Method and device for treating slurry fluidity, equipment, storage medium

Abstract

The embodiment of the application discloses a slurry flowability processing method and device, equipment and a storage medium, which determines a pipe blocking risk coefficient in real time according to a dynamic response model of pressure and flow rate corresponding to a target mine grouting pipeline. In the case that the pipe blocking risk coefficient is lower than a preset threshold, the effective diffusion radius, the filling stability index and the slurry bleeding rate of the target mine grouting pipeline are determined and input into a trained random forest regression model to obtain the residual coal oxidation inhibition rate and the CO diffusion blocking rate. At least one of the pipe blocking risk coefficient, the effective diffusion radius, the filling stability index, the slurry bleeding rate, the residual coal oxidation inhibition rate and the CO diffusion blocking rate is used to determine a slurry adjustment scheme. The embodiment of the application realizes full-chain closed-loop testing and regulation of slurry flowability from dynamic pipe blocking risk judgment, permeability evaluation, fire prevention and extinguishing efficiency prediction and parameter optimization decision, and improves the safety, adaptability and feedforward response capability of the coal mine grouting filling system.

Description

Technical Field

[0001] This application relates to the field of slurry flowability treatment, and includes, but is not limited to, a slurry flowability treatment method, apparatus, equipment, and storage medium. Background Technology

[0002] Driven by the dual demands of green mining and safe production in coal mines, gangue backfilling fire prevention and extinguishing technology has become a key means to solve the pollution from gangue accumulation and the hidden danger of spontaneous combustion of residual coal in goaf areas. Especially for near-horizontal shallow coal seam mines, the leakage of air through mining-induced fractures to the surface can easily lead to spontaneous combustion accidents. It is urgent to achieve the coordinated management of gangue resource utilization and fire zone sealing through slurry backfilling. The fluidity of the slurry directly affects its transportation efficiency, goaf infiltration coverage effect, and fire prevention and extinguishing effectiveness, and is a core indicator for the safe and stable operation of the backfilling system.

[0003] Currently, some technical methods exist for treating slurry fluidity. Existing slurry testing techniques are mostly based on static funnel methods, sedimentation methods, or single-point rotational rheometers to measure indicators, mainly focusing on single-factor evaluation of the slurry's apparent viscosity or initial yield stress. Meanwhile, some patented technologies also contain solutions for slurry performance testing, but these solutions have significant shortcomings. First, they fail to reflect the dynamic response characteristics of the slurry in complex transport paths and cannot identify the combined effects of particle size variation and shear history on pipe blockage risk. Second, goaf permeability analysis often relies solely on endpoint sampling or single-point pore pressure monitoring, making it difficult to describe the spatial heterogeneity and temporal evolution of the filling process. Third, in fire prevention and extinguishing effectiveness assessment, conventional methods rely on outcome-based observations rather than process parameter predictions, leading to delayed decision-making and adjustments to fire extinguishing effectiveness, and an inability to proactively intervene in construction mix design based on fluidity characteristics.

[0004] Furthermore, the static testing methods used in existing testing technologies are out of touch with the complex pipeline topology environment in actual wells, resulting in limited reference value for the test results. Moreover, there is a lack of effective models that link flow parameters with fire prevention and extinguishing effectiveness, making it impossible to achieve closed-loop management from testing to control. Summary of the Invention

[0005] In view of this, the slurry flowability treatment method, apparatus, equipment, and storage medium provided in the embodiments of this application can construct a key work order detection mechanism with high accuracy, fully understand the deep semantics of business work orders, and realize automatic detection of key work orders.

[0006] The slurry flowability treatment method, apparatus, equipment, and storage medium provided in this application are implemented as follows: One aspect of this application provides a method for treating the fluidity of a slurry, the method comprising: The risk coefficient of pipe blockage is determined in real time based on the dynamic response model of pressure and flow velocity corresponding to the grouting pipeline of the target mine. When the pipe blockage risk coefficient is lower than a preset threshold, the effective diffusion radius, filling stability index, and slurry bleeding rate of the target mine grouting pipeline are determined. The effective diffusion radius, filling stability index and slurry bleeding rate are input into the trained random forest regression model to obtain the residual coal oxidation inhibition rate and CO diffusion blocking rate. The slurry adjustment scheme is determined based on at least one of the following: pipe blockage risk coefficient, effective diffusion radius, filling stability index, slurry bleeding rate, residual coal oxidation inhibition rate, and CO diffusion blocking rate.

[0007] In one possible implementation, the step of determining the pipe blockage risk coefficient in real time based on the dynamic response model of pressure and flow velocity corresponding to the grouting pipeline of the target mine includes: Real-time acquisition of slurry particle size distribution and viscosity parameters of the grouting pipeline in the target mine; The particle size distribution and viscosity parameters of the grouting pipeline in the target mine are input into the dynamic response model of pressure and flow rate to obtain the pressure mutation rate and the proportion of critical particle size. The pipe blockage risk coefficient is calculated based on the pressure mutation rate and the proportion of critical particle size.

