CN110132966B - Method and system for evaluating risk of spatial position of soil pollution source - Google Patents
Method and system for evaluating risk of spatial position of soil pollution source Download PDFInfo
- Publication number
- CN110132966B CN110132966B CN201910398633.5A CN201910398633A CN110132966B CN 110132966 B CN110132966 B CN 110132966B CN 201910398633 A CN201910398633 A CN 201910398633A CN 110132966 B CN110132966 B CN 110132966B
- Authority
- CN
- China
- Prior art keywords
- risk
- images
- soil pollution
- image
- distribution
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000003900 soil pollution Methods 0.000 title claims abstract description 82
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000011156 evaluation Methods 0.000 claims abstract description 49
- 238000004364 calculation method Methods 0.000 claims abstract description 25
- 238000012545 processing Methods 0.000 claims abstract description 11
- 230000008569 process Effects 0.000 claims description 10
- 239000002689 soil Substances 0.000 claims description 10
- 238000003860 storage Methods 0.000 claims description 7
- 238000011109 contamination Methods 0.000 claims description 3
- 238000012502 risk assessment Methods 0.000 claims description 3
- 238000004590 computer program Methods 0.000 claims 3
- 238000010835 comparative analysis Methods 0.000 abstract description 5
- 239000000243 solution Substances 0.000 description 10
- 238000004891 communication Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 238000010606 normalization Methods 0.000 description 6
- 238000005457 optimization Methods 0.000 description 5
- 238000011282 treatment Methods 0.000 description 4
- 238000011284 combination treatment Methods 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000009792 diffusion process Methods 0.000 description 2
- 239000003344 environmental pollutant Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 231100000719 pollutant Toxicity 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010790 dilution Methods 0.000 description 1
- 239000012895 dilution Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000003973 irrigation Methods 0.000 description 1
- 230000002262 irrigation Effects 0.000 description 1
- 230000005923 long-lasting effect Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000004062 sedimentation Methods 0.000 description 1
- 239000010865 sewage Substances 0.000 description 1
- 239000002910 solid waste Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Educational Administration (AREA)
- Pathology (AREA)
- Biochemistry (AREA)
- Evolutionary Computation (AREA)
- Analytical Chemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Immunology (AREA)
- Primary Health Care (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Geometry (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Image Processing (AREA)
Abstract
The embodiment of the invention provides a method and a system for evaluating the risk of a spatial position of a soil pollution source, wherein the method comprises the following steps: acquiring a plurality of distribution images corresponding to each sensitive receptor in a region to be evaluated; carrying out convolution calculation on the plurality of distribution images respectively by utilizing a preset convolution window reflecting a pollution propagation model to obtain a plurality of corresponding first images, wherein each point on each first image carries a corresponding first risk value; respectively carrying out standardization processing on the multiple first images to obtain multiple corresponding second images, wherein each point on each second image carries a corresponding second risk value; and combining the plurality of second images to obtain a risk evaluation result distribution graph, wherein each point on the risk evaluation result distribution graph carries a corresponding risk value of the spatial position of the soil pollution source. The risk evaluation result distribution map can objectively reflect the risk value of the spatial position of the soil pollution source in the area to be evaluated, and can form global spatial risk comparative analysis.
Description
Technical Field
The invention relates to the technical field of environmental evaluation, in particular to a method and a system for evaluating risk of spatial position of a soil pollution source.
Background
Soil is a non-renewable resource, and it takes roughly hundreds of years to thousands of years to form one centimeter of soil. Soil pollution has the characteristics of accumulation, nonuniformity, long-term existence and the like, the migration, diffusion and dilution speed of pollutants in soil is extremely slow, and once the soil is polluted, the soil is 'long-lasting'. The soil pollution source formed by human activities discharges pollutants into soil in the modes of sewage irrigation, solid waste utilization, atmospheric sedimentation and the like to form soil pollution. Therefore, the spatial location of the soil contamination source and the distribution of its surrounding sensitive receptors are important determinants of the magnitude of its soil risk.
