CN112836842A - Watershed water environment quality prediction method and system based on source-sink risk analysis - Google Patents
Watershed water environment quality prediction method and system based on source-sink risk analysis Download PDFInfo
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Abstract
The invention provides a watershed water environment quality prediction method and a watershed water environment quality prediction system based on 'source-sink' risk analysis, which are used for analyzing control factors, material sources and basic processes influencing the watershed water chemistry change, determining the source distribution of the watershed water chemistry change, and evaluating and predicting the influence of environmental pressure change on the watershed water environment quality by constructing a 'source-sink' resistance model. The method has the advantages that the risk source distribution of the water chemistry change of the watershed is determined by analyzing the watershed water chemistry control factors, the material sources and the basic process, and the evaluation on the water chemistry change risk of the watershed is realized.
Description
Technical Field
The invention relates to the technical field of water environment prediction, in particular to a watershed water environment quality prediction method and a watershed water environment quality prediction system based on source-sink risk analysis.
Background
Rivers are an extremely important part of natural heritage. For thousands of years, humans have been using rivers, but at present few rivers are in a natural state. As one of the important water sources of human society, the water environment quality of rivers directly relates to the living environment quality and water safety of human beings. In the traditional river water environment quality monitoring, water quality data are obtained by arranging monitoring sections in functional areas with changed river water quality and water quantity and different purposes of a water body, and water environment quality indexes (WQI) are generally adopted to evaluate the water environment quality, so that most developed water environment quality prediction methods are models based on single water quality indexes. On one hand, when the river is used as a linear water body flowing along the terrain, the feasibility and the convenience of actual sampling need to be considered, and the whole process, continuous reading and dynamic monitoring of the water quality index of the whole river basin are difficult to realize. On the other hand, the influence of water on ecology and health is mainly caused by the combined action of multiple ion elements in the water, and the water environment quality cannot be comprehensively represented by a single water quality index. The method is more effective than the current single pollutant model in the aspect of simulating the contribution degree of the ion elements of the watershed water body to the water environment quality by combining multiple water quality indexes.
The water chemistry of a river is an important feature of a watershed that indicates the climate and environment through which the river passes. The chemical composition of rivers is mainly controlled by natural processes such as precipitation, weathering, soil erosion and man-made effects. By analyzing the chemical components of the river water body and various environmental factors of the basin, the chemical control factors, the material sources and the basic process of the river water can be determined.
Disclosure of Invention
The invention overcomes the defects in the prior art, and provides a watershed water environment quality prediction method and system based on 'source-sink' risk analysis.
The purpose of the invention is realized by the following technical scheme.
The watershed water environment quality prediction method based on the source-sink risk analysis is carried out according to the following steps:
s1, determining the characteristics of the water chemistry type of the watershed based on the water chemistry data of the watershed by adopting a Schuckerff classification principle, determining the source correlation factors of the water chemistry substances of the watershed through correlation and difference analysis by combining the types and the attributes of soil of the watershed, constructing a multi-factor importance scale matrix, quantifying the source strength of the water chemistry substances in different regions, refining the risk degree according to the source correlation factors, and defining the source risk grade distribution of the water chemistry substances of the watershed;
s2, acquiring spatial distribution data of the river basin related environmental factors based on river water chemical source basic process analysis, constructing a multi-factor importance scale matrix, quantifying the resistance strength of the basic process in different areas, and defining the environmental resistance risk grade distribution based on the refined risk degree;
s3, superposing the watershed water chemistry material source risk grade distribution obtained in the S1 and the environment resistance risk grade distribution obtained in the S2, obtaining strength and distribution of the watershed water chemistry source after quantification, refining the watershed water chemistry change source grade and space distribution according to the strength and distribution, establishing a watershed source-sink resistance model based on the space distribution of the various grade sources, and evaluating the river water chemistry change risk distribution condition by calculating the minimum accumulated resistance value from the various grade sources to the water body, wherein the model formula is as follows:
wherein f is an unknown negative function which is the negative correlation of the minimum cumulative resistance and ecological suitability; dijIs the spatial distance from source j to the landscape element; riThe resistance coefficient of the landscape unit to the movement process of the i is shown.
