CN118037051A - Risk identification processing method for regional burst river basin water environment - Google Patents
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Abstract
The invention discloses a risk identification processing method for a regional burst drainage basin water environment, which belongs to the technical field of risk identification processing for drainage basin burst water environments, and comprises the following steps: identifying a target river basin, and establishing a multi-source information fusion platform according to the target river basin; dividing the target area diagram to obtain a plurality of unit areas; acquiring acquisition data of a target area, and processing the acquisition data to acquire unit risk characteristics of each risk event for each unit area; evaluating the risk values of the unit areas, and merging based on the risk values of the unit areas to obtain risk areas; marking each element block corresponding to the target area; identifying a reference evaluation area in each element block; acquiring the evaluation features of the evaluation areas; calculating element values of the element blocks according to the evaluation features; setting a corresponding risk matrix according to the element values, carrying out risk assessment according to the risk matrix, obtaining a corresponding risk assessment value, and carrying out corresponding processing according to the risk assessment value.
Description
Technical Field
The invention belongs to the technical field of risk identification processing of a water environment in a river basin burst, and particularly relates to a risk identification processing method of a water environment in a regional burst river basin.
Background
The risk identification treatment of the water environment in the river basin is an important problem in the field of environmental protection, and with the acceleration of industrial development and urban process, the water environment in the river basin is increasingly prominent, such as water quality pollution, water ecological system destruction and the like. However, the existing watershed water environment risk identification processing system has some defects, such as inaccurate monitoring data, untimely early warning, and not rapid emergency response, which brings difficulty to the protection and management of the watershed water environment. Therefore, a more intelligent, efficient and reliable risk identification processing method for a river basin sudden water environment is needed to solve the problems. Based on the method, the invention provides a risk identification processing method for the regional burst drainage basin water environment.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides a risk identification processing method for regional burst drainage basin water environment.
The aim of the invention can be achieved by the following technical scheme:
A risk identification processing method for regional burst river basin water environment comprises the following steps:
Step one: identifying a target river basin, and establishing a corresponding multi-source information fusion platform according to the target river basin, wherein the multi-source information fusion platform is used for acquiring acquisition data of the target river basin;
further, the method for establishing the multi-source information fusion platform comprises the following steps:
determining each risk event according to the target river basin, and setting an acquisition item corresponding to each risk event; setting an acquisition template corresponding to each risk event according to each acquisition item; counting all the acquisition items to determine corresponding data parties; and establishing a multi-source information fusion platform according to each data party, and butting each data party through the multi-source information fusion platform.
Further, the method for determining the acquisition item comprises the following steps:
acquiring the region characteristics of the target region, and determining all equivalent regions according to the region characteristics;
Acquiring historical drainage basin data according to the target area and the equivalent area; counting each risk event according to the historical drainage basin data, and corresponding occurrence times and acquisition characteristics;
acquiring acquisition representative features of the target area corresponding to the acquisition features, and combining the acquisition features with the acquisition representative features to form corresponding sub-combinations;
Analyzing the sub-packet combination through a preset sub-packet model;
according to the formula Calculating a corresponding screening value;
Wherein: TY rate is the screening value; n o represents the occurrence times corresponding to the risk event; n U represents the number of records corresponding to the historical drainage basin data; f (a c) represents a sub-division model, a c represents a corresponding sub-division combination, c=1, 2, … …, v being a positive integer;
and eliminating risk accidents with screening values lower than the threshold value X1.
Further, the expression of the sub-sub combination is；
Wherein: a c represents a corresponding sub-combination; c=1, 2, … …, v being a positive integer.
