Real-time monitoring and intelligent control system and method for overburden rock isolation grouting

Abstract

The present application relates to coal mine overburden strata isolation grouting intelligent monitoring technical field, specifically relates to overburden strata isolation grouting real-time monitoring intelligent control system and method, overburden strata isolation grouting real-time monitoring intelligent control system includes central control module and is connected with slurry preparation module, pumping module, monitoring module, intelligent analysis module signal respectively, integrates slurry preparation, pumping control, multi-parameter monitoring and intelligent analysis in one, system realizes the automation and intelligent closed loop management of whole process. It greatly reduces the dependence on subjective experience of operating personnel, improves the consistency and repeatability of process, reduces human misjudgment and operation error, and reduces labor intensity and on-site safety risk. The system ensures the real-time matching of grouting material performance and engineering demand according to the real-time monitoring data such as flow, pressure and density, prevents pipeline blockage, insufficient grouting and other problems from the source, and improves the pre-control ability of engineering quality.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology for coal mine overburden isolation grouting, specifically to an intelligent control system and method for real-time monitoring of overburden isolation grouting. Background Technology

[0002] Coal, as my country's primary energy source, holds significant strategic importance for national energy security. my country's proven coal reserves are approximately 1.4 trillion tons, of which over 30% are located under buildings, water bodies, and railways ("three-under"), where effective mining has long been neglected, resulting in substantial resource waste. According to the "Coal Industry Development Report 2024," China's raw coal production reached 4.71 billion tons in 2023, generating approximately 808 million tons of coal gangue. However, the comprehensive utilization rate was only 58.3%, meaning that nearly 40% of the country's coal gangue remains untreated and unutilized.

[0003] For a long time, my country's large-scale, high-intensity coal mining model has had a significant impact on the ecological environment, including surface subsidence, aquifer damage, land resource depletion, and environmental pollution caused by coal gangue accumulation. Meanwhile, constrained by traditional mining technologies and safety assurance capabilities, the efficient recovery of coal resources buried under coal seams ("three underground") is difficult, further exacerbating the contradiction between resource waste and sustainable development in mining areas. Backfilling mining is considered a fundamental way to achieve the coordinated development of green coal resource development and ecological protection in mining areas. To this end, multiple ministries, including the National Energy Administration, the Ministry of Finance, the Ministry of Land and Resources, and the Ministry of Environmental Protection, jointly issued the "Guiding Opinions on Coal Mine Backfilling Mining," which clearly states that the goal is to build green, ecologically harmonious mining areas, focusing on areas buried under coal seams ("three underground") and environmentally sensitive areas, and vigorously promoting the research and application of backfilling mining technology.

[0004] In the backfilling mining technology system, overburden delamination grouting technology was once one of the main methods in the early stages. It plays a role in mitigating surface subsidence and absorbing gangue by injecting grout into the overburden delamination space. However, due to insufficient understanding of the deformation mechanism of overburden and the development law of delamination, this technology often suffers from problems such as large fluctuations in the injection-production ratio and unstable settlement reduction effect in practical applications, making it difficult to meet the strict requirements for settlement control in coal mining under buildings. After years of theoretical exploration and engineering practice, overburden isolation grouting backfilling technology has been gradually developed based on overburden delamination grouting technology. This technology abandons the traditional approach of passively "finding delamination" and transforms it into actively "creating a controllable grouting space". Its core mechanism is: by precisely controlling the grouting pressure, it plays a "supporting the upper and pressing the lower" role, recompacting the unloaded and expanded rock layer below the key layer before it breaks, thereby artificially forming a stable and continuous grouting backfilling area. This technology not only significantly improves the injection-production ratio but also constructs a long-term stable compacted support structure in the middle of the goaf, effectively maintaining the structural stability of key layers and ultimately achieving the comprehensive benefits of "one injection and five reductions"—reducing subsidence, emissions, leakage, vibration, and carbon emissions. This enables green mining and low-carbon utilization of coal resources.

[0005] However, existing grouting methods have the following drawbacks: (1) The accuracy and stability of the grouting pressure at the orifice are prerequisites for compacting the underlying rock mass of the key layer and forming effective long-term support. Existing methods often lead to pressure instability due to the lack of real-time pressure feedback and adaptive control, which in turn causes problems such as uneven grout distribution and failure of the support structure, directly affecting the success or failure of grouting and the long-term surface settlement reduction effect.

[0006] (2) The failure to establish a dynamic matching relationship between the grouting volume, grouting rate and working face advance speed can easily lead to slurry filling delay or incomplete filling of the area, resulting in the surface subsidence control and mining disturbance suppression effect being lower than the design expectation, which restricts the safe and efficient recovery of coal resources under the "three-under" pressure.

[0007] (3) The development of overburden abscission has significant spatiotemporal dynamic characteristics, and the timing of grouting depends excessively on experience. Grouting too early or too late will miss the optimal development space for abscission, resulting in a significant reduction in grout injectability, low filling efficiency, and a serious weakening of overall grouting effectiveness.

[0008] (4) From grout preparation and pumping parameter adjustment to orifice status monitoring, each step relies mainly on the subjective experience of the operators, resulting in large judgment errors and poor process consistency. This not only reduces the reliability and repeatability of the grouting process, but also increases the labor intensity and safety risks on site.

