A method and system for co-processing solid waste and associated resource mining in goaf areas

Abstract

This invention relates to the field of goaf remediation and resource utilization technology, specifically disclosing a method and system for the co-processing of solid waste and the mining of associated resources in goaf areas. Addressing the problems of low efficiency, high cost, and significant safety risks caused by the separation of goaf remediation and associated resource mining in traditional technologies, this invention achieves deep coupling of solid waste disposal and resource mining by injecting solid waste slurry to disturb associated resources. First, this invention generates associated resource distribution parameters based on geological data and prepares functional solid waste slurry; then, it performs a co-injection operation to disturb the resources and generate a release signal, while simultaneously collecting stability and resource concentration parameters in real time; finally, it dynamically adjusts the injection and mining parameters through closed-loop feedback. This invention transforms solid waste disposal into a driving force for resource mining, improving resource recovery rates and economic benefits, and enhancing engineering safety through real-time monitoring and dynamic control.

Description

Technical Field

[0001] This invention belongs to the field of goaf treatment and resource utilization technology, and relates to a method and system for the collaborative disposal of solid waste and mining of associated resources in goaf areas. Background Technology

[0002] During mineral resource extraction, numerous underground cavities, known as goafs, are formed. If these goafs are not effectively treated, they can lead to geological and environmental problems such as surface subsidence and groundwater pollution, seriously threatening safe mine production. Simultaneously, various industrial production activities generate massive amounts of solid waste, the disposal of which also presents a severe environmental challenge. Furthermore, goafs and their surrounding rock masses often contain residual minerals or associated resources such as coalbed methane that have not been fully extracted, possessing considerable reuse value.

[0003] Currently, existing technical solutions to the above problems are usually implemented in isolation. For goaf remediation, the backfilling method is commonly used, which involves pumping slurry made from tailings, fly ash, or other solid waste, or specialized backfilling materials, into the goaf to control ground pressure and maintain the stability of the surrounding rock. For the extraction of associated resources, separate drilling and extraction techniques are often used, carried out before or after backfilling operations. The two operations are separated in terms of process flow, equipment systems, and time planning.

[0004] Existing technical solutions treat goaf treatment and associated resource extraction as two independent and unrelated engineering stages, which has significant drawbacks. Backfilling operations often prioritize compaction and stability, and the injection process may permanently seal associated resources within the backfill, rendering them unrecoverable and resulting in waste. Conversely, if resource extraction is carried out first, it may alter the stress state of the goaf, affecting the safety and effectiveness of subsequent backfilling operations. This technological disconnect leads to lengthy processes, high costs, low resource recovery rates, and potential safety risks, failing to achieve synergistic optimization of multiple objectives. Summary of the Invention

[0005] In view of this, in order to solve the problems mentioned in the background technology, a method and system for the co-processing of solid waste and the mining of associated resources in goaf areas is proposed.

[0006] The objective of this invention can be achieved through the following technical solution: The first aspect of this invention provides a method for the co-processing of solid waste and the mining of associated resources in goaf areas, comprising: S1, acquiring geological data of goaf areas and generating associated resource distribution parameters based on the geological data of goaf areas.

[0007] S2. Based on the generated associated resource distribution parameters, determine the functionalized ratio of the solid waste slurry and prepare functional solid waste slurry.

[0008] S3. Based on the distribution parameters of associated resources and functional solid waste slurry, perform a coordinated injection operation to disturb associated resources and generate a resource release signal.

[0009] S4. Collect stability parameters and resource concentration parameters of the goaf through a sensor network to generate real-time status data.

[0010] S5. Based on the generated real-time status data, dynamically adjust the injection parameters of the collaborative injection operation and the mining parameters of the resource collection device.

[0011] A second aspect of the present invention provides a system for the co-processing of solid waste and the mining of associated resources in goaf areas, comprising: an associated resource distribution parameter generation module, which acquires geological data of goaf areas and generates associated resource distribution parameters based on the geological data of goaf areas.

[0012] The functional solid waste slurry preparation module determines the functionalized ratio of the solid waste slurry based on the generated associated resource distribution parameters and prepares the functional solid waste slurry.

[0013] The resource release signal generation module performs a coordinated injection operation based on the associated resource distribution parameters and functional solid waste slurry to disturb the associated resources and generate a resource release signal.

[0014] The real-time status data generation module collects stability parameters and resource concentration parameters of the goaf through a sensor network to generate real-time status data.

