CN113269395A - Water environment risk pre-judging method and system based on pressure state feedback framework - Google Patents
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Abstract
The invention relates to the technical field of water environment risk evaluation, in particular to a water environment risk pre-judging method and a water environment risk pre-judging system based on a pressure state feedback frame, wherein the method comprises the following steps: collecting historical data of the water environment risk of the river network area, and constructing an evaluation index system of the water environment risk of the river network area with a hierarchical structure relationship based on the historical data; calculating the weight of each evaluation index in the risk evaluation index system, calculating the membership degree of each evaluation index in the evaluation index system to the evaluation level, establishing a fuzzy relation matrix based on the membership degree, and determining the final evaluation result of the evaluation index based on the fuzzy relation matrix; under a pressure state feedback frame, determining an evaluation result of the water environment risk of the river network area, obtaining a risk regulation and control suggestion based on the evaluation result of the water environment risk of the river network area, and carrying out scene water environment risk pre-judgment.
Description
Technical Field
The invention relates to the technical field of water environment risk evaluation, in particular to a water environment risk pre-judging method and system based on a pressure state feedback framework.
Background
With the rapid development of social economy, industry and agriculture and the continuous acceleration of urbanization process, the environmental pressure of each region is increased day by day, and the environmental awareness of people is gradually enhanced. With the wide operation of risk evaluation concepts in various industries, water environment risk evaluation gradually enters the public field of vision. At present, common water environment risk evaluation methods include a graph superposition method, an information diffusion method, an index system method, fuzzy mathematics synthesis and the like. The water environment risk evaluation index system is an important step of water environment risk evaluation, most of the existing water environment risk evaluation index systems adopt static index systems, and an index system considering water quality prediction of a river network under a risk dynamic regulation strategy is lacked. The commonly used mathematical model for predicting the water quality of the river network mainly comprises two categories: a water quality mechanism mathematical model and a water quality data driving class mathematical model.
Most of river network water quality mathematical models adopted by the existing water environment risk evaluation adopt a single static model to predict water quality, and the applicability and the simulation precision in practical application are to be improved.
Disclosure of Invention
The invention aims to provide a water environment risk pre-judging method and system based on a pressure state feedback framework, which are used for solving one or more technical problems in the prior art and at least provide a beneficial selection or creation condition.
In order to achieve the purpose, the invention provides the following technical scheme:
a water environment risk pre-judging method based on a pressure state feedback framework comprises the following steps:
s100, collecting historical data of the water environment risk of the river network area, and constructing an evaluation index system of the water environment risk of the river network area with a hierarchical structure relationship based on the historical data;
s200, calculating the membership degree of each evaluation index in the evaluation index system to the evaluation grade, establishing a fuzzy relation matrix based on the membership degree, and determining the final evaluation result of the evaluation index based on the fuzzy relation matrix;
step S300, determining an evaluation result of the water environment risk of the river network area under a pressure state feedback framework, obtaining a risk regulation and control suggestion based on the evaluation result of the water environment risk of the river network area, and performing scene water environment risk pre-judgment.
Further, the historical data of the water environment risk in the river network area comprises: sample ammonia nitrogen, total phosphorus, dissolved oxygen, and permanganate index.
Further, the evaluation index system of the water environment risk in the river network area consists of a target layer, a standard layer and an index layer.
Further, in step S100, the constructing an evaluation index system of the water environment risk of the river network region with a hierarchical structure relationship based on the historical data includes:
and determining the weight of each evaluation index in the risk evaluation index system by using an analytic hierarchy process, and constructing a hierarchical structure relationship according to the weight and the membership of each evaluation index.
Further, the step S200 includes:
step S210, performing membership calculation on each evaluation index to obtain the membership of each evaluation index, wherein the evaluation indexes are divided into a reverse index and a forward index;
s220, establishing an index system according to each level relation of the index system, and establishing a fuzzy relation matrix according to the membership degree of each evaluation index;
and step S230, sequentially performing primary and secondary evaluation according to the weight of each evaluation index and the fuzzy relation matrix, and taking the risk grade determined according to the maximum membership rule as the final evaluation result of the evaluation index.
Further, the step S300 includes:
s310, determining the source intensity of a river network area under a pressure state feedback frame;
s320, predicting index values of an evaluation index system by adopting a prediction model system of the water environment risk of the river network area;
and S330, establishing a risk engineering regulation measure according to the water environment risk evaluation leading factor of the river network area, the river network risk grade division and the partition.
