CN116362570A - Multi-dimensional pollution analysis method and system based on big data platform - Google Patents
Multi-dimensional pollution analysis method and system based on big data platform Download PDFInfo
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Abstract
The invention discloses a multi-dimensional pollution analysis method and a system based on a big data platform. By the method, comprehensive pollution analysis and pollution loss evaluation can be performed on the farmland area in multiple dimensions, so that the pollution influence and pollution loss condition of the current farmland area can be mastered more intuitively.
Description
Technical Field
The invention relates to the field of big data analysis, in particular to a multi-dimensional pollution analysis method and system based on a big data platform.
Background
The condition of farmland crop pollution generally occurs, and in the agricultural production process, harmful substances such as chemical substances, biological substances, heavy metals and the like are contained in crops due to artificial or natural factors, so that the phenomenon of potential threat to human health is caused by exceeding the sanitary standard. Common crop pollution includes heavy metal pollution, pesticide residue, fertilizer pollution, microbial pollution and the like.
The method is subject to traditional manual experience analysis of farmland regional pollution, is difficult to accurately analyze the pollution condition of crops in the farmland, is difficult to realize scientific and accurate pollution loss evaluation, has single analysis dimension on the farmland, and further has lower risk resistance capability.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a multidimensional pollution analysis method and system based on a big data platform.
The first aspect of the invention provides a multidimensional pollution analysis method based on a big data platform, which comprises the following steps:
acquiring target farmland area information, and constructing a farmland three-dimensional model based on the target farmland area information;
the pollution monitoring data is imported into an analysis prediction model, regional pollution assessment and pollution prediction are carried out based on three dimensions of water, soil and gas, and current pollution region and pollution prediction data are obtained;
The pollution monitoring data is imported into an analysis prediction model, regional pollution assessment and pollution prediction are carried out based on three dimensions of water, soil and gas, and current pollution region and pollution prediction data are obtained;
acquiring crop growth information and crop market big data in a target farmland area;
and carrying out pollution loss evaluation on the target farmland area based on the crop growth information, the crop market big data and the pollution prediction data to obtain area pollution loss evaluation data.
In this scheme, obtain target farmland regional information, based on target farmland regional information builds farmland three-dimensional model, specifically do:
extracting farmland area, regional outline, crop distribution information, soil and stratum characteristic information from target farmland regional information;
constructing a farmland three-dimensional model based on the farmland area, the regional contour information, the soil and stratum characteristic information and the like;
based on crop distribution information, carrying out regional division of different crops from a farmland three-dimensional model to obtain a plurality of crop regions.
In this scheme, according to pollution monitoring point, carry out the multidimensional monitoring based on water-earth-gas to target farmland area through farmland monitoring module and obtain pollution monitoring data, specifically do:
Dividing a farmland plane area into N small areas based on a farmland three-dimensional model;
determining monitoring points in each small area by a random point distribution method, wherein each small area corresponds to one monitoring point in advance;
taking all the monitoring points as pollution monitoring points;
the pollution monitoring module is used for sampling and monitoring the pollution monitoring points in real time and acquiring corresponding gas monitoring data, water monitoring data and soil monitoring data;
the pollution monitoring data comprises gas monitoring data, water body monitoring data and soil monitoring data.
In this scheme, the importing of pollution monitoring data into analysis prediction model carries out regional pollution assessment and pollution prediction based on three dimensions of water-soil-gas to obtain current pollution region and pollution prediction data, including:
respectively calculating the gas pollution index, the water pollution index and the soil pollution index of each pollution monitoring point according to the gas monitoring data, the water monitoring data and the soil monitoring data;
comparing the gas pollution index, the water pollution index and the soil pollution index with a first preset pollution index, and marking a small area where a corresponding pollution monitoring point is located as a pollution area if the three pollution indexes are all larger than the first preset pollution index;
And carrying out merging operation on the adjacent polluted areas to finally form a plurality of merged areas, and marking each merged area as the current polluted area.
In this scheme, the pollution monitoring data is led into an analysis prediction model, and regional pollution assessment and pollution prediction are performed based on three dimensions of water, soil and gas to obtain current pollution region and pollution prediction data, specifically:
carrying out data standardization and normalization processing on the pollution monitoring data to obtain a standard data set;
based on the monitoring time, carrying out data serialization on the standard data set, and obtaining time series data;
constructing a pollution prediction model based on LSTM;
importing the time series data into a pollution prediction model for data prediction to obtain pollution prediction data based on three dimensions of gas, water and soil;
and importing the pollution monitoring data and the pollution prediction data into a farmland three-dimensional model, taking the current pollution area as a central area, and carrying out area pollution diffusion analysis to obtain a pollution diffusion area.
In this scheme, acquire crop growth information and crops market big data in the target farmland region, specifically:
the crop growth information comprises growth stage information and growth period information of different crops in a current target farmland area;
The crop market data includes historical sales data and historical market price data of crops.