[0008] In one possible implementation, determining the effective diffusion radius, filling stability index, and slurry bleeding rate of the target mine grouting pipeline includes: Obtain resistivity tomographic data and pore pressure time-series signal of the grouting pipeline of the target mine; The effective diffusion radius and filling stability index are calculated based on the resistivity tomography data and pore pressure time-series signal. The slurry bleeding rate is calculated based on the change in laser liquid level in the drainage trough corresponding to the grouting pipeline of the target mine.

[0009] In one possible implementation, the calculation of the effective diffusion radius and filling stability index based on the resistivity tomography data and the pore pressure time-series signal includes: The resistivity gradient field is reconstructed based on the resistivity tomography data, and the theoretical diffusion radius is determined based on the resistivity gradient field. The effective diffusion radius is obtained by correcting the theoretical diffusion radius. The filling stability index is calculated based on the pore pressure time series signal.

[0010] In one possible implementation, the method further includes: Sample data were determined by measuring the effective diffusion radius, filling stability index, and slurry bleeding rate corresponding to historical backfilling projects. The surface temperature change rate of extracted coal samples was determined as the label for the oxidation inhibition rate of residual coal, and the slope of CO concentration decrease was calculated as the label for CO diffusion blocking rate, thus obtaining the training set. A random forest regression model is trained based on the training set.

[0011] In one possible implementation, the method further includes: Based on the topology parameters of the grouting pipeline in the target mine and historical pipe blockage event data, a dynamic response model of pressure and flow velocity for the grouting pipeline in the target mine is constructed.

[0012] In one possible implementation, determining the slurry adjustment scheme based on at least one of the following: pipe blockage risk coefficient, effective diffusion radius, filling stability index, slurry bleeding rate, residual coal oxidation inhibition rate, and CO diffusion blocking rate, includes: By using a fuzzy control algorithm, based on at least one of the following factors—the pipe blockage risk coefficient, effective diffusion radius, filling stability index, slurry bleeding rate, residual coal oxidation inhibition rate, and CO diffusion blocking rate—the particle size distribution adjustment weight, water-reducing agent addition threshold, and pressure adjustment range are determined, thereby obtaining a slurry adjustment scheme.

[0013] Another aspect of the embodiments of this application provides a slurry flowability treatment apparatus, the apparatus comprising: The first parameter determination module is used to determine the pipe blockage risk coefficient in real time based on the dynamic response model of pressure and flow velocity corresponding to the grouting pipeline of the target mine. The second parameter determination module is used to determine the effective diffusion radius, filling stability index and slurry bleeding rate of the target mine grouting pipeline when the pipe blockage risk coefficient is lower than a preset threshold. The third parameter determination module is used to input the effective diffusion radius, filling stability index and slurry bleeding rate into the trained random forest regression model to obtain the residual coal oxidation inhibition rate and CO diffusion blocking rate. The scheme determination module is used to determine the slurry adjustment scheme based on at least one of the following: pipe blockage risk coefficient, effective diffusion radius, filling stability index, slurry bleeding rate, residual coal oxidation inhibition rate, and CO diffusion blocking rate.

[0014] In one possible implementation, the first parameter determining module is further configured to: Real-time acquisition of slurry particle size distribution and viscosity parameters of the grouting pipeline in the target mine; The particle size distribution and viscosity parameters of the grouting pipeline in the target mine are input into the dynamic response model of pressure and flow rate to obtain the pressure mutation rate and the proportion of critical particle size. The pipe blockage risk coefficient is calculated based on the pressure mutation rate and the proportion of critical particle size.

[0015] In one possible implementation, the second parameter determining module is further configured to: Obtain resistivity tomographic data and pore pressure time-series signal of the grouting pipeline of the target mine; The effective diffusion radius and filling stability index are calculated based on the resistivity tomography data and pore pressure time-series signal. The slurry bleeding rate is calculated based on the change in laser liquid level in the drainage trough corresponding to the grouting pipeline of the target mine.

[0016] In one possible implementation, the second parameter determining module is further configured to: The resistivity gradient field is reconstructed based on the resistivity tomography data, and the theoretical diffusion radius is determined based on the resistivity gradient field. The effective diffusion radius is obtained by correcting the theoretical diffusion radius. The filling stability index is calculated based on the pore pressure time series signal.

[0017] In one possible implementation, the device further includes: The training set determination module is used to determine sample data by using the effective diffusion radius, filling stability index and slurry bleeding rate corresponding to historical filling projects, and to determine the surface temperature change rate of extracted coal samples as the label of residual coal oxidation inhibition rate, and to calculate the slope of CO concentration decrease as the label of CO diffusion blocking rate, thus obtaining the training set; The model training module is used to train a random forest regression model based on the training set.

[0018] In one possible implementation, the device further includes: The model determination module is used to construct a dynamic response model of the pressure and flow velocity of the grouting pipeline in the target mine based on the topology parameters of the grouting pipeline and historical pipe blockage event data.

[0019] In one possible implementation, the scheme determining module is further configured to: By using a fuzzy control algorithm, based on at least one of the following factors—the pipe blockage risk coefficient, effective diffusion radius, filling stability index, slurry bleeding rate, residual coal oxidation inhibition rate, and CO diffusion blocking rate—the particle size distribution adjustment weight, water-reducing agent addition threshold, and pressure adjustment range are determined, thereby obtaining a slurry adjustment scheme.