At present, the risk analysis of the spatial position of the soil pollution source is mainly carried out according to an expert assigning method, the distance between a sensitive receptor and the soil pollution source is divided into a plurality of grades, and different scores are assigned. The characteristics of the sensitive receptors mainly comprise population number, cultivated land, water source places and the like. The existing method is mainly poor in objectivity, and the assignment and the scoring of each index have strong speciality and subjectivity; in addition, since a single pollution source is evaluated, global space risk comparative analysis cannot be formed, and references are lacked for pollution source site selection, space layout optimization and the like.
Disclosure of Invention
Embodiments of the present invention provide a method and a system for evaluating risk of spatial location of a soil pollution source, which overcome the above problems or at least partially solve the above problems.
In a first aspect, an embodiment of the present invention provides a method for evaluating risk of a spatial location of a soil pollution source, including:
acquiring a plurality of distribution images corresponding to soil pollution sensitive receptors in a region to be evaluated;
carrying out convolution calculation on the plurality of distribution images respectively by utilizing a preset convolution window reflecting a pollution propagation model to obtain a plurality of corresponding first images, wherein each point on each first image carries a corresponding first risk value;
respectively carrying out standardization processing on the multiple first images to obtain multiple corresponding second images, wherein each point on each second image carries a corresponding second risk value;
and combining the plurality of second images to obtain a risk evaluation result distribution graph, wherein each point on the risk evaluation result distribution graph carries a corresponding risk value of the spatial position of the soil pollution source.
In another aspect, an embodiment of the present invention provides a system for evaluating risk of a spatial location of a soil pollution source, including:
the distribution image acquisition module is used for acquiring a plurality of distribution images corresponding to each soil pollution sensitive receptor in the area to be evaluated;
the first image acquisition module is used for carrying out convolution calculation on the plurality of distribution images by utilizing a preset convolution window reflecting a pollution propagation model to obtain a plurality of corresponding first images, and each point on each first image carries a corresponding first risk value;
the second image acquisition module is used for respectively carrying out standardization processing on the plurality of first images to obtain a plurality of corresponding second images, and each point on each second image carries a corresponding second risk value;
and the risk evaluation result distribution map acquisition module is used for merging the plurality of second images to obtain a risk evaluation result distribution map, and each point on the risk evaluation result distribution map carries a corresponding risk value of the spatial position of the soil pollution source.
In a third aspect, an embodiment of the present invention provides a method including a processor, a communication interface, a memory, and a bus, where the processor and the communication interface complete mutual communication through the bus, and the processor may call a logic instruction in the memory to execute the method for evaluating risk of spatial position of soil pollution source provided in the first aspect.
In a fourth aspect, the embodiments of the present invention provide a non-transitory computer-readable storage medium storing computer instructions, which cause the computer to execute the method for evaluating the risk of spatial location of soil pollution source provided in the first aspect.
According to the method and the system for evaluating the risk of the spatial position of the soil pollution source, provided by the embodiment of the invention, the distribution image of each sensitive receptor in the area to be evaluated is subjected to convolution calculation, standardization treatment and combination treatment in sequence to obtain the risk evaluation result distribution map of the area to be evaluated, the risk evaluation result distribution map can objectively and professionally reflect the risk values of the spatial position of the soil pollution source at each point in the area to be evaluated, the risk evaluation result distribution map can simultaneously reflect the risk values of a plurality of pollution sources, global spatial risk comparative analysis can be formed, and references can be provided for optimization, site selection and the like of the spatial pattern of the soil pollution source.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for evaluating risk of spatial location of a soil pollution source according to an embodiment of the present invention;
FIG. 2 is a block diagram of a risk evaluation system for spatial location of a soil pollution source according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a method for evaluating risk of a spatial location of a soil pollution source according to an embodiment of the present invention, as shown in fig. 1, including:
s101, acquiring a plurality of distribution images corresponding to soil pollution sensitive receptors in a region to be evaluated;
s102, performing convolution calculation on the plurality of distribution images respectively by using a preset convolution window reflecting a pollution propagation model to obtain a plurality of corresponding first images, wherein each point on each first image carries a corresponding first risk value;
s103, respectively carrying out standardization processing on the plurality of first images to obtain a plurality of corresponding second images, wherein each point on each second image carries a corresponding second risk value;
and S104, combining the plurality of second images to obtain a risk evaluation result distribution graph, wherein each point on the risk evaluation result distribution graph carries a corresponding risk value of the spatial position of the soil pollution source.