In this scheme, carry out pollution loss evaluation to the regional pollution loss evaluation data of target farmland based on crop growth information, crop market big data, pollution prediction data, obtain regional pollution loss evaluation data, specifically be:
acquiring big market data of crops;
generating a search tag based on the target crop;
searching target crop data from the crop market big data based on the search tag to obtain the target crop big data;
carrying out data cleaning and standardization treatment on the big data of the target crops, and carrying out time serialization on the big data of the target crops after treatment to obtain serialized data of the crops;
the crop serialization data is imported into an analysis prediction model to predict sales volume data and price fluctuation of target crops, so that crop prediction sales volume data and crop market price prediction data are obtained;
and carrying out data fusion on the crop forecast sales volume data and the crop market price forecast data to obtain crop market forecast data.
In this scheme, carry out pollution loss evaluation to the regional pollution loss evaluation data of target farmland based on crop growth information, crops market big data, pollution prediction data, obtain regional pollution loss evaluation data, still include:
Acquiring a plurality of crop areas;
the crop area, the current pollution area and the pollution diffusion area are led into a farmland three-dimensional model, the intersection area part of the crop area and the current pollution area is marked as a first pollution area, and the intersection area part of the crop area and the pollution diffusion area is marked as a second pollution area;
based on crop growth information, crop market big data, a first pollution area and a second pollution area, carrying out pollution loss calculation and analysis on a target farmland area to obtain area pollution loss evaluation data;
and sending the regional pollution loss evaluation data to preset terminal equipment.
The second aspect of the present invention also provides a multi-dimensional pollution analysis system based on a big data platform, the system comprising: the multi-dimensional pollution analysis system comprises a memory and a processor, wherein the memory comprises a multi-dimensional pollution analysis program based on a big data platform, and the multi-dimensional pollution analysis program based on the big data platform realizes the following steps when being executed by the processor:
acquiring target farmland area information, and constructing a farmland three-dimensional model based on the target farmland area information;
the pollution monitoring data is imported into an analysis prediction model, regional pollution assessment and pollution prediction are carried out based on three dimensions of water, soil and gas, and current pollution region and pollution prediction data are obtained;
The pollution monitoring data is imported into an analysis prediction model, regional pollution assessment and pollution prediction are carried out based on three dimensions of water, soil and gas, and current pollution region and pollution prediction data are obtained;
acquiring crop growth information and crop market big data in a target farmland area;
and carrying out pollution loss evaluation on the target farmland area based on the crop growth information, the crop market big data and the pollution prediction data to obtain area pollution loss evaluation data.
In this scheme, obtain target farmland regional information, based on target farmland regional information builds farmland three-dimensional model, specifically do:
extracting farmland area, regional outline, crop distribution information, soil and stratum characteristic information from target farmland regional information;
constructing a farmland three-dimensional model based on the farmland area, the regional contour information, the soil and stratum characteristic information and the like;
based on crop distribution information, carrying out regional division of different crops from a farmland three-dimensional model to obtain a plurality of crop regions.
The invention discloses a multi-dimensional pollution analysis method and a system based on a big data platform. By the method, comprehensive pollution analysis and pollution loss evaluation can be performed on the farmland area in multiple dimensions, so that the pollution influence and pollution loss condition of the current farmland area can be mastered more intuitively.
Drawings
FIG. 1 shows a flow chart of a multi-dimensional pollution analysis method based on a big data platform of the present invention;
FIG. 2 shows a flow chart of the invention for constructing a three-dimensional model of a farmland;
FIG. 3 illustrates a flow chart for acquiring a current contaminated area in accordance with the present invention;
FIG. 4 shows a block diagram of a multi-dimensional pollution analysis system based on a big data platform of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
FIG. 1 shows a flow chart of a multi-dimensional pollution analysis method based on a big data platform of the present invention.
As shown in fig. 1, the first aspect of the present invention provides a multi-dimensional pollution analysis method based on a big data platform, which includes:
S102, acquiring target farmland area information, and constructing a farmland three-dimensional model based on the target farmland area information;
s104, importing the pollution monitoring data into an analysis prediction model, and carrying out regional pollution evaluation and pollution prediction based on three dimensions of water, soil and gas to obtain current pollution region and pollution prediction data;
s106, the pollution monitoring data is imported into an analysis prediction model, regional pollution assessment and pollution prediction are carried out based on three dimensions of water, soil and gas, and current pollution region and pollution prediction data are obtained;
s108, acquiring crop growth information and crop market big data in a target farmland area;
s110, carrying out pollution loss evaluation on the target farmland area based on crop growth information, crop market big data and pollution prediction data to obtain area pollution loss evaluation data.
The pollution monitoring data comprise gas monitoring data, water body monitoring data and soil monitoring data, wherein the gas monitoring data comprise atmosphere gas monitoring data and soil deep gas monitoring data of monitoring points, the water body monitoring data comprise monitoring data of surface water bodies and soil underground water bodies, the soil monitoring data comprise soil surface layer monitoring data and underground soil monitoring data, and the underground soil is underground soil with the thickness of 0-10 m. The three dimensions of water, soil and gas are specifically the dimensions of water, soil and gas above and below the ground.