[0009] (5) Key parameters such as grout density and rheology are mostly statically set based on historical experience and are not dynamically optimized according to the grouting stage and pressure changes. This can easily lead to engineering problems such as insufficient actual grouting volume and pipeline blockage, which may delay the construction period or even prevent the expected rock layer control and settlement reduction goals from being achieved.

[0010] (6) In high-extraction or complex working faces, there are many grouting pipelines and a large filling demand. Numerous parameters need to be monitored and controlled, and the system is complex. Existing methods are difficult to achieve independent and precise control and global coordination of the flow and pressure of each pipeline. The management load is heavy and prone to errors, which restricts the realization of large-scale and efficient grouting. Summary of the Invention

[0011] To address the technical shortcomings of existing overburden isolation grouting processes, such as lack of pressure control, reliance on experience-based grouting timing, poor coordination among multiple pipelines, delayed fault warnings, and high dependence on manual labor, this invention provides an intelligent control system and method for real-time monitoring of overburden isolation grouting. The technical solution adopted is as follows: An intelligent control system for real-time monitoring of overburden isolation grouting, characterized in that it includes: The central control module serves as the system's overall control unit. The pulping module receives the pulping density command issued by the central control module, automatically weighs raw materials such as fly ash, gangue powder, cement, and water, and stirs them to prepare a uniform slurry. It also provides real-time feedback on the slurry consistency, uniformity, and measured density to the central control module. The pumping module is used to receive pumping pressure and flow rate commands from the central control module, pressurize the slurry and deliver it to the grouting hole through the pipeline, and has a remote pressure adaptive adjustment function. The monitoring module is used to collect multi-dimensional grouting monitoring data in real time and upload it to the central control module. The monitoring data includes grout density, pipeline pressure, pipeline flow rate, orifice pressure, flow velocity at multiple measuring points in the pipeline, and on-site images of the orifice. The intelligent analysis module receives monitoring data forwarded by the central control module. It has built-in pipeline safety assessment model, grout leakage and pipe blockage timing identification model, grouting stage timing identification model, grout ratio dynamic optimization model, and orifice grout leakage image recognition model. It outputs pipeline safety factor, fault warning signal, current grouting stage, target grout density, critical flow velocity, water-cement ratio, and grout leakage judgment results to the central control module.

[0012] Furthermore, the modeling steps for the pipeline safety assessment model include: The outer wall of the pipe is a free surface, and the radial stress of the pipe is... It conforms to the generalized plane stress state, according to the generalized Hooke's law:

[0013] In the formula: It is circumferential stress; This is axial stress; For circumferential strain; This refers to the axial strain of the pipeline. E It is the elastic modulus; v Poisson's ratio; According to the thin-walled cylinder theory, the pressure on the outer wall of the pipe and the slurry... The relationship is:

[0014] In the formula, R is the outer diameter of the pipe, t is the pipe wall thickness, and slurry pressure is... The wall thickness t after wear can be calculated by combining the above equations, which can be obtained by measuring the slurry pressure sensor. The circumferential stress on the inner wall of the pipe can then be calculated using the following formula. :

[0015] Because the pipe wall is relatively thin, the axial strain of the pipe varies little along the wall thickness. The axial stress is estimated by estimating the internal pressure through the axial and circumferential strains of the outer wall.

[0016] Mises stress for:

[0017] In the formula: This refers to the circumferential stress on the inner wall of the pipe. This refers to the radial stress of the pipe. This refers to the axial stress of the pipe. ; Define the safety factor as M. In the formula, For the yield stress measured by the experiment, M>1.5 is defined as the safety threshold. When the safety factor is higher than the threshold, the speed-up optimization command is output, and when the safety factor is lower than the threshold, the speed-down and shutdown warning signals are output.

[0018] Furthermore, the intelligent analysis module incorporates a built-in intelligent identification model for grout leakage and pipe blockage. Pressure, flow velocity, and flow rate sensors are installed at the inlet, middle section, and end of the grouting pipeline. Using multi-point and multi-parameter time-series parameters within a sliding time window as input to the deep learning model, the module automatically extracts the fluid dynamics characteristics of pipe blockage and grout leakage, and outputs the fault type and fault section location results in real time.

[0019] Furthermore, the intelligent analysis module incorporates a grouting stage timing recognition model; it divides the entire grouting process into an un-grouting stage, a zero-pressure stage, a pressure-increasing stage, a design pressure steady-state stage, and a final pressure enhancement stage. Using the orifice pressure timing data within the sliding time window as deep learning input, it automatically extracts the characteristic patterns of pressure changes in each stage through deep learning algorithms and outputs the recognition results of the current grouting stage.

[0020] Furthermore, the intelligent analysis module incorporates a dynamic optimization model for slurry mix proportions, matching slurry parameters in stages: (1) Non-injection stage: Output continuous water injection test command, without configuring grouting slurry; (2) Zero pressure stage: The pipeline flow velocity is calculated based on the daily water injection volume, and the safe flow velocity is obtained by dividing by the safety factor. The critical grout density is matched by the critical flow velocity-density curve, and the water-cement ratio is determined by the density-water-cement ratio curve. Water injection is maintained when the water-cement ratio is >3, and grouting is started when the water-cement ratio is ≤3. The lower limit of the water-cement ratio is set to 1, and the upper limit of the grouting capacity is the pressure that the pipeline can withstand. (3) Pressure boosting stage: Calculate pipeline flow velocity based on historical daily grouting volume, match critical density and water-cement ratio, with a lower limit of water-cement ratio of 1, and simultaneously constrain orifice pressure to not exceed design pressure; (4) Pressure stabilization stage: Construct an intelligent flow prediction model, input recent flow, density and pressure data to predict subsequent flow, convert safe flow rate to match critical density, water-cement ratio ≥1, and orifice pressure controlled at 0.9-1.0 times the design pressure; (5) Final pressure enhancement stage: The intelligent flow prediction model outputs the predicted flow rate, matches the critical density and water-cement ratio, the water-cement ratio is ≥1, and the orifice pressure is controlled at 1.0-1.5 times the design pressure; if the water-cement ratio is close to 3, the shutdown command is output.