Step two: obtaining a target area diagram, and dividing the target area diagram to obtain a plurality of unit areas; acquiring acquisition data of the target area in real time through the multi-source information fusion platform, and processing the acquisition data to acquire unit risk characteristics of each risk event for each unit area;
step three: evaluating the risk value of each unit area according to each unit risk characteristic, and merging based on the risk value corresponding to each unit area to obtain each risk area;
further, the risk value calculating method includes:
Establishing an integral feature library and a target feature library, wherein the integral feature library is used for storing risk features of each unit and corresponding accident occurrence rate, which are set by combining a target area and an equivalent area; the target feature library is used for storing risk features of each unit and corresponding accident occurrence rate set according to the target area;
The unit risk features are respectively input into an integral feature library and a target feature library for matching, and corresponding initial values and target values are obtained;
identifying an initial upper limit value, an initial lower limit value, a target upper limit value and a target lower limit value which are respectively corresponding to the integral feature library and the target feature library;
according to the formula Calculating a corresponding risk value;
Wherein: f rior is a risk value; SG (k) is a symbol model, and k is input data; GL 1 is the target value; GL 0 is an initial value; GL min is the target lower limit, GL max is the target upper limit, exp is the exponential function with the base of the abnormal constant e; /(I) CL min is an initial lower limit value, and CL max is an initial upper limit value.
Further, the expression of the symbol model is:。
further, the method for merging the unit areas includes:
Step SA1: identifying a risk value and a corresponding target value of each unit area; setting a unit vector x, x= (F rior,GL1) according to the risk value and the target value of the unit area;
Step SA2: calculating a merging evaluation value between adjacent unit areas according to an evaluation formula;
The evaluation formula is: ；
Wherein: pd is a combined evaluation value; d (x 1,x2) represents the distance between two cell vectors; x 1 and x 2 represent two corresponding cell vectors, respectively;
Step SA3: combining the two unit areas with the combined evaluation value smaller than the threshold value X2 to obtain a new unit area, and setting a unit vector of the new unit area; wherein the thresholds X1, X2 are not the same as the cell vector X 1、x2;
Step SA4: step SA2 and step SA3 are looped until two unit areas having an evaluation value smaller than the threshold value X2 are not merged, and the remaining unit areas are marked as risk areas.
Step four: marking each element block corresponding to the target area; identifying risk areas in the element blocks, and marking the risk areas as evaluation areas; acquiring the parameter evaluation characteristics corresponding to the parameter evaluation area, wherein the parameter evaluation characteristics comprise areas and risk values; calculating element values corresponding to the element blocks according to the evaluation features;
Further, the element value calculating method includes:
marking the evaluation region as t, t=1, 2, … …, l being a positive integer;
Respectively marking the risk value and the face corresponding to the parameter evaluation area as Ft and At according to the parameter evaluation characteristics;
according to the formula Calculating corresponding element values; wherein: r is an element value; e is a constant.
Step five: setting a corresponding risk matrix according to each element value, carrying out risk assessment according to the risk matrix, obtaining a corresponding risk assessment value, and carrying out corresponding processing according to the risk assessment value.
Marking a risk matrix asThe element in the risk matrix is marked as r ij; i=1, 2, … …, n being a positive integer; j=1, 2, … …, m being a positive integer; the value of r ij is the corresponding element value;
according to the formula Calculating a corresponding risk assessment value;
Wherein: PU rate is a risk assessment value; the R F represents the Frobenius norm of the risk matrix R; w (R) is a front end model, and the expression is ; X3 is a threshold; EP is a real number; max (R) represents the maximum element value in the selected risk matrix; lg (x) represents a logarithmic function based on 10.