FIG. 2 shows a flow chart of the present invention for constructing a three-dimensional model of a farmland.
According to the embodiment of the invention, the method for acquiring the target farmland area information and constructing the farmland three-dimensional model based on the target farmland area information comprises the following specific steps:
s202, extracting farmland area, regional outline, crop distribution information, soil and stratum characteristic information from target farmland regional information;
s204, constructing a farmland three-dimensional model based on the farmland area, regional contour information, soil and stratum characteristic information and the like;
s206, based on crop distribution information, dividing areas of different crops from the farmland three-dimensional model to obtain a plurality of crop areas.
It should be noted that the three-dimensional model of the farmland includes all crop areas, and one crop area corresponds to a planting range of one crop. The soil and stratum characteristic information comprises information such as soil types, soil layers, soil properties of different soil layers and the like, and the crop area comprises three-dimensional areas of atmosphere, ground and underground.
According to the embodiment of the invention, according to the pollution monitoring points, the multi-dimensional monitoring based on water, soil and gas is carried out on the target farmland area through the farmland monitoring module, and pollution monitoring data are obtained, specifically:
Dividing a farmland plane area into N small areas based on a farmland three-dimensional model;
determining monitoring points in each small area by a random point distribution method, wherein each small area corresponds to one monitoring point in advance;
taking all the monitoring points as pollution monitoring points;
the pollution monitoring module is used for sampling and monitoring the pollution monitoring points in real time and acquiring corresponding gas monitoring data, water monitoring data and soil monitoring data;
the pollution monitoring data comprises gas monitoring data, water body monitoring data and soil monitoring data.
The size of the N is specifically determined by the area size of a target farmland area in the farmland three-dimensional model, the larger the area is, the larger the N is, and the area of each small area is ensured to be in a preset range after division. The gas monitoring data, the water body monitoring data and the soil pollution monitoring data comprise data of all pollution monitoring points. The farmland monitoring module comprises a gas detection device, a water body detection device and a soil sampling monitoring device.
FIG. 3 illustrates a flow chart for acquiring a currently contaminated area in accordance with the present invention.
According to the embodiment of the invention, the method for importing pollution monitoring data into an analysis prediction model and carrying out regional pollution assessment and pollution prediction based on three dimensions of water, soil and gas to obtain current pollution region and pollution prediction data comprises the following steps:
S302, respectively calculating the gas pollution index, the water pollution index and the soil pollution index of each pollution monitoring point according to the gas monitoring data, the water monitoring data and the soil monitoring data;
s304, comparing the gas pollution index, the water pollution index and the soil pollution index with a first preset pollution index, and if the three pollution indexes are all larger than the first preset pollution index, marking a small area where the corresponding pollution monitoring point is located as a pollution area;
s306, carrying out merging operation on the adjacent polluted areas and finally forming a plurality of merged areas, and marking each merged area as the current polluted area.
It should be noted that the current contaminated area is specifically an area with a high pollution degree of gas, water and soil, and the current contaminated area is generally a plurality of contaminated areas. The gas monitoring data comprise concentration values of PM2.5 in monitoring points and sulfur dioxide concentration values; the water body monitoring data comprise a water body chloride concentration value, a PH value and a sulfide concentration value; the soil monitoring data comprise soil PH value, heavy metal exceeding standard type number and heavy metal average exceeding standard rate. The method is characterized in that the content value of the heavy metals such as lead, cadmium, mercury, chromium, copper, zinc and the like in the soil at the monitoring point is detected, and the content value is compared with a standard value to obtain the standard exceeding type number and the average exceeding rate. The pH value of the soil, the number of types of the excessive heavy metal and the average excessive heavy metal can reflect the acid-base pollution and the heavy metal pollution degree of the soil.
The specific calculation formula of the gas pollution index is as follows:
in the method, in the process of the invention,
correction coefficient for gas pollution, +.>
Is a gas pollution index>
Concentration value of PM2.5, +.>
In particular to a sulfur dioxide concentration value.
The specific calculation formula of the water pollution index is as follows:
in the method, in the process of the invention,
correction coefficient for water pollution, < >>
Index of water pollution>
Is the concentration value of chloride in water body>
Is the sulfide concentration value.
The soil pollution index is specifically calculated according to the following formula:
in the method, in the process of the invention,
for soil pollution correction coefficient, < >>
Is the soil pollution index. />
For heavy metals exceeding the standard number of species->
Average overstock rate for heavy metals.