In S101, a sensitive receptor refers to a small area within the area that is sensitive to soil contamination sources, such as: agricultural land, residential land, and other important sensitive targets, among others. By analyzing the original remote sensing data to a certain extent, a plurality of distribution images only containing specific sensitive receptors can be obtained, for example, the distribution images only containing agricultural land, the distribution images only containing living places and the distribution images only containing other important sensitive targets. S102-S103 are steps of analyzing images containing different sensitive receptors, respectively.
In S102, the convolution window is a matrix in which convolution layers in the convolutional neural network perform convolution processing on data. The convolution window is used to extract the characteristic information in the input data. In a sense, a convolution window can be understood as a filter. In the convolutional neural network, a window slides, and the convolutional window calculates local data to gradually obtain a processed data matrix. The processed data matrix retains some of the characteristics of the original data. In the process of processing the picture data, the processed data of one convolution window is relative to the result of observing the picture from one dimension, and the characteristic of one dimension of the picture is reserved. In the embodiment of the invention, the convolution windows are used for simulating the soil pollution source pollution transmission diffusion, and different sensitive receptors can adopt different convolution windows or the same convolution window according to the actual conditions of different sensitive receptors.
And performing convolution calculation on the distribution image corresponding to a certain sensitive receptor by adopting a preset convolution window to obtain the distribution image after the convolution calculation, namely the first image. The corresponding first risk value carried at a certain point on a certain first image is the risk value affecting the specific sensitive receptor corresponding to the first image when the soil pollution source is arranged at the point. For example, if a sensitive receptor corresponding to a first image is a river and the first risk value corresponding to the point a on the image is 100, the risk value of the influence on the river when the soil pollution source is set at the point a is 100.
In S103, the risk assessment for the spatial position of the soil pollution source in the area to be assessed cannot only be performed on a single sensitive receptor, but the first risk values corresponding to each point in the first image corresponding to different sensitive receptors are not under the same assessment system. In order to make the various types of sensitive receptors comparable to each other, the first images need to be normalized so that the first risk values corresponding to each point in each first image are under the same evaluation system, which is obviously a prerequisite for merging the multiple images in S104.
In S104, the plurality of second images are normalized images, and in order to comprehensively consider all sensitive receptors in the area to be evaluated when performing risk evaluation on the spatial position of the soil pollution source, risks of influences of the pollution source on all sensitive receptors need to be integrated. Then, the risk value of the spatial position of the soil pollution source is the risk value which integrates the influence of the soil pollution source on all sensitive receptors in the area to be evaluated.
After the risk evaluation result distribution map of the area to be evaluated is obtained, the risk values of all points in the area can be intuitively and quickly obtained, so that references are provided for the optimization of the spatial pattern of the soil pollution source, the site selection and the like.
It can be understood that, in the risk evaluation result distribution map, the risk values of different spatial positions of the soil pollution source can be distinguished by different colors or shapes, so that the risk values of all points in the region can be acquired more intuitively and quickly.
According to the method for evaluating the risk of the spatial position of the soil pollution source provided by the embodiment of the invention, the distribution image of each sensitive receptor in the area to be evaluated is subjected to convolution calculation, standardization treatment and combination treatment in sequence to obtain the risk evaluation result distribution map of the area to be evaluated, the risk evaluation result distribution map can objectively and professionally reflect the risk values of the spatial position of the soil pollution source at each point in the area to be evaluated, the risk evaluation result distribution map can simultaneously reflect the risk values of a plurality of pollution sources, global spatial risk comparative analysis can be formed, and references can be provided for optimization, site selection and the like of the spatial pattern of the soil pollution source.
