Segmented variable-frequency grouting control method for cement-soil mixing pile

Abstract

The invention provides a control method for segmented variable-frequency grouting of a cement-soil mixing pile, which is used for solving the problem of insufficient mixing of cement paste and a soil body due to the fact that grouting parameters cannot be adaptively adjusted according to soil layer characteristics and pile length changes during construction in a complex stratum. The method comprises the following steps: dividing different stratum regions, and determining a permeability coefficient range and a compression modulus value domain index of each region; if the permeability coefficient range is higher than the preset threshold value, the grouting pressure value is increased to meet the sandy soil layer requirement; the method comprises the following steps: generating a slurry volume distribution scheme based on enhanced diffusion of a deep soil layer; if the overload risk exists, the injection speed of the corresponding area is reduced, and an optimized grouting scheme is determined; and the mixing uniformity index is monitored in the construction process, real-time feedback data is obtained based on monitoring data in combination with a monitoring feedback period and an adjustment response mechanism, parameters are iteratively adjusted, and a final variable-frequency grouting control instruction is obtained.

Description

Technical Field

[0001] This invention relates to the field of cement-soil mixing pile grouting technology, and in particular to a segmented variable frequency grouting control method for cement-soil mixing piles. Background Technology

[0002] Currently, high-pressure jet grouting and sleeve valve grouting are commonly used construction techniques for soft soil foundation treatment under bridges. However, these high-pressure grouting methods can cause displacement of existing bridge pile foundations, cracks in the bridge pavement, and even serious engineering accidents such as bridge collapse. Cement-soil piles are often used for foundation reinforcement near existing buildings because of their high pile formation efficiency, maximum utilization of the original foundation soil, and minimal impact on surrounding existing buildings during pile formation, without causing lateral extrusion of the foundation soil. However, cement-soil pile construction equipment is relatively tall, making it unsuitable for use under height-restricted conditions such as under bridges.

[0003] Therefore, when constructing in complex strata, the grouting parameters cannot be adaptively adjusted according to the soil characteristics and pile length changes, resulting in insufficient mixing of cement grout and soil. This insufficient mixing of cement grout and soil directly affects the bearing capacity and stability of the pile, becoming a key issue in improving the construction quality of cement-soil piles. Summary of the Invention

[0004] This invention provides a segmented variable frequency grouting control method for cement-soil mixing piles, which solves the problem that grouting parameters cannot be adaptively adjusted according to soil characteristics and pile length changes during construction in complex strata, resulting in insufficient mixing of cement slurry and soil.

[0005] This invention provides a segmented variable frequency grouting control method for cement-soil mixing piles, comprising: obtaining a distribution map of soil physical properties based on real-time stratum data acquired from borehole locations; The distribution map of soil physical properties is used to divide different stratigraphic regions and determine the range of permeability coefficient and compression modulus values ​​for each region. If the permeability coefficient is higher than the preset threshold, the grouting pressure value is increased to match the requirements of the sandy soil layer, and the adjusted grouting pressure sequence is obtained by combining the pressure maintenance level. Based on the adjusted grouting pressure sequence and combined with pile length data, the distribution gradient of grout injection volume is determined, and the grout flow rate distribution is further determined by combining the injection time sequence. Based on the slurry flow rate distribution, fracture pattern, and water content information, a slurry volume distribution scheme based on enhanced diffusion in deep soil layers is generated. Assess whether there is a risk of shallow soil overload in the slurry volume distribution scheme; If there is an overload risk, reduce the injection speed in the corresponding area and determine the optimized grouting scheme; Based on the optimized grouting scheme, the mixing uniformity index is monitored during construction. Based on the monitoring data, combined with the monitoring feedback cycle and adjustment response mechanism, real-time feedback data is obtained to iteratively adjust parameters and obtain the final variable frequency grouting control command.

