CN117196156A - Intelligent planning method, system and medium for pollution in-situ treatment - Google Patents
Intelligent planning method, system and medium for pollution in-situ treatment Download PDFInfo
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Abstract
The invention relates to an intelligent planning method, a system and a medium for pollution in-situ treatment, which belong to the technical field of pollution treatment. According to the invention, the cylindrical volume in the polluted area is calculated by carrying out visual analysis on the polluted area, so that the repair cost of different repair medicament types is estimated, the repair medicament type with the lowest repair cost is selected, the estimated volume of the repaired soil can be accurately estimated, and the repair cost in the soil repair process is reduced. The invention fully considers whether the self-healing property exists in the polluted soil, so that the repairing process is more reasonable.
Description
Technical Field
The invention relates to the technical field of pollution treatment, in particular to an intelligent planning method, system and medium for pollution in-situ treatment.
Background
The pollutants produced by human production activities and toxic and harmful substances produced by industrial solid wastes enter the soil, exceed the bearing capacity and range of the soil, and cause the function of the soil to be reduced. Soil pollution has the characteristics of concealment and hysteresis, is not easy to find, and has great repair difficulty. Soil pollution not only affects crops, but also indirectly harms human health. However, the restoration cost is also an important factor to be considered in the soil restoration process, and different pollution types, different pollution concentrations and different restoration agents can cause the cost of soil restoration to be different, so that how to reduce the soil restoration cost is an important technical problem of soil restoration under the condition of keeping a good restoration effect.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides an intelligent planning method, system and medium for pollution in-situ treatment.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the invention provides an intelligent planning method for pollution in-situ treatment, which comprises the following steps:
acquiring pollution investigation data information of a target area, and constructing a model according to the pollution investigation data information of the target area to generate a visual pollution model diagram of each pollution area in the target area;
Generating soil estimated repair volume data information of each polluted region according to the visualized pollution model diagram, and calculating estimated repair volume data of different repair medicament types in each region based on the soil estimated repair volume data information;
calculating the repair cost information of each repair agent type based on the estimated repair quantity data of different repair agent types, and generating a final repair agent type according to the repair cost information;
acquiring remote sensing image data information in a target area, setting soil restoration priority according to the remote sensing image data information in the target area, and generating a final restoration planning scheme based on the soil restoration priority and the final restoration agent type.
Further, in the method, pollution investigation data information of the target area is obtained, and model construction is carried out according to the pollution investigation data information of the target area, so as to generate a visual pollution model diagram of each pollution area in the target area, which specifically comprises the following steps:
the method comprises the steps of obtaining pollution investigation data information of a target area, dividing the target area into a plurality of pollution areas, and obtaining pollutant characteristic data information of each pollution area according to the pollution investigation data information of the target area;
Acquiring pollutant types of each pollutant area in a three-dimensional space, pollutant distribution positions of pollutants in the three-dimensional space and concentration data information of the pollutants in the three-dimensional space based on the pollutant characteristic data information, and constructing a visual pollution model diagram;
setting relevant color depth according to the concentration data information of the pollutants, and carrying out initialized color rendering on the visualized pollution model diagram according to the relevant color depth and the pollution distribution position of the pollutants in the three-dimensional space;
after the color rendering, generating relevant mark information according to the pollutant type and the concentration data information of the pollutant in the three-dimensional space, fusing the mark information into a visual pollution model diagram, and outputting the visual pollution model diagram.
Further, in the method, soil estimated repair volume data information of each polluted area is generated according to the visualized pollution model diagram, and the method specifically comprises the following steps:
setting relevant pollution concentration threshold information, acquiring concentration data information of pollutants at all positions in a three-dimensional space from a visual pollution model diagram, and judging whether the concentration data information of the pollutants at all positions in the three-dimensional space is smaller than the relevant pollution concentration threshold information;
When the concentration data information of the pollutants is smaller than the relevant pollutant concentration threshold information, removing the pollution positions of which the concentration data information of the pollutants is smaller than the relevant pollutant concentration threshold information from the visual pollution model diagram, and updating the visual pollution model diagram;
acquiring an updated visual pollution model diagram, acquiring limit coordinate information of pollutants in a three-dimensional space according to the updated visual pollution model diagram, and taking one of the limit coordinate information as a coordinate origin;
and constructing a cylindrical pollution volume model according to the coordinate origin and the limit coordinate information of the pollutants in the three-dimensional space, calculating the volume information of the cylindrical pollution volume model, and generating the soil estimated restoration volume data information of each polluted area.
