A mine ecological restoration evaluation analysis method and system based on big data analysis

Abstract

The application provides a mine ecological restoration evaluation analysis method and system based on big data analysis, relates to the technical field of mine ecological analysis, and comprises the following steps: S1: obtaining initial pollution degree distribution data of contaminated soil and initial particle degree distribution data of contaminated soil corresponding to the initial pollution, and establishing an initial pollution model of soil in each region of the mine; S2: collecting soil dryness degree data, and controlling the soil dryness degree data to be imported into the initial pollution model to generate a corresponding soil dryness pollution model. According to the collected initial pollution condition, the initial pollution model is established, and based on the influencing factors of soil dryness, the pollution particle distribution situation formed after soil dryness is further determined, and combined with the real-time change of the environmental wind, the dispersion situation of the pollution particles is simulated, so that the pollution particle distribution situation of each region of the mine during the restoration period is accurately determined, and the mine ecological restoration evaluation analysis efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of mine ecological analysis technology, and in particular to a method and system for evaluating and analyzing mine ecological restoration based on big data analysis. Background Technology

[0002] Mine restoration, also known as mine ecological restoration, refers to the remediation of pollution in mining wastelands. It aims to restore the damaged ecological environment and ensure the sustainable use of land resources. Mining operations generate large amounts of unusable land that cannot be remediated; this land, known as mining wasteland, is polluted by various factors related to mining activities.

[0003] In existing technologies, during the ecological restoration evaluation and analysis of mine remediation, it is often necessary to regularly sample the soil in the polluted environment. The sampling results are then used to further analyze the degree of soil pollution in each area. However, this process requires a large amount of sampling and testing costs, which brings certain difficulties to soil analysis and ecological restoration evaluation and analysis. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method and system for evaluating and analyzing mine ecological restoration based on big data analysis, which solves the technical problem in the prior art that it is not easy to quickly determine the dispersion of polluted soil particles during the mine pollution remediation process, thus affecting the cost of evaluating and analyzing mine ecological restoration.

[0005] To achieve the above and other related objectives, this invention provides a method for evaluating and analyzing mine ecological restoration based on big data analysis, comprising the following steps:

[0006] S1: Obtain the initial pollution degree distribution data of the contaminated soil and the initial particle size distribution data of the contaminated soil corresponding to the initial pollution, and establish the initial pollution model of the soil in each area of ​​the mine.

[0007] S2: Collect soil drying degree data and control the import of soil drying degree data into the initial pollution model to generate the corresponding soil drying pollution model;

[0008] Collect soil drying degree data, including: obtaining control signals affecting soil drying degree in each polluted area, and generating corresponding soil drying degree data based on the data affecting soil drying degree;

[0009] Control signals that affect the degree of soil drying include at least one of temperature signals, humidity signals, and rainfall signals;

[0010] Based on the data affecting soil drying degree, corresponding soil drying degree data are generated, including:

[0011] Based on initial values ​​of ambient temperature, ambient humidity, and rainfall. and Difference in change and The control signals corresponding to the preset differences in changes in ambient temperature, ambient humidity, and rainfall are used to calculate the soil drying degree data, i.e., the formula is:

[0012] ;

[0013] in, Data on soil drying degree; and These are the control signal output values ​​corresponding to ambient temperature, ambient humidity, and rainfall, respectively. The output is 1 when the difference between the changes in ambient temperature, ambient humidity, or rainfall exceeds the preset difference, and 0 otherwise. The degree of drying factor corresponds to the difference in ambient temperature change per unit time. The drying factor corresponds to the difference in ambient humidity per unit time. The aridification factor corresponds to the difference in rainfall variation per unit time.

[0014] The control involves importing soil drying degree data into the initial pollution model to generate a corresponding soil drying pollution model. This includes collecting initial particle size distribution data of polluted soil and initial drying degree data of soil in each region. Calculation based on soil drying level data Data on the distribution of the degree of repair drying and drying time t ,Right now And based on the distribution data of the degree of drying after repair Initial granularity distribution data Convert to corresponding repair granularity distribution data To determine the particle size of soil in each region within the drying time t;

[0015] S3: Collect environmental wind change data, simulate the distribution of polluted soil particles in the soil drying pollution model, and determine the pollution dispersion of polluted soil particles on the surrounding soil.