In the above embodiment, the acquiring a plurality of distribution images corresponding to each sensitive receptor in the region to be evaluated specifically includes:
and acquiring an original distribution image of the region to be evaluated, and extracting the plurality of distribution images corresponding to the sensitive receptors according to the land utilization classification data of the region to be evaluated.
Specifically, the land use classification data is data reflecting states, characteristics, dynamic changes and distribution characteristics of land use systems and land use elements, and human data including development and utilization, treatment and transformation, management and protection, land use planning and the like of land. Specific sensitive receptors in the area to be evaluated can be obtained according to the land use classification data, other sensitive receptors are screened out, and then a distribution image only containing the specific sensitive receptors is obtained.
In the above embodiment, before performing convolution calculation on the plurality of distribution images respectively by using a preset convolution window to obtain a plurality of corresponding first images, the method further includes:
and selecting the preset convolution window according to the mode that the sensitive receptor is influenced by the soil pollution source.
The preset convolution window is a Gaussian low-pass filter with the standard deviation of 1, and the size of the preset convolution window is determined according to the influence distance of the soil pollution source and the resolution of the distribution image.
Specifically, the law of soil pollution attenuation along with distance is clear at present, so that an available convolution window can be designed into a low-pass filter with a standard deviation of 1.0 gauss. Since the distance of transmission of the soil pollution source is typically around 1-5 km, the convolution window size can be set to 1-5 km. After the resolution of the distribution image is clarified, the size of the image scale corresponding to the convolution window can be determined.
Correspondingly, performing convolution calculation on each distribution image by using a preset convolution window to obtain a corresponding first image, which specifically comprises the following steps:
counting the number of corresponding sensitive receptors within a preset range of each point in each distribution image to obtain a first risk value corresponding to each point in each distribution image;
and assigning the first risk values corresponding to the points in each distribution image to the corresponding points to obtain the corresponding first images.
Specifically, the process of obtaining the corresponding first image from the distribution image is a process of performing convolution calculation on the distribution image, and the convolution calculation is a process of calculating the number of the sensitive receptors corresponding to each point in the distribution image within a preset range. And assigning values to corresponding points by taking the number of the sensitive receptors as a first risk value to obtain a first image.
In the above embodiment, the normalizing each first image to obtain a corresponding second image specifically includes:
normalizing the first risk values of each point in each first image to obtain second risk values corresponding to each point in each first image;
and giving the second risk values corresponding to the points in each first image to the corresponding points to obtain a corresponding second image.
Specifically, a process of acquiring a corresponding second image from the first image is a process of performing normalization calculation on the first image, and the normalization calculation adopts a normalization method to obtain a second risk value of each point. And assigning values to the corresponding points according to the second risk to obtain a second image.
In the above embodiment, the merging the multiple second images to obtain a risk evaluation result distribution map specifically includes:
carrying out weighted average on a plurality of second risk values corresponding to the same points on the plurality of second images to obtain the risk values of the spatial positions of the soil pollution sources corresponding to the points;
and assigning values to each point in the region to be evaluated by using the spatial position risk value of the soil pollution source corresponding to each point to obtain the risk evaluation result distribution map.
In the above embodiment, before performing weighted average on a plurality of second risk values corresponding to the same points on the plurality of second images to obtain the risk values of the spatial positions of the soil pollution sources corresponding to the points, the method further includes:
and acquiring the weight of the sensitive receptors corresponding to the plurality of second images in the merging process.
Specifically, the weights corresponding to different sensitive receptors can be set according to actual requirements. If the consideration of a certain sensitive receptor is emphasized, the weight setting of the sensitive receptor in the combination process can be larger. Of course, a equipartition arrangement may be employed for each sensitive receptor.
The technical solution of the embodiment of the present invention is further illustrated by an example below:
the concrete implementation process of the invention is illustrated by taking the calculation and evaluation of the spatial position risk of the soil pollution source in a certain area as an example.