Further, the prediction model system of the river network area water environment risk comprises a data driving model, a combined model and a mechanism model.
A computer-readable storage medium, wherein a water environment risk pre-judging program based on a pressure state feedback framework is stored on the computer-readable storage medium, and when being executed by a processor, the water environment risk pre-judging program based on the pressure state feedback framework realizes the steps of the water environment risk pre-judging method based on the pressure state feedback framework as described in any one of the above.
A water environment risk prediction system based on a pressure state feedback framework, the system comprising:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is enabled to implement any one of the methods for predicting risk of aquatic environment based on the pressure state feedback framework.
The invention has the beneficial effects that: the invention discloses a water environment risk pre-judging method and a water environment risk pre-judging system based on a pressure state feedback frame.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a water environment risk prediction method based on a pressure state feedback framework in an embodiment of the invention;
FIG. 2 is a schematic flow chart of a water environment risk evaluation method in an embodiment of the invention;
fig. 3 is a schematic structural diagram of a prediction model system for water environment risk in river network areas in the embodiment of the invention.
Detailed Description
The conception, specific structure and technical effects of the present application will be described clearly and completely with reference to the following embodiments and the accompanying drawings, so that the purpose, scheme and effects of the present application can be fully understood. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Referring to fig. 1 and fig. 2, as shown in fig. 1, a method for predicting water environment risk based on a pressure state feedback framework according to an embodiment of the present application is provided, where the method includes the following steps:
s100, collecting historical data of the water environment risk of the river network area, and constructing an evaluation index system of the water environment risk of the river network area with a hierarchical structure relationship based on the historical data;
s200, calculating the membership degree of each evaluation index in the evaluation index system to the evaluation grade, establishing a fuzzy relation matrix based on the membership degree, and determining the final evaluation result of the evaluation index based on the fuzzy relation matrix;
step S300, determining an evaluation result of the water environment risk of the river network area under a pressure state feedback framework, obtaining a risk regulation and control suggestion based on the evaluation result of the water environment risk of the river network area, and performing scene water environment risk pre-judgment.
In the embodiment provided by the invention, under a pressure state feedback frame, according to the analysis of the water environment pressure and state, a fuzzy comprehensive evaluation method is adopted, a water quality data drive model and a water quality mechanism model are combined for water quality prediction, and a river network area water environment risk evaluation system is used as a dynamic whole organically combined by identification, analysis and regulation, so that a comprehensive dynamic water environment risk evaluation method is constructed, the early warning of the area water environment risk level is realized, and the risk evaluation of the water environment with the water environment risk level can be carried out through a risk regulation strategy in a dynamic regulation and control mode.
In a preferred embodiment, the historical data of the water environment risk in the river network area comprises: sample ammonia nitrogen, total phosphorus, dissolved oxygen, and permanganate index.
In a preferred embodiment, the evaluation index system for the water environment risk in the river network area consists of a target layer, a criterion layer and an index layer.
As an improvement of the above embodiment, in step S100, the constructing an evaluation index system of the water environment risk of the river network region with a hierarchical structure relationship based on the historical data includes:
and determining the weight of each evaluation index in the risk evaluation index system by using an analytic hierarchy process, and constructing a hierarchical structure relationship according to the weight and the membership of each evaluation index.
Specifically, the indexes at the same level are compared pairwise by using an analytic hierarchy process according to the hierarchical structure relationship of an index system, the relative importance of the indexes is determined according to the 1-9 scale meanings, and a judgment matrix is constructed. And calculating the ratio of the index layer to the standard layer and the ratio of the standard layer to the target layer by adopting a geometric mean method, sharing a secondary weight value, and expressing the secondary weight value in a vector form. And finally, carrying out consistency check on the calculation result, and ensuring that the critical ratio is less than 0.1, namely judging that the matrix has better transitivity and consistency.
As a modification of the above embodiment, the step S200 includes:
step S210, performing membership calculation on each evaluation index to obtain the membership of each evaluation index, wherein the evaluation indexes are divided into a reverse index and a forward index, and the membership calculation mode of the evaluation indexes is as follows:
for the reverse index, the smaller the index value, the higher the water environment risk:
when x isi>ci，1When r isi，1＝1，ri，2＝ri，3＝…＝ri，K＝0；
When c is going toi，k≥xi≥ci，k+1When the temperature of the water is higher than the set temperature,
when x isi<ci，KWhen r isi，1＝ri，2＝…＝ri，K-1＝0，ri，K＝1；
For the forward index, the larger the index value, the higher the water environment risk:
when x isi<ci，1When r isi，1＝1，ri，2＝ri，3＝…＝ri，K＝0；
When c is going toi，k≤xi≤ci，k+1Then (c) is performed.