According to the embodiment of the invention, the pollution monitoring data is imported into an analysis prediction model, and regional pollution evaluation and pollution prediction are performed based on three dimensions of water, soil and gas to obtain current pollution region and pollution prediction data, specifically:
carrying out data standardization and normalization processing on the pollution monitoring data to obtain a standard data set;
based on the monitoring time, carrying out data serialization on the standard data set, and obtaining time series data;
constructing a pollution prediction model based on LSTM;
importing the time series data into a pollution prediction model for data prediction to obtain pollution prediction data based on three dimensions of gas, water and soil;
And importing the pollution monitoring data and the pollution prediction data into a farmland three-dimensional model, taking the current pollution area as a central area, and carrying out area pollution diffusion analysis to obtain a pollution diffusion area.
The pollution prediction model is specifically a long-short-term memory artificial neural network model, namely an LSTM model, and can be used for performing prediction analysis on existing pollution monitoring data to obtain pollution prediction data of a future period of time. The pollution prediction data comprise pollution prediction data of three dimensions of gas, water and soil.
The farmland three-dimensional model is a visual map model, and pollution prediction data, a current pollution area, a pollution diffusion area and a crop area can be visually displayed through the model.
According to the embodiment of the invention, the acquisition of the crop growth information and the crop market big data in the target farmland area is specifically as follows:
the crop growth information comprises growth stage information and growth period information of different crops in a current target farmland area;
the crop market data includes historical sales data and historical market price data of crops.
It should be noted that the growth stage information and the growth cycle information of the different crops specifically include the current growth stage number and the total growth stage number of the different crops.
According to the embodiment of the invention, the pollution loss evaluation is carried out on the target farmland area based on the crop growth information, the crop market big data and the pollution prediction data to obtain area pollution loss evaluation data, which comprises the following specific steps:
acquiring big market data of crops;
generating a search tag based on the target crop;
searching target crop data from the crop market big data based on the search tag to obtain the target crop big data;
carrying out data cleaning and standardization treatment on the big data of the target crops, and carrying out time serialization on the big data of the target crops after treatment to obtain serialized data of the crops;
the crop serialization data is imported into an analysis prediction model to predict sales volume data and price fluctuation of target crops, so that crop prediction sales volume data and crop market price prediction data are obtained;
and carrying out data fusion on the crop forecast sales volume data and the crop market price forecast data to obtain crop market forecast data.
The large crop market data is specifically historical sales data and historical market price data, and the historical sales data is specifically historical sales data in a target farmland area, and comprises the yield, sales volume, stagnancy volume and the like of different crops.
According to the embodiment of the invention, the method for estimating the pollution loss of the target farmland area based on the crop growth information, the crop market big data and the pollution prediction data, to obtain the regional pollution loss estimation data, further comprises the following steps:
acquiring a plurality of crop areas;
the crop area, the current pollution area and the pollution diffusion area are led into a farmland three-dimensional model, the intersection area part of the crop area and the current pollution area is marked as a first pollution area, and the intersection area part of the crop area and the pollution diffusion area is marked as a second pollution area;
based on crop growth information, crop market big data, a first pollution area and a second pollution area, carrying out pollution loss calculation and analysis on a target farmland area to obtain area pollution loss evaluation data;
and sending the regional pollution loss evaluation data to preset terminal equipment.
It should be noted that each crop area has a corresponding first contaminated area and second contaminated area. The larger the areas of the first and second contaminated areas represent a greater degree of contamination impact of the corresponding crop area.
According to the embodiment of the invention, the regional pollution loss evaluation data comprises a regional pollution loss evaluation index, and the regional pollution loss evaluation index specifically comprises the following calculation formula:
wherein T is the total number of crop areas, J is the regional pollution loss evaluation index,
for the current number of growth stages of the ith crop, < >>
For the total number of growth stages of the ith crop, < > j->
First contaminated area for the ith crop area,/->
Second contaminated area for the ith crop area,/->
Predictive marketing quantity for the ith crop, and->
Is the forecast market price of the ith crop.
The predicted market sales quantity and the predicted market price of the crops are extracted through the predicted data of the crop market. The regional pollution loss evaluation index is specifically data obtained by comprehensively analyzing and evaluating the crop growth condition, market value, predicted pollution influence and other multidimensional degrees, has higher evaluation reference value, and can more intuitively master the pollution influence condition of the current farmland region.
In addition, each crop area corresponds to one crop.
According to an embodiment of the present invention, further comprising:
Selecting one crop area as the current crop area;
acquiring a yield value of a current crop area in a preset time period from crop market big data;
calculating a first pollution area value and a second pollution area value of the current crop area according to the farmland three-dimensional model;
calculating the average value of the yield values of all the crop areas to obtain the average yield value;
if the yield value of the current crop area is smaller than the yield average value and the area values of the first pollution area and the second pollution area are both larger than the preset area value, marking the current crop area as a repaired crop area;
acquiring pollution monitoring data of a restored crop area, taking the area suitable for planting as a first target, and restoring soil as a second target, and carrying out suitable crop analysis and screening from crop planting big data based on the pollution monitoring data to obtain recommended crop information;
and sending the recommended crop information to preset terminal equipment.