(1) Acquiring high-grade remote sensing data and land utilization classification data of the last year of the evaluation area, extracting important sensitive target distribution maps such as an agricultural land distribution map, a residential land distribution map and a river distribution map, and sampling the spatial resolution of the distribution maps to 100 meters.
(2) Based on general image processing software, a convolution window of 31 × 31 is designed, and the function is a low-pass filter with standard deviation of 1.0 gauss.
(3) Performing convolution operation on the agricultural land distribution map, the residential land distribution map and the river distribution map respectively by using the convolution window to obtain a convolved image (J)1，J2，J3)。
(4) And (5) normalizing the image after the sensitive receptor is convolved. To allow various types of sensitive receptors to be compared with each other, the image after convolution is normalized, and the normalization method may be a standard normalization method. Such as J1Is standardized by
Sequentially obtaining b2、b3Wherein b is1、b2、b3Are respectively paired with J1、J2、J3Carrying out standardization to obtain an image; j. the design is a square1(i) Is J1Risk value for the ith position in the image, J1(min) is J1Minimum risk value in image, J1(max) is J1Maximum risk value in the image.
(5) And weighting to form a soil pollution source space position risk calculation result distribution graph. B ═ f1/3+b2/3+b3/3。
(6) Selecting several enterprises in the area, which are typically related to soil pollution, and forming a risk value map of the positions of the enterprises according to the calculation result in the step (5).
Fig. 2 is a block diagram of a risk evaluation system for a spatial location of a soil pollution source according to an embodiment of the present invention, as shown in fig. 2, including: a distribution image acquisition module 201, a first image acquisition module 202, a second image acquisition module 203, and a risk evaluation result distribution diagram acquisition module 204. Wherein:
the distribution image acquisition module 201 is configured to acquire a plurality of distribution images corresponding to each soil pollution sensitive receptor in the area to be evaluated. The first image obtaining module 202 is configured to perform convolution calculation on the multiple distribution images by using a preset convolution window that reflects the pollution propagation model, respectively, to obtain multiple corresponding first images, where each point on each first image carries a corresponding first risk value. The second image obtaining module 203 is configured to perform normalization processing on the multiple first images to obtain multiple corresponding second images, where each point on each second image carries a corresponding second risk value. The risk evaluation result distribution map obtaining module 204 is configured to combine the plurality of second images to obtain a risk evaluation result distribution map, and each point on the risk evaluation result distribution map carries a corresponding risk value of a spatial position of the soil pollution source.
Specifically, the distribution image acquiring module 201 is specifically configured to:
and acquiring an original distribution image of the region to be evaluated, and extracting the plurality of distribution images corresponding to the sensitive receptors according to the land utilization classification data of the region to be evaluated.
Further, the system further comprises a preset convolution window selection module, specifically configured to:
and selecting the preset convolution window according to the mode that the sensitive receptor is influenced by the soil pollution source.
Further, the first image obtaining module 202 is specifically configured to:
counting the number of corresponding sensitive receptors within a preset range of each point in each distribution image to obtain a first risk value corresponding to each point in each distribution image;
and assigning the first risk values corresponding to the points in each distribution image to the corresponding points to obtain the corresponding first images.
Further, the second image obtaining module 203 is specifically configured to:
normalizing the first risk values of each point in each first image to obtain second risk values corresponding to each point in each first image;
and giving the second risk values corresponding to the points in each first image to the corresponding points to obtain a corresponding second image.
Further, the risk evaluation result distribution diagram obtaining module 204 is specifically configured to:
carrying out weighted average on a plurality of second risk values corresponding to the same points on the plurality of second images to obtain the risk values of the spatial positions of the soil pollution sources corresponding to the points;
and assigning values to each point in the region to be evaluated by using the spatial position risk value of the soil pollution source corresponding to each point to obtain the risk evaluation result distribution map.