When x isi>ci，KWhen r isi，1＝ri，2＝…＝ri，K-1＝0，ri，K＝1；
Wherein x isiIs the index value of the i-th term, ci，kEvaluation criterion of the kth order of the i-th index, ri，kRepresenting the relative membership degree of the i index to the k level risk degree, N being the evaluation risk, etcThe number of indexes in the grade, K is the grade number of the evaluation risk degree, ai,kThe K-th level evaluation standard of the i-th index is expressed, and K belongs to [1, K ]]。
S220, establishing an index system according to each level relation of the index system, and establishing a fuzzy relation matrix according to the membership degree of each evaluation index;
wherein the fuzzy relation matrix is represented as:
and step S230, sequentially performing primary and secondary evaluation according to the weight of each evaluation index and the fuzzy relation matrix, and taking the risk grade determined according to the maximum membership rule as the final evaluation result of the evaluation index.
Specifically, the final evaluation result of the evaluation index is calculated according to the following formula:
Bi＝WiRi＝(bi1,bi2,…,biK)
B＝WR＝(b1,b2,…,bK)
wherein, biIn order to obtain the membership degree of the evaluation index to the ith grade and W is the weight of each evaluation index, in the embodiment provided by the invention, since the risk evaluation index system of the river network area water environment is divided into 3 layers, so that n is 3, and max { b is takeniAnd (6) taking the corresponding risk grade as a final evaluation result of the evaluation index.
In a preferred embodiment, the step S300 includes:
s310, determining the source intensity of a river network area under a pressure state feedback frame;
s320, predicting index values of an evaluation index system by adopting a prediction model system of the water environment risk of the river network area;
referring to fig. 3, in a preferred embodiment, the prediction model system of the river network region water environmental risk includes a data-driven model, a combined model and a mechanism model.
In a preferred embodiment, the data-driven model is determined by:
data cleaning: performing data cleaning on historical data; the step of cleaning the historical data refers to cleaning the water quality data, eliminating abnormal data and mastering the index time sequence correlation of a risk evaluation index system by using a basic statistical method and correlation analysis, so that the prediction precision is improved.
Calculating a correlation coefficient: calculating a correlation coefficient by adopting a Pearson method;
training a model: randomly splitting the washed historical data into two unequal parts, wherein more parts of data are used as training sets, and less parts of data are used as test sets; respectively establishing SARIMA and LSTM algorithm models in a training set; and (4) acting the model on the test set, feeding back the test result in real time and carrying out continuous model optimization.
In a preferred embodiment, the combined model is formed by performing optimized combination based on SARIMA and LSTM algorithm models; specifically, the combined model is determined by:
respectively calculating predicted values obtained by the SARIMA algorithm model, the LSTM algorithm model and the combined model, and taking a model with the minimum average error rate and the minimum root mean square error of the predicted values and actual values as an optimal prediction model;
selecting the optimal prediction model refers to respectively calculating predicted values obtained by the SARIMA algorithm model, the LSTM algorithm model and the combined model, selecting the optimal prediction model according to the average error rate and the root mean square error of the predicted values and the actual values, wherein the smaller the average error rate and the root mean square error of the predicted values and the actual values of the model are, the better the model is.
The average error rate is calculated as:
the root mean square error is calculated as:
wherein: xi represents the model predicted value, Yi represents the actual predicted value, and n represents the total number of predicted values.
The mechanism model is a one-dimensional river network hydrodynamic water quality model established by using data of the terrain, water level, flow and water quality of a river network through parameter adjustment and verification.
And S330, establishing a risk engineering regulation measure according to the water environment risk evaluation leading factor of the river network area, the river network risk grade division and the partition.
In this embodiment, the pressure, state, and feedback under the pressure state feedback frame are finally determined.
The pressure is obtained by carrying out strong accounting on the source of the river network area according to the current situation and the risk engineering regulation and control measures;
the states are: predicting an index value of a risk evaluation index system of the water environment of the river network region under the pressure;
the feedback is as follows: and (3) according to the water environment risk evaluation leading factors of the river network region, the classification of the river network risk grades and the partition establishment of risk engineering regulation and control measures, wherein the risk engineering regulation and control measures comprise the steps of upgrading and emission reduction, discharge port optimization, ecological water supplement and gate dam regulation and control.