S4, carrying out normalization processing on the variation curves of the water quality parameters of the watershed water body, then respectively taking one variation curve with the highest linearity of each data curve obtained by normalization processing in the different grade source risk variation curves as a reference curve, carrying out weighted average on the reference curve and other curves, wherein the weighting coefficient is in direct proportion to the corresponding linearity, and finally establishing a series of function mappings of each normalized data curve and the risk curve after weighted average to be used as a correlation function set of the water quality data and the environmental risk;
s5, setting a quantized environment pressure intensity change scene, predicting the change condition of each water quality data of the basin by referring to the water quality data and environment risk association function set obtained in S4, calculating a residual sequence between a prediction result and actual water quality monitoring data according to the actual water quality monitoring data, setting a basin water environment quality threshold, obtaining a water environment quality abnormal change value and area based on the prediction data change trend analysis, and evaluating the basin water environment quality condition.
S3 includes:
s3-1, considering accessibility of a surface unit to a watershed water body, constructing a watershed buffer area based on river water body convergent cost analysis, and refining the watershed water chemistry change source grade and space distribution on the basis of overlaying the watershed water chemistry substance source risk grade distribution obtained in S1 and the environmental resistance risk grade distribution obtained in S2;
and S3-2, calculating the minimum accumulated resistance value from each grade source to the water body by adopting a minimum accumulated resistance model, and evaluating the river water chemical change risk distribution condition according to the complete negative correlation between the accumulated cost value and the risk.
In S4, the water quality parameters include, but are not limited to, primary ion concentration: ca2+(mg/L)、Mg2+(mg/L)、K+(mg/L)、Na+(mg/L)、SO4 2-(mg/L)、HCO3 -(mg/L)、Cl-(mg/L)、NO3 -(mg/L); concentration of trace elements: hg (. mu.g/L), Ni (. mu.g/L), Co (. mu.g/L), Zn (. mu.g/L)/L)、Cu(μg/L)、Cr(μg/L)、Mn(μg/L)。
S5 includes:
s5-1, performing signal reconstruction on the predicted water quality change data by adopting a wavelet reconstruction method, comparing the signal with actual water quality monitoring data to reconstruct a residual sequence, fitting the distribution of the residual sequence by adopting a corresponding distribution function, calculating to obtain goodness-of-fit and a distribution equation, and correcting a correlation function set according to the residual distribution equation;
s5-2, carrying out spatial change trend analysis and mutation point detection on the predicted water quality change data by adopting an M-K trend detection method, calculating and distinguishing normal water quality data and abnormal water quality data based on a set watershed water environment quality threshold value, and evaluating the watershed water environment quality condition.
The watershed water environment quality prediction system based on the source-sink risk analysis comprises an acquisition terminal, a communication main control unit, a data processing unit, a central management server, a data storage server, a display unit, an FTP server and a collection terminal;
the acquisition terminal is used for acquiring the water chemistry monitoring data in the basin at irregular intervals;
the communication main control unit is used for receiving the water chemistry monitoring data transmitted by each acquisition terminal and transmitting the water chemistry monitoring data to the data processing unit;
the data processing unit is internally integrated with a watershed water environment quality prediction algorithm based on source-sink risk analysis and used for processing and predicting the received water chemistry data and calculating a water environment quality prediction result;
the central management server is used for transmitting the acquired water chemistry monitoring data, the risk distribution data, the function mapping/correction data and the water quality prediction result to a supervisor through the communication main control unit, transmitting the acquired water chemistry monitoring data, the acquired risk distribution data, the function mapping/correction data and the water quality prediction result to the collection terminal through the FTP server, and storing the acquired water chemistry monitoring data, the acquired function mapping/correction data and the water quality prediction result into the data storage server or/and transmitting the acquired water chemistry monitoring data, the acquired function mapping/;
the data storage server is used for storing all the monitoring data, the risk data, the associated data and the prediction result which are processed by the data processing unit;
the display unit is used for displaying the distribution of the watershed water risk and the environmental quality prediction result;
the FTP server is used for sending water chemistry monitoring data, risk distribution data, function mapping/correction data and a water quality prediction result in the central management server to the collection terminal;
and the collecting terminal is used for receiving and caching various system data sent by the FTP server.
The data output end of the acquisition terminal is connected with the data acquisition end of the communication main control unit, the data output end of the communication main control unit is connected with the data receiving end of the data processing unit, the result output end and the data output end of the data processing unit are both connected with the data storage end of the data storage server, the result output end of the data processing unit is also connected with the data input end of the central management server, the data output end of the central management server transmits data to a supervisor through the communication main control unit, and then sends the related data to the acquisition terminal through the FTP server, and meanwhile, the related data are stored in the data storage server or/and sent to the display unit for display.