According to the risk evaluation system for the spatial positions of the soil pollution sources, provided by the embodiment of the invention, the risk evaluation result distribution map of the area to be evaluated is obtained by sequentially performing convolution calculation, standardization treatment and combination treatment on the distribution images of all sensitive receptors in the area to be evaluated, the risk evaluation result distribution map can objectively and professionally reflect the risk values of the spatial positions of the soil pollution sources at all points in the area to be evaluated, the risk evaluation result distribution map can simultaneously reflect the risk values of a plurality of pollution sources, global spatial risk comparative analysis can be formed, and references can be provided for optimization, site selection and the like of the spatial pattern of the soil pollution sources.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device includes: a processor (processor)301, a communication Interface (communication Interface) 302, a memory (memory)303 and a bus 304, wherein the processor 301, the communication Interface 302 and the memory 303 complete communication with each other through the bus 304. Processor 301 may call logic instructions in memory 303 to perform methods including, for example: acquiring a plurality of distribution images corresponding to soil pollution sensitive receptors in a region to be evaluated; carrying out convolution calculation on the plurality of distribution images respectively by utilizing a preset convolution window reflecting a pollution propagation model to obtain a plurality of corresponding first images, wherein each point on each first image carries a corresponding first risk value; respectively carrying out standardization processing on the multiple first images to obtain multiple corresponding second images, wherein each point on each second image carries a corresponding second risk value; and combining the plurality of second images to obtain a risk evaluation result distribution graph, wherein each point on the risk evaluation result distribution graph carries a corresponding risk value of the spatial position of the soil pollution source.
The logic instructions in the memory 303 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Embodiments of the present invention provide a non-transitory computer-readable storage medium, which stores computer instructions, where the computer instructions cause the computer to perform the methods provided by the above method embodiments, for example, the methods include: acquiring a plurality of distribution images corresponding to soil pollution sensitive receptors in a region to be evaluated; carrying out convolution calculation on the plurality of distribution images respectively by utilizing a preset convolution window reflecting a pollution propagation model to obtain a plurality of corresponding first images, wherein each point on each first image carries a corresponding first risk value; respectively carrying out standardization processing on the multiple first images to obtain multiple corresponding second images, wherein each point on each second image carries a corresponding second risk value; and combining the plurality of second images to obtain a risk evaluation result distribution graph, wherein each point on the risk evaluation result distribution graph carries a corresponding risk value of the spatial position of the soil pollution source.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A risk evaluation method for spatial positions of soil pollution sources is characterized by comprising the following steps:
acquiring a plurality of distribution images corresponding to soil pollution sensitive receptors in a region to be evaluated;
carrying out convolution calculation on the plurality of distribution images respectively by utilizing a preset convolution window reflecting a pollution propagation model to obtain a plurality of corresponding first images, wherein each point on each first image carries a corresponding first risk value;
respectively carrying out standardization processing on the multiple first images to obtain multiple corresponding second images, wherein each point on each second image carries a corresponding second risk value;
and combining the plurality of second images to obtain a risk evaluation result distribution graph, wherein each point on the risk evaluation result distribution graph carries a corresponding risk value of the spatial position of the soil pollution source.
2. The method for evaluating the risk of the spatial position of the soil pollution source according to claim 1, wherein the obtaining of the plurality of distribution images corresponding to the soil pollution sensitive receptors in the area to be evaluated specifically comprises:
and acquiring an original distribution image of the region to be evaluated, and extracting the plurality of distribution images corresponding to the sensitive receptors according to the land utilization classification data of the region to be evaluated.
3. The method for evaluating the risk of the spatial position of the soil pollution source according to claim 1, wherein before performing convolution calculation on the plurality of distribution images respectively by using a preset convolution window reflecting a pollution propagation model to obtain a plurality of corresponding first images, the method further comprises:
and selecting the preset convolution window according to the mode that the sensitive receptor is influenced by the soil pollution source.