In this embodiment, starting from index data of a water environment risk evaluation index system in a river network region, three models, namely a SARIMA algorithm, an LSTM algorithm, a combination model and a one-dimensional river network hydrodynamic water quality model, are established from water quality monitoring data and a motion mechanism direction of pollutants in the water environment respectively, and indexes of the water environment risk evaluation index system in different river network regions are predicted, so that the river network region water environment risk evaluation index system is used as a dynamic whole organically combined by recognition, analysis and regulation, and a comprehensive system dynamic water environment risk evaluation method is formed. Aiming at indexes (such as dissolved oxygen, nitrogen, phosphorus and COD) of the water environment risk evaluation index system in different river network regions, SARIMA (software development technology), LSTM (least squares) algorithm and combined model simulation are applied, the prediction precision is dynamically tracked, an index optimal prediction model is adjusted, a one-dimensional river network hydrodynamic water quality model under the risk engineering regulation and control measures is established, and the prediction and prediction pre-judgment precision of the water environment risk evaluation in the river network regions can be effectively improved.
The following is a specific embodiment of the present invention:
according to the water quality data of a monitored cross section of a river in Changzhou city on a day of 2019, after water quality index damage and correlation analysis, water temperature and PH have no obvious influence on the environmental risk of river water, and no obvious correlation exists among indexes, primary index screening is carried out, then a fuzzy comprehensive evaluation method is adopted, and no risk (not monitored), extremely low risk (deviation degree of 50%), low risk (deviation degree of 30%)), moderate risk (deviation degree of 0)), high risk (deviation degree of-30%) and extremely high risk (deviation degree of-50%) are formulated according to the surface water environmental quality standard and the water quality standard deviation degree, the cross section water quality standard is a level II standard, the final water environment risk of the cross section is determined to be extremely high risk, and the risk leading factors are fecal colibacillus and ammonia nitrogen.
Table 1: index weight and fuzzy matrix in index system for water environment risk evaluation
According to the water quality data of a certain monitoring section of the river in Changzhou city in 2018 and 2020, historical data (daily average value) such as ammonia nitrogen, total phosphorus, dissolved oxygen and permanganate index of a sample are divided into 300 records in a training set, and 128 records in a testing set; respectively establishing prediction models of SARIMA and LSTM algorithms of each evaluation index of the monitoring section by using a training set, and continuously carrying out model training until the test prediction error rate is less than 30%; performing test verification based on the test set, and respectively calculating the root mean square error and the average error rate of the SARIMA, the LSTM algorithm model and the combined model by adopting an optimal prediction model selection method to obtain an optimal model of the specific index of the monitoring section; and dynamically tracking the prediction precision, analyzing the stability of the model, training the model of a new sample when the average error rate is not less than 30%, and dynamically adjusting the optimal prediction model.
According to the topography, the synchronous water level, the flow and the water quality data of the river network area where the river is located in Changzhou city, a one-dimensional river network hydrodynamic water quality model is established, the average error of the model simulation flow is 10%, and the water quality rate error is within 30%.
When the water environment risk is predicted under the conventional strong source pressure, the SARIMA, LSTM algorithm model and the combined model optimal model are adopted for prediction, the index value of the water environment risk evaluation index system is dynamically updated, the water environment risk of the section is predicted in the next 1 day, and the risk is reduced to high risk. The risk leading factors are fecal coliform and ammonia nitrogen. According to the source tracing analysis of pollutants, fecal escherichia coli and ammonia nitrogen exceeding standards are possibly influenced by sewage discharge of a sewage treatment plant, the emission standard of fecal escherichia coli and ammonia nitrogen of the sewage treatment plant is improved, and the emission amount of fecal escherichia coli and ammonia nitrogen discharged from a sewage treatment plant into a river is reduced through accounting. Under the strong pressure of the risk engineering regulation and control measure source, a one-dimensional river network hydrodynamic water quality model is adopted for prediction, the index value of the water environment risk evaluation index system is dynamically updated, the water environment risk of the section is predicted under the risk engineering regulation and control measure, and the specific risk engineering regulation and control measure is determined.
Corresponding to the method of fig. 1, an embodiment of the present invention further provides a computer-readable storage medium, where a water environment risk pre-judging program based on a pressure state feedback framework is stored on the computer-readable storage medium, and when the water environment risk pre-judging program based on the pressure state feedback framework is executed by a processor, the steps of the water environment risk pre-judging method based on the pressure state feedback framework as described in any one of the above embodiments are implemented.
