CN116384776B - Site pollution evaluation method, system and storage medium based on fuzzy evaluation - Google Patents
Site pollution evaluation method, system and storage medium based on fuzzy evaluation Download PDFInfo
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Abstract
The invention relates to a field pollution evaluation method, a field pollution evaluation system and a storage medium based on fuzzy evaluation, which belong to the technical field of pollution evaluation. According to the invention, the membership degree of the sample to other classes is inhibited by introducing the inhibition factor model, so that a reward mechanism of the maximum membership degree can be further enhanced, the convergence of a clustering algorithm can be accelerated, and the clustering efficiency is improved; on the other hand, the invention corrects the pollution diffusion result through the stress-like data of the plants, thereby obtaining more accurate pollution diffusion result and improving the evaluation accuracy of pollution diffusion.
Description
Technical Field
The invention relates to the technical field of pollution evaluation, in particular to a field pollution evaluation method, a field pollution evaluation system and a storage medium based on fuzzy evaluation.
Background
In recent years, the situations of various soil pollution types, coexistence of new and old pollution and serious inorganic and organic comprehensive pollution occur in areas such as key pollution enterprises, industrial dense areas, industrial mining areas, surrounding areas and the like, wherein mining is one of the main sources of heavy metal pollution in environmental media. The soil heavy metal pollution has the characteristics of concealment, accumulation, territory and the like, and the pollution characteristic of the clean field is the premise of safe utilization of the clean field. However, in the field pollution evaluation process, sample data needs to be converted into data which can be identified by a computer system through a certain algorithm so as to realize quick identification of pollution conditions; the fuzzy clustering algorithm is also an efficient algorithm for processing sample data, and the fuzzy clustering analysis is an analysis method for clustering the objective things by establishing a fuzzy similarity relation according to the characteristics, the affinity and the similarity among the objective things. However, the clustering efficiency of the fuzzy clustering algorithm is low when the fuzzy clustering algorithm is used for classifying polluted sample data, and the fuzzy clustering algorithm is easy to fall into a local optimal solution; second, diffusion predictions for site contamination are not accurate enough.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a field pollution evaluation method, a field pollution evaluation system and a storage medium based on fuzzy evaluation.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the first aspect of the invention provides a field pollution evaluation method based on fuzzy evaluation, which comprises the following steps:
obtaining pollution sample data information of a current target area, and clustering the pollution sample data information of the current target area through a fuzzy clustering algorithm to obtain a membership matrix corresponding to the pollution sample data;
introducing a suppression factor, correcting a membership matrix corresponding to the pollution sample data according to the suppression factor, and obtaining a membership function corresponding to the corrected pollution sample data;
obtaining membership data of each pollution sample data according to a membership function corresponding to the corrected pollution sample data, and generating a visual pollution degree distribution map based on the membership data of each pollution sample data;
and obtaining digital elevation model data information of the target area, carrying out diffusion prediction according to the digital elevation model data information of the target area and the visual pollution degree distribution map, obtaining a diffusion prediction result, and correcting the diffusion prediction result to obtain a final evaluation result.
Further, in a preferred embodiment of the present invention, the present invention clusters the information of the contaminated sample data of the current target area by a fuzzy clustering algorithm to obtain a membership matrix corresponding to the contaminated sample data, and specifically includes the following steps:
initializing a range of a clustering center, introducing a particle swarm algorithm, and setting a particle swarm scale, a maximum iteration number and a learning factor;
setting the clustering number, iteration termination conditions, maximum iteration times, gaussian kernel number, gaussian kernel parameters and constraint coefficients according to the pollution sample data information of the current target area;
according to the range of the clustering center, continuously updating the clustering center and the membership matrix through an objective function of a clustering algorithm, and stopping iteration when the maximum iteration number is reached, so as to obtain a corresponding membership matrix;
and setting the target sample as a corresponding optimal clustering center according to the corresponding membership matrix, and outputting the membership matrix corresponding to the polluted sample data.
Further, in a preferred embodiment of the present invention, an inhibitor is introduced, and a membership matrix corresponding to the pollution sample data is modified according to the inhibitor, so as to obtain a membership function corresponding to the modified pollution sample data, which specifically includes:
Obtaining membership corresponding to each sample according to a membership matrix corresponding to the pollution sample data, and constructing an inhibition factor generation model;
updating the membership degree of each sample according to the inhibition factor generation model;
after updating, acquiring a membership function corresponding to the corrected pollution sample data, and outputting the membership function corresponding to the corrected pollution sample data;
wherein the inhibitor generation model satisfies the following relationship:
；
wherein k is an inhibition factor, N is the number of samples,representing the membership degree of the sample i to the clustering center j;
after the suppression factor is found, the membership of each sample is updated by the following relationship:
；
and (3) the membership degree from the updated sample i to the clustering center j.