The pollution monitoring data comprise gas monitoring data, water monitoring data and soil pollution monitoring. It is worth mentioning that in the restored crop area, the pollution has a larger influence on the crops, the yield is far lower than the average value, namely lower than the expected value, and the invention analyzes the recommended crops with respect to the current crop area from the pollution dimension of gas, water and soil in the restored crop area by analyzing the pollution monitoring data of the restored crop area, wherein the recommended crops comprise the crop types suitable for planting in the current crop area and the crop types with restoration effect on the current crop area. The large crop planting data comprise suitable planting environments, crop properties, crop growth information, crop environment restoration effect and the like of all crops.
FIG. 4 shows a block diagram of a multi-dimensional pollution analysis system based on a big data platform of the present invention.
The second aspect of the present invention also provides a multi-dimensional pollution analysis system 4 based on a big data platform, the system comprising: a memory 41, and a processor 42, wherein the memory includes a multi-dimensional pollution analysis program based on a big data platform, and the multi-dimensional pollution analysis program based on the big data platform realizes the following steps when executed by the processor:
acquiring target farmland area information, and constructing a farmland three-dimensional model based on the target farmland area information;
the pollution monitoring data is imported into an analysis prediction model, regional pollution assessment and pollution prediction are carried out based on three dimensions of water, soil and gas, and current pollution region and pollution prediction data are obtained;
the pollution monitoring data is imported into an analysis prediction model, regional pollution assessment and pollution prediction are carried out based on three dimensions of water, soil and gas, and current pollution region and pollution prediction data are obtained;
acquiring crop growth information and crop market big data in a target farmland area;
and carrying out pollution loss evaluation on the target farmland area based on the crop growth information, the crop market big data and the pollution prediction data to obtain area pollution loss evaluation data.
The pollution monitoring data comprise gas monitoring data, water body monitoring data and soil monitoring data, wherein the gas monitoring data comprise atmosphere gas monitoring data and soil deep gas monitoring data of monitoring points, the water body monitoring data comprise monitoring data of surface water bodies and soil underground water bodies, the soil monitoring data comprise soil surface layer monitoring data and underground soil monitoring data, and the underground soil is underground soil with the thickness of 0-10 m. The three dimensions of water, soil and gas are specifically the dimensions of water, soil and gas above and below the ground.
According to the embodiment of the invention, the method for acquiring the target farmland area information and constructing the farmland three-dimensional model based on the target farmland area information comprises the following specific steps:
extracting farmland area, regional outline, crop distribution information, soil and stratum characteristic information from target farmland regional information;
constructing a farmland three-dimensional model based on the farmland area, the regional contour information, the soil and stratum characteristic information and the like;
based on crop distribution information, carrying out regional division of different crops from a farmland three-dimensional model to obtain a plurality of crop regions.
It should be noted that the three-dimensional model of the farmland includes all crop areas, and one crop area corresponds to a planting range of one crop. The soil and stratum characteristic information comprises information such as soil types, soil layers, soil properties of different soil layers and the like, and the crop area comprises three-dimensional areas of atmosphere, ground and underground.
According to the embodiment of the invention, according to the pollution monitoring points, the multi-dimensional monitoring based on water, soil and gas is carried out on the target farmland area through the farmland monitoring module, and pollution monitoring data are obtained, specifically:
dividing a farmland plane area into N small areas based on a farmland three-dimensional model;
determining monitoring points in each small area by a random point distribution method, wherein each small area corresponds to one monitoring point in advance;
taking all the monitoring points as pollution monitoring points;
the pollution monitoring module is used for sampling and monitoring the pollution monitoring points in real time and acquiring corresponding gas monitoring data, water monitoring data and soil monitoring data;
the pollution monitoring data comprises gas monitoring data, water body monitoring data and soil monitoring data.
The size of the N is specifically determined by the area size of a target farmland area in the farmland three-dimensional model, the larger the area is, the larger the N is, and the area of each small area is ensured to be in a preset range after division. The gas monitoring data, the water body monitoring data and the soil pollution monitoring data comprise data of all pollution monitoring points. The farmland monitoring module comprises a gas detection device, a water body detection device and a soil sampling monitoring device.
According to the embodiment of the invention, the method for importing pollution monitoring data into an analysis prediction model and carrying out regional pollution assessment and pollution prediction based on three dimensions of water, soil and gas to obtain current pollution region and pollution prediction data comprises the following steps:
respectively calculating the gas pollution index, the water pollution index and the soil pollution index of each pollution monitoring point according to the gas monitoring data, the water monitoring data and the soil monitoring data;
comparing the gas pollution index, the water pollution index and the soil pollution index with a first preset pollution index, and marking a small area where a corresponding pollution monitoring point is located as a pollution area if the three pollution indexes are all larger than the first preset pollution index;
and carrying out merging operation on the adjacent polluted areas to finally form a plurality of merged areas, and marking each merged area as the current polluted area.
