Mine ecology detection method and system based on big data analysis

Abstract

The invention provides a mine ecology detection method and system based on big data analysis, belongs to the technical field of mine ecology monitoring, improves the precision and timeliness of mine ecology detection, and realizes accurate capture of multi-dimensional data. In view of the defect that traditional detection depends on experience judgment and cannot be quantified, a correlation analysis model composed of multiple sub-models is trained based on a historical data set, an LSTM neural network and a DQN reinforcement learning algorithm are adopted to train a governance repair optimization model, and immediate and future repair benefits are balanced by updating functions; a staged dust source positioning result, a dust diffusion prediction result and a restoration effect evaluation data set are input into the optimization model, an optimization scheme including dust fall equipment parameter adjustment and ecological restoration auxiliary measures is generated, and a dynamic optimization closed loop from data monitoring to precise treatment is achieved; and the detection result and the restoration measure can be linked, so that the mine dust treatment strategy is analyzed and optimized according to the mine ecological feedback.

Description

Technical Field

[0001] This invention relates to the field of mine ecological monitoring technology, specifically to a mine ecological monitoring method and system based on big data analysis. Background Technology

[0002] Mining processes such as drilling, blasting, ore crushing, and mineral processing generate large amounts of production dust (such as PM10 and PM2.5 with a diameter ≤10μm). When this dust is carried by the wind to the ecological restoration area, it covers the leaves of vegetation, forming a dust film. This not only hinders sunlight and reduces the efficiency of photosynthesis, but also blocks the stomata of the leaves, affecting respiration. In the long run, this can lead to vegetation withering and shedding. At the same time, dust deposited on the soil surface will change the soil pH value, reduce permeability, and affect seed germination and root growth. Its interference with mine ecological restoration is characterized by strong concealment and long duration.

[0003] Traditional detection methods rely heavily on manual sampling and observation, which can only obtain local dust concentrations or vegetation growth status. Furthermore, manual sampling is time-consuming, prone to errors, and fails to capture dynamic changes in dust. Judging dust sources and diffusion trends solely based on experience cannot quantify the dust contribution rate of each operational stage, nor can it accurately predict the extent of dust impact on ecological restoration areas. Moreover, existing systems often only have data monitoring functions and cannot link detection results with restoration measures, making it difficult to optimize mine dust control strategies based on mine ecological feedback analysis. Therefore, this paper proposes a mine ecological detection method based on big data analysis. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a mine ecological monitoring method based on big data analysis, which can link monitoring results with remediation measures, thereby enabling the optimization of mine dust control strategies based on mine ecological feedback analysis.

[0005] The technical solution adopted in this invention is as follows: A mine ecological monitoring method based on big data analysis includes the following steps: S1: Obtain historical basic data for mine ecological monitoring, including historical dust data, historical related ecological data, and historical related operational data; S2: Perform data calibration on the historical dust data, historical associated ecological data, and historical associated operational data from step S1; S3: Based on the historical dust data, historical associated ecological data, and historical associated operational data from step S2, train a big data analysis model to generate a correlation analysis model, which includes: Dust source localization sub-model: The random forest algorithm is adopted, and a multi-classification model is constructed by integrating decision trees. Historical dust data and historical related operation data are used as feature vectors to form a model training sample set. The dust contribution rate of each operation link is output through the decision tree voting mechanism to realize the quantitative localization of dust sources. Dust diffusion prediction sub-model: The computational fluid dynamics numerical simulation method is adopted, and a physical and mathematical coupled model is constructed by combining the atmospheric diffusion equation. Historical dust data and historical related operation data are input, boundary conditions are set, and the dust diffusion trajectory at different time periods is output by solving the continuity equation, momentum equation and diffusion equation. The sub-model for evaluating the restoration effect adopts an LSTM neural network and uses its time series data processing capabilities to build a regression evaluation model. The time series of historical related ecological data is used as training samples to label the restoration effect level for the corresponding time period. S4: Real-time acquisition of dynamic data from mine ecological monitoring. The dynamic data includes real-time dust data, real-time related ecological data, and real-time related operational data. The data is stored as a sub-dataset for each time period, with one day as the preset time period. S5: Summarize the time period subsets in step S4 of the 7-day cycle, generate a dynamic dataset, and then perform data calibration on the dynamic dataset; S6: Input the dynamic dataset from step S5 into the correlation analysis model generated in step S3 to generate phased dust source location results, phased dust diffusion prediction results, and phased remediation effect evaluation datasets within a 7-day period. S7: Based on the phased repair effect evaluation dataset described in step S6, and combined with the historical associated operation data described in step S1, the governance and repair optimization model is trained using LSTM neural network and DQN reinforcement learning algorithm. S8: Input the phased dust source location results, phased dust diffusion prediction results, and phased remediation effect evaluation dataset from step S6 into the governance and remediation optimization model in step S7 to generate an optimization scheme that includes dust suppression equipment parameter adjustments and ecological restoration auxiliary measures.