Further, in a preferred embodiment of the present invention, membership data of each pollution sample data is obtained according to a membership function corresponding to the modified pollution sample data, and a visual pollution level distribution map is generated based on the membership data of each pollution sample data, which specifically includes the following steps:
acquiring membership data of each pollution sample data according to a membership function corresponding to the corrected pollution sample data, and judging whether the membership data of each pollution sample data is larger than a preset membership;
If the membership data is larger than the preset membership, obtaining pollution sample data with the membership data larger than the preset membership, and obtaining geographic position information of each pollution sample data according to the pollution sample data with the membership data larger than the preset membership;
presetting a plurality of pollution levels according to the membership degrees, and dividing the pollution levels of the pollution sample data with the membership degree data larger than the preset membership degrees according to the pollution levels to obtain pollution areas of all levels;
and constructing a visual pollution degree distribution map according to the geographical position information of each pollution sample data and the pollution areas of each degree, and outputting the visual pollution degree distribution map.
Further, in a preferred embodiment of the present invention, digital elevation model data information of a target area is obtained, and diffusion prediction is performed according to the digital elevation model data information of the target area and a visual pollution level distribution map, so as to obtain a diffusion prediction result, which specifically includes the following steps:
acquiring the geographical position information of each pollution area in the visual pollution degree distribution map, and acquiring the digital elevation model data information and stratum structure information of each pollution area according to the geographical position information of each pollution area;
Constructing stratum structure three-dimensional space data information according to the digital elevation model data information and stratum structure information of each pollution area, and acquiring groundwater flow field, soil geological feature data and ground topography structure data based on the stratum structure three-dimensional space data information;
determining pollution flow direction data of pollutants by combining groundwater flow field, soil geological feature data and ground topographic structure data;
and determining the diffusion flow direction of the pollution according to the pollution flow direction data of the pollutant, obtaining a diffusion prediction result, and outputting the diffusion prediction result.
Further, in a preferred embodiment of the present invention, the final evaluation result is obtained by correcting the diffusion prediction result, which specifically includes:
the stress data of the relevant plants under various pollution component data are obtained through big data, a database is constructed, the stress data of the relevant plants under various pollution component data are input into the database for storage, and the database is updated periodically;
the method comprises the steps of obtaining pollution component data of each pollution area, and obtaining stress-like data of the pollution component data of each pollution area on each plant at present by identifying a database according to the pollution component data of each pollution area;
Acquiring plant remote sensing image data in each current pollution area through a remote sensing technology, and acquiring plant variety stress state data in each pollution area through identifying the plant remote sensing image data;
and acquiring the position information of the plant variety corresponding to the stress-like data, and correcting the diffusion prediction result according to the position information of the plant variety corresponding to the stress-like data when the position information of the plant variety corresponding to the stress-like data does not relate to the diffusion prediction result, so as to acquire a final evaluation result.
The second aspect of the present invention provides a site pollution evaluation system based on fuzzy evaluation, the system comprising a memory and a processor, the memory comprising a site pollution evaluation method program based on fuzzy evaluation, the site pollution evaluation method program based on fuzzy evaluation realizing the following steps when executed by the processor:
obtaining pollution sample data information of a current target area, and clustering the pollution sample data information of the current target area through a fuzzy clustering algorithm to obtain a membership matrix corresponding to the pollution sample data;
introducing a suppression factor, correcting a membership matrix corresponding to the pollution sample data according to the suppression factor, and obtaining a membership function corresponding to the corrected pollution sample data;
Obtaining membership data of each pollution sample data according to a membership function corresponding to the corrected pollution sample data, and generating a visual pollution degree distribution map based on the membership data of each pollution sample data;
and obtaining digital elevation model data information of the target area, carrying out diffusion prediction according to the digital elevation model data information of the target area and the visual pollution degree distribution map, obtaining a diffusion prediction result, and correcting the diffusion prediction result to obtain a final evaluation result.