Further, in the method, estimated repair volume data of different repair agent types in each region is calculated based on the soil estimated repair volume data information, and specifically includes:
the method comprises the steps of obtaining repair agent type information of each pollution type through big data, setting keyword information according to the repair agent type information, and searching through the big data according to the keyword information to obtain repair amount data information required in unit soil volume of each repair agent type in concentration information of different soil pollutants;
Constructing a repair quantity knowledge graph, dividing the repair quantity knowledge graph into a plurality of storage spaces, and inputting the repair quantity data information required by each repair agent type in unit soil volume in the concentration information of different soil pollutants into the storage spaces for storage;
acquiring concentration data information of pollutants in all directions in the visual pollution model diagram, and calculating average pollution concentration information according to the soil estimated restoration volume data information and the concentration data information of the pollutants in all directions in the visual pollution model diagram;
and inputting the average pollution concentration information and the soil estimated repair volume data information into a repair volume knowledge graph for calculation, and generating estimated repair volume data of different repair medicament types in each region.
Further, in the method, the repair cost information of each repair agent type is calculated based on the estimated repair amount data of different repair agent types, and the final repair agent type is generated according to the repair cost information, which specifically includes:
acquiring cost data information of each repair medicament type unit volume through big data, and calculating repair cost information of each repair medicament type according to the cost data information of each repair medicament type unit volume and estimated repair quantity data of different repair medicament types;
Constructing a repair cost ranking table, inputting repair cost information of each repair medicament type into the repair cost ranking table for ranking, and obtaining a ranked repair cost ranking table;
and obtaining the repair agent type with the lowest repair cost in the repair cost ranking table after ranking, and generating a final repair agent type according to the repair agent type with the lowest repair cost in the repair cost ranking table after ranking.
Further, in the method, a soil restoration priority is set according to remote sensing image data information in a target area, and a final restoration planning scheme is generated based on the soil restoration priority and a final restoration medicament type, specifically comprising the following steps:
acquiring remote sensing image data information in a target area, filtering and denoising the remote sensing image data information to acquire an interested area in a remote sensing image, and identifying the interested area to acquire real-time plant variety data information of each polluted area in the target area;
the method comprises the steps of obtaining pollutant types of each polluted area, constructing a search tag according to the pollutant types of the polluted areas, searching through big data based on the search tag, and obtaining plant variety data information corresponding to the pollutant types of the repairable polluted areas;
Judging whether the real-time plant variety data information is matched with plant variety data information corresponding to the pollutant type, and reducing the soil restoration priority of the current polluted area when the real-time plant variety data information is matched with the plant variety data information corresponding to the pollutant type;
when the real-time plant variety data information is not matched with the plant variety data information corresponding to the pollutant type, the soil restoration priority of the current polluted area is improved, the soil restoration priority of each polluted area is obtained, and a final restoration planning scheme is generated based on the soil restoration priority and the final restoration agent type.
The second aspect of the present invention provides an intelligent planning system for pollution in-situ remediation, the system comprising a memory and a processor, the memory comprising an intelligent planning method program for pollution in-situ remediation, the intelligent planning method program for pollution in-situ remediation being executed by the processor, implementing the following steps:
acquiring pollution investigation data information of a target area, and constructing a model according to the pollution investigation data information of the target area to generate a visual pollution model diagram of each pollution area in the target area;
generating soil estimated repair volume data information of each polluted region according to the visualized pollution model diagram, and calculating estimated repair volume data of different repair medicament types in each region based on the soil estimated repair volume data information;
Calculating the repair cost information of each repair agent type based on the estimated repair quantity data of different repair agent types, and generating a final repair agent type according to the repair cost information;
acquiring remote sensing image data information in a target area, setting soil restoration priority according to the remote sensing image data information in the target area, and generating a final restoration planning scheme based on the soil restoration priority and the final restoration agent type.
Further, in the system, the soil estimated repair volume data information of each polluted area is generated according to the visualized pollution model diagram, and the method specifically comprises the following steps:
setting relevant pollution concentration threshold information, acquiring concentration data information of pollutants at all positions in a three-dimensional space from a visual pollution model diagram, and judging whether the concentration data information of the pollutants at all positions in the three-dimensional space is smaller than the relevant pollution concentration threshold information;
when the concentration data information of the pollutants is smaller than the relevant pollutant concentration threshold information, removing the pollution positions of which the concentration data information of the pollutants is smaller than the relevant pollutant concentration threshold information from the visual pollution model diagram, and updating the visual pollution model diagram;
Acquiring an updated visual pollution model diagram, acquiring limit coordinate information of pollutants in a three-dimensional space according to the updated visual pollution model diagram, and taking one of the limit coordinate information as a coordinate origin;
and constructing a cylindrical pollution volume model according to the coordinate origin and the limit coordinate information of the pollutants in the three-dimensional space, calculating the volume information of the cylindrical pollution volume model, and generating the soil estimated restoration volume data information of each polluted area.