The acquisition terminal adopts a water chemistry data acquisition terminal, and the acquisition terminal is distributed at each position of a flow domain.
The collection terminal adopts an environmental information collection terminal.
The data processing unit comprises a risk analysis module, a correlation construction module, a water environment quality prediction calculation module, a water environment quality residual calculation module and a water environment quality judgment module,
the risk analysis module is used for quantizing the acquired risk factor values into a plurality of grades, overlapping the grades to form source sequences of the grades, and acquiring environmental risk factor change data by calculating the minimum accumulated cost value;
the association building module comprises a data preprocessing unit and a function mapping unit,
the data preprocessing unit is used for standardizing water quality change data and linearly weighting the environmental risk factor change data;
the function mapping unit is used for carrying out correlation analysis processing on the water quality change data and the environmental risk factor change data processed by the data preprocessing unit, constructing a correlation function set and calculating a threshold interval for distinguishing whether the water environment quality data is abnormal or not;
the water environment quality prediction calculation module predicts the water environment quality through a function mapping set generated by the association construction module according to the input collected environment risk factor information;
the water environment quality residual error calculation module is used for calculating a residual error sequence between a water environment quality prediction result and actual water environment quality monitoring data;
and the water environment quality judgment module is used for judging the change trend and the mutation point information of the water environment quality prediction result and comparing the change trend and the mutation point information with a threshold value interval.
If the prediction result of the water environment quality judgment module exceeds the threshold interval, the current water environment quality is abnormal, otherwise, the water environment quality is normal, and the water environment quality monitoring data and the water environment quality prediction result after judgment are sent to the central management server and stored in the data storage server.
The invention has the beneficial effects that: according to the invention, the internal relation between water chemistry and different environment parameters is researched, and the watershed water environment quality prediction method and system based on source-sink risk analysis are established. The method can realize the whole-process, continuous reading and dynamic prediction of the river whole-watershed water environment quality, and give corresponding early warning information according to the predicted watershed water environment quality change state.
Drawings
FIG. 1 is a frame structure diagram of a watershed water environment quality prediction system based on a source-sink risk analysis according to the present invention;
FIG. 2 is a flow chart of the watershed water environment quality prediction method based on the source-sink risk analysis according to the present invention;
FIG. 3 is a plot of the source risk profile of the watershed material in a preferred embodiment of the invention;
FIG. 4 is a plot of the environmental resistance of a basin in a preferred embodiment of the present invention;
fig. 5 shows the water environment quality prediction status of the watershed according to the preferred embodiment of the invention.
Detailed Description
The technical solution of the present invention is further illustrated by the following specific examples.
Example 1
As shown in fig. 1, the watershed water environment quality prediction system based on the source-sink risk analysis comprises an acquisition terminal, a communication main control unit, a data processing unit, a central management server, a data storage server, a display unit, an FTP server and a collection terminal;
the acquisition terminal is used for acquiring the water chemistry monitoring data in the basin at irregular intervals;
the communication main control unit is used for receiving the water chemistry monitoring data transmitted by each acquisition terminal and transmitting the water chemistry monitoring data to the data processing unit;
the data processing unit is internally integrated with a watershed water environment quality prediction algorithm based on source-sink risk analysis and used for processing and predicting the received water chemistry data and calculating a water environment quality prediction result;
the central management server is used for transmitting the acquired water chemistry monitoring data, the risk distribution data, the function mapping/correction data and the water quality prediction result to a supervisor through the communication main control unit, transmitting the acquired water chemistry monitoring data, the acquired risk distribution data, the function mapping/correction data and the water quality prediction result to the collection terminal through the FTP server, and storing the acquired water chemistry monitoring data, the acquired function mapping/correction data and the water quality prediction result into the data storage server or/and transmitting the acquired water chemistry monitoring data, the acquired function mapping/;
the data storage server is used for storing all the monitoring data, the risk data, the associated data and the prediction result which are processed by the data processing unit;
the display unit is used for displaying the distribution of the watershed water risk and the environmental quality prediction result;
the FTP server is used for sending water chemistry monitoring data, risk distribution data, function mapping/correction data and a water quality prediction result in the central management server to the collection terminal;
and the collecting terminal is used for receiving and caching various system data sent by the FTP server.
