CN116629134A - Environmental impact prediction method and device for three-thin mineral development area - Google Patents

Environmental impact prediction method and device for three-thin mineral development area Download PDF

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CN116629134A
CN116629134A CN202310643839.6A CN202310643839A CN116629134A CN 116629134 A CN116629134 A CN 116629134A CN 202310643839 A CN202310643839 A CN 202310643839A CN 116629134 A CN116629134 A CN 116629134A
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geological disaster
score
early warning
historical
environmental impact
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CN116629134B (en
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于扬
王成辉
王登红
王伟
于沨
赵芝
刘善宝
郭娜欣
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Institute of Mineral Resources of Chinese Academy of Geological Sciences
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Institute of Mineral Resources of Chinese Academy of Geological Sciences
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    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q50/10—Services
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Abstract

The invention discloses an environmental impact prediction method and device for a three-thin mineral development area, which are characterized in that the predicted number of geological disaster hidden dangers, the predicted number of geological disaster early warning, the predicted data of water environment damage and the predicted data of soil environment damage of the three-thin mineral development area are respectively output through a pre-trained target prediction model, the predicted number of geological disaster hidden dangers, the predicted number of geological disaster early warning, the predicted data of water environment damage and the predicted data of soil environment damage are respectively calculated, a first score, a second score, a third score and a fourth score are correspondingly obtained, all scores are integrated, a first comprehensive score is obtained, the environmental impact strength is determined according to the first comprehensive score, and a corresponding environmental treatment scheme is matched in a preset environmental treatment rule based on the environmental impact strength. The invention realizes quantitative prediction of the environmental impact grade of the three-thin mineral development area and improves the acquisition efficiency of the environmental impact grade.

Description

Environmental impact prediction method and device for three-thin mineral development area
Technical Field
The invention relates to the technical field of environmental evaluation, in particular to an environmental impact prediction method and device for a three-thin mineral development area.
Background
The mine environment is a weak ring of ecological environment, and along with the rapid development of economy and society, the development scale of the mine is continuously enlarged, and the more the mine environment pollution is happened, the more frequently the mine environment pollution is happened; the method is used for developing scientific and effective evaluation of mine environment influence, is an important guarantee for sustainable development of mines, and is also a push for construction of green mines.
In the prior art, when the environmental impact is evaluated in mine development, samples are generally sampled in a mine development area, and inorganic elements and water-soluble ions in the mine area samples are analyzed by using methods such as plasma mass spectrum, plasma spectrum, ion chromatography hydride-atomic fluorescence spectrum, cold vapor-atomic fluorescence spectrum and the like to obtain analysis results, but the analysis results are not quantitatively evaluated, are all based on artificial experience judgment, and further provide management measures and suggestions for the environmental management and monitoring of rare earth ores, so that effective decision basis is lacked, and decision errors are easily caused; and when analyzing the sample, the required instrument is many and the operation is complicated, is unfavorable for improving the acquisition efficiency of environmental impact data.
Therefore, it has become urgent to quantitatively evaluate the environmental impact of mine development areas and to improve the efficiency of acquiring environmental impact data.
Disclosure of Invention
The invention aims to solve the technical problems that: the environmental impact prediction method and the device for the three-thin mineral development area are provided, quantitative prediction of the environmental impact level of the three-thin mineral development area is achieved, a corresponding treatment scheme is provided based on a prediction result, and accurate basis can be provided for decision making.
In order to solve the technical problems, the invention provides an environmental impact prediction method for a three-thin mineral development area, which comprises the following steps:
acquiring the number of historical geological disaster hidden dangers of a three-thin mineral development area, inputting the number of the historical geological disaster hidden dangers into a pre-trained first target prediction model so that the first target prediction model outputs the predicted number of the geological disaster hidden dangers, and calculating the predicted number of the geological disaster hidden dangers to obtain a first score;
acquiring historical geological disaster early warning quantity of a three-thin mineral development area, inputting the historical geological disaster early warning quantity into a pre-trained second target prediction model so that the second target prediction model outputs to obtain geological disaster early warning predicted quantity, and calculating the geological disaster early warning predicted quantity to obtain a second score;
Acquiring historical water environment damage data of a three-thin mineral development area, inputting the historical water environment damage data into a pre-trained third target prediction model so that the third target prediction model outputs water environment damage prediction data, and calculating the water environment damage prediction data to obtain a third score;
acquiring historical soil environment damage data of a three-thin mineral development area, inputting the historical soil environment damage data into a pre-trained fourth target prediction model so that the fourth target prediction model outputs soil environment damage prediction data, and calculating the soil environment damage prediction data to obtain a fourth value;
and integrating the first score, the second score, the third score and the fourth score, obtaining and determining the environmental impact strength of the three thin mineral development areas based on the first comprehensive score, and matching a corresponding environmental treatment scheme in a preset environmental treatment rule according to the environmental impact strength, wherein the preset environmental treatment rule comprises the environmental impact strength and the environmental treatment scheme.
The invention provides an environmental impact prediction method for a three-thin mineral development area, which further comprises the following steps:
Obtaining a fifth score, a sixth score, a seventh score and an eighth score corresponding to the three thin mineral development areas, and obtaining a second comprehensive score according to the fifth score, the sixth score, the seventh score and the eighth score; the fifth score, the sixth score, the seventh score and the eighth score are obtained by re-obtaining the environment treatment of the three-thin mineral development area according to the environment treatment scheme;
and re-determining the environmental impact strength of the three-thin mineral product development area according to the second comprehensive score, and re-matching the corresponding environmental treatment scheme in the environmental treatment rule.
The invention provides an environmental impact prediction method for a three-thin mineral development area, which further comprises the following steps:
setting a geological disaster hidden danger evaluation standard, a geological disaster early warning evaluation standard, a water environment damage data evaluation standard and a soil environment damage data evaluation standard.
In one possible implementation manner, according to the environmental impact strength, matching a corresponding environmental governance scheme in a preset environmental governance rule, specifically including:
acquiring the environmental impact strength, when the environmental impact strength is low, matching an environmental impact low-strength treatment scheme in a preset environmental treatment rule, when the environmental impact strength is medium, matching an environmental impact medium-strength treatment scheme in the preset environmental treatment rule, and when the environmental impact strength is high, matching an environmental impact high-strength treatment scheme in the preset environmental treatment rule.
In one possible implementation, the pre-training process of the first target prediction model specifically includes:
constructing a neural network prediction model;
acquiring the number of historical geological disaster hidden dangers corresponding to a plurality of historical years in the three-thin mineral product development area, and carrying out normalization processing on the number of the historical geological disaster hidden dangers to obtain the number of historical geological disaster hidden dangers samples;
and training the neural network prediction model by taking the number of first historical geological disaster hidden danger samples corresponding to the first historical year as model input and taking the number of second historical address disaster hidden danger samples corresponding to the second historical year as model output until the model converges, and generating a first target prediction model, wherein the first historical year and the second historical year are adjacent years.