In this embodiment, an inhibitor is introduced, and a membership matrix corresponding to the pollution sample data is modified according to the inhibitor, so as to obtain a membership function corresponding to the modified pollution sample data, which specifically includes:
obtaining membership corresponding to each sample according to a membership matrix corresponding to the pollution sample data, and constructing an inhibition factor generation model;
updating the membership degree of each sample according to the inhibition factor generation model;
after updating, acquiring a membership function corresponding to the corrected pollution sample data, and outputting the membership function corresponding to the corrected pollution sample data;
Wherein the inhibitor generation model satisfies the following relationship:
；
wherein k is an inhibition factor, N is the number of samples,representing the membership degree of the sample i to the clustering center j;
after the suppression factor is found, the membership of each sample is updated by the following relationship:
；
and (3) the membership degree from the updated sample i to the clustering center j.
In this embodiment, the final evaluation result is obtained by correcting the diffusion prediction result, which specifically includes:
the stress data of the relevant plants under various pollution component data are obtained through big data, a database is constructed, the stress data of the relevant plants under various pollution component data are input into the database for storage, and the database is updated periodically;
the method comprises the steps of obtaining pollution component data of each pollution area, and obtaining stress-like data of the pollution component data of each pollution area on each plant at present by identifying a database according to the pollution component data of each pollution area;
acquiring plant remote sensing image data in each current pollution area through a remote sensing technology, and acquiring plant variety stress state data in each pollution area through identifying the plant remote sensing image data;
And acquiring the position information of the plant variety corresponding to the stress-like data, and correcting the diffusion prediction result according to the position information of the plant variety corresponding to the stress-like data when the position information of the plant variety corresponding to the stress-like data does not relate to the diffusion prediction result, so as to acquire a final evaluation result.
A third aspect of the present invention provides a computer-readable storage medium including a fuzzy-evaluation-based site pollution evaluation method program, which when executed by a processor, implements the steps of any one of the fuzzy-evaluation-based site pollution evaluation methods.
The invention solves the defects existing in the background technology, and has the following beneficial effects:
according to the invention, the pollution sample data information of the current target area is obtained, the pollution sample data information of the current target area is clustered through a fuzzy clustering algorithm, a membership matrix corresponding to the pollution sample data is obtained, a suppression factor is further introduced, the membership matrix corresponding to the pollution sample data is corrected according to the suppression factor, a membership function corresponding to the corrected pollution sample data is obtained, membership data of each pollution sample data is obtained according to the membership function corresponding to the corrected pollution sample data, a visual pollution degree distribution map is generated based on the membership data of each pollution sample data, finally, the diffusion prediction result is obtained through obtaining the topographic data information of the target area, and the diffusion prediction result is corrected according to the topographic data information of the target area and the visual pollution degree distribution map, and the final evaluation result is obtained. According to the invention, the membership degree of the sample to other classes is inhibited by introducing the inhibition factor model, so that a reward mechanism of the maximum membership degree can be further enhanced, the convergence of a clustering algorithm can be accelerated, and the clustering efficiency is improved; on the other hand, a particle swarm algorithm is introduced to carry out iterative selection on the clustering centers, so that the number of the clustering centers is selected appropriately, and the situation of sinking into a local optimal solution is effectively avoided; in still another aspect, the invention corrects the pollution diffusion result by the stress-like data of the plant, thereby obtaining a more accurate pollution diffusion result and improving the evaluation accuracy of the pollution diffusion.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 illustrates a specific method flow diagram of a site pollution assessment method based on fuzzy evaluation;
FIG. 2 shows a first method flow diagram of a site pollution assessment method based on fuzzy evaluation;
FIG. 3 shows a second method flow diagram of a site pollution assessment method based on fuzzy evaluation;
FIG. 4 shows a system block diagram of a site pollution assessment system based on fuzzy evaluation.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the first aspect of the present invention provides a site pollution evaluation method based on fuzzy evaluation, which includes the following steps:
s102, obtaining pollution sample data information of a current target area, and clustering the pollution sample data information of the current target area through a fuzzy clustering algorithm to obtain a membership matrix corresponding to the pollution sample data;
in this step S102, the following steps are specifically included:
initializing a cluster center in a range of 40-100, introducing a particle swarm algorithm, setting the particle swarm size to be 30, the maximum iteration number to be 30 and the learning factor to be 2;
setting the clustering number N=1000 and the iteration termination condition as 10 according to the pollution sample data information of the current target area -5 The maximum iteration number is 100, the number of Gaussian kernels is 2, the Gaussian kernel parameter is 150, and the constraint coefficient is 0.6;
according to the range of the clustering center, continuously updating the clustering center and the membership matrix through an objective function of a clustering algorithm, and stopping iteration when the maximum iteration number reaches 100, so as to obtain a corresponding membership matrix;
And setting the target sample as a corresponding optimal clustering center according to the corresponding membership matrix, and outputting the membership matrix corresponding to the polluted sample data.
