An intelligent management and control method and system for mine pit gully repair

Abstract

The application discloses a kind of mine gully repair intelligent management and control method and system.The method comprises: obtaining mine three-dimensional digital base map;Real-time physical and chemical parameters of fly ash and coal gangue are obtained;Establish spontaneous combustion, stability, pollution risk quantization model, input parameter to obtain risk data;Based on three-dimensional digital base map, identify the dominant risk of each area and allocate weight, combine engineering and ecological target, solve by optimization algorithm, output the optimal mixing ratio of fly ash and coal gangue, target water content and compaction degree, and generate dynamic matching instruction after smoothing treatment.The present application realizes the whole-process intelligent management and control from material pretreatment to ecological restoration, can preventively inhibit spontaneous combustion, control settlement, prevent pollution, and improve the safety and ecological sustainability of repair engineering.

Description

Technical Field

[0001] This invention relates to the field of mine pit gully restoration technology, and in particular to an intelligent management and control method and system for mine pit gully restoration. Background Technology

[0002] Coal, as my country's primary energy source, generates massive amounts of solid waste such as coal gangue and fly ash during its mining process. Statistics show that my country's cumulative coal gangue stockpiles have exceeded 6 billion tons, and this figure continues to grow by hundreds of millions of tons annually. Long-term open-air stockpiling of coal gangue not only occupies vast amounts of land resources, but its components, such as pyrite, are highly susceptible to spontaneous combustion under suitable conditions, releasing harmful gases like SO2 and CO, and potentially triggering the formation of acid mine wastewater (AMD), causing severe heavy metal pollution to surrounding soil and water bodies. Meanwhile, fly ash, a byproduct of coal-fired power plants, is lightweight and easily generates dust, posing similar environmental risks when stockpiled.

[0003] On the other hand, underground and open-pit mining have created a large number of mining subsidence areas and open-pit mines, which have damaged the original topography and ecosystem, causing geological disaster risks such as surface collapse, landslides, and soil erosion, and seriously restricting land reclamation and ecological restoration in mining areas and surrounding areas.

[0004] To address the aforementioned problems, existing technologies have proposed remediation schemes using industrial solid wastes such as coal gangue and fly ash for mine pit backfilling. For example, this involves directly backfilling coal gangue into the mine pit, or injecting cementing materials into the coal gangue to form a solidified body, thereby reducing the contact between the coal gangue and air, lowering the risk of spontaneous combustion, and reducing the leaching of pollutants. However, these methods have the following limitations: 1. Insufficient risk control capabilities: Existing backfilling schemes mostly rely on empirical and static proportioning and construction techniques, lacking the ability to perceive the internal state of the fill material in real time. The spontaneous combustion process of coal gangue is delayed and concealed, and traditional periodic inspections and surface temperature measurements are insufficient for effective early warning and intervention before spontaneous combustion occurs.

[0005] 2. Difficulty in ensuring engineering stability: Backfilling with coal gangue alone results in poor compaction and uneven settlement; backfilling with fly ash alone leads to insufficient strength and poor stability. Although some technologies propose mixing fly ash and coal gangue, the mixing ratio is mostly determined based on experience, failing to dynamically adjust according to real-time physicochemical parameters such as the pyrite content of coal gangue and the alkalinity neutralization capacity of fly ash. This makes it difficult to effectively guarantee the long-term physical stability and chemical safety of the filler.

[0006] 3. Uncertainty in ecological restoration effect: Existing restoration methods usually separate the filling operation from the topsoil covering and vegetation restoration. After the filling is formed, the soil is directly covered and planted, ignoring the impact of possible chemical stresses (such as heavy metal leaching and abnormal pH) inside the filling on plant root growth, resulting in low vegetation survival rate and unsustainable ecological restoration effect.

[0007] 4. Low level of intelligent management and control: At present, the entire process of mine pit restoration lacks a systematic data collection, analysis and decision support platform. There are serious information silos between various processes, making it impossible to achieve closed-loop management and control of the entire process from material pretreatment and precise filling to ecological cover construction.