In one possible implementation manner, the historical geological disaster early-warning number is input to a pre-trained second target prediction model, so that the second target prediction model outputs the geological disaster early-warning predicted number, and the method specifically includes:
acquiring historical geological disaster early warning quantity, wherein the historical geological disaster early warning quantity comprises historical blue geological disaster early warning quantity, historical yellow geological disaster early warning quantity, historical orange geological disaster early warning quantity and historical red geological disaster early warning quantity;
And inputting the historical geological disaster early-warning quantity into a pre-trained second target prediction model so that the second target prediction model outputs the geological disaster early-warning predicted quantity, wherein the geological disaster early-warning predicted quantity comprises a blue geological disaster early-warning predicted quantity, a yellow geological disaster early-warning predicted quantity, an orange geological disaster early-warning predicted quantity and a red geological disaster early-warning predicted quantity.
In one possible implementation manner, the calculating process is performed on the geological disaster early warning predicted quantity to obtain a second score, which specifically includes:
comparing the geological disaster early warning predicted quantity with the geological disaster early warning critical quantity, if the geological disaster early warning predicted quantity is smaller than or equal to the geological disaster early warning critical quantity, confirming that a first early warning quantity calculation result is that the geological disaster early warning predicted quantity is smaller, and obtaining a second score according to the first early warning quantity calculation result;
if the geological disaster early warning predicted quantity is larger than the geological disaster early warning critical quantity, calculating a first duty ratio of the sum of the orange geological disaster early warning predicted quantity and the red geological disaster early warning predicted quantity in the geological disaster early warning predicted quantity, and if the first duty ratio is smaller than a preset duty ratio threshold value, confirming that a second early warning quantity calculation result is that the geological disaster early warning predicted quantity is general, and obtaining a second score according to the second early warning quantity calculation result;
And if the first duty ratio is larger than or equal to a preset duty ratio threshold, confirming that the third early warning number calculation result is that the geological disaster early warning predicted number is more, and obtaining a second score according to the third early warning number calculation result.
The invention provides an environment influence prediction device for a three-thin mineral development area, which comprises the following components: the system comprises a geological disaster hidden danger number calculation module, a geological disaster early warning number calculation module, a water environment damage number calculation module, a soil environment damage data calculation module and an environment influence intensity determination module;
the geological disaster hidden danger number calculation module is used for acquiring the historical geological disaster hidden danger number of the three thin mineral development areas, inputting the historical geological disaster hidden danger number into a pre-trained first target prediction model so that the first target prediction model outputs the geological disaster hidden danger prediction number, and calculating the geological disaster hidden danger prediction number to obtain a first score;
the geological disaster early warning number calculation module is used for acquiring the historical geological disaster early warning number of the three thin mineral development areas, inputting the historical geological disaster early warning number into a pre-trained second target prediction model so that the second target prediction model outputs the obtained geological disaster early warning predicted number, and calculating the geological disaster early warning predicted number to obtain a second score;
The water environment damage quantity calculation module is used for acquiring historical water environment damage data of three dilute mineral development areas, inputting the historical water environment damage data into a pre-trained third target prediction model so that the third target prediction model outputs water environment damage prediction data, and calculating the water environment damage prediction data to obtain a third score;
the soil environment damage data calculation module is used for acquiring historical soil environment damage data of three thin mineral development areas, inputting the historical soil environment damage data into a pre-trained fourth target prediction model so that the fourth target prediction model outputs soil environment damage prediction data, and calculating the soil environment damage prediction data to obtain a fourth score;
the environmental impact strength determining module is configured to integrate the first score, the second score, the third score and the fourth score, obtain and determine environmental impact strength of the three-thin mineral product development area based on a first comprehensive score, and match a corresponding environmental treatment scheme in a preset environmental treatment rule according to the environmental impact strength, where the preset environmental treatment rule includes the environmental impact strength and the environmental treatment scheme.
The invention provides an environment influence prediction device for a three-thin mineral development area, which further comprises: an environmental impact strength update module;
the environmental impact strength updating module is configured to obtain a fifth score, a sixth score, a seventh score, and an eighth score corresponding to the third dilute mineral development area, and obtain a second comprehensive score according to the fifth score, the sixth score, the seventh score, and the eighth score; the fifth score, the sixth score, the seventh score and the eighth score are obtained by re-obtaining the environment treatment of the three-thin mineral development area according to the environment treatment scheme;
and the environment influence intensity updating module is used for redefining the environment influence intensity of the three-thin mineral development area according to the second comprehensive score, and re-matching the corresponding environment treatment scheme in the environment treatment rule.
The invention provides an environment influence prediction device for a three-thin mineral development area, which further comprises: an evaluation standard setting module;
the evaluation standard setting module is used for setting a geological disaster hidden danger evaluation standard, a geological disaster early warning evaluation standard, a water environment damage data evaluation standard and a soil environment damage data evaluation standard.
In one possible implementation manner, the environmental impact strength determining module is configured to match, according to the environmental impact strength, a corresponding environmental governance scheme in a preset environmental governance rule, and specifically includes:
acquiring the environmental impact strength, when the environmental impact strength is low, matching an environmental impact low-strength treatment scheme in a preset environmental treatment rule, when the environmental impact strength is medium, matching an environmental impact medium-strength treatment scheme in the preset environmental treatment rule, and when the environmental impact strength is high, matching an environmental impact high-strength treatment scheme in the preset environmental treatment rule.
In one possible implementation manner, the pre-training process of the first target prediction model in the geological disaster hidden danger number calculation module specifically includes:
constructing a neural network prediction model;
acquiring the number of historical geological disaster hidden dangers corresponding to a plurality of historical years in the three-thin mineral product development area, and carrying out normalization processing on the number of the historical geological disaster hidden dangers to obtain the number of historical geological disaster hidden dangers samples;
and training the neural network prediction model by taking the number of first historical geological disaster hidden danger samples corresponding to the first historical year as model input and taking the number of second historical address disaster hidden danger samples corresponding to the second historical year as model output until the model converges, and generating a first target prediction model, wherein the first historical year and the second historical year are adjacent years.
In one possible implementation manner, the geological disaster early-warning number calculation module is configured to input the historical geological disaster early-warning number to a pre-trained second target prediction model, so that the second target prediction model outputs the obtained geological disaster early-warning predicted number, and specifically includes:
acquiring historical geological disaster early warning quantity, wherein the historical geological disaster early warning quantity comprises historical blue geological disaster early warning quantity, historical yellow geological disaster early warning quantity, historical orange geological disaster early warning quantity and historical red geological disaster early warning quantity;
and inputting the historical geological disaster early-warning quantity into a pre-trained second target prediction model so that the second target prediction model outputs the geological disaster early-warning predicted quantity, wherein the geological disaster early-warning predicted quantity comprises a blue geological disaster early-warning predicted quantity, a yellow geological disaster early-warning predicted quantity, an orange geological disaster early-warning predicted quantity and a red geological disaster early-warning predicted quantity.