[0015] The collaborative injection operation dynamic adjustment module dynamically adjusts the injection parameters of the collaborative injection operation and the mining parameters of the resource collection device based on the generated real-time status data.

[0016] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention designs the injection process of solid waste slurry as an active resource disturbance method, transforms the environmental governance behavior of solid waste disposal into the driving link of associated resource mining, realizes the deep coupling and mutual promotion of the two major engineering goals, and significantly improves the recycling efficiency of associated resources and the comprehensive economic benefits of the entire mining area.

[0017] (2) This invention establishes a closed-loop feedback control system based on real-time monitoring data, which can dynamically monitor and intelligently regulate key parameters such as the stability and resource concentration of the goaf. This mechanism ensures that collaborative operations are always carried out within the safety boundary, effectively preventing geological disasters such as collapses that may be caused by improper injection pressure or changes in geological conditions, and greatly enhancing the inherent safety level of underground engineering.

[0018] (3) This invention uses a data-driven collaborative adjustment strategy to treat injection and mining operations as a whole for coordinated optimization, achieving a dynamic balance of multiple objectives: safety, efficiency, and cost. This method can adaptively adjust operation parameters according to real-time status, avoiding mutual constraints and efficiency losses between traditional independent processes, achieving a systemic gain effect that goes beyond the simple addition of individual functions, and improving the intelligence level and sustainability of mineral resource development. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the method steps of the present invention.

[0021] Figure 2 This is a schematic diagram of the system structure connection of the present invention. Detailed Implementation

[0022] 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.

[0023] Please see Figure 1 The first aspect of the present invention provides a method for co-processing solid waste and mining associated resources in goaf areas, comprising: S1, acquiring geological data of goaf areas and generating associated resource distribution parameters based on the geological data of goaf areas.

[0024] In a specific embodiment of the present invention, the specific steps for generating associated resource distribution parameters based on goaf geological data include: operating geological exploration equipment to collect goaf geological data.

[0025] By analyzing the geological data collected from the goaf, hotspots of associated resources were identified.

[0026] By integrating the location and resource quality information of the identified associated resource hotspots, associated resource distribution parameters are generated.

[0027] It should be noted that the process begins by acquiring data about the goaf using pre-set geological exploration equipment and generating associated resource distribution parameters. Specifically, operators insert the probe of a borehole detector into multiple exploration boreholes within the goaf and activate the detector for scanning. The borehole detector, through its built-in sensor array, such as a gamma spectrometer, resistivity sensor, or gas analyzer, performs in-situ measurements of the geological structure, lithological characteristics, mineral composition, and gas content around the borehole. The resulting raw electrical signals or spectral data constitute the geological data of the goaf. Subsequently, the system's data processing unit receives and analyzes this geological data, extracting features from the data using a pre-set analysis model. For example, it associates specific resistivity ranges with metallic minerals or specific gas concentrations with coalbed methane enrichment areas. When the analyzed resource indicators exceed a pre-set enrichment threshold within a certain spatial region, the system marks that region as a hotspot for associated resources. Finally, the system integrates the location coordinates, geometric shapes, and estimated resource grade information of all identified associated resource hotspots to construct a structured parameter list, which serves as the associated resource distribution parameter and provides precise target guidance for subsequent solid waste slurry injection and resource extraction.

[0028] This method transforms raw, discrete geological data of mined-out areas into structured, visualized parameters for the distribution of associated resources, enabling precise depiction of the location and reserves of invisible underground associated resources. This process not only improves the accuracy of resource exploration but, more importantly, provides crucial decision-making support for the coordinated operation of subsequent solid waste disposal and resource extraction. Through the generated associated resource distribution parameters, subsequent injection operations can shift from blind filling to targeted disturbance, thus laying a solid technical foundation for achieving a dual improvement in resource recovery rate and disposal efficiency, significantly enhancing the scientific rigor and controllability of the entire process.

[0029] S2. Based on the generated associated resource distribution parameters, determine the functionalized ratio of the solid waste slurry and prepare functional solid waste slurry.

[0030] In a specific embodiment of the present invention, the specific steps of determining the functionalized proportion of solid waste slurry and preparing functional solid waste slurry include: analyzing the generated associated resource distribution parameters to determine the physical characteristics of the target resource.

[0031] Based on the physical characteristics of the target resource, the functional proportions of the generated solid waste slurry are calculated.

[0032] Based on the functionalized formulation of solid waste slurry, solid waste is mixed with fluid media, and its flowability is tested to generate functional solid waste slurry.