Compared with the prior art, the invention has the beneficial effects that:
The method and the system can rapidly and accurately identify and process the sudden water environment risk of the river basin, reduce the influence of risk events, strengthen the information exchange and cooperation among departments and areas in the river basin through information sharing and cooperation, and improve the management level of the whole river basin; by analyzing the target area, the acquisition items needing data acquisition are accurately determined, the existing data resources are fully utilized, all data parties are determined, and the data acquisition of the target area is realized by arranging a multi-source information fusion platform.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a risk identification processing method for a regional burst drainage basin water environment includes:
Step one: identifying a river basin needing risk identification, and marking the river basin as a target river basin; acquiring various risk events which can occur in the target river basin, such as natural disasters, nuclear pollution accidents, oil spilling accidents, accident leakage of toxic chemicals, aquatic ecosystem breakdown and the like; setting acquisition items corresponding to the risk events according to the determined risk events, namely, which data need to be acquired to carry out evaluation and judgment, and setting acquisition templates corresponding to the risk events according to the acquisition items; a risk time label corresponding to the template mark is acquired;
Counting each non-repeated acquisition item according to each acquisition template, marking the acquisition item as a source item, and determining each data party according to each source item, wherein if a weather party for weather data is acquired by self-setting related acquisition equipment, the data party is regarded as a self-provider; establishing a multi-source information fusion platform according to each data party, butting each data party through the multi-source information fusion platform, and identifying acquisition data for acquiring each requirement through the multi-source information fusion platform; and performs data processing in the aspects of corresponding formats and the like. And the multi-source information fusion platform can share data among all data parties.
In one embodiment, for setting the acquisition item, the setting may be performed directly by an existing manner; such as direct statistics or manual settings; however, the working efficiency of the existing mode is low, whether the risk event corresponding to the acquisition item is reasonable or not is not considered, and excessive resources are easy to occupy; another mode is proposed, wherein each risk event is screened firstly, and then acquisition items corresponding to the screened risk event are determined; the method for screening the risk event comprises the following steps:
Acquiring regional characteristics of a target region, such as characteristics of geographic, climate, surrounding environment and the like which have influence on risks, determining each region similar to the target region based on a similarity algorithm, marking each region meeting the requirement as an equivalent region, namely marking each region with the similarity as an equivalent region, and acquiring historical basin data equivalent to the target region, wherein the condition that the data is insufficient, representative insufficient and unilateral is possible due to the fact that the historical basin data of the target region is only relied on; acquiring corresponding historical drainage basin data according to the target area and the equivalent area; counting each risk event and the occurrence times corresponding to each risk event according to the obtained historical drainage basin data, identifying the corresponding acquisition characteristics when corresponding risk accidents occur each time, and determining according to the corresponding acquisition templates;
Acquiring the acquisition representative feature of the current target area, namely acquiring the corresponding acquisition characteristic of the target area in a period of time as the corresponding acquisition representative feature, wherein the acquisition representative feature is a range set, such as a risk accident in a rainy season, then the collection of the acquisition representative features of the target area in the rainy season, namely acquiring the representative feature, and counting the acquisition features in the rainy season according to preset time, such as preset time of 2 years, 3 years and the like;
combining the acquisition characteristics with the corresponding acquisition representative characteristics to form corresponding sub-combinations;
A large number of sub-combinations are simulated, a corresponding sub-model is built according to the sub-combinations, specifically, the sub-combinations are built based on an isolated forest algorithm, the sub-combinations are used for analyzing whether differences between acquisition characteristics and corresponding acquisition representative characteristics in the sub-combinations are abnormal, the degree that the acquisition representative characteristics are superior to the acquisition characteristics reaches a preset requirement to be regarded as abnormal, input data are sub-combinations, and output data are sub-scores, wherein the sub-scores comprise 1 or 0; the expression is ; C represents the corresponding sub-combination, and can be equal to the corresponding acquisition characteristics, the corresponding frequency data of the risk accidents and the like; c=1, 2, … …, v being a positive integer; a c denotes the corresponding sub-combination.
According to the formulaCalculating a corresponding screening value;
Wherein: TY rate is the screening value; n o represents the occurrence times corresponding to the risk event; n U represents the number of records corresponding to the historical drainage basin data; f (a c) represents a sub-division model, c=1, 2, … …, v being a positive integer.
And eliminating risk accidents with screening values lower than the threshold value X1.
By analyzing the target area, the acquisition items needing data acquisition are accurately determined, the existing data resources are fully utilized, all data parties are determined, and the data acquisition of the target area is realized by arranging a multi-source information fusion platform.