[0020] The electronic device provided in this application includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the program, it implements the method described in this application.

[0021] The computer-readable storage medium provided in this application embodiment stores a computer program thereon, which, when executed by a processor, implements the method provided in this application embodiment.

[0022] The slurry flowability treatment method, apparatus, equipment, and storage medium provided in this application embodiment determine the pipe blockage risk coefficient in real time based on the dynamic response model of pressure and flow velocity corresponding to the grouting pipeline of the target mine. When the pipe blockage risk coefficient is lower than a preset threshold, the effective diffusion radius, filling stability index, and slurry bleeding rate of the grouting pipeline of the target mine are determined. The effective diffusion radius, filling stability index, and slurry bleeding rate are input into a trained random forest regression model to obtain the residual coal oxidation inhibition rate and CO diffusion blocking rate. A slurry adjustment scheme is determined based on at least one of the pipe blockage risk coefficient, effective diffusion radius, filling stability index, slurry bleeding rate, residual coal oxidation inhibition rate, and CO diffusion blocking rate. This application embodiment realizes a closed-loop testing and control of slurry flowability from dynamic pipe blockage risk judgment, permeability performance assessment, fire prevention and extinguishing effectiveness prediction, and parameter optimization decision-making, improving the safety, adaptability, and feedforward response capability of the coal mine grouting and filling system. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating a slurry flowability treatment method according to an embodiment of this application is shown; Figure 2 A schematic diagram of a slurry flowability treatment apparatus according to an embodiment of this application is shown; Figure 3 A schematic diagram of an electronic device according to an embodiment of this application is shown. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the specific technical solutions of this application will be further described in detail below with reference to the accompanying drawings of the embodiments of this application. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0027] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0028] It should be noted that the terms "first, second, third" used in the embodiments of this application are used to distinguish similar or different objects and do not represent a specific order of objects. It can be understood that "first, second, third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0029] The slurry fluidity treatment method of this application embodiment can be executed by any electronic device capable of controlling the hot-rolled strip steel system. This electronic device may include, but is not limited to, mobile phones, wearable devices (such as smartwatches, smart bracelets, smart glasses, etc.), tablet computers, laptops, vehicle terminals, PCs (Personal Computers), etc. The functions implemented by this method can be achieved by a processor in the electronic device calling program code. Of course, the program code can be stored in a computer storage medium. Therefore, the electronic device includes at least a processor and a storage medium.

[0030] The slurry flowability treatment method of this application can be used in any application scenario that requires slurry flowability treatment. For example, this application embodiment can be applied to the application scenario of goaf filling and fire zone sealing in near-horizontal shallow coal seam mines. Alternatively, it can also be applied to the application scenario of pipe blockage risk prevention and control during gangue filling.

[0031] The following describes in detail the slurry flowability treatment scheme of the present application embodiment with reference to the accompanying drawings.

[0032] Figure 1 A flowchart illustrating a slurry flowability treatment method according to an embodiment of this application is shown. Figure 1 As shown, the slurry fluidity treatment method of this application embodiment may include the following steps S10-S40.

[0033] For ease of description, the slurry flowability processing method of this application embodiment is described using an electronic device as the execution subject. It should be understood that the execution subject of this application embodiment can also be a processor or chip in an electronic device, and this application embodiment does not impose any limitations.

[0034] Step S10: Determine the pipe blockage risk coefficient in real time based on the dynamic response model of pressure and flow velocity corresponding to the grouting pipeline of the target mine.

[0035] In one possible implementation, an electronic device is used to first determine the blockage risk index of the grouting pipeline in the target mine. This index quantifies the risk of blockage during the grout transportation process. A higher blockage risk index indicates a greater likelihood of grout deposition, accumulation, or pressure anomalies during transport, leading to a higher risk of blockage. Conversely, a lower blockage risk index indicates smoother transport and lower risk.

[0036] Optionally, the blockage wind direction coefficient can be determined using a dynamic response model of pressure and velocity obtained through simulation modeling of the target mine grouting pipeline. Specifically, electronic equipment can construct the dynamic response model of the target mine grouting pipeline based on its topology parameters and historical blockage event data. This model construction process can begin by acquiring the topology parameters of the target mine grouting pipeline, including bend curvature radius, vertical drop, and cross-sectional shape of variable diameter sections, to transform the pipeline's geometric characteristics into quantifiable flow resistance parameters.

[0037] In some embodiments, the electronic device can convert the radius of curvature of an elbow into a centrifugal force loss coefficient, the vertical drop into a static pressure compensation function, and the cross-sectional shape of a variable diameter section into a turbulence intensity correction factor, ultimately obtaining a three-dimensional pipe resistance distribution heatmap to determine the initial model, which visually displays the strength of flow resistance at various locations in the pipeline. Furthermore, the electronic device integrates historical pipe blockage event data to refine the model's accuracy. Specifically, the electronic device can extract the marginal spectrum energy of the intrinsic mode function of the pressure drop waveform through empirical mode decomposition, construct a phase shift compensation term for the transfer function, enabling the model to reproduce the historical pressure anomaly characteristics that led to pipe blockage, thus improving the accuracy of predicting current operating conditions.