The data output end of the acquisition terminal is connected with the data acquisition end of the communication main control unit, the data output end of the communication main control unit is connected with the data receiving end of the data processing unit, the result output end and the data output end of the data processing unit are both connected with the data storage end of the data storage server, the result output end of the data processing unit is also connected with the data input end of the central management server, the data output end of the central management server transmits data to a supervisor through the communication main control unit, and then sends the related data to the acquisition terminal through the FTP server, and meanwhile, the related data are stored in the data storage server or/and sent to the display unit for display.
The acquisition terminal adopts a water chemistry data acquisition terminal, and the acquisition terminal is distributed at each position of a flow domain.
The collection terminal adopts an environmental information collection terminal.
The data processing unit comprises a risk analysis module, a correlation construction module, a water environment quality prediction calculation module, a water environment quality residual calculation module and a water environment quality judgment module,
the risk analysis module is used for quantizing the acquired risk factor values into a plurality of grades, overlapping the grades to form source sequences of the grades, and acquiring environmental risk factor change data by calculating the minimum accumulated cost value;
the association building module comprises a data preprocessing unit and a function mapping unit,
the data preprocessing unit is used for standardizing water quality change data and linearly weighting the environmental risk factor change data;
the function mapping unit is used for carrying out correlation analysis processing on the water quality change data and the environmental risk factor change data processed by the data preprocessing unit, constructing a correlation function set and calculating a threshold interval for distinguishing whether the water environment quality data is abnormal or not;
the water environment quality prediction calculation module predicts the water environment quality through a function mapping set generated by the association construction module according to the input collected environment risk factor information;
the water environment quality residual error calculation module is used for calculating a residual error sequence between a water environment quality prediction result and actual water environment quality monitoring data;
and the water environment quality judgment module is used for judging the change trend and the mutation point information of the water environment quality prediction result and comparing the change trend and the mutation point information with a threshold value interval.
If the prediction result of the water environment quality judgment module exceeds the threshold interval, the current water environment quality is abnormal, otherwise, the water environment quality is normal, and the water environment quality monitoring data and the water environment quality prediction result after judgment are sent to the central management server and stored in the data storage server.
Example 2
The watershed water environment quality prediction method based on the source-sink risk analysis is carried out according to the following steps as shown in fig. 2:
s1, determining the characteristics of the water chemistry type of the watershed based on the water chemistry data of the watershed by adopting a Schuckerff classification principle, determining the source correlation factors of the water chemistry substances of the watershed through correlation and difference analysis by combining the types and the attributes of soil of the watershed, constructing a multi-factor importance scale matrix, quantifying the source strength of the water chemistry substances in different regions, refining the risk degree according to the source correlation factors, and defining the source risk grade distribution of the water chemistry substances of the watershed;
s2, acquiring spatial distribution data of the river basin related environmental factors based on river water chemical source basic process analysis, constructing a multi-factor importance scale matrix, quantifying the resistance strength of the basic process in different areas, and defining the environmental resistance risk grade distribution based on the refined risk degree;
s3, superposing the watershed water chemistry material source risk grade distribution obtained in the S1 and the environment resistance risk grade distribution obtained in the S2, obtaining strength and distribution of the watershed water chemistry source after quantification, refining the watershed water chemistry change source grade and space distribution according to the strength and distribution, establishing a watershed source-sink resistance model based on the space distribution of the various grade sources, and evaluating the river water chemistry change risk distribution condition by calculating the minimum accumulated resistance value from the various grade sources to the water body, wherein the model formula is as follows:
wherein f is an unknown negative function which is the negative correlation of the minimum cumulative resistance and ecological suitability; dijIs the spatial distance from source j to the landscape element; riThe resistance coefficient of the landscape unit to the movement process of the i is shown;
s3-1, considering accessibility of a surface unit to a watershed water body, constructing a watershed buffer area based on river water body convergent cost analysis, and refining the watershed water chemistry change source grade and space distribution on the basis of overlaying the watershed water chemistry substance source risk grade distribution obtained in S1 and the environmental resistance risk grade distribution obtained in S2;