Step two: dividing a target area into a plurality of unit blocks, presetting a corresponding size for dividing, dividing data such as a drawing and the like, and dividing the data instead of land; acquiring acquisition data of a target area in real time through a multi-source information fusion platform, and distributing the acquired acquisition data according to an acquisition template corresponding to each risk event and a unit area corresponding to the position of the acquisition template to acquire risk acquisition data corresponding to each risk event in each unit area; extracting features of each risk acquisition data to obtain corresponding unit risk features; namely, extracting data according to the corresponding acquisition items, and extracting main data;
step three: evaluating the risk value of each unit area according to the risk characteristics of each unit, and merging according to the risk value corresponding to each unit area to obtain each risk area;
The risk value calculating method comprises the following steps:
Dynamically updating and establishing an integral feature library and a target feature library, wherein the integral feature library corresponds to a target area and each equivalent area, and the target feature library corresponds to the target area; acquiring each risk feature, namely a unit risk feature, in the corresponding region, and counting the corresponding accident occurrence rate; dynamically updating according to the corresponding historical data;
Identifying unit risk features, namely respectively inputting the identified unit risk features into an integral feature library and a target feature library for matching, and obtaining accident occurrence rates of the unit risk features respectively positioned in the integral feature library and the target feature library, wherein the accident occurrence rates are respectively marked as an initial value and a target value;
Identifying the highest accident rate and the lowest accident rate which are respectively corresponding in the integral feature library and the target feature library, and respectively marking the highest accident rate and the lowest accident rate as an initial upper limit value, an initial lower limit value, a target upper limit value and a target lower limit value;
according to the formula Calculating a corresponding risk value;
wherein: f rior is a risk value; SG (k) is a symbol model, and the expression is K is the comparison of the input data, TR 1、TR2、GL1、GL0; GL 1 is the target value; GL 0 is an initial value; /(I)GL min is the target lower limit, GL max is the target upper limit, exp is the exponential function with the base of the abnormal constant e; /(I)CL min is an initial lower limit value, and CL max is an initial upper limit value.
The method for merging the unit areas comprises the following steps:
Step SA1: identifying a risk value and a corresponding target value of each unit area; a unit vector x, x= (F rior,GL1) for setting a risk value and a target value corresponding to the unit region;
Step SA2: calculating a merging evaluation value between adjacent unit areas according to an evaluation formula; the evaluation formula is:
；
Wherein: pd is a combined evaluation value; d (x 1,x2) represents the distance between two cell vectors;
Step SA3: combining the two unit areas with the combined evaluation value smaller than the threshold value X2 to obtain a new unit area; the threshold value X2 is used for merging two unit areas with little difference; the method can be directly set by professionals, and the optimal mode is to simulate according to historical data, determine how to set the threshold value X2, so that the combination is more accurate and reasonable; wherein the thresholds X1, X2 are not the same as the cell vector X 1、x2, not the same concept; the unit vectors of the new merging unit are the merging of the original two unit vectors, and the corresponding specific gravity is determined according to the corresponding areas of the unit vectors, so that the merging is performed;
Illustratively, the area ratio of the two cell regions is 0.2 and 0.8, the cell vectors are (2, 2) and (4, 4), respectively, and the cell vectors are combined to be (2×0.2+4× 0.8) = (3.6, 3.6);
Step SA4: step SA2 and step SA3 are looped until two unit areas having an evaluation value smaller than the threshold value X2 are not merged, and the remaining unit areas are marked as risk areas.
Step four: setting a target matrix template according to a target area, namely firstly dividing the target area into a corresponding number of area blocks, wherein each area block corresponds to an element bit in a matrix, and then correspondingly filling corresponding data; dividing the elements equally according to a preset area range to form a plurality of element blocks;
Identifying the distribution of risk areas in each element block, and marking the distribution as a reference evaluation area; identifying the area and risk value of each evaluation area, marking the area and risk value as evaluation features, and directly identifying the risk value according to the corresponding unit vector;
Calculating element values corresponding to the element blocks according to the evaluation features;
marking the evaluation region as t, t=1, 2, … …, l being a positive integer;
Respectively marking the risk value and the face value corresponding to the evaluation area as Ft and At;
according to the formula Calculating corresponding element values;
Wherein: r is an element value; e is a constant.
