector predicted value deviation degree based on the water environment monitoring threshold value of the ecological sensitive area, has the advantages of high judgment accuracy, simple judgment mode and easiness in operation, better accords with the environment supervision practice of the ecological sensitive area, and can provide a more accurate technical means for water environment monitoring and protection of the power transmission and transformation construction project of the ecological sensitive area.
Drawings
FIG. 1 is a flow chart of a method for monitoring and evaluating influence of water environment in power transmission and transformation construction engineering of an ecological sensitive area in the invention;
FIG. 2 is a model diagram of the water environment index prediction of the power transmission and transformation construction engineering based on a multi-index vector time sequence in the invention;
FIG. 3 is a pH value of a multi-index vector predicted value of a water environment in a power transmission and transformation construction project;
FIG. 4 is the chemical oxygen demand COD of the multi-index vector predicted value of the water environment of the power transmission and transformation construction project in the invention;
FIG. 5 shows the five-day biochemical oxygen demand BOD of the multi-index vector predicted value of the water environment of the power transmission and transformation construction project 5 ；
FIG. 6 shows the ammonia nitrogen content NH of the multi-index vector predicted value of the water environment of the power transmission and transformation construction engineering in the invention 3 -N；
FIG. 7 shows petroleum types of multi-index vector predicted values of water environment in power transmission and transformation construction engineering.
Detailed Description
The present invention will be described in further detail with reference to the following description and embodiments in conjunction with the accompanying drawings.
The principle of the invention is illustrated as follows:
the invention is used for solving the problems of water environment monitoring and protection of the power transmission and transformation construction engineering in the ecological sensitive area. The invention is based on the following features: firstly, the evolution of the water environment indexes of the ecological sensitive area along with the time has regularity, and secondly, the influence of the power transmission and transformation construction project on the water environment interferes and destroys the regularity. Therefore, the method comprises the steps of firstly taking the sewage receiving body of the power transmission and transformation construction project as a monitoring object, constructing a water environment index prediction model based on a time sequence by using a nonlinear mapping network, then constructing a learning sample according to a historical time sequence of a multi-index vector, training the water environment index nonlinear mapping network prediction model, identifying parameters of the model, finally obtaining a multi-index vector prediction value on the basis of the prediction model, and judging the influence and the degree of the water environment of the power transmission and transformation construction project according to whether the multi-index vector prediction value exceeds a water environment monitoring threshold value of an ecological sensitive area. Compared with the traditional method, the method has the advantages of more accurate judgment effect, simple judgment mode and easy operation, and can provide technical support for accurate monitoring and protection of the water environment of the power transmission and transformation construction engineering in the ecological sensitive area.
Example (b):
in fig. 1, the influence of the transmission and transformation project on the water environment mainly includes two aspects: firstly, construction wastewater comprises foundation construction slurry, construction site mixing wastewater, sand and stone washing and material cleaning and screening wastewater, machine maintenance oily wastewater and the like; and the second is domestic sewage. Therefore, in the embodiment, the water environment indexes of the sewage containing water body in the power transmission and transformation construction project focus on the following five indexes: pH value, chemical Oxygen Demand (COD), biochemical Oxygen Demand (BOD) for five days 5 Ammonia nitrogen content NH 3 N and petroleum. Meanwhile, considering that the water environment indexes are easily influenced by various factors, the following three hydrological indexes of the sewage receiving water body of the power transmission and transformation construction project are mainly concerned in the embodiment: water level, water temperature, flow rate.