[0038] After determining the dynamic response model of pressure and flow rate for the grouting pipeline in the target mine, electronic equipment can acquire the particle size distribution and viscosity parameters of the grout in the target mine in real time. Then, the particle size distribution and viscosity parameters of the grout in the target mine are input into the dynamic response model of pressure and flow rate to obtain the pressure mutation rate and critical particle size ratio. Further, the pipe blockage risk coefficient is calculated based on the pressure mutation rate and critical particle size ratio. The particle size distribution and viscosity parameters can be obtained using a laser particle size analyzer and a rotational viscometer installed in the grouting pipeline of the target mine. The particle size distribution can be a multi-peak distribution, and the viscosity parameter can be a curve showing the change in viscosity with shear rate.

[0039] Optionally, during model operation, pressure waveforms are output at key monitoring nodes in the pipeline (downstream of elbows, at diameter transitions), and empirical mode decomposition is performed on the pressure waveforms to extract pressure abrupt change characteristic values ​​(unit: MPa·s). These values ​​are directly used for the weighted calculation of the pipe blockage risk coefficient. Simultaneously, the electronic equipment calculates the critical particle size ratio based on the slurry particle size distribution and viscosity parameters, which is also used for the weighted calculation of the pipe blockage risk coefficient. This pipe blockage risk coefficient can be obtained through... The calculation yielded that, The pressure mutation rate depends entirely on the pressure waveform output by the pressure and flow velocity dynamic response model. (Critical particle size percentage) Independently derived from laser particle size analyzer.

[0040] For example, in a grouting pipeline in a certain mine, the bend curvature radius is 0.8m, the vertical drop is 12m, and the diameter of the variable section transitions from 0.5m to 0.3m. Pressure data from 50 historical pipe blockage events in the mine are extracted. The marginal spectrum energy of the intrinsic mode function of the pressure drop waveform is extracted through empirical mode decomposition, and the phase shift compensation term of the transfer function is constructed. A laser particle size analyzer is used to collect multi-peak distribution data of the slurry in real time, and a rotational viscometer is used to obtain the shear thinning curve. Monitoring nodes are set downstream of three bends and at two diameter transition points in the pipeline. When the characteristic value of the pressure change at a certain monitoring node is 0.35MPa... With a critical particle size ratio of 28% and a pressure characteristic weight of 0.6 in the bend area, the calculated pipe blockage risk coefficient for this node is 0.31.

[0041] Step S20: If the pipe blockage risk coefficient is lower than a preset threshold, determine the effective diffusion radius, filling stability index, and slurry bleeding rate of the target mine grouting pipeline.

[0042] In one possible implementation, after calculating the blockage risk coefficient of the grouting pipeline in the target mine through step S10, the electronic device compares the blockage risk coefficient with a preset threshold. Further, it determines the effective diffusion radius, filling stability index, and slurry bleeding rate of the current target mine grouting pipeline. Specifically, the electronic device can first acquire resistivity tomographic data and pore pressure time-series signals of the target mine grouting pipeline. Then, it calculates the effective diffusion radius and filling stability index based on the resistivity tomographic data and pore pressure time-series signals, and finally calculates the slurry bleeding rate based on the laser liquid level change in the corresponding drainage trough of the target mine grouting pipeline. The effective diffusion radius reflects the maximum distance that the slurry can actually penetrate and cover in the goaf gangue layer. A larger parameter indicates better slurry fluidity and a wider penetration coverage, which is beneficial for improving fire prevention and extinguishing effects. The filling stability index reflects the pressure stability and spatial uniformity of the slurry during the filling process in the goaf gangue layer. A higher index indicates smaller pore pressure fluctuations, smoother slurry filling, and less likelihood of local accumulation or voids. A lower index indicates more severe pressure fluctuations, potentially suggesting blockages in seepage channels, sudden pressure increases / decreases, and other unstable phenomena. The slurry bleeding rate is a key indicator of the slurry's tendency to separate into solid and liquid phases. Its physical significance lies in revealing the stability and uniformity of the slurry during transport and filling, as well as its potential impact on fire prevention and extinguishing effectiveness. A higher bleeding rate indicates a greater likelihood of slurry segregation, increasing the risk of pipe blockage, uneven filling, and fireproofing failure.

[0043] Optionally, the resistivity tomographic data of the target mine grouting pipeline is obtained by a three-dimensional electrode array consisting of multiple electrode points arranged in the gangue layer (goaf), and the pore pressure timing signal is obtained by an optical fiber pressure sensor array consisting of optical fiber pressure sensors buried at different depths in the gangue layer.