Further, in the present system, the estimated repair amount data of different repair agent types in each region is calculated based on the soil estimated repair volume data information, specifically including:
the method comprises the steps of obtaining repair agent type information of each pollution type through big data, setting keyword information according to the repair agent type information, and searching through the big data according to the keyword information to obtain repair amount data information required in unit soil volume of each repair agent type in concentration information of different soil pollutants;
constructing a repair quantity knowledge graph, dividing the repair quantity knowledge graph into a plurality of storage spaces, and inputting the repair quantity data information required by each repair agent type in unit soil volume in the concentration information of different soil pollutants into the storage spaces for storage;
Acquiring concentration data information of pollutants in all directions in the visual pollution model diagram, and calculating average pollution concentration information according to the soil estimated restoration volume data information and the concentration data information of the pollutants in all directions in the visual pollution model diagram;
and inputting the average pollution concentration information and the soil estimated repair volume data information into a repair volume knowledge graph for calculation, and generating estimated repair volume data of different repair medicament types in each region.
The third aspect of the present invention provides a computer readable storage medium, wherein the computer readable storage medium includes a pollution in-situ treatment intelligent planning method program, and when the pollution in-situ treatment intelligent planning method program is executed by a processor, the steps of any one of the pollution in-situ treatment intelligent planning methods are realized.
The invention solves the defects existing in the background technology, and has the following beneficial effects:
according to the method, pollution investigation data information of a target area is obtained, model construction is carried out according to the pollution investigation data information of the target area, a visual pollution model diagram of each pollution area in the target area is generated, soil estimated repair volume data information of each pollution area is further generated according to the visual pollution model diagram, estimated repair volume data of different repair agent types in each area are calculated based on the soil estimated repair volume data information, repair cost information of each repair agent type is calculated based on the estimated repair volume data of the different repair agent types, final repair agent types are generated according to the repair cost information, remote sensing image data information in the target area is finally obtained, soil repair priority is set according to the remote sensing image data information in the target area, and a final repair planning scheme is generated based on the soil repair priority and the final repair agent types. According to the invention, the cylindrical volume in the polluted area is calculated by carrying out visual analysis on the polluted area, so that the repair cost of different repair medicament types is estimated, the repair medicament type with the lowest repair cost is selected, the estimated volume of the repaired soil can be accurately estimated, and the repair cost in the soil repair process is reduced. And secondly, the method sets the related repair priority according to plant variety data growing near the polluted soil, fully considers whether the self-healing property exists in the polluted soil, and ensures that the repair process is more reasonable.
Drawings
In order to more clearly illustrate the embodiments of the application 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 application, 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 shows an overall method flow diagram of an intelligent pollution in situ remediation planning method;
FIG. 2 shows a first method flow diagram of a pollution in situ remediation intelligent planning method;
FIG. 3 shows a second method flow diagram of a pollution in situ remediation intelligent planning method;
FIG. 4 shows a system block diagram of an intelligent pollution in-situ remediation planning system
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and 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 application, however, the present application may be practiced in other ways than those described herein, and therefore the scope of the present application is not limited to the specific embodiments disclosed below.
The invention provides an intelligent planning method for pollution in-situ treatment, which comprises the following steps:
s102, acquiring pollution investigation data information of a target area, and constructing a model according to the pollution investigation data information of the target area to generate a visual pollution model diagram of each pollution area in the target area;
in step S102, the method specifically includes the following steps:
the method comprises the steps of obtaining pollution investigation data information of a target area, dividing the target area into a plurality of pollution areas, and obtaining pollutant characteristic data information of each pollution area according to the pollution investigation data information of the target area;
acquiring pollutant types of each pollutant area in a three-dimensional space, pollutant distribution positions of pollutants in the three-dimensional space and concentration data information of the pollutants in the three-dimensional space based on the pollutant characteristic data information, and constructing a visual pollution model diagram;
setting relevant color depth according to the concentration data information of the pollutants, and carrying out initialized color rendering on the visualized pollution model diagram according to the relevant color depth and the pollution distribution position of the pollutants in the three-dimensional space;
after the color rendering, generating relevant mark information according to the pollutant type and the concentration data information of the pollutant in the three-dimensional space, fusing the mark information into a visual pollution model diagram, and outputting the visual pollution model diagram.