The particle swarm algorithm is introduced to iteratively select the clustering centers, so that the number of the clustering centers is selected, and the situation of sinking into a local optimal solution is effectively avoided.
S104, introducing a suppression factor, correcting a membership matrix corresponding to the pollution sample data according to the suppression factor, and obtaining a membership function corresponding to the corrected pollution sample data;
the step of S104 specifically includes:
obtaining membership corresponding to each sample according to a membership matrix corresponding to the pollution sample data, and constructing an inhibition factor generation model;
updating the membership degree of each sample according to the inhibition factor generation model;
after updating, acquiring a membership function corresponding to the corrected pollution sample data, and outputting the membership function corresponding to the corrected pollution sample data;
wherein the inhibitor generation model satisfies the following relationship:
；
wherein k is an inhibition factor, N is the number of samples,representing the membership degree of the sample i to the clustering center j;
After the suppression factor is found, the membership of each sample is updated by the following relationship:
；
and (3) the membership degree from the updated sample i to the clustering center j.
It should be noted that, according to the above relation, the larger the maximum membership in the sample is, the closer the sample is to the class distance where the maximum membership is, at this time, the sample has a competitive advantage to the class, and should be rewarded, and the membership of the sample to other classes is suppressed. When the maximum membership of the sample is larger, the sum of the non-maximum membership is smaller, the inhibition factor is smaller, the inhibition degree of the class with the non-maximum membership is larger, and the inhibition mode accords with the mechanism of a clustering algorithm. By introducing the suppression factor model, the membership degree of the sample to other classes is suppressed, so that a reward mechanism of the maximum membership degree can be further enhanced, the convergence of a clustering algorithm can be accelerated, and the clustering efficiency is improved.
For example, the organic contaminant concentration characteristics may be expressed in terms of, for example, "Pollution-free ", and>low contamination ",">=moderate contamination "and">=severe contamination ", the corresponding fuzzy membership function may be set to +. >And so on, exemplary, sample data without contamination can be set to 0 to 0.1mol/L organic contamination by itself, and the user can set according to actual contamination criteria.
S106, obtaining membership data of each pollution sample data according to a membership function corresponding to the corrected pollution sample data, and generating a visual pollution degree distribution map based on the membership data of each pollution sample data;
as shown in fig. 2, in this step S106, specifically, the method includes:
acquiring membership data of each pollution sample data according to a membership function corresponding to the corrected pollution sample data, and judging whether the membership data of each pollution sample data is larger than a preset membership;
if the membership data is larger than the preset membership, obtaining pollution sample data with the membership data larger than the preset membership, and obtaining geographic position information of each pollution sample data according to the pollution sample data with the membership data larger than the preset membership;
presetting a plurality of pollution levels according to the membership degrees, and dividing the pollution levels of the pollution sample data with the membership degree data larger than the preset membership degrees according to the pollution levels to obtain pollution areas of all levels;
And constructing a visual pollution degree distribution map according to the geographical position information of each pollution sample data and the pollution areas of each degree, and outputting the visual pollution degree distribution map.
When the membership data is larger than the pollution-free membership data, a visual pollution degree distribution map is constructed according to the geographical position information of each pollution sample data and the pollution areas of each degree.
S108, acquiring digital elevation model data information of a target area, performing diffusion prediction according to the digital elevation model data information of the target area and a visual pollution degree distribution map, acquiring a diffusion prediction result, and correcting the diffusion prediction result to acquire a final evaluation result.
As shown in fig. 2, in step S108, a diffusion prediction is performed according to the digital elevation model data information and the visual pollution level distribution map of the target area, so as to obtain a diffusion prediction result, which specifically is:
s202, acquiring the geographical position information of each pollution area in a visual pollution degree distribution diagram, and acquiring the digital elevation model data information and stratum structure information of each pollution area according to the geographical position information of each pollution area;
S204, constructing stratum structure three-dimensional space data information according to the digital elevation model data information and stratum structure information of each pollution area, and acquiring groundwater flow field, soil geological feature data and ground topography structure data based on the stratum structure three-dimensional space data information;
s206, determining pollution flow direction data of pollutants by combining the groundwater flow field, the soil geological feature data and the ground topography structure data;
s208, determining the diffusion flow direction of the pollutants according to the pollution flow direction data of the pollutants, obtaining diffusion prediction results, and outputting the diffusion prediction results.