[0006] Furthermore, the real-time dust data includes TSP, PM2.5, and PM10 concentration data, which are acquired by laser scattering dust sensors. The laser scattering dust sensors are deployed along the perimeter of the mine restoration area and within 50-100m of the dust source, and the distance between two adjacent laser scattering dust sensors does not exceed 200m.

[0007] Furthermore, in steps S2 and S4, historical dust data, historical associated ecological data, historical associated operation data, and real-time dust data, real-time associated ecological data, and real-time associated operation data are all converted into JSON format.

[0008] Furthermore, the dust data calibration in step S2 includes: Calibration samples were acquired monthly using a gravimetric method and compared with concurrent data from a laser scattering dust sensor to calculate the calibration coefficient K. K = gravimetric measurement value / sensor measurement value. When |K~1| is greater than the calibration threshold, the historical dust data is corrected. The corrected data = original sensor data × K.

[0009] Furthermore, in step S4, Real-time dust data is acquired through laser scattering dust sensors deployed in the mine, and the data is transmitted to edge computing nodes in real time. Real-time ecological data is acquired once a day using mobile monitoring equipment, covering all fixed quadrats in the restoration area; Real-time correlated operation data is synchronously acquired through weather stations, equipment control systems, and vehicle positioning systems, and automatically summarized into time-period subsets daily.

[0010] Furthermore, in step S6, The phased dust source location results are output in the form of a pie chart, showing the dust contribution rate of each operation stage, and marking the key sources with a contribution rate of ≥20%. The phased dust diffusion prediction results output the 7-day average diffusion range, marking the affected ecological restoration areas and diffusion direction.

[0011] Furthermore, in step S1, the historical associated ecological data includes vegetation survival rate, leaf chlorophyll content, soil pH value, and soil surface dust deposition amount, and the historical associated operational data includes wind speed, wind direction, daily mining output, dust suppression measures intensity, historical remediation measures data, and historical remediation effect data.

[0012] A mine ecological monitoring system based on big data analysis, applied to the method described in any one of claims 1 to 7, comprising: The first data acquisition module is used to acquire historical basic data for mine ecological monitoring, including historical dust data, historical related ecological data, and historical related operation data, and generate a historical dataset. The calibration module calibrates the historical dust data, historical associated ecological data, and historical associated operational data in step S1. The first generation module is used to train a big data analysis model based on the historical dataset and generate a correlation analysis model. The second data acquisition module is used to acquire dynamic data of mine ecological monitoring in real time. The dynamic data includes real-time dust data, real-time associated ecological data and real-time associated operation data. It is preset that 1 day is a time period and is stored as a time period subset. The summary module is used to summarize the time period subsets in step S4 of the 7-day cycle and generate a dynamic dataset. The second generation module is used to input the dynamic dataset into the correlation analysis model generated in step S3 to generate the phased dust source location results, phased dust diffusion prediction results, and phased remediation effect evaluation dataset within the 7-day period. The third generation module is used to train a governance and repair optimization model based on the phased repair effect evaluation dataset and the historical associated operation data in step S1, using LSTM neural network and DQN reinforcement learning algorithm. The fourth generation module is used to input the phased dust source location results, the phased dust diffusion prediction results, and the phased remediation effect evaluation dataset into the governance and remediation optimization model in step S7, and generate an optimization scheme that includes dust reduction equipment parameter adjustments and ecological restoration auxiliary measures.