s3-2, calculating the minimum accumulated resistance value from each grade source to the water body by adopting a minimum accumulated resistance model, and evaluating the river water chemical change risk distribution condition according to the complete negative correlation between the accumulated cost value and the risk;
s4, carrying out normalization processing on the variation curves of the water quality parameters of the watershed water body, then respectively taking one variation curve with the highest linearity of each data curve obtained by normalization processing in the different grade source risk variation curves as a reference curve, carrying out weighted average on the reference curve and other curves, wherein the weighting coefficient is in direct proportion to the corresponding linearity, and finally establishing a series of function mappings of each normalized data curve and the risk curve after weighted average to be used as a correlation function set of the water quality data and the environmental risk;
water quality parameters include, but are not limited to, primary ion concentration: ca2+(mg/L)、Mg2+(mg/L)、K+(mg/L)、Na+(mg/L)、SO4 2-(mg/L)、HCO3 -(mg/L)、Cl-(mg/L)、NO3 -(mg/L); concentration of trace elements: hg (μ g/L), Ni (μ g/L), Co (μ g/L), Zn (μ g/L), Cu (μ g/L), Cr (μ g/L), Mn (μ g/L);
s5, setting a quantized environment pressure intensity change scene, predicting the change condition of each water quality data of the basin by referring to the water quality data and environment risk association function set obtained in S4, calculating a residual sequence between a prediction result and actual water quality monitoring data according to the actual water quality monitoring data, setting a basin water environment quality threshold, obtaining a water environment quality abnormal change value and area based on the prediction data change trend analysis, and evaluating the basin water environment quality condition;
s5-1, performing signal reconstruction on the predicted water quality change data by adopting a wavelet reconstruction method, comparing the signal with actual water quality monitoring data to reconstruct a residual sequence, fitting the distribution of the residual sequence by adopting a corresponding distribution function, calculating to obtain goodness-of-fit and a distribution equation, and correcting a correlation function set according to the residual distribution equation;
s5-2, carrying out spatial change trend analysis and mutation point detection on the predicted water quality change data by adopting an M-K trend detection method, calculating and distinguishing normal water quality data and abnormal water quality data based on a set watershed water environment quality threshold value, and evaluating the watershed water environment quality condition.
In the embodiment, the monitoring data of a river in a certain river basin is taken as a research object:
firstly, based on the Schuckleff classification method, the analysis result of the water chemistry types of the river main stream and each branch stream is Ca2+-HCO3 -Form and Na++K+-HCO3 -And (4) molding. The cluster analysis and the difference analysis are carried out on the soil physical and chemical characteristic values of the main flow and each branch flow water collecting area, the water collecting areas with the same water chemistry type have similar soil characteristics, and the significant difference between the two types is reflected in the exchange capacity of organic carbon and cations in the soil, so that the exchange capacity of the organic carbon and the cations in the soil is a key factor influencing the water chemistry type. Based on the difference analysis of the soil environment factors of the drainage basin, the loss risk distribution of the ionic elements in the soil environment of the drainage basin is constructed and identified, as shown in fig. 3.
Secondly, selecting a drainage basin typical environment factor index, performing spatial superposition analysis based on an analytic hierarchy process, and constructing and identifying drainage basin environment factor risk distribution. And (3) integrating the runoff risk distribution of the soil environment ionic elements of the drainage basin and the environmental factor risk distribution, and identifying the distribution of potential risk source regions in the vertical process of the water chemistry change of the drainage basin, as shown in fig. 4.
Thirdly, based on the watershed water cost distance analysis, combining the watershed potential risk source distribution, and finally determining the spatial distribution of the watershed water chemistry change source; on the basis of an environmental factor risk resistance datum plane, the minimum accumulated cost from the earth surface source unit to the water body unit is calculated, and the higher the accumulated cost value is, the larger the resistance received from the source unit to the water body unit is, the less the risk of the influence of the source unit on the corresponding water body unit is. Combining the results of different water collecting areas to finally obtain the risk distribution conditions of the basin water body under the action of the three water phases and different grade sources of the basin in the embodiment; and obtaining the degree of correlation as a correlation function set parameter based on the correlation analysis of a series of normalized data curves and the risk curve after weighted average.
Finally, setting the change situations of the environmental risk factors in different water periods of the embodiment, predicting the change situation of each water quality data of the basin, and evaluating the water environment quality condition of the basin, as shown in fig. 5.
The invention has been described in an illustrative manner, and it is to be understood that any simple variations, modifications or other equivalent changes which can be made by one skilled in the art without departing from the spirit of the invention fall within the scope of the invention.