[0016] S4: Based on the dispersion of pollution, redetermine the remediation evaluation level for each area of ​​the mine.

[0017] In one embodiment of the present invention, S1 includes: determining information on each polluted area of ​​the mine, marking each polluted area in an initial pollution model, and loading the corresponding initial pollution degree distribution data and initial particle size distribution data of each polluted area.

[0018] In one embodiment of the present invention, S3 further includes: importing wind force and corresponding wind direction data into the soil drying pollution model to process the remediation particulate matter distribution data. The simulation determined the drying time. The particle distribution of soil in different regions.

[0019] In one embodiment of the present invention, S4 further includes: based on the determined drying time The particle size distribution of soil in each region is determined, and the corresponding particle size range for each remediation evaluation level is determined, thereby generating the corresponding remediation evaluation level.

[0020] This invention also provides a mine ecological restoration evaluation and analysis system based on big data analysis, comprising:

[0021] The initial model building module acquires the initial pollution degree distribution data of the contaminated soil and the initial particle size distribution data of the contaminated soil corresponding to the initial pollution, and establishes the initial pollution model of the soil in each area of ​​the mine.

[0022] The drying model construction module collects soil drying degree data and controls the import of soil drying degree data into the initial pollution model to generate the corresponding soil drying pollution model.

[0023] Collect soil drying degree data, including: obtaining control signals affecting soil drying degree in each polluted area, and generating corresponding soil drying degree data based on the data affecting soil drying degree;

[0024] The control signals that affect the degree of soil drying include at least one of temperature signals, humidity signals, and rainfall signals;

[0025] Based on the data affecting soil drying degree, corresponding soil drying degree data are generated, including:

[0026] Based on initial values ​​of ambient temperature, ambient humidity, and rainfall. and Difference in change and The control signals corresponding to the preset differences in changes in ambient temperature, ambient humidity, and rainfall are used to calculate the soil drying degree data, i.e., the formula is:

[0027] ;

[0028] in, Data on soil drying degree; and These are the control signal output values ​​corresponding to ambient temperature, ambient humidity, and rainfall, respectively. The output is 1 when the difference between the changes in ambient temperature, ambient humidity, or rainfall exceeds the preset difference, and 0 otherwise. The degree of drying factor corresponds to the difference in ambient temperature change per unit time. The drying factor corresponds to the difference in ambient humidity per unit time. The aridification factor corresponds to the difference in rainfall variation per unit time.

[0029] The control process involves importing the soil drying degree data into the initial pollution model to generate a corresponding soil drying pollution model, including:

[0030] Initial soil desiccation data for each region corresponding to the initial particulate matter distribution data of contaminated soil. Calculation based on the soil drying degree data Data on the distribution of the degree of repair drying and drying time t ,Right now And based on the aforementioned repair and drying degree distribution data Initial granularity distribution data Convert to corresponding repair granularity distribution data To determine the particle size of soil in each region within the drying time t;

[0031] The wind simulation module collects environmental wind change data and simulates the distribution of polluted soil particles in the soil drying pollution model to determine the pollution dispersion of polluted soil particles on the surrounding soil.

[0032] The grading module redetermines the remediation grading level for each area of ​​the mine based on the dispersion of pollution.

[0033] As described above, the mine ecological restoration evaluation and analysis method and system based on big data analysis of the present invention has the following beneficial effects: Preliminary measurements of contaminated soil are conducted manually to obtain initial pollution level distribution data and corresponding initial particle size distribution data, and an initial pollution model for soil in each area of ​​the mine is established. Furthermore, based on whether the differences in temperature, humidity, and rainfall signals exceed preset values, control signals are output to establish a soil drying pollution model, thereby quickly establishing the redistribution of contaminated soil particles after a predetermined drying time. Combined with the influence of environmental wind changes, simulations are performed in the soil drying pollution model to ultimately determine the dispersion of contaminated soil particles on the surrounding soil at the predetermined remediation time (i.e., the predetermined drying time), thus quickly and conveniently obtaining the remediation evaluation level for each area of ​​the mine under simulation. By simulating the polluted environment in the above manner to determine the remediation evaluation level, the cost of manually measuring the amount of contaminated soil particles in each area can be reduced, while also ensuring the high efficiency of analyzing the mine soil remediation level evaluation by accurately simulating the dispersion of contaminated soil under wind action. Attached Figure Description

[0034] Figure 1 This is a flowchart of the evaluation and analysis method of the present invention.