[0013] The beneficial effects of this invention are: This invention improves the accuracy and timeliness of mine ecological monitoring, and achieves accurate capture of multi-dimensional data. In addition, to address the shortcomings of traditional monitoring that rely on experience and cannot be quantified, this invention trains a correlation analysis model composed of multiple sub-models based on historical datasets. Furthermore, it uses LSTM neural networks and DQN reinforcement learning algorithms to train a governance and restoration optimization model, and balances the immediate and future restoration benefits through an update function. Subsequently, the phased dust source location results, dust diffusion prediction results, and remediation effect evaluation dataset are input into the optimization model to generate an optimization scheme that includes dust suppression equipment parameter adjustments and ecological restoration auxiliary measures, realizing a dynamic optimization closed loop from data monitoring to precise governance; it can link detection results with remediation measures, thereby realizing the optimization of mine dust control strategies based on mine ecological feedback analysis. Attached Figure Description

[0014] Figure 1 This is a flowchart of a mine ecological monitoring method based on big data analysis, according to an embodiment of the present invention. Figure 2 This is a flowchart of the training process for the historical data processing and correlation analysis model in an embodiment of the present invention. Figure 3 This is a flowchart illustrating the dynamic data processing and phased analysis process according to an embodiment of the present invention. Figure 4 This is a flowchart illustrating the training of the governance and remediation optimization model and the generation of measures in an embodiment of the present invention. Figure 5 This is a block diagram of a mine ecological monitoring system module based on big data analysis, according to an embodiment of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] like Figures 1-5 As shown in the figure, a mine ecological monitoring method based on big data analysis according to an embodiment of the present invention includes the following steps: S1: Obtain historical basic data for mine ecological monitoring. Historical basic data includes historical dust data, historical related ecological data, and historical related operational data. Specifically, historical ecological data includes vegetation survival rate, leaf chlorophyll content, soil pH value, and soil surface dust deposition. Historical operational data includes wind speed, wind direction, daily mining output, dust suppression measures intensity, historical remediation measures data, and historical remediation effect data.

[0017] S2: Perform data calibration on the historical dust data, historical associated ecological data, and historical associated operational data from step S1; Historical dust data calibration includes: acquiring calibration samples monthly using a gravimetric method, comparing them with data from the same period from a laser scattering dust sensor, and calculating the calibration coefficient. ,when Greater than the threshold At that time, historical dust data is corrected, and the corrected data is... ; in, for ; Threshold (preset 0.1, i.e., when...) Correction is triggered when the deviation from 1 exceeds 10%. In addition, historical ecological data and historical operational data are historical measurements stored in the system logs. Only data type calibration is needed. Unifying the data types of historical ecological data and historical operational data will facilitate the training of big data analysis models.

[0018] S3: Based on the historical dust data, historical related ecological data, and historical related operational data from step S2, train a big data analysis model to generate a correlation analysis model. The correlation analysis model includes: A random forest algorithm is used to generate a dust source localization sub-model. The random forest consists of n decision trees (n is preset to 100), and the input is a feature vector of historical dust data. ( (This refers to the feature vectors of TSP, PM2.5, PM10 concentrations and spatiotemporal distribution) and historical associated operational data. ( (Daily mining output and dust suppression measures intensity) through The system integrates and outputs the dust contribution rate of each operational stage. Let be the output vector of the k-th decision tree.

[0019] Input historical dust data and historical related operation data from the historical dataset, and output the dust contribution rate of each operation link as the input of the phased source location result in step S6; when the number of new samples in the historical dataset exceeds 10% of the total number of samples, the model is automatically retrained.

[0020] A dust diffusion prediction sub-model was generated using CFD numerical simulation, based on the continuity equation. Momentum equation and diffusion equation Input historical dust data and environmental data, and output the dust diffusion trajectory. ; In the formula, air density, For time, For real-time wind speed and direction, Air pressure. Aerodynamic viscosity, It is the vector of gravitational acceleration; Dust concentration, The dust diffusion coefficient; This is the dust source item, calculated from the phased dust source location results of step S6. Among them, Positively correlated with dust particle size, PM2.5 is taken as 1.5 × 10⁻ 5 m² / s, PM10 is taken as 8×10⁻ 6 m² / s.

[0021] Input real-time dust data and real-time associated operation data from the dynamic dataset; output dust diffusion trajectory, including spatial coordinates (x, y, z) and concentration distribution C(x, y, z, t) over time t, and 7-day average diffusion range.