Claims (10)
1. The watershed water environment quality prediction method based on the source-sink risk analysis is characterized by comprising the following steps: the method comprises the following steps:
s1, determining the characteristics of the water chemistry type of the watershed based on the water chemistry data of the watershed by adopting a Schuckerff classification principle, determining the source correlation factors of the water chemistry substances of the watershed through correlation and difference analysis by combining the types and the attributes of soil of the watershed, constructing a multi-factor importance scale matrix, quantifying the source strength of the water chemistry substances in different regions, refining the risk degree according to the source correlation factors, and defining the source risk grade distribution of the water chemistry substances of the watershed;
s2, acquiring spatial distribution data of the river basin related environmental factors based on river water chemical source basic process analysis, constructing a multi-factor importance scale matrix, quantifying the resistance strength of the basic process in different areas, and defining the environmental resistance risk grade distribution based on the refined risk degree;
s3, superposing the watershed water chemistry material source risk grade distribution obtained in the S1 and the environment resistance risk grade distribution obtained in the S2, obtaining strength and distribution of the watershed water chemistry source after quantification, refining the watershed water chemistry change source grade and space distribution according to the strength and distribution, establishing a watershed source-sink resistance model based on the space distribution of the various grade sources, and evaluating the river water chemistry change risk distribution condition by calculating the minimum accumulated resistance value from the various grade sources to the water body, wherein the model formula is as follows:
wherein f is an unknown negative function which is the negative correlation of the minimum cumulative resistance and ecological suitability; dijIs the spatial distance from source j to the landscape element; riThe resistance coefficient of the landscape unit to the movement process of the i is shown;
s4, carrying out normalization processing on the variation curves of the water quality parameters of the watershed water body, then respectively taking one variation curve with the highest linearity of each data curve obtained by normalization processing in the different grade source risk variation curves as a reference curve, carrying out weighted average on the reference curve and other curves, wherein the weighting coefficient is in direct proportion to the corresponding linearity, and finally establishing a series of function mappings of each normalized data curve and the risk curve after weighted average to be used as a correlation function set of the water quality data and the environmental risk;
s5, setting a quantized environment pressure intensity change scene, predicting the change condition of each water quality data of the basin by referring to the water quality data and environment risk association function set obtained in S4, calculating a residual sequence between a prediction result and actual water quality monitoring data according to the actual water quality monitoring data, setting a basin water environment quality threshold, obtaining a water environment quality abnormal change value and area based on the prediction data change trend analysis, and evaluating the basin water environment quality condition.
2. The watershed water environment quality prediction method based on the source-sink risk analysis as claimed in claim 1, wherein: s3 includes:
s3-1, considering accessibility of a surface unit to a watershed water body, constructing a watershed buffer area based on river water body convergent cost analysis, and refining the watershed water chemistry change source grade and space distribution on the basis of overlaying the watershed water chemistry substance source risk grade distribution obtained in S1 and the environmental resistance risk grade distribution obtained in S2;
and S3-2, calculating the minimum accumulated resistance value from each grade source to the water body by adopting a minimum accumulated resistance model, and evaluating the river water chemical change risk distribution condition according to the complete negative correlation between the accumulated cost value and the risk.
3. The watershed water environment quality prediction method based on the source-sink risk analysis as claimed in claim 1, wherein: in S4, the water quality parameters include, but are not limited to, primary ion concentration: ca2+(mg/L)、Mg2+(mg/L)、K+(mg/L)、Na+(mg/L)、SO4 2-(mg/L)、HCO3 -(mg/L)、Cl-(mg/L)、NO3 -(mg/L); concentration of trace elements: hg (. mu.g/L), Ni (. mu.g/L), Co (. mu.g/L), Zn (. mu.g/L), Cu (. mu.g/L), Cr (. mu.g/L), Mn (. mu.g/L).
4. The watershed water environment quality prediction method based on the source-sink risk analysis as claimed in claim 1, wherein: s5 includes:
s5-1, performing signal reconstruction on the predicted water quality change data by adopting a wavelet reconstruction method, comparing the signal with actual water quality monitoring data to reconstruct a residual sequence, fitting the distribution of the residual sequence by adopting a corresponding distribution function, calculating to obtain goodness-of-fit and a distribution equation, and correcting a correlation function set according to the residual distribution equation;
s5-2, carrying out spatial change trend analysis and mutation point detection on the predicted water quality change data by adopting an M-K trend detection method, calculating and distinguishing normal water quality data and abnormal water quality data based on a set watershed water environment quality threshold value, and evaluating the watershed water environment quality condition.