[0021] Furthermore, the intelligent analysis module has a built-in image recognition model for slurry leakage from the orifice. It receives real-time images of the orifice, dynamically determines the slurry leakage status, and feeds back the recognition results to the central control module.

[0022] Furthermore, the pulping module includes a high-precision weighing system, a multi-stage mixer, a mixing tank, and an online slurry density detection device, enabling automatic raw material feeding, continuous mixing, and real-time feedback of slurry density.

[0023] Furthermore, the pumping module includes multiple sets of high-pressure grouting pumps, branch delivery pipelines, and electrically controlled valve groups, supporting independent pressure and flow regulation of multiple pipelines and global coordinated grouting.

[0024] A method for real-time monitoring and intelligent control of overburden isolation grouting, characterized by a real-time monitoring and intelligent control system for overburden isolation grouting, comprising the following steps: After the S1 grouting hole is formed and the pipeline system is installed, the central control module sends water injection commands to the pumping module at regular intervals to test the pressurized water injection in the borehole. The S2 monitoring module collects orifice pressure and grouting flow data in real time and uploads them to the central control module. The S3 central control module forwards the monitoring data to the intelligent analysis module, which uses the grouting stage timing identification model to determine the current grouting stage and transmits the results back. If S4 is determined to be an uninjectable stage, the continuous water injection test S1-S3 will be executed repeatedly. If S5 is determined to be in the zero-pressure stage: The S51 intelligent analysis module outputs the target slurry density and critical flow velocity; The S52 central control module sends the pulping density command to the pulp preparation module; The S53 monitoring module collects the measured slurry density. Once the density reaches the standard, the central control module sends a pumping command of 1.25 times the critical flow rate to the pumping module. The S54 monitoring module continuously collects pipeline pressure data and uploads it to the central control module, which then forwards it to the intelligent analysis module to calculate the pipeline safety factor. When the safety factor is greater than 1.6, the pumping module increases the pumping speed by 10%, and this process is repeated until the safety factor approaches 1.6. If S6 is determined to be in the boost phase: The S61 intelligent analysis module outputs the target slurry density, and the central control module controls the slurry preparation module to mix the slurry. After the S62 slurry density reaches the standard, start pumping for grouting. The S63 collects pipeline pressure and orifice pressure in real time and calculates the pipeline safety factor. When the safety factor is greater than 1.6 and the orifice pressure is lower than the design pressure, the pumping module increases the pumping speed by 10%. If S7 is determined to be in the pressure stabilization stage: the intelligent analysis module outputs the matching slurry density, and after the slurry meets the standard, the pumping module will stabilize the orifice pressure at 0.9-1.0 times the design pressure and continue grouting; If S8 is determined to be in the final pressure strengthening stage: the intelligent analysis module updates the target slurry density every half day. After the slurry reaches the standard, the pumping module controls the orifice pressure to be grouted within the range of 1.0-1.5 times the design pressure. The intelligent analysis module monitors the water-cement ratio and issues a shutdown command when the water-cement ratio approaches 3. The S9 monitoring module collects images of the wellhead in real time and transmits them to the intelligent analysis module. The image recognition model determines whether grout is leaking from the wellhead. When grout leakage is detected, the central control module immediately issues a stop grouting command to the pumping module and outputs an audible and visual alarm to prompt the operator to take action.

[0025] Furthermore, the pipeline safety factor calculation in steps S54 and S63 adopts a pipeline safety assessment model; In step S9, the identification of slurry overflow from the orifice uses an image recognition model; The real-time identification of pipeline blockage and grout leakage faults adopts a time-series identification model, which simultaneously triggers alarms and flow and pressure reduction controls when a fault occurs.

[0026] The beneficial effects of this invention are as follows: The central control module is connected to the pulping module, pumping module, monitoring module, and intelligent analysis module, integrating pulp preparation, pumping control, multi-parameter monitoring, and intelligent analysis into one system. This achieves fully automated and intelligent closed-loop management of the entire process. It significantly reduces reliance on operator experience, improves process consistency and repeatability, reduces human error and operational inaccuracies, and simultaneously lowers labor intensity and on-site safety risks.

[0027] Based on real-time monitoring data such as flow rate, pressure, and density, and combined with models such as critical velocity-density curves and water-cement ratio relationships, the system dynamically optimizes grout density, mix proportions, and pumping parameters. This ensures real-time matching of grouting material performance with project requirements, preventing problems such as pipe blockage and insufficient grouting volume from the source, and improving the pre-control capability of project quality.