The digital elevation model is a physical ground model which represents ground elevation in the form of a group of ordered value arrays, and is a branch of a digital terrain model (Digital Terrain Model, DTM for short), and other various terrain characteristic values can be derived from the model. DTM is generally considered to be a spatial distribution describing a linear and nonlinear combination of various topographical factors including elevation, such as slope, slope direction, rate of change of slope, etc. The terrain data are closely related to the diffusion of the soil pollution, such as inclined planes, and the soil pollution is mainly diffused along the runoff direction of water and moisture according to the principle that water flows downwards. The soil geological features comprise soil types and migration characteristics of soil type pollutants (such as migration characteristics of pollutants with various concentrations in soil), wherein the groundwater flow field is the flow direction of groundwater in a polluted area.
As shown in fig. 3, in step S108, the final evaluation result is obtained by correcting the diffusion prediction result, which specifically includes:
s302, obtaining stress data of related plants under various pollution component data through big data, constructing a database, inputting the stress data of the related plants under the various pollution component data into the database for storage, and updating the database periodically;
s304, acquiring pollution component data of each pollution area, and acquiring stress-like data of the pollution component data of each pollution area on each plant at present by identifying a database according to the pollution component data of each pollution area;
s306, acquiring plant remote sensing image data in each current pollution area through a remote sensing technology, and acquiring plant variety stress state data in each pollution area through identifying the plant remote sensing image data;
s308, acquiring position information of the plant variety corresponding to the stress-like data, and correcting the diffusion prediction result according to the position information of the plant variety corresponding to the stress-like data when the position information of the plant variety corresponding to the stress-like data does not relate to the diffusion prediction result, so as to acquire a final evaluation result.
In this embodiment, the type of the pollutant may cause a stress property to the plant, and the acid soil may reduce the number of beneficial microorganisms in the soil and inhibit the growth and activity of the beneficial microorganisms, thereby acting as an obstacle to the decomposition of organic matters in the soil and the circulation of elements such as nitrogen, phosphorus, potassium, and sulfur. Moreover, acid soil can cause germ breeding and root disease increase, which results in slow growth of plants and even death of plants. For example, aluminum ions in the soil in acid soil can cause harm to wheat seedlings and can have a great influence on the relative yield of barley. The relative yield of barley is greatly reduced with the increase of exchangeable aluminum content in the soil. At the same time, the crop is cultivated in the acid soil, which is easy to cause stiff seedling and elder Miao, and the quality of the crop is degraded. The method is used for predicting the diffusion of pollution by combining the stress characters of the plants, so that the prediction accuracy of the diffusion prediction result is improved. The plant remote sensing image data can be identified by an image identification technology, such as the identification of plant variety stress state data in the directions of neural network, big data analysis and the like.
It is to be noted that, the invention suppresses the membership degree of the sample to other classes by introducing the suppression factor model, can further strengthen the reward mechanism of the maximum membership degree, can accelerate the convergence of the clustering algorithm, and improves the clustering efficiency; on the other hand, a particle swarm algorithm is introduced to carry out iterative selection on the clustering centers, so that the number of the clustering centers is selected appropriately, and the situation of sinking into a local optimal solution is effectively avoided; in still another aspect, the invention corrects the pollution diffusion result by the stress-like data of the plant, thereby obtaining a more accurate pollution diffusion result and improving the evaluation accuracy of the pollution diffusion.
In addition, the invention can also comprise the following steps:
acquiring a pollution diffusion result of each pollution area and geographical position data information where the pollution diffusion result is located, and constructing visual diffusion range data according to the pollution diffusion result of each pollution area;
searching through map resource data information according to the geographic position data information, acquiring map resource data information of the geographic position of each pollution area, and judging whether the map resource data information of the geographic position of each pollution area is preset map resource data information or not;
If the map resource data information of the geographic position of each pollution area is preset map resource data information, calculating the Euclidean distance according to the visual diffusion range data and the preset map resource data information;
when the Euclidean distance is lower than a preset distance range threshold, corresponding early warning information is generated according to the visual diffusion range data and preset map resource data information, and a corresponding soil treatment direction is generated.