[0008] Therefore, how to overcome the above-mentioned defects of the existing technology and provide a method and system that can prevent spontaneous combustion, control settlement, prevent pollution, and realize intelligent and precise management and control of the mine pit restoration process has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0009] The present invention aims to overcome the shortcomings of the prior art and solve the technical problems in the backfilling of mine pits and trenches that cannot prevent spontaneous combustion, control settlement and prevent pollution, as well as the problem that the lack of systematic and intelligent management and control in the mine pit restoration process leads to uncertainty in the ecological restoration effect.

[0010] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: In a first aspect, the present invention provides an intelligent management and control method for mine pit gully repair, comprising the following steps: Obtain a 3D digital base map of the target mine pit and gullies; Obtain real-time physicochemical parameters of fly ash and coal gangue used for backfilling; Establish a risk quantification model system, which includes at least a spontaneous combustion risk model, a stability risk model, and a pollution risk model; The real-time materialization parameters are input into the risk quantification model system for normalization and weighted calculation. Combined with the environmental information of the three-dimensional digital base map, structured risk data is obtained. Based on the three-dimensional digital base map, the dominant risk types in different areas of the target mine pit and gully are identified, and optimized weight coefficients are dynamically assigned to each area to determine the priority of risk control and ecological restoration in that area. Using preset engineering and ecological goals as decision variables and preset process constraints as constraint boundaries, the optimization algorithm solves the problem based on the priorities and risk data, and outputs the process parameters of optimal mixing ratio of fly ash and coal gangue, target moisture content and compaction degree. The process parameters are smoothed in space and time to generate dynamic balancing instructions to guide construction.

[0011] As a preferred technical solution, the method further includes: The target mine pit trench is filled according to the dynamic balancing command, and a sensor network is deployed in the filling body to continuously monitor the physical stability and chemical safety of the filling body. Based on the monitoring data from the sensor network, the spontaneous combustion risk of the filler is assessed and classified in real time, and corresponding risk warnings are issued according to the spontaneous combustion risk level.

[0012] As a further preferred technical solution, the real-time assessment and classification of the spontaneous combustion risk of the filler specifically includes: The temperature and oxygen concentration data inside the filling material are acquired in real time, and the spatial temperature gradient and the heating trend at specific points are calculated. Based on the data, multiple early warning indicators are calculated, including at least: determining whether there are sensor locations that exceed a preset absolute temperature threshold; determining whether there are abnormal spatial temperature gradient regions that exceed a preset gradient threshold; and determining whether there are specific sensor locations whose temperature values ​​have a heating rate exceeding a preset trend threshold over a continuous time period. Correlation analysis and cross-validation of oxygen concentration data were performed on the locations that triggered temperature warnings; When only one of the absolute temperature threshold, gradient threshold, or trend threshold is triggered, it is determined to be a medium-level warning; When the absolute temperature threshold and either the gradient threshold or the trend threshold are triggered simultaneously, it is determined to be a high-level warning; When the conditions for a high-level warning are met, and cross-verification confirms that the oxygen concentration at the corresponding location has decreased significantly and synchronously, it is determined to be a severe alarm.

[0013] In a second aspect, the present invention provides a mine pit gully repair system for performing the intelligent control method for mine pit gully repair as described in any of the above claims, comprising: Pre-processing center, operation module, overlay building unit, and monitoring and maintenance platform; The pretreatment center is used for online detection and dynamic proportioning of backfill materials, and includes: The monitoring module is used for online analysis of the pyrite content of coal gangue and the chemical composition of fly ash; A humidity sensor is used to monitor the real-time moisture content of fly ash. The central control unit has built-in processing logic for the above method, used to generate the dynamic balancing command; A conditioning device, connected to the central control unit, is used to add water or dust suppressant to fly ash according to the dynamic balancing command, so as to adjust it to the target state. The operation module is connected to the preprocessing center and is used to perform filling operations according to the dynamic balancing instructions. It includes: A GNSS positioning system, installed on paving equipment and rollers, is used to precisely control the paving thickness and compaction trajectory according to the dynamic balancing instructions. The sensor network includes multiple sensors arranged in a preset grid within the filling material during the filling process, used to monitor the temperature, oxygen concentration, and humidity data inside the filling material in real time, and transmit the data back to the monitoring and maintenance platform. The covering construction unit is used to construct an ecological soil layer suitable for plant growth on the surface of the filled body after filling, and includes: The decision module is used to obtain the leaching toxicity test results of fly ash and, based on the results, determine whether an artificial anti-seepage lining layer needs to be laid before constructing the ecological soil layer. The database stores climate, soil, and native plant information for the target mine pit gully area, and is used to recommend the optimal soil cover thickness and pioneer plant species combination based on the judgment results of the decision module. The monitoring and maintenance platform is connected to the pretreatment center and the operation module respectively, and is used to collect, analyze and provide risk warnings for the entire process from material pretreatment to ecological restoration.