[0028] The system integrates multiple fault diagnosis models, including pipeline safety assessment, intelligent identification of grout leakage and blockage, and early warning of grout leakage risk at orifices. Based on multi-source data fusion analysis, it can achieve timely detection, precise location, and early warning of early abnormal conditions. This enables maintenance personnel to intervene proactively, effectively preventing major engineering accidents and ensuring the continuous and safe execution of grouting operations. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the system architecture of the present invention; Detailed Implementation To make the objectives, technical solutions, and advantages of this invention clearer, the invention will now be described in further detail with reference to the accompanying drawings and the following embodiments, so that the public can better understand the implementation method of this invention. The specific implementation scheme of this invention is as follows: The intelligent control system for real-time monitoring of overburden isolation grouting is characterized by a central control module. This module serves as the system's overall control unit and core control unit, responsible for coordinating and directing the operation of all subsequent modules. It features a human-machine interface, supports centralized setting of grouting process parameters, visual display of process data, alarm prompts for abnormal states, and real-time issuance of control commands. Through this module, the system can achieve centralized monitoring, intelligent analysis, and automatic control of the entire grouting process, significantly improving the reliability, safety, and efficiency of grouting projects.

[0030] The pulping module receives pulping density instructions from the central control module, automatically weighs fly ash, gangue powder, cement, and water raw materials, and mixes them to prepare a uniform slurry. It also provides real-time feedback on slurry consistency, uniformity, and measured density to the central control module. The pulping module includes a high-precision weighing unit, a multi-stage mixer, a mixing tank, and an online slurry density detection device, enabling automatic raw material feeding, continuous mixing, and real-time feedback on slurry density.

[0031] The pumping module receives pumping pressure and flow rate commands from the central control module, pressurizes the grout, and delivers it through pipelines to the grouting holes. It includes a high-pressure grouting pump, delivery pipelines, and a set of valves. It supports remote pressure regulation and flow control, and features remote adaptive pressure adjustment to adapt to different geological conditions and grouting stages. The pumping module comprises multiple high-pressure grouting pumps, branch delivery pipelines, and electrically controlled valve assemblies, supporting independent pressure and flow control for multiple pipelines and global coordinated grouting.

[0032] The monitoring module is used to collect multi-dimensional grouting monitoring data in real time and upload it to the central control module. The monitoring data includes grout density, pipeline pressure, pipeline flow rate, orifice pressure, flow velocity at multiple measuring points in the pipeline, and on-site images of the orifice. The grout density is measured in real time by a grout density detection device, and the grout state is tested by a grout pressure monitoring instrument. Pipeline pressure and flow rate data are collected in real time with the help of pipeline pressure sensors and grout flow meters. High-speed cameras installed at the grouting orifice monitor in real time whether grout leakage occurs and can be linked with sensor data for analysis.

[0033] The intelligent analysis module receives monitoring data forwarded by the central control module. It incorporates a pipeline safety assessment model, a grout leakage and blockage timing identification model, a grouting stage timing identification model, a grout mix dynamic optimization model, and a grout leakage image recognition model. It outputs pipeline safety factors, fault warning signals, current grouting stage, target grout density, critical flow velocity, water-cement ratio, and grout leakage judgment results to the central control module. Based on multi-source data fusion analysis, it enables timely detection, precise location, and early warning of early abnormal conditions. This allows maintenance personnel to intervene proactively, effectively preventing major engineering accidents and ensuring continuous and safe grouting operations.

[0034] Specifically, for a thin-walled cylindrical pipe subjected to a uniform internal pressure pi, the inner wall of the pipe is where the Mises stress is greatest. Therefore, a pipe safety status assessment method based on the inner wall Mises stress is established, and the steps include: The outer wall of the pipe is a free surface, and the radial stress of the pipe is... It conforms to the generalized plane stress state, according to the generalized Hooke's law:

[0035] In the formula: It is circumferential stress; This is axial stress; For circumferential strain; This refers to the axial strain of the pipeline. E is the elastic modulus; v is Poisson's ratio; Circumferential and axial strains can be measured by strain gauges, while elastic modulus E and Poisson's ratio v are mechanical parameters of the steel pipe and can be measured in advance.

[0036] According to the thin-walled cylinder theory, the pressure on the outer wall of the pipe and the slurry... The relationship is:

[0037] In the formula, R is the outer diameter of the pipe, t is the pipe wall thickness, and slurry pressure is... The wall thickness t after wear can be calculated by combining the above equations, which can be obtained by measuring the slurry pressure sensor. The circumferential stress on the inner wall of the pipe can then be calculated using the following formula. :

[0038] Because the pipe wall is relatively thin, the axial strain of the pipe varies little along the wall thickness. The axial stress is estimated by estimating the internal pressure through the axial and circumferential strains of the outer wall.

[0039] Mises stress for:

[0040] In the formula: This refers to the circumferential stress on the inner wall of the pipe. This refers to the radial stress of the pipe. This refers to the axial stress of the pipe. ; Define the safety factor as M. In the formula, For the yield stress measured by the experiment, M>1.5 is defined as the safety threshold. When the safety factor is higher than the threshold, the speed-up optimization command is output, and when the safety factor is lower than the threshold, the speed-down and shutdown warning signals are output.

[0041] It should be noted that grout leakage and pipe blockage are two typical and serious faults in grouting pipeline systems. Both are essentially abnormalities in the system's fluid dynamics, specifically reflected in the instability and deviation of key parameters such as pressure and flow rate. The intelligent analysis module incorporates a time-series identification model for grout leakage and pipe blockage. Pressure, velocity, and flow sensors are deployed at the inlet, middle, and end of the grouting pipeline. Using multi-point, multi-parameter time-series parameters within a sliding time window as input to the deep learning model, it automatically extracts the fluid dynamic characteristics of pipe blockage and grout leakage, and outputs the fault type and fault section location results in real time.