It should be noted that the current contaminated area is specifically an area with a high pollution degree of gas, water and soil, and the current contaminated area is generally a plurality of contaminated areas. The gas monitoring data comprise concentration values of PM2.5 in monitoring points and sulfur dioxide concentration values; the water body monitoring data comprise a water body chloride concentration value, a PH value and a sulfide concentration value; the soil monitoring data comprise soil PH value, heavy metal exceeding standard type number and heavy metal average exceeding standard rate. The method is characterized in that the content value of the heavy metals such as lead, cadmium, mercury, chromium, copper, zinc and the like in the soil at the monitoring point is detected, and the content value is compared with a standard value to obtain the standard exceeding type number and the average exceeding rate. The pH value of the soil, the number of types of the excessive heavy metal and the average excessive heavy metal can reflect the acid-base pollution and the heavy metal pollution degree of the soil.
The specific calculation formula of the gas pollution index is as follows:
in the method, in the process of the invention,
correction coefficient for gas pollution, +.>
Is a gas pollution index>
Concentration value of PM2.5, +.>
In particular to a sulfur dioxide concentration value.
The specific calculation formula of the water pollution index is as follows:
in the method, in the process of the invention,
correction coefficient for water pollution, < >>
Is the index of water pollution，/>
Is the concentration value of chloride in water body>
Is the sulfide concentration value.
The soil pollution index is specifically calculated according to the following formula:
in the method, in the process of the invention,
for soil pollution correction coefficient, < >>
Is the soil pollution index. />
For heavy metals exceeding the standard number of species->
Average overstock rate for heavy metals.
According to the embodiment of the invention, the pollution monitoring data is imported into an analysis prediction model, and regional pollution evaluation and pollution prediction are performed based on three dimensions of water, soil and gas to obtain current pollution region and pollution prediction data, specifically:
carrying out data standardization and normalization processing on the pollution monitoring data to obtain a standard data set;
based on the monitoring time, carrying out data serialization on the standard data set, and obtaining time series data;
constructing a pollution prediction model based on LSTM;
importing the time series data into a pollution prediction model for data prediction to obtain pollution prediction data based on three dimensions of gas, water and soil;
And importing the pollution monitoring data and the pollution prediction data into a farmland three-dimensional model, taking the current pollution area as a central area, and carrying out area pollution diffusion analysis to obtain a pollution diffusion area.
The pollution prediction model is specifically a long-short-term memory artificial neural network model, namely an LSTM model, and can be used for performing prediction analysis on existing pollution monitoring data to obtain pollution prediction data of a future period of time. The pollution prediction data comprise pollution prediction data of three dimensions of gas, water and soil.
The farmland three-dimensional model is a visual map model, and pollution prediction data, a current pollution area, a pollution diffusion area and a crop area can be visually displayed through the model.
According to the embodiment of the invention, the acquisition of the crop growth information and the crop market big data in the target farmland area is specifically as follows:
the crop growth information comprises growth stage information and growth period information of different crops in a current target farmland area;
the crop market data includes historical sales data and historical market price data of crops.
It should be noted that the growth stage information and the growth cycle information of the different crops specifically include the current growth stage number and the total growth stage number of the different crops.
According to the embodiment of the invention, the pollution loss evaluation is carried out on the target farmland area based on the crop growth information, the crop market big data and the pollution prediction data to obtain area pollution loss evaluation data, which comprises the following specific steps:
acquiring big market data of crops;
generating a search tag based on the target crop;
searching target crop data from the crop market big data based on the search tag to obtain the target crop big data;
carrying out data cleaning and standardization treatment on the big data of the target crops, and carrying out time serialization on the big data of the target crops after treatment to obtain serialized data of the crops;
the crop serialization data is imported into an analysis prediction model to predict sales volume data and price fluctuation of target crops, so that crop prediction sales volume data and crop market price prediction data are obtained;
and carrying out data fusion on the crop forecast sales volume data and the crop market price forecast data to obtain crop market forecast data.
The large crop market data is specifically historical sales data and historical market price data, and the historical sales data is specifically historical sales data in a target farmland area, and comprises the yield, sales volume, stagnancy volume and the like of different crops.
According to the embodiment of the invention, the method for estimating the pollution loss of the target farmland area based on the crop growth information, the crop market big data and the pollution prediction data, to obtain the regional pollution loss estimation data, further comprises the following steps:
acquiring a plurality of crop areas;
the crop area, the current pollution area and the pollution diffusion area are led into a farmland three-dimensional model, the intersection area part of the crop area and the current pollution area is marked as a first pollution area, and the intersection area part of the crop area and the pollution diffusion area is marked as a second pollution area;
based on crop growth information, crop market big data, a first pollution area and a second pollution area, carrying out pollution loss calculation and analysis on a target farmland area to obtain area pollution loss evaluation data;
and sending the regional pollution loss evaluation data to preset terminal equipment.
It should be noted that each crop area has a corresponding first contaminated area and second contaminated area. The larger the areas of the first and second contaminated areas represent a greater degree of contamination impact of the corresponding crop area.