In fig. 2, the model employs a hierarchical structure of input layer-hidden layer-output layer. In the input layer, the input of the nonlinear mapping network prediction model is the input of the s-th input-output mapping (M) s ,M s+1 ,…,M s+N-1 ) (ii) a In the output layer, the output of the model is the multi-index vector predicted value at the s + N prediction time
In the hidden layer, the number of hidden layer nodes is equal to the input dimension N of the input-output mapping, and the nth node is marked as U n ，n∈[1,2,…,N]. For the nth node, four neurons f, i, g and o are adopted to realize the coordination memory of the long-term trend and the short-term fluctuation of the time sequence, and the corresponding neuron output is as follows:
f n ＝σ(w f Z n +R f H n-1 +b f )
i n ＝σ(w i Z n +R i H n-1 +b i )
g n ＝δ(w g Z n +R g H n-1 +b g )
o n ＝σ(w o Z n +R o H n-1 +b o )
wherein f is n ，i n ，g n And o n Excitation outputs of four neurons, f, i, g and o, σ (·) and δ (·) are sigmoid and tanh functions, respectively, b f ，b i ，b g And b o The deviation of four neurons, f, i, g and o, respectively, w f ，w i ， w g And w o Input weights, R, for four neurons, f, i, g and o, respectively f ，R i ，R g And R o The regression weights for the four neurons, f, i, g, and o, respectively. According to the model, the n-th node U n Long term memory of C n And short term memory H n The following are calculated respectively:
wherein H n-1 And C n-1 Respectively represent the n-1 th node U n-1 Short term memory and long term memory. When N = N, then there are:
this means the Nth node U N Output H of N Can be obtained by iterative calculations. At this time H N Namely the multi-index vector predicted value at the s + N prediction time
In the embodiment, the sampling time interval is counted by days under the supporting of the sampling and detection technology, and the water environment indexes of the sampling are pH value, COD (chemical oxygen demand) and BOD (biochemical oxygen demand) 5 、NH 3 N and petroleum, and the sampled hydrological environment indexes are water level, water temperature and flow rate. Input the methodThe input dimension N of the output map is 50, the number of hidden layer nodes is 50, the number of training samples is 395, and the dimensions of the input weight and the regression weight are both 200 × 8.
Fig. 3 to 7 show the deviation condition of the multi-index vector predicted value of the water environment of the power transmission and transformation construction project relative to the water environment monitoring threshold value of the ecological sensitive area. In the figures, the ordinate respectively represents the pH value, the Chemical Oxygen Demand (COD) and the five-day Biochemical Oxygen Demand (BOD) of the multi-index vector predicted value of the water environment of the power transmission and transformation construction engineering 5 Ammonia nitrogen content NH 3 N and petroleum. 5 times of iterative prediction is carried out on the multi-index vector of the water environment, and the 396 th to 400 th sampling moments of the abscissa are corresponded. The water environment monitoring threshold is determined according to the upper bound and the lower bound of the up-to-standard historical water environment index of the ecological sensitive area. The upper and lower limits of the pH are 8 and 7.5, respectively; the upper and lower limits of the chemical oxygen demand COD are 14.5mg/L and 11mg/L respectively; biochemical oxygen demand BOD five days 5 The upper and lower limits of (A) are 2mg/L and 1.5mg/L, respectively; ammonia nitrogen content NH 3 The upper and lower limits of N are 0.5mg/L and 0.3mg/L, respectively; the upper and lower limits of petroleum are 0.03mg/L and 0mg/L, respectively. The five water environment multi-index vector predicted values are all in the upper bound and the lower bound of the water environment monitoring threshold, which shows that the power transmission and transformation construction project does not influence the water environment, and the influence degree belongs to 0.
Claims (3)
1. The method for monitoring and evaluating the influence of the water environment in the power transmission and transformation construction engineering of the ecological sensitive area is characterized by comprising the following steps of:
s1, acquiring water environment indexes including pH value, COD (chemical oxygen demand) and BOD (biochemical oxygen demand) of sewage receiving water body of power transmission and transformation construction engineering at monitoring section and sampling point 5 、NH 3 -N and petroleum, constituting a water environment index time series: x t ＝{x 1，t ，x 2，t ，x 3，t ，x 4，t ，x 5，t }＝{x l，t |l＝[1，2，…，5]Where T is the sampling time, T is the [1,2, \ 8230;, T]T represents the maximum sampling time, and l is the first water environment index x l，t Number of (2), x 1，t Denotes the pH value, dimensionless; x is the number of 2，t Representing chemical oxygen demand COD, singlyIn milligrams per liter; x is the number of 3，t Indicating biochemical oxygen demand BOD for five days 5 In milligrams per liter; x is a radical of a fluorine atom 4，t Represents the content of ammonia nitrogen NH 3 -N in milligrams per liter; x is a radical of a fluorine atom 5，t Expressed as petroleum in milligrams per liter;