In one possible implementation manner, the geological disaster early warning number calculation module is configured to calculate and process the geological disaster early warning predicted number to obtain a second score, and specifically includes:
Comparing the geological disaster early warning predicted quantity with the geological disaster early warning critical quantity, if the geological disaster early warning predicted quantity is smaller than or equal to the geological disaster early warning critical quantity, confirming that a first early warning quantity calculation result is that the geological disaster early warning predicted quantity is smaller, and obtaining a second score according to the first early warning quantity calculation result;
if the geological disaster early warning predicted quantity is larger than the geological disaster early warning critical quantity, calculating a first duty ratio of the sum of the orange geological disaster early warning predicted quantity and the red geological disaster early warning predicted quantity in the geological disaster early warning predicted quantity, and if the first duty ratio is smaller than a preset duty ratio threshold value, confirming that a second early warning quantity calculation result is that the geological disaster early warning predicted quantity is general, and obtaining a second score according to the second early warning quantity calculation result;
and if the first duty ratio is larger than or equal to a preset duty ratio threshold, confirming that the third early warning number calculation result is that the geological disaster early warning predicted number is more, and obtaining a second score according to the third early warning number calculation result.
The invention also provides a terminal device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the environment influence prediction method and device for the three-thin mineral development area are realized when the processor executes the computer program.
The invention also provides a computer readable storage medium, which comprises a stored computer program, wherein the computer program is used for controlling equipment where the computer readable storage medium is located to execute the environmental impact prediction method of the three-thin mineral development area according to any one of the above.
Compared with the prior art, the environment influence prediction method and device for the three-thin mineral development area have the following beneficial effects:
respectively outputting the geological disaster hidden danger prediction quantity, the geological disaster early warning prediction quantity, the water environment damage prediction data and the soil environment damage prediction data of the three thin mineral development areas through a pre-trained target prediction model, respectively calculating the geological disaster hidden danger prediction quantity, the geological disaster early warning prediction quantity, the water environment damage prediction data and the soil environment damage prediction data to correspondingly obtain a first score, a second score, a third score and a fourth score, integrating all the scores to obtain a first comprehensive score, determining the environmental impact strength according to the first comprehensive score, and simultaneously matching a corresponding environmental treatment scheme in a preset environmental treatment rule based on the environmental impact strength; compared with the prior art, the technical scheme of the invention respectively carries out prediction calculation processing on geological disaster hidden danger, geological disaster early warning, water environment damage data and soil environment damage data of the dilute mineral development area through pre-training a plurality of target prediction models to obtain corresponding scores, realizes quantitative prediction on environmental impact levels of the three dilute mineral development areas based on the scores, provides a corresponding treatment scheme based on the prediction results, can provide accurate basis for decision makers, and reduces decision errors; meanwhile, in quantitative prediction, future data are predicted based on historical data, so that the problems that in the prior art, in order to acquire environmental impact levels, the process steps are complex and a large number of instruments are needed are avoided, and the acquisition efficiency of the environmental impact data is improved.
Drawings
FIG. 1 is a schematic flow chart of one embodiment of a method for predicting environmental impact of a three-thin mineral development area provided by the present invention;
FIG. 2 is a schematic diagram of an embodiment of an environmental impact prediction apparatus for a three-thin mineral development area provided by the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of a method for predicting environmental impact in a three-thin mineral development area according to the present invention, as shown in fig. 1, the method includes steps 101 to 105, specifically as follows:
step 101: the method comprises the steps of obtaining the number of historical geological disaster hidden dangers of a three-thin mineral development area, inputting the number of the historical geological disaster hidden dangers into a pre-trained first target prediction model, enabling the first target prediction model to output the predicted number of the geological disaster hidden dangers, and calculating the predicted number of the geological disaster hidden dangers to obtain a first score.
In one embodiment, a neural network prediction model is constructed, wherein the neural network prediction model is a BP neural network prediction model.
In an embodiment, the number of historical geological disaster hidden dangers corresponding to a plurality of historical years in the three-thin mineral development area is obtained, the number of historical geological disaster hidden dangers corresponding to the plurality of historical years is normalized to obtain the number of historical geological disaster hidden dangers, the number of historical geological disaster hidden dangers is divided into a training set and a testing set according to a preset proportion, the neural network prediction model is trained based on the training set, and the model effect of the neural network prediction model is detected based on the testing set.
In one embodiment, model training is performed on the neural network prediction model; specifically, the neural network prediction model is trained by taking the number of first historical geological disaster hidden danger samples corresponding to a first historical year as model input and the number of second historical address disaster hidden danger samples corresponding to a second historical year as model output, until the model converges, a first target prediction model is generated, wherein the first historical year and the second historical year are adjacent years.
As an illustration of model training of the neural network predictive model: acquiring the number of historical geological disaster hidden danger samples corresponding to a plurality of historical years in the three-thin mineral development area, wherein the historical years are 2015, 2016, 2017, 2018, 2019 and 2020; inputting the number of geological disaster hidden danger samples corresponding to 2015 into the neural network prediction model so that the neural network prediction model outputs the number of geological disaster hidden danger samples corresponding to 2016, and comparing the number of geological disaster hidden danger samples corresponding to 2016 with the number of substantial geological disaster hidden danger samples corresponding to 2016; or inputting the number of geological disaster hidden danger samples corresponding to 2016 years into the neural network prediction model, so that the neural network prediction model outputs the number of geological disaster hidden danger samples corresponding to 2017 years, comparing the number of geological disaster hidden danger samples corresponding to 2017 years with the number of substantial geological disaster hidden danger samples corresponding to 2017 years, and the like, until the prediction precision of the neural network prediction model reaches a preset precision value, stopping training the neural network prediction model, and generating a first target prediction model.
In an embodiment, the latest historical number of geological disaster hidden dangers is obtained, and the latest historical number of geological disaster hidden dangers is input into the first target prediction model, so that the first target prediction model outputs the predicted number of geological disaster hidden dangers in a preset year.
Preferably, the number of the latest historical geological disaster hidden dangers is the number of the historical geological disaster hidden dangers corresponding to the previous year of the current year; the preset year is the current year.
In one embodiment, the number of potential address hazards includes the number of potential points of unstable slopes, potential landslides, potential collapses, potential debris flows and potential ground subsidence in three thin mineral development areas that may jeopardize the life and property safety of people, and landslides, collapses, debris flows, ground subsidence that have occurred but are currently unstable.