The method can be used for carrying out visual treatment on pollution, so that pollution data are more clear. The visual pollution model diagram can comprise a pollution three-dimensional model diagram, a two-dimensional section model diagram and the like.
S104, generating soil estimated repair volume data information of each polluted region according to the visual pollution model diagram, and calculating estimated repair volume data of different repair medicament types in each region based on the soil estimated repair volume data information;
as shown in fig. 2, in step S104, soil estimated repair volume data information of each contaminated area is generated according to the visualized pollution model diagram, which specifically includes:
s202, setting relevant pollution concentration threshold information, acquiring concentration data information of pollutants at all positions in a three-dimensional space from a visual pollution model diagram, and judging whether the concentration data information of the pollutants at all positions in the three-dimensional space is smaller than the relevant pollution concentration threshold information;
s204, when the concentration data information of the pollutants is smaller than the relevant pollutant concentration threshold information, removing the pollution positions of which the concentration data information of the pollutants is smaller than the relevant pollutant concentration threshold information from the visual pollution model diagram, and updating the visual pollution model diagram;
S206, acquiring an updated visual pollution model diagram, acquiring limit coordinate information of pollutants in a three-dimensional space according to the updated visual pollution model diagram, and taking one of the limit coordinate information as a coordinate origin;
s208, constructing a cylindrical pollution volume model according to the origin of coordinates and the ultimate coordinate information of the pollutants in the three-dimensional space, calculating the volume information of the cylindrical pollution volume model, and generating the soil estimated restoration volume data information of each polluted area.
In the method, a cylindrical pollution volume model can be constructed according to the limit coordinate information of pollutants in a three-dimensional space, and the actual irregular pollution volume model is manufactured into the cylindrical pollution volume model, so that the repair agent surrounds the cylindrical pollution volume model to determine the repair range in the repair process, and the method is more in line with the actual repair situation.
As shown in fig. 3, the method for calculating estimated repair volume data of different repair agent types in each region based on the estimated repair volume data information of soil specifically includes:
s302, acquiring repair agent type information of each pollution type through big data, setting keyword information according to the repair agent type information, and searching through the big data according to the keyword information to acquire repair amount data information required in unit soil volume of each repair agent type in concentration information of different soil pollutants;
S304, constructing a repair quantity knowledge graph, dividing the repair quantity knowledge graph into a plurality of storage spaces, and inputting repair quantity data information required in each repair agent type unit soil volume in concentration information of different soil pollutants into the storage spaces for storage;
s306, acquiring concentration data information of pollutants in all directions in the visual pollution model diagram, and calculating average pollution concentration information according to the soil estimated restoration volume data information and the concentration data information of the pollutants in all directions in the visual pollution model diagram;
and S308, inputting the average pollution concentration information and the soil estimated repair volume data information into a repair quantity knowledge graph for calculation, and generating estimated repair quantity data of different repair agent types in each region.
In the actual pollution in-situ remediation process, the concentration information of different soil pollutants is inconsistent with the required remediation amount data information in each remediation agent type unit soil volume due to the related chemical reaction or physical reaction, and the estimated remediation amount of different remediation agent types in each pollution area can be calculated by the method, so that the pollution in-situ remediation planning is more reasonable.
It should be noted that, in this scheme, the data such as the soil restoration target value, the soil permeability coefficient, the groundwater level and the groundwater flow direction, the in-situ injection process influence radius, etc. may be fused to calculate the estimated restoration data of different restoration agent types in each region, for example, different soil permeability coefficients may influence the permeability of the agent in the soil, so that different agent amounts may be required to permeate into the same depth region. As another example, different soil types are different in the penetration amount of the chemical, such as sand and clay, and under the condition that other conditions are the same, the sand is better in the penetration than the clay, and the chemical amount required to reach the same depth is inconsistent. The water level of the groundwater also affects the penetration of the chemical, and the soil is more moist as the water level approaches the groundwater, so that the rapid flow and adhesion of the chemical to the soil are reduced.