[0014] Thirdly, the present invention provides a dynamic balancing device for mine pit gully repair, comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the method described above.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. Implement preventative risk management This invention establishes three quantitative models—spontaneous combustion risk, stability risk, and pollution risk—and combines them with real-time physicochemical parameters for forward-looking risk prediction. It can proactively identify potential risks before and during construction, and by dynamically adjusting the proportion, moisture content, and compaction degree of the filling material, it can suppress the spontaneous combustion tendency of coal gangue, control uneven settlement, and prevent heavy metal pollution from the source, thus achieving a fundamental shift from "passive response" to "proactive prevention."

[0016] 2. Improve project stability and safety This invention dynamically generates the optimal mixing ratio through a multi-objective optimization algorithm, matching the alkalinity neutralization capacity of fly ash with the acid-producing potential of coal gangue, effectively reducing the risk of acidic leachate formation. Simultaneously, by precisely controlling the target moisture content and compaction degree, the density and mechanical strength of the filler are significantly improved, ensuring long-term engineering stability. Experimental verification shows that the filler formed using this method exhibits superior key indicators such as compressive strength and permeability coefficient compared to traditional empirical mixing schemes.

[0017] 3. Construct a fully intelligent closed-loop management system. This invention establishes a precise digital twin model of the restoration area using a three-dimensional digital base map, serving as the spatial benchmark for all decisions; it achieves online material detection and dynamic proportioning through a pre-processing center; it enables precise control and status awareness of the filling process through operational modules; it facilitates scientific decision-making for ecological restoration through overlay construction units; and it enables full-process data aggregation and risk warning through a monitoring and maintenance platform. These five modules work collaboratively to form a complete closed loop of perception-prediction-decision-execution-feedback, achieving intelligent and precise management and control of the mine restoration process.

[0018] 4. Achieve multi-level early warning and graded response for spontaneous combustion risk. This invention constructs a multi-level spontaneous combustion risk early warning system by deploying a sensor network in a grid pattern within the filling material to monitor key parameters such as temperature and oxygen concentration in real time. Combined with multi-dimensional early warning indicators such as absolute temperature thresholds, spatial temperature gradients, and temperature rise trends, the system effectively avoids false alarms and missed alarms through cross-validation of oxygen concentration data. Corresponding response measures can be taken according to different early warning levels, significantly improving the ability to prevent and control spontaneous combustion risks.

[0019] 5. Ensure the long-term sustainability of ecological restoration. This invention introduces a decision-making mechanism in the capping construction stage, scientifically determining whether an artificial impermeable lining layer is needed based on the leaching toxicity test results of fly ash, effectively blocking the migration pathway of pollutants into the topsoil. Simultaneously, by combining regional climate, soil, and native plant information databases, it recommends the optimal capping thickness and pioneer plant species combination, significantly improving vegetation survival rate and ecological restoration effects, achieving an organic unity between engineering safety and ecological sustainability. Attached Figure Description

[0020] Figure 1 This is a flowchart of the intelligent management and control method for mine pit and gully restoration disclosed in the embodiments of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0022] I. System Overall Architecture like Figure 1 As shown in the figure, an intelligent management and control system for mine pit and gully repair provided by an embodiment of the present invention includes: a pretreatment center 100, an operation module 200, a cover layer construction unit 300, and a monitoring and maintenance platform 400.