[0022] It should be noted that the time series of historical ecological data includes monthly changes in vegetation survival rate, leaf chlorophyll content, soil pH, and soil surface dust deposition.

[0023] An LSTM neural network is used to generate a sub-model for evaluating the repair effect, using a forgetting gate. Input gate and output gate Construct memory units and input historical related ecological data time series. ,pass Output repair effect levels 1-5; Among them, the restoration effect level 1 is: vegetation survival rate <50%, soil pH value <5.0 or >8.5, and soil surface dust deposition >500g / m²; Level 2 restoration effect: vegetation survival rate 50%~69%, soil pH value 5.0~5.5 or 8.0~8.5, soil surface dust deposition 300~500g / m²; Level 3 restoration effect: vegetation survival rate 70%~84%, soil pH value 5.5~6.5 or 7.0~8.0, and surface dust deposition 100~300g / m². Level 4 restoration effect: vegetation survival rate 85%~94%, soil pH value 6.5~7.0, and surface dust deposition 50~100g / m². Level 5 restoration effect: vegetation survival rate ≥95%, soil pH value 6.5~7.0, and surface dust deposition <50g / m².

[0024] Specifically, input the time series of related ecological data from historical or dynamic datasets. Output the interim evaluation results after the 7-day dynamic dataset is generated.

[0025] S4: Real-time acquisition of dynamic data from mine ecological monitoring. The dynamic data includes real-time dust data, real-time related ecological data, and real-time related operational data. The data is stored as a sub-dataset for each time period, with one day as the preset time period. S5: Summarize the time period subsets in step S4 of the 7-day cycle, generate a dynamic dataset, and then perform data calibration on the dynamic dataset; The data calibration for the dynamic dataset is the same as the data calibration step in step S2.

[0026] S6: Input the dynamic dataset from step S5 into the correlation analysis model generated in step S3 to generate the phased dust source location results, phased dust diffusion prediction results, and phased remediation effect evaluation dataset within the 7-day period. The phased dust source location results are output in the form of a pie chart showing the dust contribution rate of each operation stage, and key sources with a contribution rate of ≥20% are marked; the phased dust diffusion prediction results output the 7-day average diffusion range, and mark the affected ecological restoration areas and diffusion direction.

[0027] S7: Based on the phased remediation effect evaluation dataset from step S6, and combined with the historical associated operation data from step S1, an LSTM neural network and DQN reinforcement learning algorithm are used to train the governance and remediation optimization model. DQN passed Update Q-value function ( As an ecological state, As a repair measure, (for reward value) In the formula, To control the update magnitude for the learning rate, The discount factor balances immediate rewards with future rewards; The optimal action value for the next state is determined by this. The governance and remediation optimization model is trained using historical remediation measure data and historical remediation effect data, and iterates continuously. The value, ultimately in the current state Choose below The action with the highest value This is the optimal measure.

[0028] S8: Input the phased dust source location results, phased dust diffusion prediction results, and phased remediation effect evaluation dataset from step S6 into the governance and remediation optimization model in step S7 to generate an optimization scheme that includes dust suppression equipment parameter adjustments and ecological restoration auxiliary measures.

[0029] Specifically, in step S4, dynamic data of mine ecological monitoring is acquired in real time, including: acquiring TSP, PM2.5 and PM10 concentration data through a laser scattering dust sensor with an accuracy of 0.1 μg / m³. The laser scattering dust sensor is deployed along the perimeter of the mine restoration area and within 50-100m of the dust source, and the distance between two adjacent laser scattering dust sensors does not exceed 200m.

[0030] Specifically, in step S4, the real-time acquisition of dynamic data includes: real-time dust data is acquired through an IoT sensor network deployed in the mine, with an acquisition frequency set to once every 2 minutes, and the data is transmitted to the edge computing node in real time; real-time associated ecological data is acquired once a day using mobile monitoring equipment, covering all fixed sample plots in the remediation area; and real-time associated operational data is acquired synchronously through weather stations, equipment control systems, and vehicle positioning systems, and is automatically summarized into time period subsets daily.