s2, acquiring hydrological indexes of the sewage containing body of the power transmission and transformation construction project at the monitoring section and the sampling point, wherein the hydrological indexes comprise water level, water temperature and flow velocity, and a hydrological index time sequence is formed: y is t ＝{H t ，C t ，V t Where T is the sampling time, T is the [1,2, \ 8230;, T]，H t Represents the water level in meters; c t Denotes temperature in degrees celsius; v t Representing the water flow velocity in meters per second;
s3, constructing a multi-index vector historical time sequence of the power transmission and transformation construction nano-sewage body through the water environment index time sequence and the hydrological index time sequence:
M＝{M t |t∈[1，2，…，T]}
wherein M is t ＝{X′ t ，Y t }，X′ t Is X t Is a subset of
X′ t Evaluation factors of pH value, COD and BOD according to water environment influence 5 、NH 3 N and one or more indexes in the petroleum, wherein the specific index number is comprehensively determined according to the collection monitoring technical conditions and the environmental protection supervision requirements of the ecological sensitive area;
s4, according to the multi-index vector historical time sequence M = { M = t |t∈[1，2，…，T]Constructing an input-output mapping; and if N is the input dimension of the input-output mapping and N is less than T, the historical time sequence M of the multi-index vector can construct T-N input-output mappings:
(M 1 ，M 2 ，…，M N )→M N+1
(M 2 ，M 3 ，…，M N+1 )→M N+2
…
(M T-N ，M T-N+1 ，…，M T-1 )→M T
thus, the input-output mapping described above is as follows:
(M s ，M s+1 ，…，M s+N-1 )→M s+N ，s∈[1，2，…，T-N]
s represents the serial number of the s-th input-output mapping, and has the value of [1,2, \8230 ], T-N]An integer in between; (M) s ，M s+1 ，…，M s+N-1 ) Representing the input of the s-th input-output mapping, M s+N Represents the expected output of the s-th input-output mapping;
s5, constructing a power transmission and transformation construction engineering water environment index nonlinear mapping network prediction model based on a multi-index vector historical time sequence, wherein the input of the model is the input of input-output mapping, and the output of the model is a multi-index vector predicted value at the prediction moment;
the nonlinear mapping network prediction model adopts a layered structure of an input layer, a hidden layer and an output layer: in the input layer, the input of the nonlinear mapping network prediction model is the input of the s-th input-output mapping (M) s ，M s+1 ，…，M s+N-1 ) (ii) a In the output layer, the output of the model is the multi-index vector predicted value at the s + N prediction time
In the hidden layer, the number of hidden layer nodes is equal to the input dimension N of the input-output mapping, and the nth node is marked as U n ，n∈[1，2，…，N](ii) a For the nth node, four neurons f, i, g and o are adopted to realize the coordination memory of the long-term trend and the short-term fluctuation of the time sequence, and the corresponding neuron output is as follows:
f n ＝σ(w f Z n +R f H n-1 +b f )
i n ＝σ(w i Z n +R i H n-1 +bi)
g n ＝δ(w g Z n +R g H n-1 +b g )
o n ＝σ(w o Z n +R o H n-1 +b o )
wherein f is n ，i n ，g n And o n Excitation outputs of four neurons, f, i, g and o, respectively, sigma (-) and delta (-) are sigmoid and tanh functions, respectively, b f ，b i ，b g And b o Deviation of four neurons, respectively f, i, g and o, w f ，w i ，w g And w o Input weights, R, for four neurons, f, i, g and o, respectively f ，R i ，R g And R o Regression weights for four neurons, f, i, g, and o, respectively; according to the model, the n-th node U n Long term memory of C n And short term memory H n The following are calculated respectively:
wherein H n-1 And C n-1 Respectively represent the n-1 th node U n-1 Short-term memory and long-term memory; when N = N, then there are:
this means the Nth node U N Output H of N Can be obtained by iterative calculation, at this time H N Namely the multi-index vector predicted value at the s + N prediction time
S6, training the power transmission and transformation construction engineering water environment index nonlinear mapping network prediction model according to the T-N input-output mappings, and identifying parameters of the model; the method comprises the following specific steps:
in S4, according to a multi-index vector historical time sequence M = { M = { M } t |t∈[1，2，…，T]T-N input-output maps are constructed:
(M s ，M s+1 ，…，M s+N-1 )→M s+N ，s∈[1，2，…，T-N]
s5, according to the nonlinear mapping network prediction model of the water environment indexes of the power transmission and transformation construction project, setting the input of the model as the input of input-output mapping (M) s ，M s+1 ，…，M s+N-1 ) The output of the model is a multi-index vector predicted value at the s + N prediction time