[0023] The pretreatment center 100 is used for online detection and dynamic proportioning of backfill materials. It includes a monitoring module 110, a humidity sensor 120, a central control unit 130, and a conditioning device 140. The monitoring module 110 preferably employs a near-infrared spectrometer for online analysis of the pyrite content of coal gangue and the chemical composition of fly ash (such as CaO, SiO2, Al2O3, etc.). The humidity sensor 120 monitors the real-time moisture content of the fly ash. The central control unit 130 incorporates the intelligent control method logic of this invention to generate dynamic balancing instructions. The conditioning device 140, according to the instructions from the central control unit 130, adds water or dust suppressant to the fly ash to adjust it to the target state.

[0024] The operation module 200 is connected to the pre-processing center 100 and is used to perform filling operations according to dynamic balancing instructions. It includes a GNSS positioning system 210 and a sensor network 220. The GNSS positioning system 210 is installed on the paving equipment and roller to precisely control the paving thickness and compaction trajectory. The sensor network 220 includes multiple sensors deployed in the filling body according to a preset grid (e.g., 10m × 10m) during the filling process to monitor the temperature, oxygen concentration, and humidity data inside the filling body in real time, and transmits the data back to the monitoring and maintenance platform 400 wirelessly.

[0025] The cover layer construction unit 300 is used to construct an ecological soil layer suitable for plant growth on the surface of the filled body after filling. It includes a decision module 310 and a database 320. The decision module 310 is used to obtain the leaching toxicity test results of fly ash and, based on the results, determine whether an artificial impermeable lining layer needs to be laid before constructing the ecological soil layer. The database 320 stores climate, soil, and native plant information of the target mine pit gully area and is used to recommend the optimal cover thickness and pioneer plant species combination based on the judgment results of the decision module 310.

[0026] The monitoring and maintenance platform 400 is connected to the pretreatment center 100 and the operation module 200, respectively, and is used for data aggregation, analysis, and risk warning of the entire process from material pretreatment to ecological restoration. The monitoring and maintenance platform 400 includes a data server, an application server, and an early warning terminal, and can realize functions such as real-time data display, historical data query, and early warning information push.

[0027] II. Specific Implementation Steps of Intelligent Control Methods The implementation steps of the intelligent management and control method for mine pit and gully restoration provided by the present invention will be described in detail below with reference to specific embodiments.

[0028] Step 100: Obtain a 3D digital base map of the target mine pit and gullies. Aerial surveying using unmanned aerial vehicles (UAVs) and ground-penetrating radar (GPR) was employed to conduct detailed mapping of the target mine pit and gullies. Specifically, the UAV, equipped with a five-lens oblique photography camera, flew at an altitude of 120m with a forward overlap of 80% and a lateral overlap of 70%, acquiring high-resolution image data. The GPR, using a 100MHz shielded antenna, probed along a pre-defined survey line, acquiring geological structure information within a 0-20m depth. The acquired data was processed using 3D modeling software to generate a 3D digital base map containing topography, slope, and hydrogeological conditions, serving as the spatial reference for all subsequent decision-making.

[0029] Step 200: Obtain real-time physicochemical parameters of fly ash and coal gangue, and conduct a risk assessment. When backfill materials arrive at the site, the real-time physicochemical parameters of fly ash and coal gangue are obtained online through the monitoring module 110 of the pretreatment center 100. Taking a certain mining area as an example, the pyrite content of coal gangue is 3.2%, and the calorific value is 4.8 MJ / kg; the CaO content of fly ash is 8.5%, and the loss on ignition is 3.2%.

[0030] Establish a risk quantification model system, including a spontaneous combustion risk model, a stability risk model, and a pollution risk model: Spontaneous combustion risk model: Based on parameters such as pyrite content, calorific value, and particle size distribution of coal gangue, and combined with the theory of thermal spontaneous combustion, a multiphysics field model is constructed that couples oxygen transport, exothermic oxidation reaction, and heat dissipation through pores to predict the spontaneous combustion tendency index Rfire (with a value ranging from 0 to 1, where a larger value indicates a higher risk of spontaneous combustion) under different stacking conditions. In this embodiment, Rfire is calculated to be 0.65.