S106, calculating the repair cost information of each repair agent type based on the estimated repair quantity data of different repair agent types, and generating a final repair agent type according to the repair cost information;
In step S106, the method specifically includes the following steps:
acquiring cost data information of each repair medicament type unit volume through big data, and calculating repair cost information of each repair medicament type according to the cost data information of each repair medicament type unit volume and estimated repair quantity data of different repair medicament types;
constructing a repair cost ranking table, inputting repair cost information of each repair medicament type into the repair cost ranking table for ranking, and obtaining a ranked repair cost ranking table;
and obtaining the repair agent type with the lowest repair cost in the repair cost ranking table after ranking, and generating a final repair agent type according to the repair agent type with the lowest repair cost in the repair cost ranking table after ranking.
The method can be used for selecting the type of the repair medicament with the lowest repair cost and reducing the repair cost.
S108, acquiring remote sensing image data information in the target area, setting soil restoration priority according to the remote sensing image data information in the target area, and generating a final restoration planning scheme based on the soil restoration priority and the final restoration medicament type.
In step S108, the method may further include the steps of:
acquiring remote sensing image data information in a target area, filtering and denoising the remote sensing image data information to acquire an interested area in a remote sensing image, and identifying the interested area to acquire real-time plant variety data information of each polluted area in the target area;
the method comprises the steps of obtaining pollutant types of each polluted area, constructing a search tag according to the pollutant types of the polluted areas, searching through big data based on the search tag, and obtaining plant variety data information corresponding to the pollutant types of the repairable polluted areas;
judging whether the real-time plant variety data information is matched with plant variety data information corresponding to the pollutant type, and reducing the soil restoration priority of the current polluted area when the real-time plant variety data information is matched with the plant variety data information corresponding to the pollutant type;
when the real-time plant variety data information is not matched with the plant variety data information corresponding to the pollutant type, the soil restoration priority of the current polluted area is improved, the soil restoration priority of each polluted area is obtained, and a final restoration planning scheme is generated based on the soil restoration priority and the final restoration agent type.
It should be noted that, in this embodiment, the relevant plant variety has the capability of repairing pollution, for example, mango trees can absorb heavy metal pollution, and when the plant variety data information corresponding to the real-time plant variety data information pollutant type is matched, the pollution area is described as having the capability of repairing itself, so that the soil repair priority of the current pollution area is reduced, and the rationality of soil repair planning is improved.
In addition, the invention can also comprise the following steps:
acquiring weather type data information in a target area within preset time, judging whether the weather type data information is of a preset weather type, and if the weather type data information is of the preset weather type, acquiring estimated rainfall data information of the preset weather type;
acquiring soil permeability data information of each polluted area at each depth gradient, and calculating water content estimated flow characteristic data according to the estimated rainfall data information of the preset weather type and the soil permeability data information of each polluted area at each depth gradient;
acquiring historical migration characteristic data of pollutants in the water flowing process, and predicting pollution migration depth data in the water flowing process according to the estimated flow characteristic data of the water and the historical migration characteristic data of the pollutants in the water flowing process;
And acquiring a visual pollution model diagram of each pollution area, and updating the visual pollution model diagram of each pollution area according to the pollution migration depth data when the water flows.
The soil permeability is the permeability of the soil to surface water. Is one of the main traits affecting soil erosion. Depending on the texture, structure, porosity, moisture, cross-sectional configuration, etc. of the soil. Generally, the soil with thicker texture, good structure, larger pores and smaller humidity has easier water seepage and larger water permeability, and the surface runoff is reduced. On the contrary, the soil is slow in water seepage and small in water permeability, the surface runoff is increased, and the erosion effect on the soil is also enhanced. In the soil cross-sectional configuration, when the water permeability of each layer is not uniform, the soil permeability is often determined by the layer with the smallest water permeability. The closer the layer with smaller water permeability is to the ground, the larger the effect is, so that the stronger water and soil loss is easy to cause. The method is characterized in that the weather type is rainfall, pollutants in soil can slowly migrate to soil at the lower layer from soil at the upper layer along with water flowing due to the influence of rainfall, so that the volume of soil to be repaired can be increased, historical migration characteristic data of the pollutants can reflect migration characteristics of the water flowing when the water flows, and the pollutants in soil with certain soil permeability can migrate to a preset depth if certain data rainfall is obtained.
In addition, the invention can also comprise the following steps:
the method comprises the steps of obtaining a visual pollution model diagram of each pollution area, obtaining geographic position information of each pollution area, and obtaining map resource data information of each pollution area within a preset range through map software according to the geographic position information;
judging whether the map resource data information contains preset map resource data information or not, and if so, acquiring pollutant distribution information in a three-dimensional space in a visual pollution model diagram;
judging whether the pollutant distribution information is transmitted to the map resource data information, and adjusting the corresponding pollution area to the highest treatment priority when the pollutant distribution information is transmitted to the map resource data information;
and readjusting the current final repair planning scheme according to the highest treatment priority, and generating an adjusted repair planning scheme.