[0044] In some embodiments, after acquiring resistivity tomography data, the electronic device can first reconstruct the resistivity gradient field based on the resistivity tomography data, determine the theoretical diffusion radius based on the resistivity gradient field, and then correct the theoretical diffusion radius to obtain the effective diffusion radius. Specifically, the electronic device can inject gradient current into the gangue layer through a three-dimensional electrode array, measure the potential response at each electrode point, and obtain spatial potential distribution data. Then, the inverse problem is solved using the alternating direction multiplier method with L1 norm constraints, transforming the potential distribution into a resistivity gradient field. A three-dimensional resistivity distribution matrix was obtained, reflecting the resistivity differences at different locations in the gangue layer. High resistivity typically corresponds to pores or air, while low resistivity corresponds to slurry-filled areas. Then, CT scans or cross-sectional photographs of the gangue layer were performed to obtain a binary image of the pore structure, where pores are represented by white and the framework by black. Then, images of different sizes were used... A grid-covered image. Count the number of grids required to cover the porous region for each size. .draw and The relationship diagram. The slope is the fractal dimension. Further, the boundary between the filled and unfilled regions of the slurry is identified from the resistivity gradient field, and a theoretical diffusion radius is estimated. Because the pore structure of gangue exhibits fractal characteristics, the actual diffusion path is longer than the straight-line distance. Therefore, the fractal dimension is used to correct the theoretical diffusion radius to obtain the effective diffusion radius. ,in As a correction factor, usually Larger pores have more complex structures, resulting in a smaller effective diffusion radius after correction. This is because the bypass path is longer, and the straight-line distance covered by the same volume of slurry is shorter.

[0045] In other embodiments, after acquiring the pore pressure timing signal, the electronic device can analyze the pressure timing signal at each measuring point. Empirical mode decomposition is performed, which decomposes the data into several intrinsic mode functions (IMFs) and a residual term. ,in, For the first The first measuring point Each IMF component represents a pressure fluctuation pattern at different time scales. Let be the residual. Empirical Mode Decomposition (EMD) can adaptively extract local oscillation features from a signal without pre-setting basis functions. Then, for each measurement point, the variance of all its IMF components is calculated. Let the th... The first measuring point The variance of each IMF component is The larger the variance, the more drastic the pressure fluctuations corresponding to that IMF component, and the more unstable the filling process. Typically, the first few major IMF components (those with high energy proportions) are selected for analysis. The reciprocals of the variances of each IMF component are then weighted or summed to obtain the filling stability index.

[0046] For example, when the calculated pipe blockage risk coefficient of 0.31 is lower than the set threshold of 0.5, the electronic device can activate the permeability analysis module. Gradient current is injected through a three-dimensional electrode array, and the resistivity gradient field is reconstructed using the alternating direction multiplier method constrained by the L1 norm. Combined with the fractal dimension of the gangue pores (1.8) to correct the diffusion path, the effective diffusion radius is calculated to be 4.2m. Fiber optic pressure sensors are deployed at three different depths in the gangue layer. The intrinsic mode functions of pressure fluctuations are obtained through empirical mode decomposition, and the inverse variance is used to generate a filling stability index of 0.86. Based on the laser-induced liquid level change in the drainage trough, the slurry bleeding rate is calculated to be 3.5%.

[0047] Step S30: Input the effective diffusion radius, filling stability index and slurry bleeding rate into the trained random forest regression model to obtain the residual coal oxidation inhibition rate and CO diffusion blocking rate.

[0048] In one possible implementation, after calculating the effective diffusion radius, filling stability index, and slurry bleeding rate, the electronic device can input these parameters into a trained random forest regression model to obtain the residual coal oxidation inhibition rate and the CO diffusion blocking rate. The residual coal oxidation inhibition rate represents the slurry's ability to prevent spontaneous combustion and temperature rise of coal; the CO diffusion blocking rate represents the slurry's efficiency in sealing fissures and preventing carbon monoxide leakage. Both quantify the fire prevention and extinguishing effects from the perspectives of ignition source suppression and gas sealing, respectively.

[0049] Optionally, the electronic device can determine sample data based on the effective diffusion radius, filling stability index, and slurry bleeding rate corresponding to historical filling projects, and determine the surface temperature change rate of extracted coal samples as the label for residual coal oxidation inhibition rate, and calculate the slope of CO concentration decrease as the label for CO diffusion blocking rate, thus obtaining a training set. Then, a random forest regression model is trained based on the training set.

[0050] For example, electronic devices collect 200 sets of historical backfilling engineering data from the mine, perform time-domain differential processing on the infrared thermal imager temperature rise curves to extract oxidation inhibition rate labels, and simultaneously analyze gas chromatograph CO concentration data to obtain diffusion blocking rate labels. The effective diffusion radius of 4.2m is logarithmically transformed, the filling stability index of 0.86 is compressed using the Sigmoid function, and the bleeding rate is normalized to 3.5% before being input into a pre-trained random forest model. The model contains 100 decision trees, and the feature weights are optimized through out-of-bag error estimation, ultimately outputting an oxidation inhibition rate of 89% and a CO diffusion blocking rate of 92%.

[0051] Step S40: Determine the slurry adjustment scheme based on at least one of the following: pipe blockage risk coefficient, effective diffusion radius, filling stability index, slurry bleeding rate, residual coal oxidation inhibition rate, and CO diffusion blocking rate.