[0031] Stability risk model: Mechanical parameters (internal friction angle, cohesion, and compression modulus) of fly ash and coal gangue were obtained through indoor geotechnical tests. Based on the terrain and slope conditions provided by the three-dimensional digital base map, the safety factor under different filling ratios was analyzed using finite element numerical simulation to obtain the stability risk index Rstability (value range 0-1). In this embodiment, Rstability = 0.35 was calculated.

[0032] Pollution risk model: By analyzing the acid production potential of coal gangue and the alkaline neutralization potential of fly ash through static leaching experiments, a hydrogeochemical coupling model was constructed to simulate the leaching and migration process of pollutants under rainfall infiltration conditions, and the pollution risk index Rpollution (range 0-1) was obtained. In this embodiment, Rpollution was calculated to be 0.42.

[0033] The three risk indices are normalized and then weighted and summed according to preset weighting coefficients to obtain structured risk data R: In this embodiment, weights W1=0.4, W2=0.3, and W3=0.3 are set according to the characteristics of the project, and R=0.4×0.65+0.3×0.35+0.3×0.42=0.491 is calculated.

[0034] Step 300: Generate dynamic balancing instructions Based on a 3D digital base map, the dominant risk types in different areas of the target mine pit are identified. In this embodiment, the mine pit slope area is mainly characterized by stability risk (weighting coefficient W2 is increased to 0.6), the bottom water catchment area is mainly characterized by pollution risk (weighting coefficient W2 is increased to 0.5), and other areas are mainly characterized by spontaneous combustion risk (weighting coefficient W1 is increased to 0.5).

[0035] Using pre-set engineering objectives (e.g., compaction degree ≥93%, bearing capacity ≥150kPa) and ecological objectives (e.g., pH value 6.5-8.5, heavy metal leaching concentration below national standard limits) as decision variables, and process constraints (e.g., fly ash content 20%-50%, moisture content 15%-25%) as boundary constraints, a genetic algorithm was used to perform multi-objective optimization based on the risk priority of each region, outputting the process parameters for different regions. The optimization results are shown in Table 1. Table 1 Dynamic Balancing Instructions for Different Regions Spatial interpolation and smoothing are performed on the above process parameters to eliminate parameter jumps between adjacent regions, generate continuous and executable dynamic balancing instructions, and send them to the preprocessing center 100 and the operation module 200.

[0036] Step 400: Perform the filling operation and monitor in real time. According to the dynamic balancing instructions, the pretreatment center 100 adds water or dust suppressant to the fly ash through the conditioning device 140 to adjust its moisture content to the target value, and mixes it evenly with the coal gangue in the optimal ratio. The operation module 200 receives the mixed backfill material and adopts a "sandwich" alternating paving process: first, a 0.4-0.6 meter thick layer of coal gangue is laid, followed by a 0.2-0.3 meter thick layer of fly ash, with the total height of a single layer controlled at 15-20 meters. The GNSS positioning system 210 guides the paving equipment and roller in real time to ensure that the paving thickness and compaction trajectory meet the design requirements.

[0037] During the filling process, a sensor network 220 is deployed in a 10m×10m grid, and temperature, oxygen concentration, and humidity sensors are buried at different depths (1m, 3m, and 5m below the surface) in the filling body. Data is collected in real time and transmitted back to the monitoring and maintenance platform 400 via wireless network.

[0038] Step 500: Spontaneous Combustion Risk Warning The monitoring and maintenance platform 400 processes and analyzes the real-time collected data. Taking a certain sensor location as an example, the temperature measured at a certain moment is 42℃ and the oxygen concentration is 19.5%. The calculated temperature rise rate of this location over the past 24 hours is 1.8℃ / day, and the spatial temperature gradient calculated from the adjacent sensors is 3.2℃ / m.

[0039] The judgment is made based on the preset warning thresholds (absolute temperature threshold 60℃, gradient threshold 5℃ / m, trend threshold 2℃ / day): Absolute temperature 42℃ < 60℃, not triggered; Gradient 3.2℃ / m < 5℃ / m, not triggered; Trend 1.8℃ / day < 2℃ / day, not triggered.

[0040] The current status is normal and no warning is triggered.