In one embodiment, setting a geological disaster hidden danger evaluation standard; specifically, the set geological disaster hidden danger evaluation criteria comprise a small number of geological disaster hidden danger predictions, a general number of geological disaster hidden danger predictions and a large number of geological disaster hidden danger predictions, wherein a first score corresponding to the small number of geological disaster hidden danger is 3 points; the first score corresponding to the number of geological disaster hidden dangers is generally 2; the first score corresponding to the greater number of the geological disaster hidden dangers is 1 minute.
In an embodiment, the predicted number of geological disaster hidden dangers is calculated to obtain a first score.
Specifically, a first number proportion of the geological disaster hidden danger prediction number in a first geological disaster hidden danger critical number is calculated, if the first number proportion is smaller than or equal to a first preset number proportion threshold value, the first hidden danger number calculation result is confirmed to be smaller than the geological disaster hidden danger number, and a first score is obtained according to the first hidden danger number calculation result.
Specifically, if the first number duty ratio is greater than a first preset number duty ratio threshold, and the first number duty ratio is less than or equal to the second preset number duty ratio threshold, confirming that the second hidden danger number calculation result is that the geological disaster early warning number is general, and obtaining a first score according to the second hidden danger number calculation result.
Specifically, if the first number duty ratio is greater than the second preset number duty ratio threshold, confirming that the third hidden danger number calculation result is that the geological disaster early warning number is more, and obtaining a first score according to the third hidden danger number calculation result.
Preferably, the setting of the critical quantity of the geological disaster hidden danger can be obtained from the relevant standards of the geological disaster hidden danger based on the mine type.
Preferably, the first number is set to 60% and the second preset number is set to 100% with respect to the threshold.
Step 102: the method comprises the steps of obtaining historical geological disaster early warning quantity of a three-thin mineral product development area, inputting the historical geological disaster early warning quantity into a pre-trained second target prediction model, enabling the second target prediction model to output and obtain geological disaster early warning predicted quantity, and calculating and processing the geological disaster early warning predicted quantity to obtain a second score.
In one embodiment, a neural network prediction model is constructed; the neural network prediction model is a BP neural network prediction model.
In an embodiment, the historical geological disaster early warning number corresponding to a plurality of historical years in the three-thin mineral development area is obtained, and the historical geological disaster early warning number is normalized to obtain the number of historical geological disaster hidden danger samples; and training the neural network prediction model by taking the number of first historical geological disaster early warning samples corresponding to the first historical year as model input and the number of second historical address disaster early warning samples corresponding to the second historical year as model output, until the model converges, generating a second target prediction model, wherein the first historical year and the second historical year are adjacent years.
In an embodiment, the latest historical geological disaster early warning number is obtained, and the latest historical geological disaster early warning number is input into the second target prediction model, so that the second target prediction model outputs the geological disaster early warning predicted number of a preset year.
Specifically, the historical geological disaster early warning number is obtained, wherein the historical geological disaster early warning number comprises historical blue geological disaster early warning number, historical yellow geological disaster early warning number, historical orange geological disaster early warning number and historical red geological disaster early warning number.
Specifically, the historical geological disaster early-warning quantity is input into a second target prediction model, so that the target prediction model outputs the geological disaster early-warning predicted quantity, wherein the geological disaster early-warning predicted quantity comprises a blue geological disaster early-warning predicted quantity, a yellow geological disaster early-warning predicted quantity, an orange geological disaster early-warning predicted quantity and a red geological disaster early-warning predicted quantity.
In one embodiment, the geological disaster early warning number refers to disaster early warning data issued by geological and meteorological departments according to the current environment; the blue geological disaster early warning means that the possibility of geological disasters is generally expected to happen; the yellow geological disaster early warning means that the possibility of occurrence of geological disasters is expected to be high; orange geological disaster early warning means that the possibility of occurrence of geological disasters is expected to be high; red geological disaster early warning means that the probability of occurrence of geological disasters is extremely high.
In one embodiment, a geological disaster early warning evaluation standard is set; specifically, the set geological disaster early warning evaluation criteria comprise a small number of geological disaster early warning, a general number of geological disaster early warning and a large number of geological disaster early warning, wherein the second score corresponding to the small number of geological disaster early warning is 3 minutes; the second score corresponding to the geological disaster early warning quantity is generally 2 points; the second score corresponding to the large number of geological disaster early warning is 1 minute.
In an embodiment, the geological disaster early warning prediction number is calculated to obtain a second score.
Specifically, comparing the geological disaster early warning predicted quantity with the geological disaster early warning critical quantity, if the geological disaster early warning predicted quantity is smaller than or equal to the geological disaster early warning critical quantity, confirming that a first early warning quantity comparison result is smaller than the geological disaster early warning quantity, and obtaining a second score according to the first early warning quantity comparison result.
Specifically, if the geological disaster early warning predicted quantity is larger than the geological disaster early warning critical quantity, calculating a first duty ratio of the sum of the orange geological disaster early warning predicted quantity and the red geological disaster early warning predicted quantity in the geological disaster early warning predicted quantity, and if the first duty ratio is smaller than a preset duty ratio threshold value, confirming that a second early warning quantity comparison result is that the geological disaster early warning quantity is common, and obtaining a second score according to the second early warning quantity comparison result.
Specifically, if the first duty ratio is greater than or equal to a preset duty ratio threshold, confirming that the third early warning number comparison result is that the geological disaster early warning number is more, and obtaining a second score according to the third early warning number comparison result.
In an embodiment, calculating a first duty ratio of a sum of the orange geological disaster early warning predicted quantity and the red geological disaster early warning predicted quantity in the geological disaster early warning predicted quantity; specifically, a first weight value corresponding to the early-warning predicted quantity of orange geological disasters and a second weight value corresponding to the early-warning predicted quantity of red geological disasters are obtained, the early-warning predicted quantity of orange geological disasters and the early-warning predicted quantity of red geological disasters are substituted into a first duty ratio calculation formula, and a first duty ratio of the early-warning predicted quantity of orange geological disasters and the early-warning predicted quantity of red geological disasters in the early-warning predicted quantity of geological disasters is calculated, wherein the first duty ratio calculation formula is as follows:
N total (S) ＝N b +N y +N c +N r ；
Wherein P is a first duty ratio, a is a first weight value, and a is a constant; n (N) c Orange field The mass disaster early warning forecast quantity, b is a second weight value, and is a constant, N r For red geological disaster early warning prediction quantity, N Total (S) The early warning forecast quantity of the geological disaster is provided; n (N) b For blue geological disaster early warning prediction quantity, N y And the yellow geological disaster early warning forecast quantity is obtained.
Preferably, the setting of the geological disaster early warning critical quantity can be obtained from the geological disaster early warning related standard based on the mine type.
Preferably, the preset duty cycle threshold may be set to 50%.
Step 103: acquiring historical water environment damage data of a three-thin mineral development area, inputting the historical water environment damage data into a pre-trained third target prediction model, so that the third target prediction model outputs water environment damage prediction data, and calculating the water environment damage prediction data to obtain a third score.