[0052] In one possible implementation, the electronic device can determine a slurry adjustment scheme based on at least one of the following: pipe blockage risk coefficient, effective diffusion radius, filling stability index, slurry bleeding rate, residual coal oxidation inhibition rate, and CO diffusion blocking rate. This slurry adjustment scheme is used to adjust the slurry parameters in the grouting pipeline of the target mine, and may include, for example, particle size distribution adjustment weights, water-reducing agent addition thresholds, and pressure adjustment ranges. The drawback of this scheme can be mitigated by a fuzzy control algorithm. Specifically, the electronic device can use a fuzzy control algorithm to determine the particle size distribution adjustment weights, water-reducing agent addition thresholds, and pressure adjustment ranges based on at least one of the following: pipe blockage risk coefficient, effective diffusion radius, filling stability index, slurry bleeding rate, residual coal oxidation inhibition rate, and CO diffusion blocking rate, thereby obtaining the slurry adjustment scheme.

[0053] Optionally, after determining the parameters, the electronic device can determine a fuzzy adjustment scheme based on preset fuzzy rules. For example, if the risk of pipe blockage is high, the particle size distribution adjustment weight is increased; if the filling is unstable, the water-reducing agent addition threshold is increased; if the fireproofing effect is not up to standard, the pressure adjustment range is expanded or increased. Then, the precise adjustment value is calculated using the centroid method. For example, the above fuzzy judgment results are calculated using the centroid method to obtain three specific instructions: particle size distribution adjustment weight, water-reducing agent addition threshold, and pressure adjustment range. Further, after executing the instructions, the device is retested, and the confidence level of the adjustment rules is adjusted based on the actual effect (e.g., calibrated to 0.92 in the example) to make the next decision more accurate. In other words, based on the various indicators calculated above, fuzzy rules are used to determine and calculate the adjustment values ​​for particle size, water-reducing agent, and pressure, while automatically correcting the rules based on the actual effect.

[0054] For example, the electronic device can construct a fuzzy interface with a current pipe blockage risk coefficient of 0.31 (medium risk), a filling stability index of 0.86 (stable), an oxidation inhibition rate of 89% (acceptable), and a CO diffusion blocking rate of 92% (excellent). Based on the fuzzy rule base, an inference mechanism is initiated, determining the particle size distribution adjustment weight to be 0.4, the water-reducing agent addition threshold to be 0.8%, and the pressure regulation range to be 1.2 - 1.8 MPa. Control commands are generated by defuzzifying using the center-of-gravity method and embedded in a feedback loop, calibrating the rule confidence level to 0.92.

[0055] Based on the above technical features, this application embodiment constructs a pressure-flow velocity dynamic response model through a working condition simulation module, integrating pipeline structural characteristics and historical pipe blockage data to achieve dynamic and accurate quantitative judgment of pipe blockage risk, overcoming the limitation of traditional static testing in failing to reflect the dynamic response of the slurry transportation process; secondly, the permeability analysis module introduces three-dimensional resistivity scanning and pore pressure fluctuation collaborative analysis, combined with fractal dimension correction of diffusion path, to comprehensively evaluate the permeability diffusion radius, filling stability, and bleeding characteristics of the slurry in heterogeneous gangue structures, providing reliable structural parameter support for fire prevention and extinguishing effectiveness, and making up for the one-sided defects of existing permeability analysis; thirdly, effectiveness The prediction module utilizes a random forest regression model to predict the residual coal oxidation inhibition rate and CO diffusion blocking rate based on flowability parameters without the need for on-site fire extinguishing tests. This significantly improves the response speed of fire prevention and extinguishing assessment and solves the problem of delayed fire performance response in traditional methods. Finally, the optimization decision module uses a fuzzy control algorithm to transform multidimensional physical indicators into suggestions for adjusting construction parameters such as particle size distribution adjustment weights, water-reducing agent addition thresholds, and pressure regulation ranges. It also embeds feedback closed-loop real-time calibration rule confidence levels, achieving linked feedback between flowability control and fire prevention and extinguishing objectives. This enhances the adaptability and safe operation of the grouting system in complex mine environments. In summary, this invention forms a complete closed-loop testing and control system from pipe blockage risk assessment, permeability performance evaluation, fire prevention and extinguishing effectiveness prediction to parameter optimization decision-making.

[0056] It should be understood that although the steps in the above flowcharts are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the above flowcharts may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0057] Based on the foregoing embodiments, this application provides a slurry flowability treatment device, which includes various modules and units included in each module, and can be implemented by a processor; of course, it can also be implemented by specific logic circuits; in the implementation process, the processor can be a central processing unit (CPU), microprocessor (MPU), digital signal processor (DSP) or field programmable gate array (FPGA), etc.

[0058] Figure 2 A schematic diagram of a slurry flowability treatment apparatus according to an embodiment of this application is shown. Figure 2As shown, the slurry flowability treatment device in this embodiment includes: The first parameter determination module 20 is used to determine the pipe blockage risk coefficient in real time based on the dynamic response model of pressure and flow velocity corresponding to the grouting pipeline of the target mine. The second parameter determination module 21 is used to determine the effective diffusion radius, filling stability index and slurry bleeding rate of the target mine grouting pipeline when the pipe blockage risk coefficient is lower than a preset threshold. The third parameter determination module 22 is used to input the effective diffusion radius, filling stability index and slurry bleeding rate into the trained random forest regression model to obtain the residual coal oxidation inhibition rate and CO diffusion blocking rate. The scheme determination module 23 is used to determine the slurry adjustment scheme based on at least one of the following: pipe blockage risk coefficient, effective diffusion radius, filling stability index, slurry bleeding rate, residual coal oxidation inhibition rate, and CO diffusion blocking rate.