[0035] Figure 2 This is an architecture diagram of the evaluation and analysis system of the present invention. Detailed Implementation

[0036] Please see Figure 1 This invention provides a method for evaluating and analyzing mine ecological restoration based on big data analysis, comprising the following steps:

[0037] S1: Obtain the initial pollution degree distribution data of the contaminated soil and the initial particle size distribution data of the contaminated soil corresponding to the initial pollution, and establish the initial pollution model of the soil in each area of ​​the mine.

[0038] S2: Collect soil drying degree data and control the import of soil drying degree data into the initial pollution model to generate the corresponding soil drying pollution model;

[0039] S3: Collect environmental wind change data, simulate the distribution of polluted soil particles in the soil drying pollution model, and determine the pollution dispersion of polluted soil particles on the surrounding soil.

[0040] S4: Based on the dispersion of pollution, the remediation evaluation level of each area of ​​the mine is re-determined.

[0041] As can be seen from the above, during the restoration of damaged mine ecosystems, especially when environmental soil is polluted, methods such as on-site sampling of various polluted areas in the mine are used to pre-determine the initial pollution level distribution data and the corresponding initial particle size distribution data of the polluted soil in each area. Furthermore, based on the initial pollution level distribution data and the initial particle size distribution data, an initial pollution model for the soil in each area of ​​the mine is established to reflect the initial pollution status of the mine's environmental soil. Under environmental conditions affecting soil drying, the degree of soil drying will change, and the amount of soil particles formed will differ depending on the degree of drying. Therefore, when subjected to wind, polluted soil can also pollute other surrounding areas. When calculating the degree of soil drying, data on the degree of soil drying affected by environmental conditions is first collected and then imported into the established initial pollution model to obtain a soil drying pollution model, thereby determining the amount of polluted soil particles formed by the affected drying. Furthermore, by monitoring environmental wind changes around the contaminated soil in the mine, the specific distribution of contaminated soil particles after being dispersed by wind under corresponding wind strength and direction conditions is simulated in the soil drying pollution model. Based on the final distribution of contaminated soil particles in various areas of the mine, the corresponding remediation evaluation level for each area can be determined. This method effectively reduces the need for soil testing and analysis to further determine the soil remediation evaluation level during the mine ecological soil remediation process. Instead, it determines the final distribution of soil particles in each area based on the degree of soil drying, and then accurately simulates the dispersion of contaminated soil under wind action through wind simulation, thus ensuring the high efficiency of the analysis of mine soil remediation level evaluation.

[0042] Step S1 includes: determining the information of each polluted area in the mine, marking each polluted area in the initial pollution model, and loading the corresponding initial pollution degree distribution data and initial particle size distribution data of each polluted area.

[0043] In one embodiment of the present invention, the initial pollution level distribution data and the initial particulate matter level distribution data can be determined by on-site sampling. Moreover, the pollution levels of the formed pollution areas are different. Therefore, by marking each pollution area separately, the pollution areas can be treated separately.

[0044] Furthermore, in step S2, soil drying degree data is collected, including: obtaining control signals affecting the soil drying degree corresponding to each polluted area, and generating corresponding soil drying degree data based on the data affecting the soil drying degree.

[0045] In one embodiment of the present invention, in order to better generate soil drying degree data, it is necessary to collect control signals that affect the soil drying degree, so as to determine whether to use the data affecting the soil drying degree to generate the corresponding soil drying degree data based on the obtained control signals. This reduces the amount of computation and processing required to obtain the corresponding soil drying degree data.

[0046] Control signals affecting soil drying levels include at least one of temperature, humidity, and rainfall signals. During the soil drying process into granular form, it is influenced by various environmental factors, such as temperature, humidity, and rainfall. Therefore, by using temperature, humidity, and rainfall sensors to detect these signals in each polluted area, appropriate soil drying level data can be generated when these factors significantly affect the degree of soil drying.