The preset map resource data information comprises rivers, farms, underground wells, agricultural irrigation wells and the like, and the rationality of the soil remediation planning scheme can be improved through the method.
The second aspect of the present invention provides an intelligent pollution in-situ remediation planning system 4, where the system 4 includes a memory 41 and a processor 62, and the memory 41 includes an intelligent pollution in-situ remediation planning method program, and when the intelligent pollution in-situ remediation planning method program is executed by the processor 62, the following steps are implemented:
acquiring pollution investigation data information of a target area, and constructing a model according to the pollution investigation data information of the target area to generate a visual pollution model diagram of each pollution area in the target area;
generating soil estimated repair volume data information of each polluted region according to the visualized pollution model diagram, and calculating estimated repair volume data of different repair medicament types in each region based on the soil estimated repair volume data information;
calculating the repair cost information of each repair agent type based on the estimated repair quantity data of different repair agent types, and generating a final repair agent type according to the repair cost information;
acquiring remote sensing image data information in a target area, setting soil restoration priority according to the remote sensing image data information in the target area, and generating a final restoration planning scheme based on the soil restoration priority and the final restoration agent type.
Further, in the system, the soil estimated repair volume data information of each polluted area is generated according to the visualized pollution model diagram, and the method specifically comprises the following steps:
setting relevant pollution concentration threshold information, acquiring concentration data information of pollutants at all positions in a three-dimensional space from a visual pollution model diagram, and judging whether the concentration data information of the pollutants at all positions in the three-dimensional space is smaller than the relevant pollution concentration threshold information;
when the concentration data information of the pollutants is smaller than the relevant pollutant concentration threshold information, removing the pollution positions of which the concentration data information of the pollutants is smaller than the relevant pollutant concentration threshold information from the visual pollution model diagram, and updating the visual pollution model diagram;
acquiring an updated visual pollution model diagram, acquiring limit coordinate information of pollutants in a three-dimensional space according to the updated visual pollution model diagram, and taking one of the limit coordinate information as a coordinate origin;
and constructing a cylindrical pollution volume model according to the coordinate origin and the limit coordinate information of the pollutants in the three-dimensional space, calculating the volume information of the cylindrical pollution volume model, and generating the soil estimated restoration volume data information of each polluted area.
Further, in the present system, the estimated repair amount data of different repair agent types in each region is calculated based on the soil estimated repair volume data information, specifically including:
the method comprises the steps of obtaining repair agent type information of each pollution type through big data, setting keyword information according to the repair agent type information, and searching through the big data according to the keyword information to obtain repair amount data information required in unit soil volume of each repair agent type in concentration information of different soil pollutants;
constructing a repair quantity knowledge graph, dividing the repair quantity knowledge graph into a plurality of storage spaces, and inputting the repair quantity data information required by each repair agent type in unit soil volume in the concentration information of different soil pollutants into the storage spaces for storage;
acquiring concentration data information of pollutants in all directions in the visual pollution model diagram, and calculating average pollution concentration information according to the soil estimated restoration volume data information and the concentration data information of the pollutants in all directions in the visual pollution model diagram;
and inputting the average pollution concentration information and the soil estimated repair volume data information into a repair volume knowledge graph for calculation, and generating estimated repair volume data of different repair medicament types in each region.
The third aspect of the present application provides a computer readable storage medium, wherein the computer readable storage medium includes a pollution in-situ treatment intelligent planning method program, and when the pollution in-situ treatment intelligent planning method program is executed by a processor, the steps of any one of the pollution in-situ treatment intelligent planning methods are realized.
In the several embodiments provided by the present 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 (10)
1. An intelligent planning method for pollution in-situ treatment is characterized by comprising the following steps:
acquiring pollution investigation data information of a target area, and performing model construction according to the pollution investigation data information of the target area to generate a visual pollution model diagram of each pollution area in the target area;
generating soil estimated repair volume data information of each polluted region according to the visual pollution model diagram, and calculating estimated repair volume data of different repair medicament types in each region based on the soil estimated repair volume data information;
calculating the repair cost information of each repair agent type based on the estimated repair quantity data of the different repair agent types, and generating a final repair agent type according to the repair cost information;
Acquiring remote sensing image data information in a target area, setting soil restoration priority according to the remote sensing image data information in the target area, and generating a final restoration planning scheme based on the soil restoration priority and a final restoration medicament type.