[0059] In one possible implementation, the first parameter determining module 20 is further configured to: Real-time acquisition of slurry particle size distribution and viscosity parameters of the grouting pipeline in the target mine; The particle size distribution and viscosity parameters of the grouting pipeline in the target mine are input into the dynamic response model of pressure and flow rate to obtain the pressure mutation rate and the proportion of critical particle size. The pipe blockage risk coefficient is calculated based on the pressure mutation rate and the proportion of critical particle size.

[0060] In one possible implementation, the second parameter determining module 21 is further configured to: Obtain resistivity tomographic data and pore pressure time-series signal of the grouting pipeline of the target mine; The effective diffusion radius and filling stability index are calculated based on the resistivity tomography data and pore pressure time-series signal. The slurry bleeding rate is calculated based on the change in laser liquid level in the drainage trough corresponding to the grouting pipeline of the target mine.

[0061] In one possible implementation, the second parameter determining module 21 is further configured to: The resistivity gradient field is reconstructed based on the resistivity tomography data, and the theoretical diffusion radius is determined based on the resistivity gradient field. The effective diffusion radius is obtained by correcting the theoretical diffusion radius. The filling stability index is calculated based on the pore pressure time series signal.

[0062] In one possible implementation, the device further includes: The training set determination module is used to determine sample data by using the effective diffusion radius, filling stability index and slurry bleeding rate corresponding to historical filling projects, and to determine the surface temperature change rate of extracted coal samples as the label of residual coal oxidation inhibition rate, and to calculate the slope of CO concentration decrease as the label of CO diffusion blocking rate, thus obtaining the training set; The model training module is used to train a random forest regression model based on the training set.

[0063] In one possible implementation, the device further includes: The model determination module is used to construct a dynamic response model of the pressure and flow velocity of the grouting pipeline in the target mine based on the topology parameters of the grouting pipeline and historical pipe blockage event data.

[0064] In one possible implementation, the scheme determining module 23 is further configured to: By using a fuzzy control algorithm, based on at least one of the following factors—the pipe blockage risk coefficient, effective diffusion radius, filling stability index, slurry bleeding rate, residual coal oxidation inhibition rate, and CO diffusion blocking rate—the particle size distribution adjustment weight, water-reducing agent addition threshold, and pressure adjustment range are determined, thereby obtaining a slurry adjustment scheme.

[0065] The descriptions of the above device embodiments are similar to those of the above method embodiments, and have similar beneficial effects. For technical details not disclosed in the device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0066] It should be noted that, in the embodiments of this application... Figure 2 The module division of the slurry flowability treatment device shown is illustrative and represents only a logical functional division; in actual implementation, other division methods may be used. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, exist as separate physical units, or have two or more units integrated into one unit. The integrated units can be implemented in hardware, as software functional units, or a combination of both.

[0067] It should be noted that, in the embodiments of this application, if the above-described methods are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0068] Figure 3 A schematic diagram of an electronic device according to an embodiment of this application is shown. For example... Figure 3 As shown in the figure, this application provides an electronic device, which can be a server, and its internal structure diagram can be as follows. Figure 3 As shown, the electronic device includes a processor 320, a memory, and a transceiver 340 connected via a system bus 310. The processor 320 provides computing and control capabilities. The memory includes a non-volatile storage medium 331 and internal memory 332. The non-volatile storage medium 331 stores an operating system, computer programs, and a database. The internal memory 332 provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium 331. The database stores data. The transceiver 340 communicates with external terminals via a network connection. The computer program is executed by the processor 320 to implement the aforementioned methods.

[0069] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor 320, implements the steps of the method provided in the above embodiments.

[0070] This application provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the steps in the method provided in the above-described method embodiments.

[0071] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0072] In one possible implementation, the apparatus provided in this application can be implemented as a computer program, which can be configured as follows: Figure 3 The device operates on the electronic device shown. The memory of the electronic device can store the various program modules that make up the above-described apparatus. The computer program composed of the various program modules causes the processor 320 to execute the steps of the methods in the various embodiments of this application described in this specification.

[0073] It should be noted that the descriptions of the storage medium and device embodiments above are similar to the descriptions of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium, storage medium, and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0074] It should be understood that the phrases "one embodiment," "an embodiment," or "some embodiments" mentioned throughout the specification mean that a specific feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, phrases such as "in one possible implementation," "in one embodiment," or "in some embodiments" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely for descriptive purposes and do not represent the superiority or inferiority of the embodiments. The descriptions of the various embodiments above tend to emphasize the differences between the various embodiments; their similarities or commonalities can be referred to mutually, and for the sake of brevity, they will not be repeated here.