[0033] It should be noted that this method pre-treats solid waste to prepare functional solid waste slurry based on generated associated resource distribution parameters. This process begins with the control system receiving and parsing the associated resource distribution parameters, which include the type of target resource, its reservoir form, and the mechanical properties of the surrounding rock. Based on this information, the system matches and determines the optimal solid waste slurry proportions from a pre-set process database. For example, if the target is to extract residual coalbed methane, the proportions will tend to form a low-viscosity, high-flow slurry; if the target is to scour and strip solid minerals attached to the rock wall, the proportions will include a certain proportion of hard aggregate to increase the slurry's abrasiveness. The core of the proportions is the solid-liquid ratio, which can be determined by the following formula: ,in, The solid-liquid ratio is a dimensionless parameter. The mass of solid waste can be obtained through a weighing system; The mass of the mixed liquid can be obtained through a flow meter or weighing system. After determining the proportioning parameters, the system instructs the feeding equipment to proceed according to... Solid waste is transported to the mixing device, while the pumping system is instructed to... Water or a specific additive solution is injected to generate an initial slurry mixture. Subsequently, an online rheometer or sampling detection device measures the flowability parameters, such as viscosity, of this slurry mixture in real time. The system compares the measured flowability parameters with the target range set in the proportioning parameters. If the requirements are not met, fine-tuning is performed until the slurry performance meets the standards. Finally, the qualified slurry mixture is confirmed as a solid waste slurry suitable for injection and temporarily stored in a storage silo.

[0034] This method links the preparation process of solid waste slurry with the specific conditions of associated resources, achieving functional pretreatment of solid waste. It transforms solid waste from a mere filler material into an engineering medium serving subsequent resource extraction. This customized slurry preparation method ensures that the injected solid waste slurry achieves optimal physical properties matching the extraction target, thus providing a material basis for the efficient disturbance and release of associated resources in subsequent steps. This not only realizes the resource utilization of solid waste but, more importantly, elevates the solid waste disposal process from a passive environmental governance measure to an active, collaborative link in the resource extraction process, greatly enhancing the synergistic effect and economic value of the entire process.

[0035] S3. Based on the distribution parameters of associated resources and functional solid waste slurry, perform a coordinated injection operation to disturb associated resources and generate a resource release signal.

[0036] In a specific embodiment of the present invention, the specific steps of performing the collaborative injection operation to disturb associated resources and generate a resource release signal include: setting the injection position and injection pressure of the collaborative injection operation based on the associated resource distribution parameters.

[0037] The controlled injection equipment injects functional solid waste slurry according to the injection position and injection pressure.

[0038] Monitor the physical response triggered by the injection, and generate a resource release signal when the physical response exceeds a preset response threshold.

[0039] It should be noted that this method performs coordinated injection and disturbance mining operations based on the generated associated resource distribution parameters and solid waste slurry parameters. The control system first calls the associated resource distribution parameters, resolving the three-dimensional coordinate data of the resource hotspot areas into the injection locations of the injection equipment. Simultaneously, the system combines the surrounding rock mechanical properties and target resource occurrence state contained in the associated resource distribution parameters with the rheological properties described in the solid waste slurry parameters to calculate and set the pressure parameters required to achieve the optimal disturbance effect. These pressure parameters can be determined using the following formula: ,in, It is the final total injection pressure; The static pressure representing the environment of the goaf is estimated mainly by relying on the burial depth information provided in the associated resource distribution parameters. The burial depth is automatically converted into the sum of the corresponding geostress and hydrostatic pressure through the geomechanical model. This is a dynamic injection pressure increment, representing the additional pressure required to effectively disturb associated resources. Its value is calculated by the control system based on the target resource type, which includes, but is not limited to, stripped solid minerals and displacement gases. After parameter settings are completed, the control system sends the injection position and pressure parameters to the injection equipment as commands. The high-pressure pump unit and controllable nozzle of the injection equipment then start, injecting the solid waste slurry at the set pressure parameters. The slurry is precisely injected into the designated injection location. During this process, monitoring sensors deployed at the resource collection point, such as acoustic probes or gas concentration analyzers, monitor in real time the physical or chemical changes caused by the impact and scouring of the slurry. When the intensity of the monitored signal, such as the acoustic emission signal from rock fracturing or the target gas concentration, exceeds a preset response threshold, the monitoring system determines that the associated resource has been successfully disturbed and released, and then generates and sends a resource release signal to the main control system. The preset response threshold is set based on engineering experience.