[0031] Sources contributing ≥30% of dust are prioritized for treatment, with specific measures such as adjusting dust suppression equipment parameters to their upper limits: spray pressure 1.2 MPa, flow rate 20 L / min. Sources contributing 20%-29% of dust are then treated, with parameters adjusted to the mid-to-upper limits: pressure 0.8-1.0 MPa, flow rate 15-18 L / min. Simultaneous manual dust removal measures are implemented.

[0032] In addition, the vegetation survival rate is verified by random sampling. For example, 30 plants are sampled from each quadrat, and the deviation between the actual number of surviving plants and the statistical number of vegetation monitoring sensors is calculated. If the deviation is greater than 5%, it is corrected to the actual value.

[0033] Soil pH values ​​are measured monthly using a laboratory potentiometric method on calibrated samples, and compared with field sensor data. The calibration coefficient K = laboratory value divided by sensor value; corrections are made when K-1 > 0.05. Daily mining output is measured by comparing weighbridge data with statistical data from the equipment control system; if the deviation > 3%, the weighbridge data is used for correction.

[0034] A mine ecological monitoring system based on big data analysis includes: The first data acquisition module is used to acquire historical basic data for mine ecological monitoring. The historical basic data includes historical dust data, historical related ecological data, and historical related operation data, and generates a historical dataset. The calibration module calibrates the historical dust data, historical associated ecological data, and historical associated operational data in step S1. The first generation module is used to train a big data analysis model based on historical datasets and generate a correlation analysis model. The second data acquisition module is used to acquire dynamic data of mine ecological monitoring in real time. The dynamic data includes real-time dust data, real-time related ecological data and real-time related operation data. It is preset to store the data as a time period of 1 day as a time period subset. The summary module is used to summarize the time period subsets in step S4 of the 7-day cycle and generate a dynamic dataset. The second generation module is used to input the dynamic dataset into the correlation analysis model generated in step S3 to generate the phased dust source location results, phased dust diffusion prediction results, and phased remediation effect evaluation dataset within the 7-day period. The third generation module is used to train the governance and remediation optimization model based on the phased remediation effect evaluation dataset and combined with the historical associated operation data in step S1, using LSTM neural network and DQN reinforcement learning algorithm. The fourth generation module is used to input the phased dust source location results, phased dust diffusion prediction results, and phased remediation effect evaluation dataset into the governance and remediation optimization model in step S7, and generate an optimization plan that includes dust suppression equipment parameter adjustments and ecological restoration auxiliary measures.