5. The prediction system of the watershed water environment quality prediction method based on the source-sink risk analysis as claimed in any one of claims 1 to 4, is characterized in that: the system comprises an acquisition terminal, a communication main control unit, a data processing unit, a central management server, a data storage server, a display unit, an FTP server and a collection terminal;
the acquisition terminal is used for acquiring the water chemistry monitoring data in the basin at irregular intervals;
the communication main control unit is used for receiving the water chemistry monitoring data transmitted by each acquisition terminal and transmitting the water chemistry monitoring data to the data processing unit;
the data processing unit is internally integrated with a watershed water environment quality prediction algorithm based on source-sink risk analysis and used for processing and predicting the received water chemistry data and calculating a water environment quality prediction result;
the central management server is used for transmitting the acquired water chemistry monitoring data, the risk distribution data, the function mapping/correction data and the water quality prediction result to a supervisor through the communication main control unit, transmitting the acquired water chemistry monitoring data, the acquired risk distribution data, the function mapping/correction data and the water quality prediction result to the collection terminal through the FTP server, and storing the acquired water chemistry monitoring data, the acquired function mapping/correction data and the water quality prediction result into the data storage server or/and transmitting the acquired water chemistry monitoring data, the acquired function mapping/;
the data storage server is used for storing all the monitoring data, the risk data, the associated data and the prediction result which are processed by the data processing unit;
the display unit is used for displaying the distribution of the watershed water risk and the environmental quality prediction result;
the FTP server is used for sending water chemistry monitoring data, risk distribution data, function mapping/correction data and a water quality prediction result in the central management server to the collection terminal;
and the collecting terminal is used for receiving and caching various system data sent by the FTP server.
6. The watershed water environment quality prediction system based on the source-sink risk analysis according to claim 5, characterized in that: the data output end of the acquisition terminal is connected with the data acquisition end of the communication main control unit, the data output end of the communication main control unit is connected with the data receiving end of the data processing unit, the result output end and the data output end of the data processing unit are both connected with the data storage end of the data storage server, the result output end of the data processing unit is also connected with the data input end of the central management server, the data output end of the central management server transmits data to a supervisor through the communication main control unit, and then sends the related data to the acquisition terminal through the FTP server, and meanwhile, the related data are stored in the data storage server or/and sent to the display unit for display.
7. The watershed water environment quality prediction system based on the source-sink risk analysis according to claim 6, characterized in that: the acquisition terminal adopts a water chemistry data acquisition terminal, and the acquisition terminal is distributed at each position of a flow domain.
8. The watershed water environment quality prediction system based on the source-sink risk analysis according to claim 6, characterized in that: the collection terminal adopts an environmental information collection terminal.
9. The watershed water environment quality prediction system based on the source-sink risk analysis according to claim 6, characterized in that: the data processing unit comprises a risk analysis module, a correlation construction module, a water environment quality prediction calculation module, a water environment quality residual calculation module and a water environment quality judgment module,
the risk analysis module is used for quantizing the acquired risk factor values into a plurality of grades, overlapping the grades to form source sequences of the grades, and acquiring environmental risk factor change data by calculating the minimum accumulated cost value;
the association building module comprises a data preprocessing unit and a function mapping unit,
the data preprocessing unit is used for standardizing water quality change data and linearly weighting the environmental risk factor change data;
the function mapping unit is used for carrying out correlation analysis processing on the water quality change data and the environmental risk factor change data processed by the data preprocessing unit, constructing a correlation function set and calculating a threshold interval for distinguishing whether the water environment quality data is abnormal or not;
the water environment quality prediction calculation module predicts the water environment quality through a function mapping set generated by the association construction module according to the input collected environment risk factor information;
the water environment quality residual error calculation module is used for calculating a residual error sequence between a water environment quality prediction result and actual water environment quality monitoring data;
and the water environment quality judgment module is used for judging the change trend and the mutation point information of the water environment quality prediction result and comparing the change trend and the mutation point information with a threshold value interval.
10. The watershed water environment quality prediction system based on the source-sink risk analysis according to claim 9, wherein: if the prediction result of the water environment quality judgment module exceeds the threshold interval, the current water environment quality is abnormal, otherwise, the water environment quality is normal, and the water environment quality monitoring data and the water environment quality prediction result after judgment are sent to the central management server and stored in the data storage server.
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