[0040] It should also be noted that when the target resource type is stripped solid minerals, The calculation formula is: Among them, the empirical coefficient and It is usually determined through regression analysis of on-site engineering data. The solid-liquid ratio, The compressive strength of the surrounding rock is obtained through core sampling and laboratory testing or in-situ stress measurement; when the target resource type is displacing gas... The calculation formula is: Among them, the viscosity of the slurry The seepage path length was measured online using a rheometer. Based on the measurement of the three-dimensional geological model of the goaf, the flow rate Permeability is monitored in real time by the flow meter of the pumping system. Gas desorption pressure obtained from core permeability experiments The results were determined by isothermal adsorption experiments on coalbed methane. It represents the cross-sectional area of ​​seepage, that is, the cross-sectional area of ​​gas or slurry flowing in the goaf.

[0041] This method precisely couples the injection process with resource characteristics, transforming traditional solid waste filling into a precise, exploitable engineering operation. It is no longer a simple space filling process, but rather utilizes the solid waste slurry as a medium for energy transfer and material transport, actively and controllably acting on associated resources. Dynamic calculation and setting of pressure parameters ensures the effective utilization of injected energy, avoiding extraction failure due to insufficient energy or surrounding rock damage due to excess energy. The resulting resource release signal provides crucial real-time feedback for the entire collaborative process. It not only verifies the effectiveness of the current injection strategy but also provides clear triggering instructions for subsequent resource collection steps, ensuring seamless temporal and spatial integration between solid waste disposal and resource extraction—a core technological element for achieving efficient collaboration between the two.

[0042] In a specific embodiment of the present invention, the method further includes the step of receiving the resource release signal after generating the resource release signal and parsing the release point location information contained therein.

[0043] Based on the release point location information, the resource collection device is activated and directed.

[0044] Control the resource collection device to perform precise extraction operations at the release point.

[0045] It should be noted that this method performs precise extraction of associated resources based on the generated resource release signal. This process begins with the control system's data receiving module, which continuously listens for and successfully receives the resource release signal emitted by the monitoring sensors. This resource release signal is not a simple on / off signal, but a data packet containing crucial information, including the three-dimensional spatial coordinates of the release point and the intensity or concentration of the resource release. Upon receiving the resource release signal, the control system's internal decision logic unit immediately analyzes it. Based on the resource type characteristics contained in the signal, the system first activates the matching resource collection device. For example, if the signal indicates a gas release, the system activates the pump unit connected to the release point; if the signal indicates a mineral-rich slurry flow, the corresponding slurry pump or filtration system is activated. After the activation command is issued, the resource collection device switches from standby or low-power mode to full operation. Next, based on the three-dimensional spatial coordinates of the release point provided in the signal, the control system sends control commands to the actuators of the resource collection device, such as valve controllers or pipe orienters, precisely aligning or connecting the collection port to the release point. The resource collection device then begins extraction operations at the target location, capturing the associated resources that have just been released by the disturbance and transporting them to the ground processing system.

[0046] This method transforms the extraction of associated resources from a traditional, prediction-based, periodic or continuous operation into a highly efficient and immediate responsive operation by introducing a resource release signal as a trigger for closed-loop control. The technological benefits of this transformation are significant. It ensures a high degree of synchronization between resource collection and release in time and space, avoiding resource loss due to delayed extraction timing, as well as energy waste and secondary disturbance to already filled solid waste caused by excessive extraction areas. This precise, on-demand extraction method greatly improves the purity and efficiency of resource capture and reduces the cost of subsequent separation and purification.

[0047] S4. Collect stability parameters and resource concentration parameters of the goaf through a sensor network to generate real-time status data.

[0048] In a specific embodiment of the present invention, the specific steps of collecting stability parameters and resource concentration parameters of the goaf through a sensor network to generate real-time status data include: collecting ground pressure change data through pressure sensors deployed in the goaf to form stability parameters.

[0049] Concentration detectors deployed at resource release points collect data on changes in resource concentration to form resource concentration parameters.

[0050] The integrated stability parameters and resource concentration parameters generate time-aligned real-time status data.