4. The method for evaluating the risk of the spatial position of the soil pollution source according to claim 3, wherein the preset convolution window is a Gaussian low-pass filter with a standard deviation of 1, and the size of the preset convolution window is determined according to the influence distance of the soil pollution source and the resolution of the plurality of distribution images; in a corresponding manner, the first and second electrodes are,
performing convolution calculation on each distribution image by using a preset convolution window to obtain a corresponding first image, which specifically comprises the following steps:
counting the number of corresponding sensitive receptors within a preset range of each point in each distribution image to obtain a first risk value corresponding to each point in each distribution image;
and assigning the first risk values corresponding to the points in each distribution image to the corresponding points to obtain the corresponding first images.
5. The method for evaluating the risk of the spatial position of the soil pollution source according to claim 1, wherein each first image is normalized to obtain a corresponding second image, and the method specifically comprises the following steps:
normalizing the first risk values of each point in each first image to obtain second risk values corresponding to each point in each first image;
and giving the second risk values corresponding to the points in each first image to the corresponding points to obtain a corresponding second image.
6. The method for evaluating the risk of the spatial position of the soil pollution source according to claim 1, wherein the step of combining the plurality of second images to obtain a risk evaluation result distribution map specifically comprises:
carrying out weighted average on a plurality of second risk values corresponding to the same points on the plurality of second images to obtain the risk values of the spatial positions of the soil pollution sources corresponding to the points;
and assigning values to each point in the region to be evaluated by using the spatial position risk value of the soil pollution source corresponding to each point to obtain the risk evaluation result distribution map.
7. The method for evaluating the risk of the spatial location of the soil pollution source according to claim 6, wherein before the step of performing weighted average on the plurality of second risk values corresponding to the same point on the plurality of second images to obtain the risk value of the spatial location of the soil pollution source corresponding to each point, the method further comprises:
and acquiring the weight of the sensitive receptors corresponding to the plurality of second images in the merging process.
8. A soil pollution source spatial location risk assessment system, comprising:
the distribution image acquisition module is used for acquiring a plurality of distribution images corresponding to each soil pollution sensitive receptor in the area to be evaluated;
the first image acquisition module is used for carrying out convolution calculation on the plurality of distribution images by utilizing a preset convolution window reflecting a pollution propagation model to obtain a plurality of corresponding first images, and each point on each first image carries a corresponding first risk value;
the second image acquisition module is used for respectively carrying out standardization processing on the plurality of first images to obtain a plurality of corresponding second images, and each point on each second image carries a corresponding second risk value;
and the risk evaluation result distribution map acquisition module is used for merging the plurality of second images to obtain a risk evaluation result distribution map, and each point on the risk evaluation result distribution map carries a corresponding risk value of the spatial position of the soil pollution source.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the processor executes the program to implement the steps of the method for assessing risk of spatial location of a soil pollution source according to any one of claims 1 to 7.
10. A non-transitory computer readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the method for assessing risk of spatial location of a soil contamination source according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910398633.5A CN110132966B (en) | 2019-05-14 | 2019-05-14 | Method and system for evaluating risk of spatial position of soil pollution source |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910398633.5A CN110132966B (en) | 2019-05-14 | 2019-05-14 | Method and system for evaluating risk of spatial position of soil pollution source |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110132966A CN110132966A (en) | 2019-08-16 |
| CN110132966B true CN110132966B (en) | 2021-09-10 |
Family
ID=67573939