[0042] Specifically, to achieve accurate and early fault diagnosis and section location, high-precision pressure, velocity, and flow meters are simultaneously deployed at three key nodes: the pipeline inlet, middle, and end, forming a synchronized monitoring system for the entire pipeline network. By collecting real-time time-series data of pressure, flow rate, and velocity at these three points, a time-series identification model for grout leakage and pipe blockage can be constructed. This model uses multi-point, multi-parameter time-series data within a sliding time window (e.g., 5 minutes) as input, and employs time-series analysis methods such as deep learning to deeply analyze both pipe blockage and grout leakage. The time-series identification model can identify grout leakage and pipe blockage in real time.

[0043] It should be noted that the intelligent analysis module has a built-in grouting stage timing recognition model; the entire grouting process is divided into five continuous stages: the non-grouting stage, the zero-pressure stage, characterized by the orifice pressure being close to zero, where the system needs to conduct grouting tests and determine whether the grouting volume needs to be increased; the pressure-increasing stage, where the orifice pressure is between zero and the design pressure, where the system needs to gradually increase the grouting volume to push the pressure up; the design pressure steady-state stage, where the orifice pressure is stable near the design value, where the system needs to maintain the grouting volume and the working face advancement speed in sync to maintain pressure stability; and the final pressure enhancement stage, where the system needs to strengthen pressure monitoring and dynamically adjust the grout density and grouting parameters to ensure that the pressure is within the safe final pressure range.

[0044] A time-series identification model for grouting stages with orifice pressure as input was constructed. The model takes orifice pressure time-series data collected within a sliding time window (e.g., 3 minutes) as input, automatically extracts the feature patterns of pressure changes in each stage through a deep learning algorithm, and outputs the identification result of the current grouting stage.

[0045] Furthermore, the intelligent analysis module incorporates a dynamic optimization model for slurry mix proportions, matching slurry parameters in stages: (1) Non-injection stage: Output continuous water injection test command, without configuring grouting slurry.

[0046] (2) Zero-pressure stage: First, conduct water injection tests to calculate the water injection volume in one day. Calculate the flow rate using the relationship between flow rate and velocity. Based on the current flow rate, divide by a safety factor (e.g., 1.5) to obtain a safe flow rate. The slurry may deposit under certain conditions during pipeline transportation. There is a critical velocity; above this velocity, the slurry will not deposit. The critical velocity is related to the slurry density; the higher the density, the lower the critical velocity. Find a corresponding critical density on the critical velocity-density curve. Based on this critical density, find the corresponding water-cement ratio on the density-water-cement ratio curve. If the water-cement ratio is greater than 3, continue water injection. If it is less than 3, start grouting. When the water-cement ratio is less than 1, take the water-cement ratio as 1. The upper limit of grouting capacity is the pressure that the pipeline can withstand. The water-cement ratio refers to the ratio of the mass of water to the mass of fly ash. The slurry density and the water-cement ratio are negatively correlated.

[0047] (3) Pressure boosting stage: Based on the grouting volume of the previous day, calculate the flow rate using the relationship between flow rate and velocity. Based on the current flow rate, divide by the safety factor (e.g., 1.5) to obtain a safe flow rate. Find a corresponding critical density on the critical flow rate-density curve. When the water-cement ratio is less than 1, take the water-cement ratio as 1. The upper limit of grouting capacity is the pressure that the pipeline can withstand. At the same time, pay attention to the orifice pressure not being greater than the design value.

[0048] (4) Pressure Stabilization Stage: A smart recognition model is trained using field monitoring data. Input information includes the current grouting stage, flow rate, grout density, and pressure data over the past ten minutes. The output is the predicted future flow rate. Based on the predicted flow rate, the water-cement ratio is calculated according to the flow rate-velocity relationship. The safe flow rate is obtained by dividing the current flow rate by a safety factor (e.g., 1.5), and the corresponding critical density is determined through the critical velocity-density curve. If the water-cement ratio is less than 1, it is set to water-cement ratio = 1, and the orifice pressure should be controlled within the design pressure range of (0.9-1.0).

[0049] (5) Final pressure enhancement stage: Construct an intelligent flow prediction model. Input information includes the current grouting stage, flow rate, grout density, and pressure data over the past ten minutes. Output is the predicted future flow rate. Based on the predicted flow rate, calculate the water-cement ratio according to the flow rate-velocity relationship. Divide the current flow rate by a safety factor (e.g., 1.5) to obtain the safe flow rate, and then determine the corresponding critical density through the critical flow rate-density curve. If the water-cement ratio is less than 1, take the value as water-cement ratio = 1. If the water-cement ratio is greater than 3, stop grouting, and at the same time, the orifice pressure should be controlled within the design pressure range of (1.0-1.5).

[0050] Based on a grouting stage timing identification model with orifice pressure as input, the system can automatically and accurately identify different stages of overburden delamination development and grouting filling, and intelligently recommend the best grouting timing and parameters accordingly, so that the grouting operation is always within the optimal time and space window, greatly improving the injectability of grout and filling efficiency.