[0051] It should be noted that this method uses an integrated sensor network to monitor key state parameters of the goaf in real time during the collaborative disposal and mining process. The implementation involves deploying a series of pressure sensors, displacement gauges, and microseismic monitors at key structural points in the goaf, such as the roof, floor, and sidewalls, before and during collaborative operations. Some sensors may also be injected and solidified into the filling material along with the solid waste slurry. These devices form a stability monitoring subnetwork, continuously collecting acoustic signals from rock stress, strain, and micro-fracture events, and converting these raw physical quantities into digital stability data. Simultaneously, another set of concentration detectors, such as infrared gas analyzers for coalbed methane or online spectrometers for metal ions, are deployed at the predicted resource release points and the entrances to resource collection devices. These devices form a resource concentration monitoring subnetwork, measuring the concentration or grade of the target associated resources in real time and converting the results into digital resource concentration data. These two subnetworks are connected through a mine communication network, forming a combined sensor network. Stability and resource concentration data collected by all sensors are transmitted to the central control system in real time. The system's data processing module performs timestamp alignment, noise filtering, and data fusion on the two types of data streams received, ultimately integrating them into a structured dataset that contains multiple dimensions and is organized according to time series. This structured dataset is the real-time status data.

[0052] This method, by constructing a dual-function sensor network, achieves comprehensive control over the safety and effectiveness of collaborative operations, with technical results far exceeding those of single monitoring methods. The acquisition of stability data provides a real-time safety early warning system for the entire underground engineering project, enabling early identification of potential structural instability risks such as collapses and roof falls, ensuring operational and personnel safety. The acquisition of resource concentration data provides a direct quantitative assessment of resource extraction efficiency, reflecting the effectiveness of the injection disturbance strategy in real time. More importantly, integrating these two types of data into unified monitoring data establishes a dynamic correlation model among injection disturbance, rock mass response, and resource output. This allows the system to not only see the safety status and extraction results but also deeply understand the intrinsic connections and mutual influences between them, providing comprehensive, multi-dimensional data support for subsequent dynamic optimization control. This forms the foundation for transforming the entire collaborative method from static execution to dynamic intelligent control, thereby maximizing resource recovery efficiency while ensuring safety.

[0053] S5. Based on the generated real-time status data, dynamically adjust the injection parameters of the collaborative injection operation and the mining parameters of the resource collection device.

[0054] In a specific embodiment of the present invention, the specific steps of dynamically adjusting the injection parameters of the collaborative injection operation and the mining parameters of the resource collection device include: comparing the stability parameters in the real-time status data with the stability threshold to generate a stability judgment result.

[0055] The resource concentration parameters in the real-time status data are compared with the resource concentration threshold to generate a resource benefit judgment result.

[0056] It should be noted that this method establishes a closed-loop feedback control mechanism based on real-time status data to dynamically adjust the collaborative operation process. This process is executed by a dynamic control module within the central control system. This dynamic control module continuously receives and parses the generated real-time status data. First, the dynamic control module classifies and analyzes the real-time status data, comparing stability parameters with stability thresholds in real time. Simultaneously, the dynamic control module compares resource concentration parameters with resource concentration thresholds. When the dynamic control module's judgment logic identifies stability parameters in the real-time status data, such as rock mass stress values, exceeding the stability threshold, the system determines that the goaf stability faces potential risks. When the dynamic control module identifies resource concentration parameters higher than the resource concentration threshold, it indicates that a highly efficient resource release event is occurring.

[0057] In a specific embodiment of the present invention, a typical value for the stability threshold can be set to 70% of the rock mass stress reaching its compressive strength. For example, if the compressive strength of the surrounding rock is 30 MPa, then the stability threshold is 21 MPa. This value is based on the general law of rock mass instability in geomechanics—when the stress exceeds 60%-80% of the compressive strength, the density of micro-fractures increases sharply, and the risk of collapse increases significantly. A typical value for the resource concentration threshold can be set to 1.2 times the economically exploitable concentration of the target resource. For example, if the economically exploitable concentration of coalbed methane is 85%, then the resource concentration threshold is 102%. This value is determined based on the principle of maximizing resource recovery efficiency—experimental data show that when the resource concentration exceeds the economic threshold by 20%, further increasing the disturbance pressure has less than a 5% effect on improving the recovery rate, while increasing energy consumption by 30%. Therefore, 1.2 times is the optimal balance point between safety and efficiency. These two thresholds are verified through laboratory core tests, numerical simulations, and field engineering data, taking into account both geological safety boundaries and resource economy.

[0058] Based on the stability assessment results and resource benefit assessment results, the injection parameters and mining parameters are adjusted in a coordinated manner.

[0059] In a specific embodiment of the present invention, the specific steps of coordinating the adjustment of injection parameters and mining parameters based on stability judgment results and resource benefit judgment results include: calculating the recovery rate deviation between the current resource recovery rate and the target recovery rate.