According to the embodiment of the invention, the regional pollution loss evaluation data comprises a regional pollution loss evaluation index, and the regional pollution loss evaluation index specifically comprises the following calculation formula:
wherein T is the total number of crop areas, J is the regional pollution loss evaluation index,
for the current number of growth stages of the ith crop, < >>
For the total number of growth stages of the ith crop, < > j->
First contaminated area for the ith crop area,/->
Second contaminated area for the ith crop area,/->
Predictive marketing quantity for the ith crop, and->
Is the forecast market price of the ith crop.
The predicted market sales quantity and the predicted market price of the crops are extracted through the predicted data of the crop market. The regional pollution loss evaluation index is specifically data obtained by comprehensively analyzing and evaluating the crop growth condition, market value, predicted pollution influence and other multidimensional degrees, has higher evaluation reference value, and can more intuitively master the pollution influence condition of the current farmland region.
In addition, each crop area corresponds to one crop.
According to an embodiment of the present invention, further comprising:
Selecting one crop area as the current crop area;
acquiring a yield value of a current crop area in a preset time period from crop market big data;
calculating a first pollution area value and a second pollution area value of the current crop area according to the farmland three-dimensional model;
calculating the average value of the yield values of all the crop areas to obtain the average yield value;
if the yield value of the current crop area is smaller than the yield average value and the area values of the first pollution area and the second pollution area are both larger than the preset area value, marking the current crop area as a repaired crop area;
acquiring pollution monitoring data of a restored crop area, taking the area suitable for planting as a first target, and restoring soil as a second target, and carrying out suitable crop analysis and screening from crop planting big data based on the pollution monitoring data to obtain recommended crop information;
and sending the recommended crop information to preset terminal equipment.
The pollution monitoring data comprise gas monitoring data, water monitoring data and soil pollution monitoring. It is worth mentioning that in the restored crop area, the pollution has a larger influence on the crops, the yield is far lower than the average value, namely lower than the expected value, and the invention analyzes the recommended crops with respect to the current crop area from the pollution dimension of gas, water and soil in the restored crop area by analyzing the pollution monitoring data of the restored crop area, wherein the recommended crops comprise the crop types suitable for planting in the current crop area and the crop types with restoration effect on the current crop area. The large crop planting data comprise suitable planting environments, crop properties, crop growth information, crop environment restoration effect and the like of all crops.
The invention discloses a multi-dimensional pollution analysis method and a system based on a big data platform. By the method, comprehensive pollution analysis and pollution loss evaluation can be performed on the farmland area in multiple dimensions, so that the pollution influence and pollution loss condition of the current farmland area can be mastered more intuitively.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in 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 of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. The multidimensional pollution analysis method based on the big data platform is characterized by comprising the following steps of:
acquiring target farmland area information, and constructing a farmland three-dimensional model based on the target farmland area information;
according to the pollution monitoring points, the multi-dimensional monitoring based on water, soil and gas is carried out on the target farmland area through a farmland monitoring module, and pollution monitoring data are obtained;
the pollution monitoring data is imported into an analysis prediction model, regional pollution assessment and pollution prediction are carried out based on three dimensions of water, soil and gas, and current pollution region and pollution prediction data are obtained;
acquiring crop growth information and crop market big data in a target farmland area;
and carrying out pollution loss evaluation on the target farmland area based on the crop growth information, the crop market big data and the pollution prediction data to obtain area pollution loss evaluation data.
2. The multi-dimensional pollution analysis method based on the big data platform according to claim 1, wherein the obtaining of the target farmland area information and the construction of the farmland three-dimensional model based on the target farmland area information are specifically as follows:
extracting farmland area, regional outline, crop distribution information, soil and stratum characteristic information from target farmland regional information;
Constructing a farmland three-dimensional model based on the farmland area, the regional contour information, the soil and stratum characteristic information and the like;
based on crop distribution information, carrying out regional division of different crops from a farmland three-dimensional model to obtain a plurality of crop regions.
3. The multi-dimensional pollution analysis method based on the big data platform according to claim 2, wherein the multi-dimensional monitoring based on water, soil and gas is performed on the target farmland area through the farmland monitoring module according to the pollution monitoring points, and pollution monitoring data is obtained specifically as follows:
dividing a farmland plane area into N small areas based on a farmland three-dimensional model;
determining monitoring points in each small area by a random point distribution method, wherein each small area corresponds to one monitoring point in advance;
taking all the monitoring points as pollution monitoring points;
the pollution monitoring module is used for sampling and monitoring the pollution monitoring points in real time and acquiring corresponding gas monitoring data, water monitoring data and soil monitoring data;
the pollution monitoring data comprises gas monitoring data, water body monitoring data and soil monitoring data.