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910398633.5A Expired - Fee Related CN110132966B (en) | 2019-05-14 | 2019-05-14 | Method and system for evaluating risk of spatial position of soil pollution source |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110132966B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113610167B (en) * | 2021-08-10 | 2022-06-24 | 宿迁旺春机械制造有限公司 | Equipment risk detection method based on metric learning and visual perception |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101982823A (en) * | 2010-09-29 | 2011-03-02 | 重庆大学 | Risk assessment system of soil heavy metal pollution on human health |
| CN102270325A (en) * | 2011-07-12 | 2011-12-07 | 北京师范大学 | Method for evaluating vulnerability of regional environment risk receptor |
| CN108985147A (en) * | 2018-05-31 | 2018-12-11 | 成都通甲优博科技有限责任公司 | Object detection method and device |
-
2019
- 2019-05-14 CN CN201910398633.5A patent/CN110132966B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101982823A (en) * | 2010-09-29 | 2011-03-02 | 重庆大学 | Risk assessment system of soil heavy metal pollution on human health |
| CN102270325A (en) * | 2011-07-12 | 2011-12-07 | 北京师范大学 | Method for evaluating vulnerability of regional environment risk receptor |
| CN108985147A (en) * | 2018-05-31 | 2018-12-11 | 成都通甲优博科技有限责任公司 | Object detection method and device |
Non-Patent Citations (4)
| Title |
|---|
| Multivariate spatial modelling through a convolution-based skewed process;Hamid Zareifard et.al;《Stochastic Environmental Research and Risk Assessment》;20190221;第33卷;657-671 * |
| 土壤污染遥感研究进展及应用展望;熊文成等;《环境与可持续发展》;20171231(第6期);51-54 * |
| 基于地统计学的土壤重金属分布与污染风险评价;杨奇勇等;《农业机械学报》;20171231;第48卷(第12期);248-254 * |
| 基于多源遥感的典型污染场地土壤污染监测初步探讨;熊文成等;《中囯环境科学学会学术年会论文集》;20171231;1996-2002 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110132966A (en) | 2019-08-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Klingseisen et al. | Geomorphometric landscape analysis using a semi-automated GIS-approach | |
| Dezhkam et al. | Performance evaluation of land change simulation models using landscape metrics | |
| Richardson et al. | Uncertainty in urban forest canopy assessment: Lessons from Seattle, WA, USA | |
| Liao et al. | Assessment of urbanization-induced ecological risks in an area with significant ecosystem services based on land use/cover change scenarios | |
| Bogawski et al. | Predicting the onset of Betula pendula flowering in Poznań (Poland) using remote sensing thermal data | |
| US10984518B2 (en) | Methods and systems for assessing the quality of geospatial data | |
| Tsarouchi et al. | Historical and future land-cover changes in the Upper Ganges basin of India | |
| Orenstein et al. | Methodology matters: Measuring urban spatial development using alternative methods | |
| Lee et al. | Estimating conservation metrics from atlas data: the case of southern African endemic birds | |
| Walz et al. | Integrating time and the third spatial dimension in landscape structure analysis | |
| Estévez et al. | Spatial regression test for ensuring temperature data quality in southern Spain | |
| CN110132966B (en) | Method and system for evaluating risk of spatial position of soil pollution source | |
| Triantakonstantis et al. | A spatially heterogeneous expert based (SHEB) urban growth model using model regionalization | |
| Ma et al. | Individual structure mapping over six million trees for New York City USA | |
| Cunningham et al. | A statistical methodology for tracking long-term change in reporting rates of birds from volunteer-collected presence–absence data | |
| Lagarias | Exploring land use policy scenarios with the use of a cellular automata-based model: urban sprawl containment and sustainable development in Thessaloniki | |
| Murray et al. | Using fractal analysis of crown images to measure the structural condition of trees | |
| Tiwari et al. | Markov random field-based method for super-resolution mapping of forest encroachment from remotely sensed ASTER image | |
| CN113240340B (en) | Soybean planting area analysis method, device, equipment and medium based on fuzzy classification | |
| CN115345488A (en) | Wetland ecosystem service evaluation method, device, server and medium | |
| CN115879594A (en) | Urban settlement population distribution trend prediction method based on geographic detector | |
| Karmshahi et al. | Application of an integrated CA-Markov model in simulating spatiotemporal changes in forest cover: a case study of Malekshahi county forests, Ilam province | |
| Martínez-Navarro et al. | The earliest Ethiopian wolf: implications for the species evolution and its future survival | |
| Grima et al. | Methodological approach for the analysis of groundwater quality in the framework of the Groundwater Directive | |
| Badia et al. | LiDAR technology to map forest continuity: A municipality tool to prevent forest fires in a Wildland–Urban interface |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20210910 |