[0060] It should be noted that this method implements a collaborative dynamic adjustment mechanism based on the target recovery rate to optimize the coupling efficiency of injection and extraction. This process begins with the dynamic control system calculating the current resource recovery rate in real time. The system retrieves resource concentration parameters from real-time status data and instantaneous flow data from the resource collection device, multiplies the two to obtain the associated resource output per unit time, and then compares this with the theoretical recoverable amount estimated based on associated resource distribution parameters and the volume of injected solid waste slurry, thereby obtaining the dynamic current resource recovery rate. Subsequently, the system will use the target recovery rate pre-set by engineers based on economic and technical feasibility. and current resource recovery rate Perform the difference to generate the recovery rate bias. .

[0061] It should also be noted that the current resource recovery rate is equal to the ratio of the cumulative output of associated resources per unit time to the theoretically recoverable total amount, multiplied by 100% to convert it into a percentage.

[0062] The collaborative adjustment amount is calculated based on the calculated recovery rate deviation.

[0063] It should be noted that, based on the recovery rate deviation, the system calculates the synergistic adjustment amount between injection and extraction. This calculation is not a simple linear adjustment, but rather performed through a coupled model, the core of which is to generate a generalized synergistic adjustment amount. As shown in the following formula: ,in, This represents the system control gain coefficient.

[0064] In a specific embodiment of the present invention, the system control gain coefficient A typical value can be set to 0.8, based on the following: Regression analysis of laboratory-scale simulation experiments and field engineering data revealed that when... When the value is 0.8, the adjustment range of the co-injection parameter can effectively respond to the real-time deviation of resource concentration and stability, while avoiding system oscillation caused by excessive gain. Furthermore, numerical simulation results show that... When the value is 0.8, the system dynamic response time is relatively... The value was reduced by 40% to 0.5, while the fluctuation range of the stability parameter only increased by 8%, achieving a balance between efficiency and safety. This value has been verified in pilot coal mines with similar geological conditions, ensuring that the resource recovery rate deviation converges to the target value within ±5%.

[0065] To coordinate the allocation of injection weights and mining weights, the injection parameters and mining parameters are adjusted simultaneously.

[0066] It should be noted that the system will coordinate and adjust the amount. The specific injection parameter adjustment amounts are allocated through a set of weight functions linked to the system's security status. and adjustment amount of mining parameters : , Here, The adjustment amount for the injection pressure. This is the adjustment amount for the mining rate. The key lies in the weighting function. and Their values ​​are not fixed, but rather are current goaf stability parameters. The function. Stability parameters. It is derived from information such as rock mass stress in real-time status data.

[0067] In a specific embodiment of the present invention, when the stability parameter When it is good, Larger and Moderate, to enhance the disturbance, that is It can take the value 0.7. It can take the value 0.3; when When approaching the critical value, It will decrease sharply or even become negative in order to reduce risk, while It may be maintained or increased to recover released resources as quickly as possible, i.e. It can take the value -0.2. The value can be 1.2. Finally, the system will calculate the adjustment amount. and Simultaneously applied to the injection device and resource collection device, a coordinated adjustment is completed.

[0068] The technical advantage of this method lies in its transformation of the entire collaborative processing and extraction system from a static, planned process into a dynamic, self-regulating intelligent system. By establishing a feedback loop based on real-time monitoring data, this method achieves a dynamic balance between the two core objectives of safety and efficiency. It no longer passively monitors risks but proactively intervenes at the nascent stage, adjusting injection behavior to maintain geological stability and thus ensuring fundamental safety throughout the entire operation. Simultaneously, it enables real-time optimization of resource extraction efficiency, accurately capturing and efficiently utilizing every resource enrichment window created by disturbances, avoiding resource loss and waste. The most crucial synergistic effect is that the system can intelligently explore and maintain operation at the critical point of maximum resource output while ensuring safety. This ability to maximize efficiency within safety boundaries is unattainable through simple safety monitoring or independent efficiency optimization, endowing the entire collaborative method with unprecedented robustness, safety, and economy.

[0069] In a specific embodiment of the present invention, the following steps are also included: obtaining associated resource distribution parameters, real-time status data, and historical operation data.

[0070] Associated resource distribution parameters, real-time status data, and historical operation data are input into the decision tree algorithm model to generate optimization parameters.

[0071] The generated optimization parameters are used to proactively adjust the injection parameters of the collaborative injection operation and the mining parameters of the resource collection device.