It should be noted that, the preset map resource data information may be conditions such as drinking wells, irrigation wells, poultry cultivation areas, etc., and when the euclidean distance between the visual diffusion range data and the preset map resource data information is lower than the preset distance range threshold, the user is prompted to perform rapid early warning. Map software can be used for acquiring map resource data information of the geographic position of each polluted area.
In addition, the diffusion prediction result is corrected according to the position information of the stress-like data corresponding to the plant variety, and a final evaluation result is obtained, and the method specifically comprises the following steps: acquiring root mean growth depth data information of each type of plant variety through big data, and inputting the root mean growth depth data information of each type of plant variety into a database for storage; inputting the plant varieties corresponding to the current area into the database for matching, obtaining the root system average growth depth data information of the plant varieties corresponding to the current area, and obtaining the plant varieties corresponding to the stress-like data; extracting root system average growth depth data information of corresponding plant varieties containing stress-like data, and sorting according to the root system average growth depth data information to obtain a sorting result; and acquiring maximum root mean growth depth data information from the sequencing result as pollutant migration depth data, and correcting a diffusion prediction result according to the pollutant migration depth data to acquire a final evaluation result.
In this embodiment, the plant absorbs water mainly through the root system, so that the polluted components enter the plant, the pollutant migration depth can be further accurately obtained by the method, the diffusion prediction result is corrected according to the pollutant migration depth data, and the accuracy of the diffusion prediction result is improved.
As shown in fig. 4, the second aspect of the present invention provides a field pollution evaluation system 4 based on fuzzy evaluation, the system includes a memory 41 and a processor 62, the memory 41 includes a field pollution evaluation method program based on fuzzy evaluation, and when the field pollution evaluation method program based on fuzzy evaluation is executed by the processor 62, the following steps are implemented:
obtaining pollution sample data information of a current target area, and clustering the pollution sample data information of the current target area through a fuzzy clustering algorithm to obtain a membership matrix corresponding to the pollution sample data;
introducing a suppression factor, correcting a membership matrix corresponding to the pollution sample data according to the suppression factor, and obtaining a membership function corresponding to the corrected pollution sample data;
obtaining membership data of each pollution sample data according to a membership function corresponding to the corrected pollution sample data, and generating a visual pollution degree distribution map based on the membership data of each pollution sample data;
And obtaining digital elevation model data information of the target area, carrying out diffusion prediction according to the digital elevation model data information of the target area and the visual pollution degree distribution map, obtaining a diffusion prediction result, and correcting the diffusion prediction result to obtain a final evaluation result.
In this embodiment, an inhibitor is introduced, and a membership matrix corresponding to the pollution sample data is modified according to the inhibitor, so as to obtain a membership function corresponding to the modified pollution sample data, which specifically includes:
obtaining membership corresponding to each sample according to a membership matrix corresponding to the pollution sample data, and constructing an inhibition factor generation model;
updating the membership degree of each sample according to the inhibition factor generation model;
after updating, acquiring a membership function corresponding to the corrected pollution sample data, and outputting the membership function corresponding to the corrected pollution sample data;
wherein the inhibitor generation model satisfies the following relationship:
；
wherein k is an inhibition factor, N is the number of samples,representing the membership degree of the sample i to the clustering center j;
after the suppression factor is found, the membership of each sample is updated by the following relationship:
；
And (3) the membership degree from the updated sample i to the clustering center j.
In this embodiment, the final evaluation result is obtained by correcting the diffusion prediction result, which specifically includes:
the stress data of the relevant plants under various pollution component data are obtained through big data, a database is constructed, the stress data of the relevant plants under various pollution component data are input into the database for storage, and the database is updated periodically;
the method comprises the steps of obtaining pollution component data of each pollution area, and obtaining stress-like data of the pollution component data of each pollution area on each plant at present by identifying a database according to the pollution component data of each pollution area;
acquiring plant remote sensing image data in each current pollution area through a remote sensing technology, and acquiring plant variety stress state data in each pollution area through identifying the plant remote sensing image data;
and acquiring the position information of the plant variety corresponding to the stress-like data, and correcting the diffusion prediction result according to the position information of the plant variety corresponding to the stress-like data when the position information of the plant variety corresponding to the stress-like data does not relate to the diffusion prediction result, so as to acquire a final evaluation result.