In one embodiment, a neural network prediction model is constructed; the neural network prediction model is a BP neural network prediction model.
In one embodiment, historical water environment damage data corresponding to a plurality of historical years in the three-dilute mineral development area are obtained, and normalization processing is carried out on the historical water environment damage data to obtain historical water environment damage sample data; and training the neural network prediction model by taking the number of first historical water environment damage samples corresponding to the first historical year as model input and the number of second historical water environment damage samples corresponding to the second historical year as model output until the model converges, and generating a third target prediction model, wherein the first historical year and the second historical year are adjacent years.
In an embodiment, the latest historical water environment damage data is obtained, and the latest historical water environment damage data is input into the third target prediction model, so that the third target prediction model outputs the water environment damage prediction data of a preset year.
In one embodiment, setting a water environment damage data evaluation standard; specifically, the set water environment damage data evaluation criteria comprise less water environment damage data, general water environment damage data and more water environment damage data, wherein the second score corresponding to the less water environment damage data is 3 minutes; the second score corresponding to the water environment damage data is generally 2 minutes; the second score corresponding to more water environment damage data is 1 score.
In one embodiment, the water environment damage prediction data is calculated to obtain a third score.
Specifically, calculating a second number of the water environment damage prediction data in the critical value of the water environment damage data, if the second number of the water environment damage prediction data is smaller than or equal to a third preset number of the water environment damage prediction data, confirming that a first water environment calculation result is that the water environment damage data is smaller, and obtaining a third score according to the first water environment calculation result.
Specifically, if the second number duty ratio is greater than a third preset number duty ratio threshold, and the second number duty ratio is less than or equal to the second preset number duty ratio threshold, confirming that a second water environment calculation result is that the water environment damage data is general, and obtaining a third score according to the second water environment calculation result.
Specifically, if the second number duty ratio is greater than the fourth preset number duty ratio threshold, confirming that a third water environment calculation result is that the water environment damage data are more, and obtaining a third score according to the third water environment calculation result.
Preferably, the setting of the critical value of the water environment damage data can be obtained from the related standards of the water environment based on the mine type.
Preferably, the third number is set to 60% and the fourth preset number is set to 100% with respect to the threshold.
Step 104: and acquiring historical soil environment damage data of a three-thin mineral product development area, inputting the historical soil environment damage data into a pre-trained fourth target prediction model, so that the fourth target prediction model outputs the soil environment damage prediction data, and performing calculation processing on the soil environment damage prediction data to obtain a fourth value.
In one embodiment, a neural network prediction model is constructed; the neural network prediction model is a BP neural network prediction model.
In one embodiment, historical soil environment damage data corresponding to a plurality of historical years in the three-thin mineral development area is obtained, and the historical soil environment damage data is normalized to obtain historical soil environment damage sample data; and training the neural network prediction model by taking the number of first historical soil environment damage samples corresponding to the first historical year as model input and the number of second historical soil environment damage samples corresponding to the second historical year as model output until the model converges, and generating a fourth target prediction model, wherein the first historical year and the second historical year are adjacent years.
In an embodiment, the latest historical soil environment damage data is obtained, and the latest historical soil environment damage data is input into the fourth target prediction model, so that the fourth target prediction model outputs soil environment damage prediction data of a preset year.
In one embodiment, soil environment damage data evaluation criteria are set; specifically, the set soil environment damage data evaluation criteria comprise less soil environment damage data, general soil environment damage data and more soil environment damage data, wherein the second score corresponding to less soil environment damage data is 3 points; the second score corresponding to the soil environment damage data is generally 2 points; the second score corresponding to more soil environment damage data is 1 score.
In one embodiment, the soil environment damage prediction data is calculated to obtain a fourth score.
Specifically, calculating a third quantity ratio of the soil environment damage prediction data in the first soil environment damage data critical value, if the third quantity ratio is less than or equal to the fifth preset quantity ratio threshold value, confirming that the first soil environment calculation result is that the soil environment damage data is less, and obtaining a fourth value according to the first soil environment calculation result.
Specifically, if the third number duty ratio is greater than a third preset number duty ratio threshold, and the third number duty ratio is less than or equal to the sixth preset number duty ratio threshold, determining that the second soil environment calculation result is that the soil environment damage data is less, and obtaining a fourth score according to the second soil environment calculation result.
Specifically, if the third number duty ratio is greater than the sixth preset number duty ratio threshold, determining that the third soil environment calculation result is that the soil environment damage data are more, and obtaining a fourth score according to the third soil environment calculation result.
Preferably, the setting of the critical value of the soil environment damage data can be obtained from the soil environment related standard based on the mine type.
Preferably, the fifth number is set to 60% and the sixth preset number is set to 100% with respect to the threshold.
Step 105: and integrating the first score, the second score, the third score and the fourth score, obtaining and determining the environmental impact strength of the three thin mineral development areas based on the first comprehensive score, and matching a corresponding environmental treatment scheme in a preset environmental treatment rule according to the environmental impact strength, wherein the preset environmental treatment rule comprises the environmental impact strength and the environmental treatment scheme.
In an embodiment, an environmental impact strength score table is set, and the first integrated score is compared with the environmental impact strength score table, where the environmental impact strength score table includes integrated scores and corresponding environmental impact strengths.
Preferably, when the first integrated score is 4-6, the corresponding environmental impact strength is high; when the first comprehensive score is 7-9, the corresponding environmental impact strength is the middle; when the first comprehensive score is 10-12, the corresponding environmental impact strength is low.
In an embodiment, the environmental impact strength is obtained, when the environmental impact strength is low, a corresponding environmental impact low-strength treatment scheme is matched in a preset environmental treatment rule, when the environmental impact strength is medium, a corresponding environmental impact medium-strength treatment scheme is matched in a preset environmental treatment rule, and when the environmental impact strength is high, a corresponding environmental impact high-strength treatment scheme is matched in a preset environmental treatment rule.
In one embodiment, a fifth score, a sixth score, a seventh score and an eighth score corresponding to the three thin mineral development areas are obtained, and a second comprehensive score is obtained according to the fifth score, the sixth score, the seventh score and the eighth score; the fifth score, the sixth score, the seventh score and the eighth score are obtained by re-obtaining the environment treatment of the three-thin mineral development area according to the environment treatment scheme; re-determining the environmental impact strength of the three-dilute mineral development area according to the second comprehensive score, and re-matching the corresponding environmental treatment scheme in the environmental treatment rule; the method can update the corresponding environmental impact strength in time and update the corresponding environmental treatment scheme in real time based on the change of the environment of the three-thin mineral development area.