[0041] Suppose that on a certain day, the temperature at this location is measured to rise to 58℃, with a heating rate of 2.5℃ / day and a spatial temperature gradient of 4.8℃ / m. At this time: Absolute temperature 58℃ < 60℃, not triggered; Gradient 4.8℃ / m < 5℃ / m, not triggered; The trend threshold is triggered when the temperature rises from 2.5℃ / day to 2℃ / day.

[0042] The alert level was determined to be medium. The monitoring and maintenance platform 400 issued a visual alarm on the monitoring interface and increased the data collection frequency from once per hour to once every 10 minutes.

[0043] If the temperature continues to rise to 62℃, and the spatial temperature gradient reaches 5.2℃ / m, then: If the absolute temperature is 62℃ > 60℃, trigger action. Gradient 5.2℃ / m > 5℃ / m, triggering; Trend 2.5℃ / day > 2℃ / day, triggered.

[0044] Simultaneous triggering of both the absolute temperature threshold and the gradient threshold indicates a high-level warning. The system automatically sends a remote alarm message to the administrator and activates the pre-installed inert gas injection system to inject nitrogen into the abnormal area to suppress the oxidation reaction.

[0045] If, under advanced warning conditions, the oxygen concentration at the corresponding location is detected to have dropped significantly from 19.5% to 15.2%, and cross-verification confirms that a spontaneous combustion reaction is occurring, it is classified as a severe alarm. The system operates the inert gas injection system at maximum power and activates the spray cooling system in conjunction with it to spray inhibitor slurry onto the abnormal area, achieving rapid fire extinguishing and cooling.

[0046] Step 600: Cover Construction and Ecological Restoration After the fill material is stockpiled and stabilized naturally for 6 months, the cover layer is constructed. The decision module 310 of the cover layer construction unit 300 obtains the leaching toxicity test results of the fly ash on the surface of the fill material. In this embodiment, the test results show that the heavy metal leaching concentration is lower than the Class III limit of the "Surface Water Environmental Quality Standard", and the pH value is 8.2, indicating weak alkalinity. Based on this, the decision module 310 determines that there is no need to lay an artificial impermeable lining and that soil can be directly used for backfilling.

[0047] The target area stored in database 320 has a semi-arid continental climate with an average annual precipitation of 350 mm and an average annual evaporation of 1800 mm. The soil type is chestnut calcareous soil, and the native plants are mainly alfalfa, clover, and caragana. Based on the above information, decision module 310 recommends an optimal soil cover thickness of 50 cm and a pioneer plant species combination of alfalfa and clover (ratio 1:1).

[0048] Construction workers carried out soil covering and vegetation planting according to the recommended plan. Subsequent monitoring showed that the vegetation survival rate was over 85%, the coverage rate reached 70%, and the ecological restoration effect was good.

[0049] The above embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Based on the technical solutions of the present invention, those skilled in the art can make the following modifications according to actual application scenarios, and all such modifications fall within the protection scope of the present invention: The risk quantification model system is not limited to the three models of spontaneous combustion risk, stability risk, and pollution risk. Dust risk model, geological disaster risk model, etc. can be added as needed. The optimization algorithm is not limited to genetic algorithm, but may employ other intelligent optimization algorithms such as particle swarm optimization, simulated annealing algorithm, and ant colony optimization algorithm. The sensor network is not limited to temperature, oxygen concentration, and humidity sensors; pressure sensors, pH sensors, conductivity sensors, etc., can be added as needed. The warning levels are not limited to three levels: intermediate, advanced, and severe; more levels can be added as needed. The early warning response measures are not limited to inert gas injection and spray cooling; grouting reinforcement and flame-retardant covering can be added as needed. The decision-making basis for the construction unit of the cover layer is not limited to the leaching toxicity test results, but may include indicators such as soil salinity, alkalinity and organic matter content.