Step five: setting a corresponding risk matrix according to the element values, carrying out risk assessment according to the risk matrix, obtaining a corresponding risk assessment value, and carrying out corresponding processing according to the risk assessment value.
Correspondingly bringing each element value into an element position to form a corresponding risk matrix, and marking the risk matrix asThe element in the risk matrix is marked as r ij; i=1, 2, … …, n being a positive integer; j=1, 2, … …, m being a positive integer; the value of r ij is the corresponding element value;
according to the formula Calculating a corresponding risk assessment value;
Wherein: PU rate is a risk assessment value; the R F represents the Frobenius norm of the risk matrix R; w (R) is a front end model, and the expression is ; X3 is a threshold value, and represents that a single element exceeds the standard; the EP is real, which is equivalent to infinity, and a larger value is generally selected at will, so that the problem is directly judged; max (R) represents the maximum element value in the selected risk matrix; lg (x) represents a logarithmic function based on 10.
Setting a risk evaluation value range of each risk event according to simulation, setting corresponding risk grades according to risk conditions corresponding to different risk evaluation values, pairing the risk grades with the current processing mode, and then directly processing the risk evaluation values according to the processing mode corresponding to the calculated risk evaluation values. Exemplary pollution source control, pollution diffusion prevention and emergency treatment measures; the risk identification and the space positioning are combined through the GIS technology, so that accurate risk positioning and dynamic tracking are realized.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (9)
1. The risk identification processing method for the regional burst river basin water environment is characterized by comprising the following steps of:
Step one: identifying a target river basin, and establishing a corresponding multi-source information fusion platform according to the target river basin, wherein the multi-source information fusion platform is used for acquiring acquisition data of the target river basin;
Step two: obtaining a target area diagram, and dividing the target area diagram to obtain a plurality of unit areas; acquiring acquisition data of the target area in real time through the multi-source information fusion platform, and processing the acquisition data to acquire unit risk characteristics of each risk event for each unit area;
step three: evaluating the risk value of each unit area according to each unit risk characteristic, and merging based on the risk value corresponding to each unit area to obtain each risk area;
Step four: marking each element block corresponding to the target area; identifying risk areas in the element blocks, and marking the risk areas as evaluation areas; acquiring the parameter evaluation characteristics corresponding to the parameter evaluation area, wherein the parameter evaluation characteristics comprise areas and risk values; calculating element values corresponding to the element blocks according to the evaluation features;
Step five: setting a corresponding risk matrix according to each element value, carrying out risk assessment according to the risk matrix, obtaining a corresponding risk assessment value, and carrying out corresponding processing according to the risk assessment value.
2. The risk identification processing method for the regional burst drainage basin water environment according to claim 1, wherein the method for establishing the multi-source information fusion platform comprises the following steps:
determining each risk event according to the target river basin, and setting an acquisition item corresponding to each risk event; setting an acquisition template corresponding to each risk event according to each acquisition item; counting all the acquisition items to determine corresponding data parties; and establishing a multi-source information fusion platform according to each data party, and butting each data party through the multi-source information fusion platform.