In summary, according to the environmental impact prediction method for the three-thin mineral development area, a plurality of target prediction models are pre-trained, the number of geological disaster hidden dangers, the geological disaster early warning number, the water environment damage number and the soil environment damage number of the three-thin mineral development area are predicted based on each target prediction model, the prediction results are calculated respectively to obtain corresponding scores, quantitative prediction of the environmental impact level of the three-thin mineral development area is realized based on the scores, a corresponding treatment scheme is provided based on the prediction results, accurate basis can be provided for a decision maker, and decision errors are reduced; meanwhile, in order to evaluate the environmental impact of the three-thin mineral development area in the prior art, multiple samples in the area need to be collected, data extraction analysis and the like are carried out on the samples, the problems of complex operation and a large number of required instruments can be avoided, and the acquisition efficiency of environmental impact data is improved.
Example 2
Referring to fig. 2, fig. 2 is a schematic structural diagram of an embodiment of an environmental impact prediction apparatus for a three-thin mineral development area provided by the present invention, and as shown in fig. 2, the apparatus includes a geological disaster hidden danger number calculation module 201, a geological disaster early warning number calculation module 202, a water environment damage number calculation module 203, a soil environment damage data calculation module 204, and an environmental impact strength determination module 205, which are specifically as follows:
the geological disaster hidden danger number calculation module 201 is configured to obtain the historical geological disaster hidden danger number of the three thin mineral development areas, input the historical geological disaster hidden danger number to a pre-trained first target prediction model, so that the first target prediction model outputs the geological disaster hidden danger prediction number, and calculate the geological disaster hidden danger prediction number to obtain a first score.
The geological disaster early-warning number calculation module 202 is configured to obtain a historical geological disaster early-warning number of a three-thin mineral development area, input the historical geological disaster early-warning number to a pre-trained second target prediction model, so that the second target prediction model outputs a geological disaster early-warning predicted number, and calculate the geological disaster early-warning predicted number to obtain a second score.
The water environment damage number calculation module 203 is configured to obtain historical water environment damage data of three dilute mineral development areas, input the historical water environment damage data to a pre-trained third target prediction model, so that the third target prediction model outputs water environment damage prediction data, and calculate the water environment damage prediction data to obtain a third score.
The soil environment damage data calculation module 204 is configured to obtain historical soil environment damage data of a three-thin mineral development area, input the historical soil environment damage data to a pre-trained fourth target prediction model, so that the fourth target prediction model outputs soil environment damage prediction data, and calculate the soil environment damage prediction data to obtain a fourth score.
The environmental impact strength determining module 205 is configured to integrate the first score, the second score, the third score and the fourth score, obtain and determine an environmental impact strength of the three-thin mineral development area based on the first integrated score, and match a corresponding environmental treatment scheme in a preset environmental treatment rule according to the environmental impact strength, where the preset environmental treatment rule includes the environmental impact strength and the environmental treatment scheme.
The environment influence prediction device for the three-thin mineral development area provided by the embodiment of the invention further comprises: an environmental impact strength update module.
In an embodiment, the environmental impact strength updating module is configured to obtain a fifth score, a sixth score, a seventh score, and an eighth score corresponding to the three thin mineral development areas, and obtain a second integrated score according to the fifth score, the sixth score, the seventh score, and the eighth score; the fifth score, the sixth score, the seventh score and the eighth score are obtained by re-obtaining the environment treatment of the three-thin mineral product development area according to the environment treatment scheme.
In an embodiment, the environmental impact strength updating module is configured to redetermine the environmental impact strength of the three thin mineral development areas according to the second comprehensive score, and re-match the corresponding environmental governance scheme in the environmental governance rule.
The environment influence prediction device for the three-thin mineral development area provided by the embodiment of the invention further comprises: and an evaluation standard setting module.
In an embodiment, the evaluation standard setting module is configured to set a geological disaster hidden danger evaluation standard, a geological disaster early warning evaluation standard, a water environment damage data evaluation standard and a soil environment damage data evaluation standard.
In an embodiment, the environmental impact strength determining module is configured to match a corresponding environmental governance scheme in a preset environmental governance rule according to the environmental impact strength, and specifically includes: acquiring the environmental impact strength, when the environmental impact strength is low, matching an environmental impact low-strength treatment scheme in a preset environmental treatment rule, when the environmental impact strength is medium, matching an environmental impact medium-strength treatment scheme in the preset environmental treatment rule, and when the environmental impact strength is high, matching an environmental impact high-strength treatment scheme in the preset environmental treatment rule.
In an embodiment, the pre-training process of the first target prediction model in the geological disaster hidden danger number calculation module specifically includes: constructing a neural network prediction model; acquiring the number of historical geological disaster hidden dangers corresponding to a plurality of historical years in the three-thin mineral product development area, and carrying out normalization processing on the number of the historical geological disaster hidden dangers to obtain the number of historical geological disaster hidden dangers samples; and training the neural network prediction model by taking the number of first historical geological disaster hidden danger samples corresponding to the first historical year as model input and taking the number of second historical address disaster hidden danger samples corresponding to the second historical year as model output until the model converges, and generating a first target prediction model, wherein the first historical year and the second historical year are adjacent years.
In an embodiment, the geological disaster early-warning number calculation module is configured to input the historical geological disaster early-warning number to a pre-trained second target prediction model, so that the second target prediction model outputs the obtained geological disaster early-warning predicted number, and specifically includes: acquiring historical geological disaster early warning quantity, wherein the historical geological disaster early warning quantity comprises historical blue geological disaster early warning quantity, historical yellow geological disaster early warning quantity, historical orange geological disaster early warning quantity and historical red geological disaster early warning quantity; and inputting the historical geological disaster early-warning quantity into a pre-trained second target prediction model so that the second target prediction model outputs the geological disaster early-warning predicted quantity, wherein the geological disaster early-warning predicted quantity comprises a blue geological disaster early-warning predicted quantity, a yellow geological disaster early-warning predicted quantity, an orange geological disaster early-warning predicted quantity and a red geological disaster early-warning predicted quantity.
In an embodiment, the geological disaster early warning number calculation module is configured to calculate and process the geological disaster early warning predicted number to obtain a second score, and specifically includes: comparing the geological disaster early warning predicted quantity with the geological disaster early warning critical quantity, if the geological disaster early warning predicted quantity is smaller than or equal to the geological disaster early warning critical quantity, confirming that a first early warning quantity calculation result is that the geological disaster early warning predicted quantity is smaller, and obtaining a second score according to the first early warning quantity calculation result; if the geological disaster early warning predicted quantity is larger than the geological disaster early warning critical quantity, calculating a first duty ratio of the sum of the orange geological disaster early warning predicted quantity and the red geological disaster early warning predicted quantity in the geological disaster early warning predicted quantity, and if the first duty ratio is smaller than a preset duty ratio threshold value, confirming that a second early warning quantity calculation result is that the geological disaster early warning predicted quantity is general, and obtaining a second score according to the second early warning quantity calculation result; and if the first duty ratio is larger than or equal to a preset duty ratio threshold, confirming that the third early warning number calculation result is that the geological disaster early warning predicted number is more, and obtaining a second score according to the third early warning number calculation result.