A third aspect of the present invention provides a computer-readable storage medium including a fuzzy-evaluation-based site pollution evaluation method program, which when executed by a processor, implements the steps of any one of the fuzzy-evaluation-based site pollution evaluation methods.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. The field pollution evaluation method based on fuzzy evaluation is characterized by comprising the following steps of:
obtaining pollution sample data information of a current target area, and clustering the pollution sample data information of the current target area through a fuzzy clustering algorithm to obtain a membership matrix corresponding to the pollution sample data;
introducing a suppression factor, correcting a membership matrix corresponding to the pollution sample data according to the suppression factor, and obtaining a membership function corresponding to the corrected pollution sample data;
obtaining membership data of each pollution sample data according to a membership function corresponding to the corrected pollution sample data, and generating a visual pollution degree distribution map based on the membership data of each pollution sample data;
acquiring digital elevation model data information of a target area, performing diffusion prediction according to the digital elevation model data information of the target area and a visual pollution degree distribution map, acquiring a diffusion prediction result, and correcting the diffusion prediction result to acquire a final evaluation result;
Introducing a suppression factor, correcting a membership matrix corresponding to the pollution sample data according to the suppression factor, and obtaining a membership function corresponding to the corrected pollution sample data, wherein the method specifically comprises the following steps of:
obtaining membership corresponding to each sample according to a membership matrix corresponding to the pollution sample data, and constructing an inhibition factor generation model;
updating the membership degree of each sample according to the inhibition factor generation model;
after updating, acquiring a membership function corresponding to the corrected pollution sample data, and outputting the membership function corresponding to the corrected pollution sample data;
wherein the inhibitor generation model satisfies the following relationship:
；
wherein k is an inhibition factor, N is the number of samples,representing the membership degree of the sample i to the clustering center j;
after the suppression factor is found, the membership of each sample is updated by the following relationship:
；
and (3) the membership degree from the updated sample i to the clustering center j.
2. The field pollution evaluation method based on fuzzy evaluation according to claim 1, wherein the pollution sample data information of the current target area is clustered by a fuzzy clustering algorithm to obtain a membership matrix corresponding to the pollution sample data, and specifically comprises the following steps:
Initializing a range of a clustering center, introducing a particle swarm algorithm, and setting a particle swarm scale, a maximum iteration number and a learning factor;
setting the clustering number, iteration termination conditions, maximum iteration times, gaussian kernel number, gaussian kernel parameters and constraint coefficients according to the pollution sample data information of the current target area;
according to the range of the clustering center, continuously updating the clustering center and the membership matrix through an objective function of a clustering algorithm, and stopping iteration when the maximum iteration number is reached, so as to obtain a corresponding membership matrix;
and setting the target sample as a corresponding optimal clustering center according to the corresponding membership matrix, and outputting the membership matrix corresponding to the polluted sample data.
3. The site pollution evaluation method based on fuzzy evaluation of claim 1, wherein the membership data of each pollution sample data is obtained according to the membership function corresponding to the modified pollution sample data, and a visual pollution degree distribution map is generated based on the membership data of each pollution sample data, and specifically comprises the following steps:
obtaining membership data of each pollution sample data according to a membership function corresponding to the corrected pollution sample data, and judging whether the membership data of each pollution sample data is larger than a preset membership;
If the membership data is larger than the preset membership, obtaining pollution sample data with the membership data larger than the preset membership, and obtaining geographic position information of each pollution sample data according to the pollution sample data with the membership data larger than the preset membership;
presetting a plurality of pollution levels according to the membership degrees, and dividing the pollution levels of the pollution sample data with the membership degree data larger than the preset membership degrees according to the pollution levels to obtain pollution areas of all levels;
and constructing a visual pollution degree distribution map according to the geographical position information of the pollution sample data and the pollution areas of various degrees, and outputting the visual pollution degree distribution map.
4. The field pollution evaluation method based on fuzzy evaluation of claim 1, wherein the method comprises the steps of obtaining digital elevation model data information of a target area, and performing diffusion prediction according to the digital elevation model data information of the target area and a visual pollution degree distribution map to obtain a diffusion prediction result, and specifically comprises the following steps:
acquiring the geographical position information of each pollution area in the visual pollution degree distribution map, and acquiring the digital elevation model data information and stratum structure information of each pollution area according to the geographical position information of each pollution area;
Constructing stratum structure three-dimensional space data information according to the digital elevation model data information and stratum structure information of each pollution area, and acquiring groundwater flow field, soil geological feature data and ground topography structure data based on the stratum structure three-dimensional space data information;
determining pollution flow direction data of pollutants by combining groundwater flow field, soil geological feature data and ground topographic structure data;
and determining the diffusion flow direction of the pollution according to the pollution flow direction data of the pollutant, obtaining a diffusion prediction result, and outputting the diffusion prediction result.