2. The intelligent planning method for pollution in-situ treatment according to claim 1, wherein the method is characterized by obtaining pollution investigation data information of a target area, performing model construction according to the pollution investigation data information of the target area, and generating a visual pollution model diagram of each pollution area in the target area, and specifically comprises the following steps:
the method comprises the steps of obtaining pollution investigation data information of a target area, dividing the target area into a plurality of pollution areas, and obtaining pollutant characteristic data information of each pollution area according to the pollution investigation data information of the target area;
acquiring pollutant types of each pollutant area in a three-dimensional space, pollutant distribution positions of pollutants in the three-dimensional space and concentration data information of the pollutants in the three-dimensional space based on the pollutant characteristic data information, and constructing a visual pollution model diagram;
setting relevant color depth according to the concentration data information of the pollutants, and carrying out initialized color rendering on the visual pollution model diagram according to the relevant color depth and the pollution distribution position of the pollutants in a three-dimensional space;
After color rendering, generating relevant marking information according to the pollutant type and the pollutant concentration data information in the three-dimensional space, fusing the marking information into the visual pollution model diagram, and outputting the visual pollution model diagram.
3. The intelligent planning method for pollution in-situ remediation according to claim 1, wherein the generating of the soil estimated remediation volume data information of each pollution area according to the visual pollution model map specifically comprises:
setting relevant pollution concentration threshold information, acquiring concentration data information of pollutants at all positions in a three-dimensional space from the visual pollution model diagram, and judging whether the concentration data information of the pollutants at all positions in the three-dimensional space is smaller than the relevant pollution concentration threshold information;
when the concentration data information of the pollutants is smaller than the relevant pollutant concentration threshold information, removing the pollution positions of which the concentration data information of the pollutants is smaller than the relevant pollutant concentration threshold information from the visual pollution model diagram, and updating the visual pollution model diagram;
Acquiring an updated visual pollution model diagram, acquiring limit coordinate information of pollutants in a three-dimensional space according to the updated visual pollution model diagram, and taking one of the limit coordinate information as a coordinate origin;
and constructing a cylindrical pollution volume model according to the coordinate origin and the limit coordinate information of the pollutant in the three-dimensional space, calculating the volume information of the cylindrical pollution volume model, and generating the soil estimated restoration volume data information of each polluted area.
4. The intelligent planning method for pollution in-situ remediation according to claim 1, wherein the calculating of estimated remediation data of different remediation agent types in each region based on the estimated remediation volume data information of the soil specifically comprises:
acquiring repair agent type information of each pollution type through big data, setting keyword information according to the repair agent type information, and searching through big data according to the keyword information to acquire repair amount data information required in unit soil volume of each repair agent type in concentration information of different soil pollutants;
constructing a repair quantity knowledge graph, dividing the repair quantity knowledge graph into a plurality of storage spaces, and inputting the repair quantity data information required in each repair agent type unit soil volume in the concentration information of different soil pollutants into the storage spaces for storage;
Acquiring concentration data information of pollutants in all directions in a visual pollution model diagram, and calculating average pollution concentration information according to the soil estimated restoration volume data information and the concentration data information of the pollutants in all directions in the visual pollution model diagram;
and inputting the average pollution concentration information and the soil estimated repair volume data information into the repair volume knowledge graph for calculation, and generating estimated repair volume data of different repair agent types in each region.
5. The intelligent planning method for pollution in-situ remediation according to claim 1, wherein calculating the repair cost information of each repair agent type based on the estimated repair quantity data of the different repair agent types, and generating the final repair agent type according to the repair cost information, specifically comprises:
acquiring cost data information of unit volume of each repair medicament type through big data, and calculating repair cost information of each repair medicament type according to the cost data information of the unit volume of each repair medicament type and estimated repair quantity data of different repair medicament types;
constructing a repair cost ranking table, inputting the repair cost information of each repair medicament type into the repair cost ranking table for ranking, and obtaining a repair cost ranking table with the ranking completed;
And obtaining the repair agent type with the lowest repair cost in the repair cost ranking table after the ranking is finished, and generating a final repair agent type according to the repair agent type with the lowest repair cost in the repair cost ranking table after the ranking is finished.