[0047] Furthermore, based on the data affecting soil drying degree, corresponding soil drying degree data are generated, including:

[0048] Based on initial values ​​of ambient temperature, ambient humidity, and rainfall. and Difference in change and The control signals corresponding to the preset differences in changes in ambient temperature, ambient humidity, and rainfall are used to calculate the soil drying degree data, i.e., the formula is:

[0049] ;

[0050] in, Data on soil drying degree; and These are the control signal output values ​​corresponding to ambient temperature, ambient humidity, and rainfall, respectively. The output is 1 when the difference between the changes in ambient temperature, ambient humidity, or rainfall exceeds the preset difference, and 0 otherwise. The degree of drying factor corresponds to the difference in ambient temperature change per unit time. The drying factor corresponds to the difference in ambient humidity per unit time. The drying factor corresponds to the difference in rainfall per unit time.

[0051] In one embodiment of the present invention, since the environmental factors affecting soil drying degree—ambient temperature, ambient humidity, and rainfall—are independent of each other, the soil drying degree data is calculated using a product method. Furthermore, during the calculation, ambient temperature, ambient humidity, and rainfall may exceed preset variation values; therefore, by… , and Assigning a 0 or 1 output allows for relatively rapid analysis and measurement of soil drying level data. That is, in and If one or more values ​​in the equation are 0, it indicates that the corresponding value was not considered a factor influencing the degree of soil drying and is not included in the calculation. Furthermore... There are cases of positive and negative correlation, that is, when A positive value indicates an increase in ambient temperature, a decrease in ambient humidity, and the absence of rainfall. Meanwhile... A negative value indicates a decrease in ambient temperature, an increase in ambient humidity, or an increase in rainfall.

[0052] The control involves importing soil drying degree data into the initial pollution model to generate a corresponding soil drying pollution model. This includes collecting initial particle size distribution data of polluted soil and initial drying degree data of soil in each region. Calculation based on soil drying level data Data on the distribution of the degree of repair drying and drying time t ,Right now And based on the distribution data of the degree of drying after repair Initial granularity distribution data Convert to corresponding repair granularity distribution data To determine the particle size of soil in each region within the drying time t.

[0053] In one embodiment of the present invention, when obtaining a corresponding soil drying pollution model using soil drying degree data, the initial drying degree data of soil in each region corresponding to the measured initial particle size distribution data of the polluted soil is used. Calculations were performed. Initial soil drying data for each region was obtained based on the initial granulation distribution data of the contaminated soil. At that time, the initial particle size distribution of contaminated soil was detected at fixed points using methods such as manual inspection. After obtaining the initial particle size distribution data, it was then converted into the corresponding initial drying degree data of soil in each area. And then based on the collected difference in changes... and Calculated ;

[0054] Specifically, the soil drying degree data, K, is imported into the initial pollution model. K then interacts with the initial drying degree data Ti and drying time t for each region in the initial pollution model to obtain the distribution data of remediation drying degree for each region. Data on the distribution of the degree of drying during restoration and soil desiccation data Since the data is of the same type, further analysis is needed based on the distribution data of the degree of drying after repair. The correspondence between soil particle size distribution data and the remediation particle size distribution data, and then the initial particle size distribution data. Convert to corresponding repair granularity distribution data This allows us to obtain the particle size of the soil in each region within the drying time t.

[0055] Step S3 also includes: importing wind speed and corresponding wind direction data into the soil drying pollution model to process the remediation particulate matter distribution data. The simulation determined the particle distribution of soil in each region within the drying time t.

[0056] In one embodiment of the present invention, after obtaining the particle size of the soil in each region within the drying time t, a soil drying pollution model is obtained, which represents the particle size distribution of the soil in each region within the drying time t. During this period, the wind speed and corresponding wind direction data are detected, and the wind speed and corresponding wind direction data are imported into the soil drying pollution model for simulation, i.e., by repairing the particle size distribution data. Simulations were conducted to determine the particle distribution in the soil of each area under real-time wind force and corresponding wind direction data. This was done to determine the extent of the impact of pollutant particles on the surrounding areas.