The model parameters to be identified are W:
W＝{W i ，W r ，b}
W i ＝{w f ，w i ，w g ，w o }
W r ＝{R f ，R i ，R g ，R o }
b＝{b f ，b i ，b g ，b o }
wherein, W i Input weights w representing four neurons of f, i, g and o f ，w i ，w g And w o A set of (a); w r Regression weights R representing four neurons of f, i, g and o f ，R i ，R g And R o B represents the deviation b of four neurons of f, i, g and o f ，b i ，b g And b o A set of (a);
the identification process of the model parameter W is as follows:
first, the training error E of the model is calculated train ：
The loss function J (W) for this model was determined as:
alpha is an adjusting factor used for optimizing the training error E in priority during iterative calculation train (the first item on the right side of the equal sign of the J (W) expression) and the priority optimization model parameter W (the second item on the right side of the equal sign of the J (W) expression) are weighed;
then, the model parameter W is determined by iterative calculation using the following formula:
beta is the learning rate in the iterative computation, and the change of W in each iterative computation is ensured to be in a proper range;
s7, according to the model parameter W finally determined in the S6, predicting the multi-index vector predicted value at the T +1 moment by adopting a power transmission and transformation construction engineering water environment index nonlinear mapping network prediction model: setting the input of the model to (M) T-N+1 ，M T-N+2 ，…，M T ) The output of the model is the multi-index vector predicted value at the T +1 th prediction time
S8, calculating deviation degree of the multi-index vector predicted value according to the water environment monitoring threshold value of the ecological sensitive area, and judging the water environment influence and evaluation influence degree of the power transmission and transformation construction project, wherein the method specifically comprises the following steps:
s8-1, setting a water environment monitoring threshold value gamma = { gamma = of the ecological sensitive area according to the quality standard of the national surface water environment and historical water environment indexes of the ecological sensitive area l L =1, \ 8230 |, 5}, wherein
Is the first water environment index x l，t The upper bound of the threshold value is set,γ l is the first water environment index x l，t A lower threshold bound;
s8-2, calculating a multi-index vector predicted value according to the water environment monitoring threshold value of the ecological sensitive area
Degree of deviation of (e) = { e = [ (. Epsilon) l L =1, ·,5}, wherein
As the degree of deviation in the first water environment index,ε l the deviation degree under the first water environment index;
andε l calculated by the following formula:
wherein, the first and the second end of the pipe are connected with each other,
for multi-index vector prediction
Middle water environment index x l，t A predicted value at the T +1 th prediction time;
s8-3, judging the influence and the degree of the water environment of the power transmission and transformation construction engineering according to the deviation epsilon of the multi-index vector predicted value:
(1) If it is
And isε l And if the influence degree belongs to the following conditions, the influence of the power transmission and transformation construction engineering on the upper boundary of the water environment threshold value is more than or equal to 0, and the l belongs to (1., 5):
(2) If it isε l Is < 0 and
then the electric transmission and transformation construction project influences the lower boundary of the water environment threshold, and the influence degree belongs to:
∈＝{maxε l |ε l ＜0∩l∈[1，2，…，5]}
(3) If it is
And is provided withε l If the value is less than 0, l belongs to (1, 1.. Multidot.5), the electric transmission and transformation construction engineering influences the upper and lower water environment thresholds, and the influence degree belongs to:
(4) If it is
And is
The electric transmission and transformation construction project does not influence the water environment, and the influence degree belongs to 0.
2. The method for monitoring and evaluating the water environment and the influence of the power transmission and transformation construction engineering in the ecological sensitive area according to claim 1, which is characterized by comprising the following steps of: in S5, the adjustment factor α takes a value of 0.1.
3. The method for monitoring and evaluating the water environment in the power transmission and transformation construction project of the ecological sensitive area according to claim 1, which is characterized by comprising the following steps: in S5, the learning rate β in the iterative computation takes a value of 0.001.
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