[0051] It should be noted that the intelligent analysis module has a built-in image recognition model for slurry leakage from the orifice. It receives real-time images of the orifice, dynamically determines the slurry leakage status, and feeds back the recognition results to the central control module.

[0052] A real-time monitoring and intelligent control method for overburden isolation grouting, based on an intelligent control system for real-time monitoring of overburden isolation grouting, includes the following steps: After the S1 grouting hole is formed and the pipeline system is installed, the central control module sends water injection commands to the pumping module at regular intervals to test the pressurized water injection in the borehole. The S2 monitoring module collects orifice pressure and grouting flow data in real time and uploads them to the central control module. The S3 central control module forwards the monitoring data to the intelligent analysis module, which uses the grouting stage timing identification model to determine the current grouting stage and transmits the results back. If S4 is determined to be an uninjectable stage, the continuous water injection test S1-S3 will be executed repeatedly. If S5 is determined to be in the zero-pressure stage: The S51 intelligent analysis module outputs the target slurry density and critical flow velocity; The S52 central control module sends the pulping density command to the pulp preparation module; The S53 monitoring module collects the measured slurry density. Once the density reaches the standard, the central control module sends a pumping command of 1.25 times the critical flow rate to the pumping module. The S54 monitoring module continuously collects pipeline pressure data and uploads it to the central control module, which then forwards it to the intelligent analysis module to calculate the pipeline safety factor. The pipeline safety factor calculation uses a pipeline safety assessment model. When the safety factor is greater than 1.6, the pumping module increases the pumping speed by 10%, and this process is repeated until the safety factor approaches 1.6. If S6 is determined to be in the boost phase: The S61 intelligent analysis module outputs the target slurry density, and the central control module controls the slurry preparation module to mix the slurry. After the S62 slurry density reaches the standard, start pumping for grouting. The S63 collects pipeline pressure and orifice pressure in real time, calculates the pipeline safety factor, and uses a pipeline safety assessment model for the calculation. When the safety factor is greater than 1.6 and the orifice pressure is lower than the design pressure, the pumping module increases the pumping speed by 10%. If S7 is determined to be in the pressure stabilization stage: the intelligent analysis module outputs the matching slurry density, and after the slurry meets the standard, the pumping module will stabilize the orifice pressure at 0.9-1.0 times the design pressure and continue grouting; If S8 is determined to be in the final pressure strengthening stage: the intelligent analysis module updates the target slurry density every half day. After the slurry reaches the standard, the pumping module controls the orifice pressure to be grouted within the range of 1.0-1.5 times the design pressure. The intelligent analysis module monitors the water-cement ratio and issues a shutdown command when the water-cement ratio approaches 3. The S9 monitoring module collects images of the wellhead in real time and transmits them to the intelligent analysis module. The image recognition model identifies grout leakage from the wellhead. When grout leakage is detected, the central control module immediately issues a stop grouting command to the pumping module and outputs an audible and visual alarm to prompt the operator to take action.

[0053] The real-time identification of pipeline blockage and grout leakage faults adopts a time-series identification model, which simultaneously triggers alarms and flow and pressure reduction controls when a fault occurs.

[0054] The working principle and process of this invention are as follows: (1) Phased intelligent grouting main process: The grouting hole construction is completed, and the grouting pipeline, sensors, and borehole camera are all in place; the central control module outputs water injection commands at regular intervals to control the pumping module to inject pressurized water into the borehole to test the injectability of the rock strata. The monitoring module collects the orifice pressure and instantaneous grouting flow rate in real time and uploads them to the central control module's human-machine interface for storage and display. The central control module forwards pressure and flow time-series data to the intelligent analysis module, and the grouting stage identification model outputs the current grouting stage and sends it back to the central control module. If the stage is identified as uninjectable, the water injection monitoring process will continue to be cyclical until the stage is switched. Zero-pressure stage execution steps: The intelligent analysis module calculates the target slurry density and pipeline critical flow velocity; the central control module sends the density parameters to the slurry preparation module to start automatic slurry mixing; the monitoring module transmits the measured slurry density back in real time. After the density matches the target value, the pumping module is controlled to deliver the slurry at 1.25 times the critical flow velocity; pipeline pressure data is continuously collected to calculate the pipeline safety factor. When the safety factor is greater than 1.6, the pumping speed is increased by 10%, and the cycle is adjusted until the safety factor approaches 1.6. The pressurization stage execution steps are as follows: the intelligent analysis module outputs the slurry density adapted to the pressurization conditions, and the slurry preparation module automatically mixes the slurry; after the slurry meets the standard, grouting is started; the pipeline and orifice pressure are monitored in real time. If the pipeline safety factor is greater than 1.6 and the orifice pressure has not reached the design pressure, the pumping speed is automatically increased by 10% to accelerate the pressurization process. The pressure stabilization stage execution steps are as follows: The intelligent analysis module outputs the special slurry mix ratio for the pressure stabilization stage. After the slurry mix is ​​completed, the system automatically adjusts the pumping pressure to maintain the orifice pressure stably within the range of 0.9-1.0 times the design pressure, and continuously fills the overburden isolation space. Final pressure strengthening stage execution steps: The intelligent analysis module updates the optimal grout density every half day. After the grout preparation meets the standard, grouting is performed. The borehole pressure is controlled at 1.0-1.5 times the design pressure. The water-cement ratio is continuously monitored. When the water-cement ratio approaches 3, the central control module issues a stop command to end the grouting operation of this borehole.