[0075] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three kinds of relationships. For example, object A and / or object B can represent three situations: object A exists alone, object A and object B exist simultaneously, and object B exists alone.

[0076] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple modules or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or modules can be electrical, mechanical, or other forms.

[0078] The modules described above as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules. They may be located in one place or distributed across multiple network units. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0079] In addition, each functional module in the various embodiments of this application can be integrated into one processing unit, or each module can be a separate unit, or two or more modules can be integrated into one unit; the integrated modules can be implemented in hardware or in the form of hardware plus software functional units.

[0080] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0081] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0082] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0083] The features disclosed in the several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0084] The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0085] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for treating the fluidity of a slurry, characterized in that, The method includes: The risk coefficient of pipe blockage is determined in real time based on the dynamic response model of pressure and flow velocity corresponding to the grouting pipeline of the target mine. When the pipe blockage risk coefficient is lower than a preset threshold, the effective diffusion radius, filling stability index, and slurry bleeding rate of the target mine grouting pipeline are determined. The effective diffusion radius, filling stability index, and slurry bleeding rate are input into the trained random forest regression model to obtain the residual coal oxidation inhibition rate and CO diffusion blocking rate. The slurry adjustment scheme is determined based on at least one of the following: pipe blockage risk coefficient, effective diffusion radius, filling stability index, slurry bleeding rate, residual coal oxidation inhibition rate, and CO diffusion blocking rate.

2. The method according to claim 1, characterized in that, The method of determining the pipe blockage risk coefficient in real time based on the dynamic response model of pressure and flow velocity corresponding to the grouting pipeline of the target mine includes: Real-time acquisition of slurry particle size distribution and viscosity parameters of the grouting pipeline in the target mine; The particle size distribution and viscosity parameters of the grouting pipeline in the target mine are input into the dynamic response model of pressure and flow rate to obtain the pressure mutation rate and the proportion of critical particle size. The pipe blockage risk coefficient is calculated based on the pressure mutation rate and the proportion of critical particle size.

3. The method according to claim 1, characterized in that, Determining the effective diffusion radius, filling stability index, and slurry bleeding rate of the grouting pipeline in the target mine includes: Obtain resistivity tomographic data and pore pressure time-series signal of the grouting pipeline of the target mine; The effective diffusion radius and filling stability index are calculated based on the resistivity tomography data and pore pressure time-series signal. The slurry bleeding rate is calculated based on the change in laser liquid level in the drainage trough corresponding to the grouting pipeline of the target mine.

4. The method according to claim 3, characterized in that, The calculation of the effective diffusion radius and filling stability index based on the resistivity tomography data and pore pressure time-series signal includes: The resistivity gradient field is reconstructed based on the resistivity tomography data, and the theoretical diffusion radius is determined based on the resistivity gradient field. The effective diffusion radius is obtained by correcting the theoretical diffusion radius. The filling stability index is calculated based on the pore pressure time series signal.

5. The method according to claim 1, characterized in that, The method further includes: Sample data were determined by measuring the effective diffusion radius, filling stability index, and slurry bleeding rate corresponding to historical backfilling projects. The surface temperature change rate of extracted coal samples was determined as the label for the oxidation inhibition rate of residual coal, and the slope of CO concentration decrease was calculated as the label for CO diffusion blocking rate, thus obtaining the training set. A random forest regression model is trained based on the training set.

6. The method according to claim 1, characterized in that, The method further includes: Based on the topology parameters of the grouting pipeline in the target mine and historical pipe blockage event data, a dynamic response model of pressure and flow velocity for the grouting pipeline in the target mine is constructed.

7. The method according to claim 1, characterized in that, The process of determining the slurry adjustment scheme based on at least one of the following: pipe blockage risk coefficient, effective diffusion radius, filling stability index, slurry bleeding rate, residual coal oxidation inhibition rate, and CO diffusion blocking rate, includes: By using a fuzzy control algorithm, based on at least one of the following factors—the pipe blockage risk coefficient, effective diffusion radius, filling stability index, slurry bleeding rate, residual coal oxidation inhibition rate, and CO diffusion blocking rate—the particle size distribution adjustment weight, water-reducing agent addition threshold, and pressure adjustment range are determined, thereby obtaining a slurry adjustment scheme.

8. A slurry flowability treatment device, characterized in that, The device includes: The first parameter determination module is used to determine the pipe blockage risk coefficient in real time based on the dynamic response model of pressure and flow velocity corresponding to the grouting pipeline of the target mine. The second parameter determination module is used to determine the effective diffusion radius, filling stability index and slurry bleeding rate of the target mine grouting pipeline when the pipe blockage risk coefficient is lower than a preset threshold. The third parameter determination module is used to input the effective diffusion radius, filling stability index and slurry bleeding rate into the trained random forest regression model to obtain the residual coal oxidation inhibition rate and CO diffusion blocking rate. The scheme determination module is used to determine the slurry adjustment scheme based on at least one of the following: pipe blockage risk coefficient, effective diffusion radius, filling stability index, slurry bleeding rate, residual coal oxidation inhibition rate, and CO diffusion blocking rate.

9. An electronic device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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