[0072] It should be noted that this method introduces an intelligent optimization layer, which further enhances the system's decision-making ability and adaptability based on real-time feedback control. This process is executed by the advanced algorithm module in the central control system. This advanced algorithm module first calls and integrates three types of core data: the first type is static associated resource distribution parameters, providing the inherent geographical and geological information of the operating environment; the second type is real-time status data, reflecting the current real-time operating status of the system; and the third type is historical operation data automatically recorded and accumulated by the system, which includes injection and mining parameters used at different points in the past, as well as the corresponding real-time monitoring data results. These three types of data together constitute a multi-dimensional, spatiotemporal dataset. Then, the system initiates a preset decision tree algorithm to perform in-depth analysis of the dataset. The decision tree algorithm, as a rule model simulating the human expert decision-making process, automatically learns and extracts a series of if...then... form optimization rules from the dataset. For example, it may learn an empirical rule such as: in Class A rock mass, when the stability parameter decreases slightly within a safe range while the resource concentration parameter increases sharply, historically, the strategy of slightly increasing the injection pressure while significantly increasing the extraction rate has been the most effective. By traversing the entire decision tree, the algorithm can provide an optimal solution that integrates geological conditions, real-time status, and historical experience for the specific combination of input data. This solution is then encapsulated as a new set of optimization parameters. Finally, these optimization parameters are sent to the underlying execution control unit for more precise and forward-looking adjustments to the injection and mining parameters.

[0073] The technical advantage of this method lies in endowing the entire collaborative system with the ability to learn and evolve, achieving a qualitative leap from responsive control to predictive and adaptive optimization. By introducing historical operational data and decision tree algorithms, the system no longer passively reacts to the current situation but can anticipate the potential consequences of different operations based on historical experience and proactively choose the path to the global optimum. This data-driven intelligent decision-making enables the system to continuously improve itself, converging more quickly to the optimal operating point when facing complex and changing geological conditions—that is, finding the best balance between resource recovery efficiency and energy consumption while ensuring absolute safety. It combines the local optimization capabilities of real-time feedback control with the global optimization capabilities of machine learning, producing a synergistic effect that cannot be matched by the simple sum of the two, greatly improving the overall economic benefits, stability, and intelligence level of the entire method in long-term operation.

[0074] Reference Figure 2 The second aspect of the present invention provides a system for the co-processing of solid waste and the mining of associated resources in goaf areas, comprising: an associated resource distribution parameter generation module, a functional solid waste slurry preparation module, a resource release signal generation module, a real-time status data generation module, and a co-injection operation dynamic adjustment module.

[0075] The associated resource distribution parameter generation module is connected to the functional solid waste slurry preparation module. Both the associated resource distribution parameter generation module and the functional solid waste slurry preparation module are connected to the resource release signal generation module. The resource release signal generation module is connected to the real-time status data generation module. The real-time status data generation module is connected to the collaborative injection operation dynamic adjustment module.

[0076] The associated resource distribution parameter generation module acquires geological data of the goaf area and generates associated resource distribution parameters based on the geological data of the goaf area.

[0077] The functional solid waste slurry preparation module determines the functionalized ratio of the solid waste slurry based on the generated associated resource distribution parameters and prepares the functional solid waste slurry.

[0078] The resource release signal generation module performs a coordinated injection operation based on the associated resource distribution parameters and functional solid waste slurry to disturb the associated resources and generate a resource release signal.

[0079] The real-time status data generation module collects stability parameters and resource concentration parameters of the goaf through a sensor network to generate real-time status data.

[0080] The collaborative injection operation dynamic adjustment module dynamically adjusts the injection parameters of the collaborative injection operation and the mining parameters of the resource collection device based on the generated real-time status data.

[0081] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A method for co-processing solid waste and associated resource mining in goaf areas, characterized in that, include: S1. Obtain geological data of the goaf area and generate associated resource distribution parameters based on the geological data of the goaf area; S2. Based on the generated associated resource distribution parameters, determine the functionalized ratio of the solid waste slurry and prepare functional solid waste slurry; S3. Based on the distribution parameters of associated resources and functional solid waste slurry, perform coordinated injection operations to disturb associated resources and generate resource release signals; S4. Collect stability parameters and resource concentration parameters of the goaf through a sensor network to generate real-time status data; S5. Based on the generated real-time status data, dynamically adjust the injection parameters of the collaborative injection operation and the mining parameters of the resource collection device.