[0055] (2) Emergency Implementation Procedure for Image Recognition of Grout Leakage from the Orifice: The monitoring module's high-definition camera continuously collects real-time images of the grouting hole, and the image data stream is transmitted to the intelligent analysis module in real time. The intelligent analysis module has a built-in visual recognition model that can determine whether there are characteristics of slurry overflow or ground slurry seepage in the image frame by frame. If the model outputs the grout leakage identification result, it will be immediately sent back to the central control module. The central control module will simultaneously perform two actions: send a stop grouting command to the pumping module to cut off the grout delivery; and activate the audible and visual alarm on the human-machine interface and prompt the operator to go to the grouting hole to handle and block the grout leakage.

[0056] (3) Pipeline safety and leak detection early warning: Pressure, velocity, and flow rate sensors are simultaneously installed at the pipeline inlet, middle section, and end to continuously collect time-series data. On the one hand, the intelligent analysis module runs the pipeline safety assessment model to calculate the Mises stress and safety factor of the pipe wall in real time and predict the risk of pipe wear and bursting; on the other hand, it inputs 5-minute sliding window time series data into the deep learning fault model to identify abnormal fluid characteristics such as pipe blockage and grout leakage. When any risk triggers the warning threshold, the central control module automatically reduces the pump flow and pressure, and the human-machine interface marks the location of the risk pipeline and the type of fault. Maintenance personnel can inspect the pipeline in advance to avoid grouting interruption and grout leakage and pollution.

Claims

1. An intelligent control system for real-time monitoring of overburden isolation grouting, characterized in that, include: The central control module serves as the system's overall control unit. The pulping module receives the pulping density command issued by the central control module, automatically weighs fly ash, gangue powder, and water raw materials, and stirs them to prepare a uniform slurry. It also provides real-time feedback on the slurry consistency, uniformity, and measured density to the central control module. The pumping module is used to receive pumping pressure and flow rate commands from the central control module, pressurize the slurry and deliver it to the grouting hole through the pipeline, and has a remote pressure adaptive adjustment function. The monitoring module is used to collect multi-dimensional grouting monitoring data in real time and upload it to the central control module. The monitoring data includes grout density, pipeline pressure, pipeline flow rate, orifice pressure, flow velocity at multiple measuring points in the pipeline, and on-site images of the orifice. The intelligent analysis module receives monitoring data forwarded by the central control module. It has built-in pipeline safety assessment model, grout leakage and pipe blockage timing identification model, grouting stage timing identification model, grout ratio dynamic optimization model, and orifice grout leakage image recognition model. It outputs pipeline safety factor, fault warning signal, current grouting stage, target grout density, critical flow velocity, water-cement ratio, and grout leakage judgment results to the central control module.

2. The intelligent control system for real-time monitoring of overburden isolation grouting according to claim 1, characterized in that, The modeling steps for pipeline safety assessment include: The outer wall of the pipe is a free surface, and the radial stress of the pipe is... It conforms to the generalized plane stress state, according to the generalized Hooke's law: In the formula: It is circumferential stress; For axial stress; For circumferential strain; For the axial strain of the pipeline; E It is the elastic modulus; v Poisson's ratio; According to the thin-walled cylinder theory, the pressure on the outer wall of the pipe and the slurry... The relationship is: In the formula, R is the outer diameter of the pipe, t is the pipe wall thickness, and slurry pressure is... The wall thickness t after wear can be calculated by combining the above equations, which can be obtained by measuring the slurry pressure sensor. The circumferential stress on the inner wall of the pipe can then be calculated using the following formula. : Because the pipe wall is relatively thin, the axial strain of the pipe varies little along the wall thickness. The axial stress is estimated by estimating the internal pressure through the axial and circumferential strains of the outer wall. Mises stress for: In the formula: This refers to the circumferential stress on the inner wall of the pipe. This refers to the radial stress of the pipe. This refers to the axial stress of the pipe. ； Define the safety factor as M. In the formula, For the yield stress measured by the experiment, M>1.5 is defined as the safety threshold. When the safety factor is higher than the threshold, the speed-up optimization command is output, and when the safety factor is lower than the threshold, the speed-down and shutdown warning signals are output.

3. The intelligent control system for real-time monitoring of overburden isolation grouting according to claim 1, characterized in that, The intelligent analysis module has a built-in intelligent identification model for grout leakage and pipe blockage. Pressure, flow velocity, and flow rate sensors are installed at the inlet, middle section, and end of the grouting pipeline. The deep learning model is input with multi-point and multi-parameter time series parameters within a sliding time window. The model automatically extracts the fluid dynamic characteristics of pipe blockage and grout leakage and outputs the fault type and fault section location results in real time.

4. The intelligent control system for real-time monitoring of overburden isolation grouting according to claim 1, characterized in that, The intelligent analysis module has a built-in grouting stage time sequence recognition model. It divides the entire grouting process into the non-grouting stage, zero pressure stage, pressure increase stage, design pressure steady state stage, and final pressure enhancement stage. It uses the orifice pressure time sequence data within the sliding time window as deep learning input, and automatically extracts the feature patterns of pressure changes in each stage through deep learning algorithms, and outputs the recognition results of the current grouting stage.