It will be clear to those skilled in the art that, for convenience and brevity of description, reference may be made to the corresponding process in the foregoing method embodiment for the specific working process of the above-described apparatus, which is not described in detail herein.
It should be noted that the above embodiment of the environmental impact prediction apparatus for a three-thin mineral development area is merely illustrative, and the modules described as separate components may or may not be physically separated, and components displayed as modules may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
On the basis of the embodiment of the environmental impact prediction method of the three-thin mineral development area, another embodiment of the present invention provides an environmental impact prediction terminal device of the three-thin mineral development area, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor executes the computer program to implement the environmental impact prediction method of the three-thin mineral development area according to any one of the embodiments of the present invention.
Illustratively, in this embodiment the computer program may be partitioned into one or more modules, which are stored in the memory and executed by the processor to perform the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program in the environmental impact prediction terminal device of the three-dilute mineral development zone.
The environmental impact prediction terminal equipment of the three-thin mineral product development area can be computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud server. The environmental impact prediction terminal device of the triple-thin mining development area may include, but is not limited to, a processor, a memory.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the environmental impact prediction terminal device of the three-thin-mineral development area, and connects the respective parts of the environmental impact prediction terminal device of the entire three-thin-mineral development area using various interfaces and lines.
The memory may be used to store the computer program and/or module, and the processor may implement various functions of the environmental impact prediction terminal device of the three-thin mineral development area by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the cellular phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
On the basis of the embodiment of the environmental impact prediction method of the three-thin mineral development area, another embodiment of the present invention provides a storage medium, where the storage medium includes a stored computer program, and when the computer program runs, the device where the storage medium is controlled to execute the environmental impact prediction method of the three-thin mineral development area of any embodiment of the present invention.
In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, where the computer program code may be in a source code form, an object code form, an executable file, or some intermediate form, and so on. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
In summary, the invention discloses an environmental impact prediction method and device for a three-thin mineral development area, which are characterized in that the prediction quantity of geological disaster hidden dangers, the prediction quantity of geological disaster early warning, the prediction data of water environment damage and the prediction data of soil environment damage of the three-thin mineral development area are respectively output through a pre-trained target prediction model, the prediction quantity of geological disaster hidden dangers, the prediction quantity of geological disaster early warning, the prediction data of water environment damage and the prediction data of soil environment damage are respectively calculated, a first score, a second score, a third score and a fourth score are correspondingly obtained, all scores are integrated, a first comprehensive score is obtained, the environmental impact strength is determined according to the first comprehensive score, and meanwhile, a corresponding environmental management scheme is matched in a preset environmental management rule based on the environmental impact strength. Compared with the prior art, the technical scheme of the invention realizes quantitative prediction of the environmental impact grade of the three-thin mineral development area, and improves the acquisition efficiency of the environmental impact grade.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and substitutions can be made by those skilled in the art without departing from the technical principles of the present invention, and these modifications and substitutions should also be considered as being within the scope of the present invention.

Claims (10)

1. A method for predicting environmental impact in a three-thin mineral development area, comprising:
acquiring the number of historical geological disaster hidden dangers of a three-thin mineral development area, inputting the number of the historical geological disaster hidden dangers into a pre-trained first target prediction model so that the first target prediction model outputs the predicted number of the geological disaster hidden dangers, and calculating the predicted number of the geological disaster hidden dangers to obtain a first score;
acquiring historical geological disaster early warning quantity of a three-thin mineral development area, inputting the historical geological disaster early warning quantity into a pre-trained second target prediction model so that the second target prediction model outputs to obtain geological disaster early warning predicted quantity, and calculating the geological disaster early warning predicted quantity to obtain a second score;
acquiring historical water environment damage data of a three-thin mineral development area, inputting the historical water environment damage data into a pre-trained third target prediction model so that the third target prediction model outputs water environment damage prediction data, and calculating the water environment damage prediction data to obtain a third score;
Acquiring historical soil environment damage data of a three-thin mineral development area, inputting the historical soil environment damage data into a pre-trained fourth target prediction model so that the fourth target prediction model outputs soil environment damage prediction data, and calculating the soil environment damage prediction data to obtain a fourth value;
and integrating the first score, the second score, the third score and the fourth score, obtaining and determining the environmental impact strength of the three thin mineral development areas based on the first comprehensive score, and matching a corresponding environmental treatment scheme in a preset environmental treatment rule according to the environmental impact strength, wherein the preset environmental treatment rule comprises the environmental impact strength and the environmental treatment scheme.
2. The environmental impact prediction method of a triple dilute mineral development zone of claim 1, further comprising:
obtaining a fifth score, a sixth score, a seventh score and an eighth score corresponding to the three thin mineral development areas, and obtaining a second comprehensive score according to the fifth score, the sixth score, the seventh score and the eighth score; the fifth score, the sixth score, the seventh score and the eighth score are obtained by re-obtaining the environment treatment of the three-thin mineral development area according to the environment treatment scheme;
And re-determining the environmental impact strength of the three-thin mineral product development area according to the second comprehensive score, and re-matching the corresponding environmental treatment scheme in the environmental treatment rule.
3. The environmental impact prediction method of a triple dilute mineral development zone of claim 1, further comprising:
setting a geological disaster hidden danger evaluation standard, a geological disaster early warning evaluation standard, a water environment damage data evaluation standard and a soil environment damage data evaluation standard.
4. The method for predicting the environmental impact of a three-thin mineral development area according to claim 1, wherein the matching of the corresponding environmental governance scheme in the preset environmental governance rule according to the environmental impact strength specifically comprises:
acquiring the environmental impact strength, when the environmental impact strength is low, matching an environmental impact low-strength treatment scheme in a preset environmental treatment rule, when the environmental impact strength is medium, matching an environmental impact medium-strength treatment scheme in the preset environmental treatment rule, and when the environmental impact strength is high, matching an environmental impact high-strength treatment scheme in the preset environmental treatment rule.
5. The method for predicting environmental impact in a three-thin mineral development area of claim 1, wherein the pre-training process of the first target prediction model specifically comprises:
constructing a neural network prediction model;
acquiring the number of historical geological disaster hidden dangers corresponding to a plurality of historical years in the three-thin mineral product development area, and carrying out normalization processing on the number of the historical geological disaster hidden dangers to obtain the number of historical geological disaster hidden dangers samples;
and training the neural network prediction model by taking the number of first historical geological disaster hidden danger samples corresponding to the first historical year as model input and taking the number of second historical address disaster hidden danger samples corresponding to the second historical year as model output until the model converges, and generating a first target prediction model, wherein the first historical year and the second historical year are adjacent years.