[0057] Furthermore, step S4 also includes: determining the particle distribution range corresponding to each remediation evaluation level based on the particle distribution of soil in each region within the determined drying time t, and generating the corresponding remediation evaluation level.

[0058] In one embodiment of the present invention, after obtaining the particle distribution of soil in each region within the drying time t, the distribution of pollutant particles in each region is reconfirmed to analyze and determine the remediation evaluation level. This allows for relatively quick and convenient simulation analysis based on the dispersion of pollutant particles to obtain the evaluation analysis results of mine ecological restoration.

[0059] Please see Figure 2 The present invention also provides a mine ecological restoration evaluation and analysis system based on big data analysis, comprising:

[0060] The initial model building module acquires the initial pollution degree distribution data of the contaminated soil and the initial particle size distribution data of the contaminated soil corresponding to the initial pollution, and establishes the initial pollution model of the soil in each area of ​​the mine.

[0061] The drying model construction module collects soil drying degree data and controls the import of soil drying degree data into the initial pollution model to generate the corresponding soil drying pollution model.

[0062] The wind simulation module collects environmental wind change data and simulates the distribution of polluted soil particles in the soil drying pollution model to determine the pollution dispersion of polluted soil particles on the surrounding soil.

[0063] The grading module redetermines the remediation grading level for each area of ​​the mine based on the dispersion of pollution.

[0064] In one embodiment of the present invention, an initial model construction module is used to establish initial pollution models for soil in each region based on initial pollution degree distribution data and initial particulate matter distribution data. A drying model construction module is used to further analyze the initial pollution models by combining soil drying degree data, thereby obtaining a soil drying pollution model after changes in pollutant particles. A wind simulation module is then used to simulate real-time environmental wind changes, thereby simulating the dispersion of pollutant soil particles on the surrounding soil. Finally, in the grade evaluation module, the remediation evaluation grade for each area of ​​the mine is re-determined based on the pollution dispersion. This enables rapid evaluation and analysis of mine ecological restoration.

[0065] In summary, this invention uses manual methods to conduct preliminary measurements of contaminated soil, obtaining initial pollution level distribution data and corresponding initial particle size distribution data, and establishing initial pollution models for soil in various mine areas. Furthermore, based on whether the differences in temperature, humidity, and rainfall signals exceed preset thresholds, control signals are output to establish a soil drying pollution model, thereby quickly establishing the redistribution of contaminated soil particles after a predetermined drying time. In conjunction with the influence of environmental wind changes, simulations are performed within the soil drying pollution model to ultimately determine the dispersion of contaminated soil particles on surrounding soil at the predetermined remediation time (i.e., the predetermined drying time), thus quickly and conveniently determining the remediation evaluation level for each mine area under simulation. By simulating the contaminated environment to determine the remediation evaluation level, this method reduces the cost of periodically measuring contaminated soil particle amounts in each area manually, while also accurately simulating the dispersion of contaminated soil under wind action, ensuring the high efficiency of analyzing mine soil remediation level evaluations. Therefore, this invention effectively overcomes the various shortcomings of existing technologies and has high industrial application value.

[0066] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for evaluating and analyzing mine ecological restoration based on big data analysis, characterized in that, Includes the following steps: S1: Obtain the initial pollution degree distribution data of the contaminated soil and the initial particle size distribution data of the contaminated soil corresponding to the initial pollution, and establish the initial pollution model of the soil in each area of ​​the mine. S2: Collect soil drying degree data and control the import of the soil drying degree data into the initial pollution model to generate the corresponding soil drying pollution model; Collect soil drying degree data, including: obtaining control signals affecting soil drying degree in each polluted area, and generating corresponding soil drying degree data based on the data affecting soil drying degree; The control signals that affect the degree of soil drying include at least one of temperature signals, humidity signals, and rainfall signals; Based on the data affecting soil drying degree, corresponding soil drying degree data are generated, including: Based on initial values ​​of ambient temperature, ambient humidity, and rainfall. and Difference in change and The control signals corresponding to the preset differences in changes in ambient temperature, ambient humidity, and rainfall are used to calculate the soil drying degree data, i.e., the formula is: ； in, Data on soil drying degree; and These are the control signal output values ​​corresponding to ambient temperature, ambient humidity, and rainfall, respectively. The output is 1 when the difference between the changes in ambient temperature, ambient humidity, or rainfall exceeds the preset difference, and 0 otherwise. The degree of drying factor corresponds to the difference in ambient temperature change per unit time. The drying factor corresponds to the difference in ambient humidity per unit time. The aridification factor corresponds to the difference in rainfall variation per unit time. The control process involves importing the soil drying degree data into the initial pollution model to generate a corresponding soil drying pollution model, including: Initial soil desiccation data for each region corresponding to the initial particulate matter distribution data of contaminated soil. Calculation based on the soil drying degree data Data on the distribution of the degree of repair drying and drying time t ,Right now And based on the repair drying degree distribution data Initial granularity distribution data Convert to corresponding repair granularity distribution data To determine the particle size of soil in each region within the drying time t; S3: Collect environmental wind change data, simulate the distribution of polluted soil particles in the soil drying pollution model, and determine the pollution dispersion of polluted soil particles on the surrounding soil. S4: Based on the pollution dispersion, redetermine the remediation evaluation level for each area of ​​the mine.