6. The intelligent planning method for pollution in-situ remediation according to claim 1, wherein the method is characterized by setting a soil remediation priority according to remote sensing image data information in the target area, and generating a final remediation planning scheme based on the soil remediation priority and a final remediation agent type, and specifically comprises the following steps:
acquiring remote sensing image data information in a target area, filtering and denoising the remote sensing image data information to acquire an interested area in a remote sensing image, and identifying the interested area to acquire real-time plant variety data information of each polluted area in the target area;
acquiring the pollutant type of each polluted area, constructing a search tag according to the pollutant type of the polluted area, and searching through big data based on the search tag to acquire plant variety data information corresponding to the pollutant type of each repairable polluted area;
Judging whether the real-time plant variety data information is matched with plant variety data information corresponding to the pollutant type, and reducing the soil restoration priority of the current polluted area when the real-time plant variety data information is matched with the plant variety data information corresponding to the pollutant type;
when the real-time plant variety data information is not matched with the plant variety data information corresponding to the pollutant type, the soil restoration priority of the current polluted area is improved, the soil restoration priority of each polluted area is obtained, and a final restoration planning scheme is generated based on the soil restoration priority and the final restoration agent type.
7. The intelligent pollution in-situ treatment planning system is characterized by comprising a memory and a processor, wherein the memory comprises an intelligent pollution in-situ treatment planning method program, and the intelligent pollution in-situ treatment planning method program is executed by the processor and comprises the following steps:
acquiring pollution investigation data information of a target area, and performing model construction according to the pollution investigation data information of the target area to generate a visual pollution model diagram of each pollution area in the target area;
Generating soil estimated repair volume data information of each polluted region according to the visual pollution model diagram, and calculating estimated repair volume data of different repair medicament types in each region based on the soil estimated repair volume data information;
calculating the repair cost information of each repair agent type based on the estimated repair quantity data of the different repair agent types, and generating a final repair agent type according to the repair cost information;
acquiring remote sensing image data information in a target area, setting soil restoration priority according to the remote sensing image data information in the target area, and generating a final restoration planning scheme based on the soil restoration priority and a final restoration medicament type.
8. The intelligent pollution in-situ remediation planning system of claim 7, wherein the generating of soil pre-estimated remediation volume data information for each contaminated area according to the visual pollution model map specifically comprises:
setting relevant pollution concentration threshold information, acquiring concentration data information of pollutants at all positions in a three-dimensional space from the visual pollution model diagram, and judging whether the concentration data information of the pollutants at all positions in the three-dimensional space is smaller than the relevant pollution concentration threshold information;
When the concentration data information of the pollutants is smaller than the relevant pollutant concentration threshold information, removing the pollution positions of which the concentration data information of the pollutants is smaller than the relevant pollutant concentration threshold information from the visual pollution model diagram, and updating the visual pollution model diagram;
acquiring an updated visual pollution model diagram, acquiring limit coordinate information of pollutants in a three-dimensional space according to the updated visual pollution model diagram, and taking one of the limit coordinate information as a coordinate origin;
and constructing a cylindrical pollution volume model according to the coordinate origin and the limit coordinate information of the pollutant in the three-dimensional space, calculating the volume information of the cylindrical pollution volume model, and generating the soil estimated restoration volume data information of each polluted area.
9. The intelligent pollution in-situ remediation planning system of claim 7, wherein calculating estimated remediation data for different remediation agent types in each zone based on the soil estimated remediation volume data information, specifically comprises:
acquiring repair agent type information of each pollution type through big data, setting keyword information according to the repair agent type information, and searching through big data according to the keyword information to acquire repair amount data information required in unit soil volume of each repair agent type in concentration information of different soil pollutants;
Constructing a repair quantity knowledge graph, dividing the repair quantity knowledge graph into a plurality of storage spaces, and inputting the repair quantity data information required in each repair agent type unit soil volume in the concentration information of different soil pollutants into the storage spaces for storage;
acquiring concentration data information of pollutants in all directions in a visual pollution model diagram, and calculating average pollution concentration information according to the soil estimated restoration volume data information and the concentration data information of the pollutants in all directions in the visual pollution model diagram;
and inputting the average pollution concentration information and the soil estimated repair volume data information into the repair volume knowledge graph for calculation, and generating estimated repair volume data of different repair agent types in each region.
10. A computer readable storage medium, characterized in that the computer readable storage medium comprises a pollution in-situ remediation intelligent planning method program, which when executed by a processor, implements the steps of the pollution in-situ remediation intelligent planning method according to any one of claims 1-6.
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