4. The multi-dimensional pollution analysis method based on big data platform according to claim 3, wherein the step of importing pollution monitoring data into an analysis prediction model, and performing regional pollution assessment and pollution prediction based on three dimensions of water, soil and gas to obtain current pollution region and pollution prediction data comprises the following steps:
Respectively calculating the gas pollution index, the water pollution index and the soil pollution index of each pollution monitoring point according to the gas monitoring data, the water monitoring data and the soil monitoring data;
comparing the gas pollution index, the water pollution index and the soil pollution index with a first preset pollution index, and marking a small area where a corresponding pollution monitoring point is located as a pollution area if the three pollution indexes are all larger than the first preset pollution index;
and carrying out merging operation on the adjacent polluted areas to finally form a plurality of merged areas, and marking each merged area as the current polluted area.
5. The multidimensional pollution analysis method based on the big data platform according to claim 4, wherein the pollution monitoring data is imported into an analysis prediction model, and regional pollution evaluation and pollution prediction are performed based on three dimensions of water, soil and gas to obtain current pollution region and pollution prediction data, specifically:
carrying out data standardization and normalization processing on the pollution monitoring data to obtain a standard data set;
based on the monitoring time, carrying out data serialization on the standard data set, and obtaining time series data;
constructing a pollution prediction model based on LSTM;
Importing the time series data into a pollution prediction model for data prediction to obtain pollution prediction data based on three dimensions of water, soil and gas;
and importing the pollution monitoring data and the pollution prediction data into a farmland three-dimensional model, taking the current pollution area as a central area, and carrying out area pollution diffusion analysis to obtain a pollution diffusion area.
6. The multi-dimensional pollution analysis method based on the big data platform according to claim 5, wherein the obtaining of the crop growth information and the crop market big data in the target farmland area is specifically:
the crop growth information comprises growth stage information and growth period information of different crops in a current target farmland area;
the crop market data includes historical sales data and historical market price data of crops.
7. The multidimensional pollution analysis method based on the big data platform according to claim 6, wherein the pollution loss evaluation is performed on the target farmland area based on the crop growth information, the big data of the crop market and the pollution prediction data, so as to obtain area pollution loss evaluation data, specifically:
acquiring big market data of crops;
Generating a search tag based on the target crop;
searching target crop data from the crop market big data based on the search tag to obtain the target crop big data;
carrying out data cleaning and standardization treatment on the big data of the target crops, and carrying out time serialization on the big data of the target crops after treatment to obtain serialized data of the crops;
the crop serialization data is imported into an analysis prediction model to predict sales volume data and price fluctuation of target crops, so that crop prediction sales volume data and crop market price prediction data are obtained;
and carrying out data fusion on the crop forecast sales volume data and the crop market price forecast data to obtain crop market forecast data.
8. The multi-dimensional pollution analysis method based on a big data platform according to claim 7, wherein the pollution loss evaluation is performed on the target farmland area based on the crop growth information, the big data of the crop market and the pollution prediction data, so as to obtain area pollution loss evaluation data, and further comprising:
acquiring a plurality of crop areas;
the crop area, the current pollution area and the pollution diffusion area are led into a farmland three-dimensional model, the intersection area part of the crop area and the current pollution area is marked as a first pollution area, and the intersection area part of the crop area and the pollution diffusion area is marked as a second pollution area;
Based on crop growth information, crop market big data, a first pollution area and a second pollution area, carrying out pollution loss calculation and analysis on a target farmland area to obtain area pollution loss evaluation data;
and sending the regional pollution loss evaluation data to preset terminal equipment.
9. A multi-dimensional pollution analysis system based on a big data platform, the system comprising: the multi-dimensional pollution analysis system comprises a memory and a processor, wherein the memory comprises a multi-dimensional pollution analysis program based on a big data platform, and the multi-dimensional pollution analysis program based on the big data platform realizes the following steps when being executed by the processor:
acquiring target farmland area information, and constructing a farmland three-dimensional model based on the target farmland area information;
according to the pollution monitoring points, the multi-dimensional monitoring based on water, soil and gas is carried out on the target farmland area through a farmland monitoring module, and pollution monitoring data are obtained;
the pollution monitoring data is imported into an analysis prediction model, regional pollution assessment and pollution prediction are carried out based on three dimensions of water, soil and gas, and current pollution region and pollution prediction data are obtained;
acquiring crop growth information and crop market big data in a target farmland area;
And carrying out pollution loss evaluation on the target farmland area based on the crop growth information, the crop market big data and the pollution prediction data to obtain area pollution loss evaluation data.
10. The multi-dimensional pollution analysis system based on the big data platform according to claim 9, wherein the obtaining the target farmland area information and constructing a farmland three-dimensional model based on the target farmland area information specifically comprises:
extracting farmland area, regional outline, crop distribution information, soil and stratum characteristic information from target farmland regional information;
constructing a farmland three-dimensional model based on the farmland area, the regional contour information, the soil and stratum characteristic information and the like;
based on crop distribution information, carrying out regional division of different crops from a farmland three-dimensional model to obtain a plurality of crop regions.
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