3. The risk identification processing method for the regional burst drainage basin water environment according to claim 2, wherein the method for determining the acquisition items comprises the following steps:
acquiring the region characteristics of the target region, and determining all equivalent regions according to the region characteristics;
Acquiring historical drainage basin data according to the target area and the equivalent area; counting each risk event according to the historical drainage basin data, and corresponding occurrence times and acquisition characteristics;
acquiring acquisition representative features of the target area corresponding to the acquisition features, and combining the acquisition features with the acquisition representative features to form corresponding sub-combinations;
Analyzing the sub-packet combination through a preset sub-packet model;
according to the formula Calculating a corresponding screening value;
Wherein: TY rate is the screening value; n o represents the occurrence times corresponding to the risk event; n U represents the number of records corresponding to the historical drainage basin data; f (a c) represents a sub-division model, a c represents a corresponding sub-division combination, c=1, 2, … …, v being a positive integer;
and eliminating risk accidents with screening values lower than the threshold value X1.
4. The risk identification processing method for regional burst drainage basin water environment according to claim 3, wherein the expression of the sub-combination is that；
Wherein: a c represents a corresponding sub-combination; c=1, 2, … …, v being a positive integer.
5. The risk identification processing method for the regional burst drainage basin water environment according to claim 1, wherein the risk value calculation method comprises the following steps:
Establishing an integral feature library and a target feature library, wherein the integral feature library is used for storing risk features of each unit and corresponding accident occurrence rate, which are set by combining a target area and an equivalent area; the target feature library is used for storing risk features of each unit and corresponding accident occurrence rate set according to the target area;
The unit risk features are respectively input into an integral feature library and a target feature library for matching, and corresponding initial values and target values are obtained;
identifying an initial upper limit value, an initial lower limit value, a target upper limit value and a target lower limit value which are respectively corresponding to the integral feature library and the target feature library;
according to the formula Calculating a corresponding risk value;
Wherein: f rior is a risk value; SG (k) is a symbol model, and k is input data; GL 1 is the target value; GL 0 is an initial value; GL min is the target lower limit, GL max is the target upper limit, exp is the exponential function with the base of the abnormal constant e; /(I) CL min is an initial lower limit value, and CL max is an initial upper limit value.
6. The risk identification processing method for regional burst drainage basin water environment according to claim 5, wherein the expression of the symbol model is:。
7. The method for risk identification treatment of regional burst drainage basin environments according to claim 5, wherein the method for merging the unit regions comprises the following steps:
Step SA1: identifying a risk value and a corresponding target value of each unit area; setting a unit vector x, x= (F rior,GL1) according to the risk value and the target value of the unit area;
Step SA2: calculating a merging evaluation value between adjacent unit areas according to an evaluation formula;
The evaluation formula is: ；
Wherein: pd is a combined evaluation value; d (x 1,x2) represents the distance between two cell vectors; x 1 and x 2 represent two corresponding cell vectors, respectively;
Step SA3: combining the two unit areas with the combined evaluation value smaller than the threshold value X2 to obtain a new unit area, and setting a unit vector of the new unit area; wherein the thresholds X1, X2 are not the same as the cell vector X 1、x2;
Step SA4: step SA2 and step SA3 are looped until two unit areas having an evaluation value smaller than the threshold value X2 are not merged, and the remaining unit areas are marked as risk areas.
8. The risk identification processing method for the regional burst drainage basin water environment according to claim 1, wherein the element value calculation method comprises the following steps:
marking the evaluation region as t, t=1, 2, … …, l being a positive integer;
Respectively marking the risk value and the face corresponding to the parameter evaluation area as Ft and At according to the parameter evaluation characteristics;
according to the formula Calculating corresponding element values; wherein: r is an element value; e is a constant.
9. The risk identification processing method for the regional burst drainage basin water environment according to claim 1, wherein the risk assessment value calculating method comprises the following steps:
marking a risk matrix as The element in the risk matrix is marked as r ij; i=1, 2, … …, n being a positive integer; j=1, 2, … …, m being a positive integer; the value of r ij is the corresponding element value;
according to the formula Calculating a corresponding risk assessment value;
Wherein: PU rate is a risk assessment value; the R F represents the Frobenius norm of the risk matrix R; w (R) is a front end model, and the expression is ; X3 is a threshold; EP is a real number; max (R) represents the maximum element value in the selected risk matrix; lg (x) represents a logarithmic function based on 10.
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