5. The method for evaluating field pollution based on fuzzy evaluation of claim 1, wherein the final evaluation result is obtained by correcting the diffusion prediction result, specifically comprising:
obtaining stress data of related plants under various pollution component data through big data, constructing a database, inputting the stress data of the related plants under various pollution component data into the database for storage, and periodically updating the database;
the method comprises the steps of obtaining pollution component data of each pollution area, and obtaining stress-like data of the pollution component data of each pollution area on each plant at present by identifying the database according to the pollution component data of each pollution area;
Acquiring plant remote sensing image data in each pollution area currently through a remote sensing technology, identifying the plant remote sensing image data to acquire plant varieties in each pollution area, and acquiring stress state data of the plant varieties;
and acquiring the position information of the plant variety corresponding to the stress-like data, and correcting the diffusion prediction result according to the position information of the plant variety corresponding to the stress-like data when the position information of the plant variety corresponding to the stress-like data does not relate to the diffusion prediction result, so as to acquire a final evaluation result.
6. The field pollution evaluation system based on fuzzy evaluation is characterized by comprising a memory and a processor, wherein the memory comprises a field pollution evaluation method program based on fuzzy evaluation, and when the field pollution evaluation method program based on fuzzy evaluation is executed by the processor, the following steps are realized:
obtaining pollution sample data information of a current target area, and clustering the pollution sample data information of the current target area through a fuzzy clustering algorithm to obtain a membership matrix corresponding to the pollution sample data;
Introducing a suppression factor, correcting a membership matrix corresponding to the pollution sample data according to the suppression factor, and obtaining a membership function corresponding to the corrected pollution sample data;
obtaining membership data of each pollution sample data according to a membership function corresponding to the corrected pollution sample data, and generating a visual pollution degree distribution map based on the membership data of each pollution sample data;
acquiring digital elevation model data information of a target area, performing diffusion prediction according to the digital elevation model data information of the target area and a visual pollution degree distribution map, acquiring a diffusion prediction result, and correcting the diffusion prediction result to acquire a final evaluation result;
introducing a suppression factor, correcting a membership matrix corresponding to the pollution sample data according to the suppression factor, and obtaining a membership function corresponding to the corrected pollution sample data, wherein the method specifically comprises the following steps of:
obtaining membership corresponding to each sample according to a membership matrix corresponding to the pollution sample data, and constructing an inhibition factor generation model;
updating the membership degree of each sample according to the inhibition factor generation model;
After updating, acquiring a membership function corresponding to the corrected pollution sample data, and outputting the membership function corresponding to the corrected pollution sample data;
wherein the inhibitor generation model satisfies the following relationship:
；
wherein k is an inhibition factor, N is the number of samples,representing the membership degree of the sample i to the clustering center j;
after the suppression factor is found, the membership of each sample is updated by the following relationship:
；
and (3) the membership degree from the updated sample i to the clustering center j.
7. The system for evaluating field pollution based on fuzzy evaluation of claim 6, wherein the final evaluation result is obtained by correcting the diffusion prediction result, specifically comprising:
obtaining stress data of related plants under various pollution component data through big data, constructing a database, inputting the stress data of the related plants under various pollution component data into the database for storage, and periodically updating the database;
the method comprises the steps of obtaining pollution component data of each pollution area, and obtaining stress-like data of the pollution component data of each pollution area on each plant at present by identifying the database according to the pollution component data of each pollution area;
Acquiring plant remote sensing image data in each current pollution area through a remote sensing technology, and acquiring plant variety stress state data in each pollution area through identifying the plant remote sensing image data;
and acquiring the position information of the plant variety corresponding to the stress-like data, and correcting the diffusion prediction result according to the position information of the plant variety corresponding to the stress-like data when the position information of the plant variety corresponding to the stress-like data does not relate to the diffusion prediction result, so as to acquire a final evaluation result.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a fuzzy-evaluation-based site pollution evaluation method program, which, when executed by a processor, implements the steps of the fuzzy-evaluation-based site pollution evaluation method according to any one of claims 1-5.
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