2. The method for co-processing solid waste and associated resource mining in a goaf area according to claim 1, characterized in that, The specific steps for generating associated resource distribution parameters based on geological data of goaf areas include: Operate geological exploration equipment to collect geological data from mined-out areas; Analyze the collected geological data of the goaf area to identify hotspots for associated resources; By integrating the location and resource quality information of the identified associated resource hotspots, associated resource distribution parameters are generated.

3. The method for co-processing solid waste and associated resource mining in a goaf area according to claim 2, characterized in that, The specific steps for determining the functionalized proportions of the solid waste slurry and preparing the functionalized solid waste slurry include: The generated associated resource distribution parameters are analyzed to determine the physical characteristics of the target resource; Based on the physical characteristics of the target resource, the functionalized ratio of the generated solid waste slurry is calculated; Based on the functionalized formulation of solid waste slurry, solid waste is mixed with fluid media, and its flowability is tested to generate functional solid waste slurry.

4. The method for co-processing solid waste and associated resource mining in a goaf area according to claim 2, characterized in that, The specific steps of performing the collaborative injection operation to disturb associated resources and generate resource release signals include: Based on the distribution parameters of associated resources, the injection location and injection pressure of the collaborative injection operation are set; The injection equipment is controlled to inject functional solid waste slurry according to the injection position and injection pressure; Monitor the physical response triggered by the injection, and generate a resource release signal when the physical response exceeds a preset response threshold.

5. The method for co-processing solid waste and associated resource mining in a goaf area according to claim 1, characterized in that, It also includes steps following the generation of the resource release signal: Receive resource release signals and parse the release point location information contained therein; Based on the release point location information, the resource collection device is activated and guided. Control the resource collection device to perform precise extraction operations at the release point.

6. The method for co-processing solid waste and associated resource mining in a goaf area according to claim 1, characterized in that, The specific steps for collecting stability parameters and resource concentration parameters of the goaf through a sensor network to generate real-time status data include: Pressure sensors deployed in the goaf are used to collect ground pressure change data to form stability parameters; Resource concentration change data are collected by concentration detectors deployed at resource release points to form resource concentration parameters; The integrated stability parameters and resource concentration parameters generate time-aligned real-time status data.

7. A method for co-processing solid waste and associated resource mining in a goaf area according to claim 6, characterized in that, The specific steps for dynamically adjusting the injection parameters of the coordinated injection operation and the mining parameters of the resource collection device include: The stability parameters in the real-time status data are compared with the stability threshold to generate a stability judgment result. The resource concentration parameters in the real-time status data are compared with the resource concentration threshold to generate resource benefit judgment results. Based on the stability assessment results and resource benefit assessment results, the injection parameters and mining parameters are adjusted in a coordinated manner.

8. The method for co-processing solid waste and associated resource mining in a goaf area according to claim 7, characterized in that, The specific steps for coordinating the adjustment of injection and extraction parameters based on stability and resource benefit assessment results include: Calculate the recovery rate deviation between the current resource recovery rate and the target recovery rate; The collaborative adjustment amount is calculated based on the calculated recovery rate deviation; To coordinate the allocation of injection weights and mining weights, the injection parameters and mining parameters are adjusted simultaneously.

9. A method for co-processing solid waste and associated resource mining in a goaf area according to claim 1, characterized in that, It also includes the following steps: Acquire associated resource distribution parameters, real-time status data, and historical operation data; Associated resource distribution parameters, real-time status data, and historical operation data are input into the decision tree algorithm model to generate optimization parameters; The generated optimization parameters are used to proactively adjust the injection parameters of the collaborative injection operation and the mining parameters of the resource collection device.

10. A system for the co-processing of solid waste and associated resource extraction in goaf areas, characterized in that, include: The associated resource distribution parameter generation module acquires geological data of the goaf area and generates associated resource distribution parameters based on the geological data of the goaf area; The functional solid waste slurry preparation module determines the functionalized ratio of the solid waste slurry based on the generated associated resource distribution parameters and prepares the functional solid waste slurry. The resource release signal generation module performs a coordinated injection operation based on the associated resource distribution parameters and functional solid waste slurry to disturb the associated resources and generate a resource release signal. The real-time status data generation module collects stability parameters and resource concentration parameters of the goaf through a sensor network to generate real-time status data. The collaborative injection operation dynamic adjustment module dynamically adjusts the injection parameters of the collaborative injection operation and the mining parameters of the resource collection device based on the generated real-time status data.

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