5. The intelligent control system for real-time monitoring of overburden isolation grouting according to claim 4, characterized in that, The intelligent analysis module incorporates a dynamic optimization model for slurry mix proportions, matching slurry parameters in stages: (1) Non-injection stage: Output continuous water injection test command, without configuring grouting slurry; (2) Zero pressure stage: The pipeline flow velocity is calculated based on the daily water injection volume, and the safe flow velocity is obtained by dividing by the safety factor. The critical grout density is matched by the critical flow velocity-density curve, and the water-cement ratio is determined by the density-water-cement ratio curve. Water injection is maintained when the water-cement ratio is >3, and grouting is started when the water-cement ratio is ≤3. The lower limit of the water-cement ratio is set to 1, and the upper limit of the grouting capacity is the pressure that the pipeline can withstand. (3) Pressure boosting stage: Calculate pipeline flow velocity based on historical daily grouting volume, match critical density and water-cement ratio, with a lower limit of water-cement ratio of 1, and simultaneously constrain orifice pressure to not exceed design pressure; (4) Pressure stabilization stage: Construct an intelligent flow prediction model, input recent flow, density and pressure data to predict subsequent flow, convert safe flow rate to match critical density, water-cement ratio ≥1, and orifice pressure controlled at 0.9-1.0 times the design pressure; (5) Final pressure enhancement stage: The intelligent flow prediction model outputs the predicted flow rate, matches the critical density and water-cement ratio, the water-cement ratio is ≥1, and the orifice pressure is controlled at 1.0-1.5 times the design pressure; if the water-cement ratio is close to 3, the shutdown command is output.

6. The intelligent control system for real-time monitoring of overburden isolation grouting according to claim 1, characterized in that, The intelligent analysis module has a built-in image recognition model for slurry leakage from the orifice. It receives real-time images of the orifice, dynamically determines the slurry leakage status, and feeds back the recognition results to the central control module.

7. The intelligent control system for real-time monitoring of overburden isolation grouting according to claim 1, characterized in that, The pulping module includes a high-precision weighing system, a multi-stage mixer, a mixing tank, and an online slurry density detection device, enabling automatic raw material feeding, continuous mixing, and real-time feedback of slurry density.

8. The intelligent control system for real-time monitoring of overburden isolation grouting according to claim 1, characterized in that, The pumping module includes multiple sets of high-pressure grouting pumps, branch delivery pipelines, and electrically controlled valve groups, supporting independent pressure and flow regulation of multiple pipelines and global coordinated grouting.

9. A real-time monitoring and intelligent control method for grouting for overburden isolation, characterized in that, The intelligent control system for real-time monitoring of overburden isolation grouting according to any one of claims 1-8 includes the following steps: After the S1 grouting hole is formed and the pipeline system is installed, the central control module sends water injection commands to the pumping module at regular intervals to test the pressurized water injection in the borehole. The S2 monitoring module collects orifice pressure and grouting flow data in real time and uploads them to the central control module. The S3 central control module forwards the monitoring data to the intelligent analysis module, which determines the current grouting stage through the grouting stage timing identification model and sends back the results. If S4 is determined to be an uninjectable stage, the continuous water injection test S1-S3 will be executed repeatedly; If S5 is determined to be in the zero-pressure stage: The S51 intelligent analysis module outputs the target slurry density and critical flow velocity; The S52 central control module sends the pulping density command to the pulp preparation module; The S53 monitoring module collects the measured slurry density. Once the density reaches the standard, the central control module sends a pumping command of 1.25 times the critical flow rate to the pumping module. The S54 monitoring module continuously collects pipeline pressure data and uploads it to the central control module, which then forwards it to the intelligent analysis module to calculate the pipeline safety factor. When the safety factor is greater than 1.6, the pumping module increases the pumping speed by 10%, and this process is repeated until the safety factor approaches 1.

6. If S6 is determined to be in the boost phase: The S61 intelligent analysis module outputs the target slurry density, and the central control module controls the slurry preparation module to mix the slurry. After the S62 slurry density reaches the standard, start pumping for grouting. The S63 collects pipeline pressure and orifice pressure in real time and calculates the pipeline safety factor. When the safety factor is greater than 1.6 and the orifice pressure is lower than the design pressure, the pumping module increases the pumping speed by 10%. If S7 is determined to be in the pressure stabilization stage: the intelligent analysis module outputs the matching slurry density, and after the slurry meets the standard, the pumping module will stabilize the orifice pressure at 0.9-1.0 times the design pressure and continue grouting; If S8 is determined to be in the final pressure strengthening stage: the intelligent analysis module updates the target slurry density every half day. After the slurry reaches the standard, the pumping module controls the orifice pressure to be grouted within the range of 1.0-1.5 times the design pressure. The intelligent analysis module monitors the water-cement ratio and issues a shutdown command when the water-cement ratio approaches 3. The S9 monitoring module collects images of the wellhead in real time and transmits them to the intelligent analysis module. The image recognition model determines whether grout is leaking from the wellhead. When grout leakage is detected, the central control module immediately issues a stop grouting command to the pumping module and outputs an audible and visual alarm to prompt the operator to take action.

10. The intelligent control method for real-time monitoring of overburden isolation grouting according to claim 9, characterized in that: The pipeline safety factor calculation in steps S54 and S63 uses a pipeline safety assessment model; In step S9, the identification of slurry overflow from the orifice uses an image recognition model; The real-time identification of pipeline blockage and grout leakage faults adopts a time-series identification model, which simultaneously triggers alarms and flow and pressure reduction controls when a fault occurs.

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