[0035] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0036] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0037] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0038] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0039] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0040] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0041] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0042] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A mine ecological detection method based on big data analysis, characterized in that, Comprise the following steps: S1: obtain the historical basic data of mine ecological detection, the historical basic data includes historical dust data, historical associated ecological data, historical associated operation data; S2: the historical dust data, historical associated ecological data, historical associated operation data in step S1 are calibrated; S3: based on the historical dust data, historical associated ecological data, historical associated operation data in step S2, train a big data analysis model, generate an associated analysis model, the associated analysis model includes: Dust source positioning submodel: adopt random forest algorithm, construct multi-classification model through integrated decision tree, use historical dust data and historical associated operation data as feature vector, form model training sample set, output dust contribution rate of each operation link through decision tree voting mechanism, realize quantitative positioning of dust source; Dust diffusion prediction submodel: adopt computational fluid dynamics numerical simulation method, combine atmospheric diffusion equation to construct physical and mathematical coupling model, input historical dust data and historical associated operation data, set boundary conditions, output dust diffusion trajectory at different time periods by solving continuity equation, momentum equation and diffusion equation; Repair effect evaluation submodel, adopt LSTM neural network, utilize its time series data processing capability to construct regression evaluation model, use time series sequence of historical associated ecological data as training sample, label repair effect grade of corresponding period; S4: real-time acquisition of dynamic data of mine ecological detection, dynamic data includes real-time dust data, real-time associated ecological data and real-time associated operation data, preset 1 day as 1 period to be stored as period sub-data set; S5: the period sub-data set in step S4 in 7-day cycle is summarized, a dynamic data set is generated, and then the dynamic data set is calibrated; S6: the dynamic data set in step S5 is input into the associated analysis model generated in step S3, and stage dust source positioning result, stage dust diffusion prediction result and stage repair effect evaluation data set in 7-day cycle are generated; S7: based on the stage repair effect evaluation data set in step S6, combined with the historical associated operation data in step S1, adopt LSTM neural network and DQN reinforcement learning algorithm to train governance and repair optimization model; S8: the stage dust source positioning result, the stage dust diffusion prediction result and the stage repair effect evaluation data set in step S6 are input into the governance and repair optimization model in step S7, and a measure optimization scheme including dust reduction equipment parameter adjustment and ecological repair auxiliary measures is generated. 2.The mine ecological detection method based on big data analysis according to claim 1, characterized in that, The real-time dust data includes TSP, PM2.5 and PM10 concentration data, which are obtained by a laser scattering dust sensor, the laser scattering dust sensor is arranged along the periphery of the mine repair area and within 50-100m of the dust source, the distance between two adjacent laser scattering dust sensors is not more than 200m. 3.The mine ecological detection method based on big data analysis according to claim 1, characterized in that, In steps S2 and S4, the historical dust data, historical associated ecological data, historical associated operation data and real-time dust data, real-time associated ecological data and real-time associated operation data are all converted into JSON format. 4.The mine ecological detection method based on big data analysis according to claim 2, characterized in that, The dust data calibration in step S2 includes: Synchronously obtain calibration samples every month by weight method, compare with the synchronous data of the laser scattering dust sensor, calculate the calibration coefficient K: K = weight method determination value / sensor determination value, when |K ~ 1| is greater than the calibration threshold, correct the historical dust data, and the corrected data = sensor original data * K. 5.The mine ecological detection method based on big data analysis according to claim 2, characterized in that, In step S4, Real-time dust data is obtained by deploying a laser scattering dust sensor in the mine, and the data is transmitted to the edge computing node in real time; Real-time associated ecological data is obtained once a day by a mobile monitoring device, covering all fixed sample plots in the restoration area; Real-time associated operation data is synchronously obtained through a weather station, a device control system and a vehicle positioning system, and is automatically summarized into a time period sub-data set every day. 6.The mine ecological detection method based on big data analysis according to claim 1, characterized in that, In step S6, The phase dust source positioning result is output in the form of a pie chart, and the key sources with a contribution rate of more than 20% are marked; The phase dust diffusion prediction result outputs the 7-day average diffusion range, and marks the affected ecological restoration area and the diffusion direction. 7.The mine ecological detection method based on big data analysis of claim 1, wherein, In step S1, the historical associated ecological data includes vegetation survival rate, leaf chlorophyll content, soil pH value and soil surface dust deposition amount, and the historical associated operation data includes wind speed, wind direction, mine daily production, dust suppression measure intensity, historical restoration measure data and historical restoration effect data.

8. A mine ecological detection system based on big data analysis, applied to the method of any one of claims 1-6, characterized in that, It includes: A first data acquisition module is configured to acquire historical basic data of mine ecological detection, wherein the historical basic data includes historical dust data, historical associated ecological data and historical associated operation data, and a historical data set is generated; A calibration module is configured to calibrate the historical dust data, the historical associated ecological data and the historical associated operation data in step S1; A first generation module is configured to train a big data analysis model based on the historical data set, and generate an associated analysis model; A second data acquisition module is configured to acquire dynamic data of mine ecological detection in real time, wherein the dynamic data includes real-time dust data, real-time associated ecological data and real-time associated operation data, and 1 day is preset as a time period to be summarized and stored as a time period sub-data set; A summary module is configured to summarize the time period sub-data set in step S4 in a 7-day cycle, and generate a dynamic data set; A second generation module is configured to input the dynamic data set into the associated analysis model generated in step S3, and generate a phase dust source positioning result, a phase dust diffusion prediction result and a phase restoration effect evaluation data set in the 7-day cycle; A third generation module is configured to train a governance and restoration optimization model based on the phase restoration effect evaluation data set and the historical associated operation data in step S1, using an LSTM neural network and a DQN reinforcement learning algorithm; A fourth generation module is configured to input the phase dust source positioning result, the phase dust diffusion prediction result and the phase restoration effect evaluation data set into the governance and restoration optimization model in step S7, and generate a measure optimization scheme including dust suppression equipment parameter adjustment and ecological restoration auxiliary measures.

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