Corresponding to the method in fig. 1, an embodiment of the present invention further provides a water environment risk pre-determination system based on a pressure state feedback framework, where the system includes:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is enabled to implement the method for predicting risk of aquatic environment based on the pressure state feedback framework according to any of the embodiments.
The contents in the above method embodiments are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above method embodiment, and the beneficial effects achieved by the present system embodiment are also the same as those achieved by the above method embodiment.
The Processor may be a Central-Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application-Specific-Integrated-Circuit (ASIC), a Field-Programmable Gate array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general processor can be a microprocessor or the processor can be any conventional processor and the like, the processor is a control center of the water environment risk pre-judging system based on the pressure state feedback framework, and various interfaces and lines are utilized to connect various parts of the operable device of the whole water environment risk pre-judging system based on the pressure state feedback framework.
The memory can be used for storing the computer program and/or the module, and the processor can realize various functions of the water environment risk pre-judging system based on the pressure state feedback framework by operating or executing the computer program and/or the module stored in the memory and calling the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart-Media-Card (SMC), a Secure-Digital (SD) Card, a Flash-memory Card (Flash-Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
While the description of the present application has been made in considerable detail and with particular reference to a few illustrated embodiments, it is not intended to be limited to any such details or embodiments or any particular embodiments, but it is to be construed that the present application effectively covers the intended scope of the application by reference to the appended claims, which are interpreted in view of the broad potential of the prior art. Further, the foregoing describes the present application in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial changes from the present application, not presently foreseen, may nonetheless represent equivalents thereto.
Claims (9)
1. A water environment risk pre-judging method based on a pressure state feedback framework is characterized by comprising the following steps:
s100, collecting historical data of the water environment risk of the river network area, and constructing an evaluation index system of the water environment risk of the river network area with a hierarchical structure relationship based on the historical data;
s200, calculating the membership degree of each evaluation index in the evaluation index system to the evaluation grade, establishing a fuzzy relation matrix based on the membership degree, and determining the final evaluation result of the evaluation index based on the fuzzy relation matrix;
step S300, determining an evaluation result of the water environment risk of the river network area under a pressure state feedback framework, obtaining a risk regulation and control suggestion based on the evaluation result of the water environment risk of the river network area, and performing scene water environment risk pre-judgment.
2. The method for predicting water environment risk based on the pressure state feedback framework as claimed in claim 1, wherein the historical data of the water environment risk in the river network area comprises: sample ammonia nitrogen, total phosphorus, dissolved oxygen, and permanganate index.
3. The method for pre-judging water environment risk based on the pressure state feedback framework as claimed in claim 2, wherein the evaluation index system of water environment risk in river network area is composed of a target layer, a standard layer and an index layer.
4. The method as claimed in claim 3, wherein in step S100, the step of constructing an evaluation index system of the water environment risk of the river network region with a hierarchical structure relationship based on the historical data includes:
and determining the weight of each evaluation index in the risk evaluation index system by using an analytic hierarchy process, and constructing a hierarchical structure relationship according to the weight and the membership of each evaluation index.
5. The method for predicting water environment risk based on pressure state feedback framework according to claim 4, wherein the step S200 comprises:
step S210, performing membership calculation on each evaluation index to obtain the membership of each evaluation index, wherein the evaluation indexes are divided into a reverse index and a forward index;
s220, establishing an index system according to each level relation of the index system, and establishing a fuzzy relation matrix according to the membership degree of each evaluation index;
and step S230, sequentially performing primary and secondary evaluation according to the weight of each evaluation index and the fuzzy relation matrix, and taking the risk grade determined according to the maximum membership rule as the final evaluation result of the evaluation index.
6. The method for predicting water environment risk based on pressure state feedback framework according to claim 5, wherein the step S300 comprises:
s310, determining the source intensity of a river network area under a pressure state feedback frame;
s320, predicting index values of an evaluation index system by adopting a prediction model system of the water environment risk of the river network area;
and S330, establishing a risk engineering regulation measure according to the water environment risk evaluation leading factor of the river network area, the river network risk grade division and the partition.
7. The method for predicting water environment risk based on the pressure state feedback framework as claimed in claim 6, wherein the model system for predicting water environment risk in the river network region comprises a data-driven model, a combined model and a mechanism model.
8. A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when being executed by a processor, the computer program implements the steps of the method for predicting water environment risk based on pressure state feedback framework according to any one of claims 1 to 7.
9. A water environment risk pre-judging system based on a pressure state feedback framework is characterized by comprising:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor may implement the method for predicting risk of aquatic environment according to any one of claims 1 to 7 based on the pressure state feedback framework.
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