2. The evaluation and analysis method for mine ecological restoration based on big data analysis according to claim 1, characterized in that: S1 includes: The information of each polluted area in the mine is determined, and each polluted area is marked in the initial pollution model. The corresponding initial pollution degree distribution data and initial particulate matter degree distribution data of each polluted area are loaded.

3. The evaluation and analysis method for mine ecological restoration based on big data analysis according to claim 2, characterized in that: S3 further includes: The wind force and corresponding wind direction data are imported into the soil drying pollution model to analyze the remediation particulate matter distribution data. The simulation determined the particle distribution of soil in each region within the drying time t.

4. The evaluation and analysis method for mine ecological restoration based on big data analysis according to claim 3, characterized in that: S4 further includes: Based on the particle size distribution of soil in each region within the determined drying time t, the corresponding particle size distribution range for each remediation evaluation level is determined, and the corresponding remediation evaluation level is generated.

5. A mine ecological restoration evaluation and analysis system based on big data analysis, characterized in that, include: An initial model building module acquires the initial pollution degree distribution data of contaminated soil and the initial particle size distribution data of contaminated soil corresponding to the initial pollution, and establishes an initial pollution model for soil in various areas of the mine. A drying model construction module collects soil drying degree data and controls the import of the soil drying degree data into the initial pollution model to generate a corresponding soil drying pollution model. Collect soil drying degree data, including: obtaining control signals affecting soil drying degree in each polluted area, and generating corresponding soil drying degree data based on the data affecting soil drying degree; The control signals that affect the degree of soil drying include at least one of temperature signals, humidity signals, and rainfall signals; Based on the data affecting soil drying degree, corresponding soil drying degree data are generated, including: Based on initial values ​​of ambient temperature, ambient humidity, and rainfall. and Difference in change and The control signals corresponding to the preset differences in changes in ambient temperature, ambient humidity, and rainfall are used to calculate the soil drying degree data, i.e., the formula is: ； in, Data on soil drying degree; and These are the control signal output values ​​corresponding to ambient temperature, ambient humidity, and rainfall, respectively. The output is 1 when the difference between the changes in ambient temperature, ambient humidity, or rainfall exceeds the preset difference, and 0 otherwise. The degree of drying factor corresponds to the difference in ambient temperature change per unit time. The drying factor corresponds to the difference in ambient humidity per unit time. The aridification factor corresponds to the difference in rainfall variation per unit time. The control process involves importing the soil drying degree data into the initial pollution model to generate a corresponding soil drying pollution model, including: Initial soil desiccation data for each region corresponding to the initial particulate matter distribution data of contaminated soil. Calculation based on the soil drying degree data Data on the distribution of the degree of repair drying and drying time t ,Right now And based on the repair drying degree distribution data Initial granularity distribution data Convert to corresponding repair granularity distribution data To determine the particle size of soil in each region within the drying time t; The wind simulation module collects environmental wind change data and simulates the distribution of polluted soil particles in the soil drying pollution model to determine the pollution dispersion of polluted soil particles on the surrounding soil. The rating module redetermines the remediation rating of each area of ​​the mine based on the pollution dispersion status.

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