6. The method for predicting environmental impact in a three-thin mineral development area according to claim 1, wherein the step of inputting the historical geological disaster early-warning number into a pre-trained second target prediction model to enable the second target prediction model to output the geological disaster early-warning predicted number comprises the following steps:
Acquiring historical geological disaster early warning quantity, wherein the historical geological disaster early warning quantity comprises historical blue geological disaster early warning quantity, historical yellow geological disaster early warning quantity, historical orange geological disaster early warning quantity and historical red geological disaster early warning quantity;
and inputting the historical geological disaster early-warning quantity into a pre-trained second target prediction model so that the second target prediction model outputs the geological disaster early-warning predicted quantity, wherein the geological disaster early-warning predicted quantity comprises a blue geological disaster early-warning predicted quantity, a yellow geological disaster early-warning predicted quantity, an orange geological disaster early-warning predicted quantity and a red geological disaster early-warning predicted quantity.
7. The method for predicting environmental impact in a three-thin mineral development area according to claim 6, wherein the calculating the predicted number of geological disaster early warning to obtain the second score specifically comprises:
comparing the geological disaster early warning predicted quantity with the geological disaster early warning critical quantity, if the geological disaster early warning predicted quantity is smaller than or equal to the geological disaster early warning critical quantity, confirming that a first early warning quantity calculation result is that the geological disaster early warning predicted quantity is smaller, and obtaining a second score according to the first early warning quantity calculation result;
If the geological disaster early warning predicted quantity is larger than the geological disaster early warning critical quantity, calculating a first duty ratio of the sum of the orange geological disaster early warning predicted quantity and the red geological disaster early warning predicted quantity in the geological disaster early warning predicted quantity, and if the first duty ratio is smaller than a preset duty ratio threshold value, confirming that a second early warning quantity calculation result is that the geological disaster early warning predicted quantity is general, and obtaining a second score according to the second early warning quantity calculation result;
and if the first duty ratio is larger than or equal to a preset duty ratio threshold, confirming that the third early warning number calculation result is that the geological disaster early warning predicted number is more, and obtaining a second score according to the third early warning number calculation result.
8. An environmental impact prediction apparatus for a three-thin mineral development area, comprising: the system comprises a geological disaster hidden danger number calculation module, a geological disaster early warning number calculation module, a water environment damage number calculation module, a soil environment damage data calculation module and an environment influence intensity determination module;
the geological disaster hidden danger number calculation module is used for acquiring the historical geological disaster hidden danger number of the three thin mineral development areas, inputting the historical geological disaster hidden danger number into a pre-trained first target prediction model so that the first target prediction model outputs the geological disaster hidden danger prediction number, and calculating the geological disaster hidden danger prediction number to obtain a first score;
The geological disaster early warning number calculation module is used for acquiring the historical geological disaster early warning number of the three thin mineral development areas, inputting the historical geological disaster early warning number into a pre-trained second target prediction model so that the second target prediction model outputs the obtained geological disaster early warning predicted number, and calculating the geological disaster early warning predicted number to obtain a second score;
the water environment damage quantity calculation module is used for acquiring historical water environment damage data of three dilute mineral development areas, inputting the historical water environment damage data into a pre-trained third target prediction model so that the third target prediction model outputs water environment damage prediction data, and calculating the water environment damage prediction data to obtain a third score;
the soil environment damage data calculation module is used for acquiring historical soil environment damage data of three thin mineral development areas, inputting the historical soil environment damage data into a pre-trained fourth target prediction model so that the fourth target prediction model outputs soil environment damage prediction data, and calculating the soil environment damage prediction data to obtain a fourth score;
The environmental impact strength determining module is configured to integrate the first score, the second score, the third score and the fourth score, obtain and determine environmental impact strength of the three-thin mineral product development area based on a first comprehensive score, and match a corresponding environmental treatment scheme in a preset environmental treatment rule according to the environmental impact strength, where the preset environmental treatment rule includes the environmental impact strength and the environmental treatment scheme.
9. A terminal device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing an environmental impact prediction method and apparatus method of a triple-thin mineral development zone according to any one of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored computer program, wherein the computer program, when run, controls a device in which the computer readable storage medium is located to perform the environmental impact prediction method of the triple-thin mineral development zone according to any one of claims 1 to 7.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109086935A (en) * 2018-08-07 2018-12-25 中国地质环境监测院 A kind of Prediction of Regional Geo-hazards method
CN110503805A (en) * 2018-05-16 2019-11-26 中国石油化工股份有限公司天然气分公司 Pipeline Geological Hazards Monitoring method for early warning and device
CN111144651A (en) * 2019-12-26 2020-05-12 杭州鲁尔物联科技有限公司 Geological disaster prediction method, device and equipment
CN111160794A (en) * 2019-12-31 2020-05-15 中国地质科学院探矿工艺研究所 Geological environment bearing capacity analysis method based on AHP-GIS coupling analysis
CN112819208A (en) * 2021-01-19 2021-05-18 武汉中地云申科技有限公司 Spatial similarity geological disaster prediction method based on feature subset coupling model
CN115249331A (en) * 2022-09-22 2022-10-28 山东世纪矿山机电有限公司 Mine ecological safety identification method based on convolutional neural network model

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110503805A (en) * 2018-05-16 2019-11-26 中国石油化工股份有限公司天然气分公司 Pipeline Geological Hazards Monitoring method for early warning and device
CN109086935A (en) * 2018-08-07 2018-12-25 中国地质环境监测院 A kind of Prediction of Regional Geo-hazards method
CN111144651A (en) * 2019-12-26 2020-05-12 杭州鲁尔物联科技有限公司 Geological disaster prediction method, device and equipment
CN111160794A (en) * 2019-12-31 2020-05-15 中国地质科学院探矿工艺研究所 Geological environment bearing capacity analysis method based on AHP-GIS coupling analysis
CN112819208A (en) * 2021-01-19 2021-05-18 武汉中地云申科技有限公司 Spatial similarity geological disaster prediction method based on feature subset coupling model
CN115249331A (en) * 2022-09-22 2022-10-28 山东世纪矿山机电有限公司 Mine ecological safety identification method based on convolutional neural network model

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
WU QIANG 等: "Study on assessment of mine environments", 《ACTA GEOLOGICA SINICA(ENGLISH EDITION)》, vol. 82, no. 5, pages 1027 - 1034, XP071919670, DOI: 10.1111/j.1755-6724.2008.tb00659.x *
于扬 等: "川西甲基卡大型锂资源基地绿色调查及环境评价指标 体系的建立", 《岩矿测试》, vol. 38, no. 5, pages 534 - 544 *
于扬 等: "稀土矿区环境调查SMAIMA方法体系、评价模型及其应用——以赣南离子吸附型稀土矿山为例", 《地球学报》, vol. 38, no. 3, pages 335 - 344 *
李东 等: "BP 神经网络和SVM在矿山环境评价中的应用分析", 《干旱区地理》, vol. 38, no. 1, pages 128 - 134 *

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