[0050] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent management and control of mine pit gully restoration, characterized in that, Includes the following steps: Obtain a 3D digital base map of the target mine pit and gullies; Obtain real-time physicochemical parameters of fly ash and coal gangue used for backfilling; Establish a risk quantification model system, which includes at least a spontaneous combustion risk model, a stability risk model, and a pollution risk model; The real-time materialization parameters are input into the risk quantification model system for normalization and weighted calculation. Combined with the environmental information of the three-dimensional digital base map, structured risk data is obtained. Based on the three-dimensional digital base map, the dominant risk types in different areas of the target mine pit and gully are identified, and optimized weight coefficients are dynamically assigned to each area to determine the priority of risk control and ecological restoration in that area. Using preset engineering and ecological goals as decision variables and preset process constraints as constraint boundaries, the optimization algorithm solves the problem based on the priorities and risk data, and outputs the process parameters of optimal mixing ratio of fly ash and coal gangue, target moisture content and compaction degree. The process parameters are smoothed in space and time to generate dynamic balancing instructions to guide construction.

2. The intelligent management and control method for mine pit gully restoration according to claim 1, characterized in that, Also includes: The target mine pit trench is filled according to the dynamic balancing command, and a sensor network is deployed in the filling body to continuously monitor the physical stability and chemical safety of the filling body. Based on the monitoring data from the sensor network, the spontaneous combustion risk of the filler is assessed and classified in real time, and corresponding risk warnings are issued according to the spontaneous combustion risk level.

3. The intelligent management and control method for mine pit gully restoration according to claim 2, characterized in that, The real-time assessment and classification of the spontaneous combustion risk of the filler specifically includes: The temperature and oxygen concentration data inside the filling material are acquired in real time, and the spatial temperature gradient and the heating trend at specific points are calculated. Based on the data, multiple early warning indicators are calculated, including at least: determining whether there are sensor locations that exceed a preset absolute temperature threshold; determining whether there are abnormal spatial temperature gradient regions that exceed a preset gradient threshold; and determining whether there are specific sensor locations whose temperature values ​​have a heating rate exceeding a preset trend threshold over a continuous time period. Correlation analysis and cross-validation of oxygen concentration data were performed on the locations that triggered temperature warnings; When only one of the absolute temperature threshold, gradient threshold, or trend threshold is triggered, it is determined to be a medium-level warning; When the absolute temperature threshold and either the gradient threshold or the trend threshold are triggered simultaneously, it is determined to be a high-level warning; When the conditions for a high-level warning are met, and cross-verification confirms that the oxygen concentration at the corresponding location has decreased significantly and synchronously, it is determined to be a severe alarm.

4. A mine pit gully repair system, used to execute the intelligent control method for mine pit gully repair as described in any one of claims 1 to 3, characterized in that, include: The pretreatment center, used for online testing and dynamic proportioning of backfill materials, includes: The monitoring module is used for online analysis of the pyrite content of coal gangue and the chemical composition of fly ash; A humidity sensor is used to monitor the real-time moisture content of fly ash. The central control unit has a built-in function to implement the method of claim 1, and is used to generate the dynamic balancing command; A conditioning device, connected to the central control unit, is used to add water or dust suppressant to fly ash according to the dynamic balancing command, so as to adjust it to the target state. The operation module, connected to the preprocessing center, is used to perform filling operations according to the dynamic balancing instructions, and includes: A GNSS positioning system, installed on paving equipment and rollers, is used to precisely control the paving thickness and compaction trajectory according to the dynamic balancing instructions. The sensor network includes multiple sensors arranged in a preset grid within the filling material during the filling process, used to monitor and transmit real-time data on temperature, oxygen concentration, and humidity inside the filling material. A cover layer building unit, used to construct an ecological soil layer suitable for plant growth on the surface of a completed fill body, comprising: The decision module is used to obtain the leaching toxicity test results of fly ash and, based on the results, determine whether an artificial anti-seepage lining layer needs to be laid before constructing the ecological soil layer. The database stores climate, soil, and native plant information for the target mine pit gully area, and is used to recommend the optimal soil cover thickness and pioneer plant species combination based on the judgment results of the decision module. The monitoring and maintenance platform is connected to the pretreatment center and the operation module respectively, and is used to collect, analyze and provide risk warnings for the entire process from material pretreatment to ecological restoration.

5. A dynamic balancing device for mine pit gully repair, characterized in that, include: At least one processor; And a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the method as described in any one of claims 1 to 3.

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