[0006] Preferably, real-time formation data is obtained from the borehole location, and combined with rock hardness and soil particle size analysis to generate a distribution map of soil physical properties; The distribution map of soil physical properties is analyzed and different strata regions are divided. The range of permeability coefficient and the range of compression modulus value of each region are determined. If the permeability coefficient range exceeds the preset threshold, the grouting pressure value is adjusted according to the requirements of sandy soil layer to generate an adjusted grouting pressure sequence. The depth gradient is calculated based on the adjusted grouting pressure sequence and pile length data, and the grout flow rate distribution is generated by combining the injection time sequence. When in deep soil layers, a grout volume distribution scheme to enhance diffusion is generated based on the grout flow rate distribution, crack distribution pattern, and water content ratio. If there is an overload risk in the shallow soil layer in the grout volume distribution scheme, the injection speed in the corresponding area is reduced according to the curing time span to generate an optimized grouting scheme. The mixing uniformity index is monitored based on the optimized grouting scheme, and the feedback cycle is iterated. Based on the iteration results, parameters are adjusted to generate real-time feedback data.

[0007] Preferred, The soil physical property data is standardized to generate a property dataset in a unified format; Grouping the characteristic datasets yields preliminary stratigraphic region division results. If the boundaries of the regions in the division results are unclear, spatial analysis can be used to adjust the boundaries and determine clear stratigraphic regions. The statistical range of permeability coefficient for each region is calculated based on the stratigraphic region to obtain the permeability coefficient distribution; the statistical range of compressibility modulus for each region is calculated based on the stratigraphic region to obtain the compressibility modulus value range. The distribution of permeability coefficient and the range of compressibility modulus are superimposed to generate a comprehensive physical property distribution map of the stratigraphic region; the significance of the property distribution of each region is verified by the comprehensive physical property distribution map, and the final stratigraphic region division and property value range are determined. The soil physical property data is based on real-time geological data obtained from borehole locations.

[0008] Preferably, if the permeability coefficient exceeds a preset threshold, the difference between the permeability coefficient and the preset threshold is compared, the adjustment coefficient is recalculated, and an initial pressure adjustment value is obtained; based on the initial pressure adjustment value and the characteristic parameters of the sandy soil layer, a pressure adjustment sequence matching the soil layer characteristics is generated. The pressure change trend is obtained from the pressure adjustment sequence, and the stability condition is determined by combining the pressure maintenance level. If the stability condition is met, the optimized pressure sequence is obtained. If the pressure values ​​in the optimized pressure sequence exceed the safe range, the pressure sequence parameters are adjusted using the gradient descent algorithm to obtain a safe pressure sequence. The final grouting pressure sequence is generated based on the safety pressure sequence and the soil layer characteristic matching results. Key pressure points are extracted from the final grouting pressure sequence to determine the sequence stability, and the grouting execution sequence is obtained. A control signal sequence is generated based on the grouting execution sequence and output to the grouting equipment to complete pressure optimization control.

[0009] Preferably, the grouting pressure sequence and pile length data are obtained to calculate the depth gradient and obtain the depth change rate; the grout injection volume distribution is analyzed based on the depth change rate and injection time sequence to determine the injection volume distribution pattern. If the injection volume distribution pattern deviates from the preset uniform distribution threshold by more than a specified range, the grouting pressure sequence is adjusted by a weighted average method to obtain the adjusted pressure sequence.

[0010] Preferably, the slurry flow rate distribution is calculated based on the adjusted pressure sequence and injection time sequence to obtain the flow rate distribution characteristics; The diffusion range of the slurry is divided into zones based on the flow rate distribution characteristics, and the zoning results of the diffusion range are determined. If there are anomalous zones in the diffusion range zoning results, the flow rate of the anomalous zones is recalculated using time series analysis to obtain the corrected flow rate distribution. Based on the corrected flow rate distribution and the depth change rate, an optimized sequence for slurry injection volume is generated, and the final optimized distribution is determined.

[0011] Preferably, the geological structure characteristics of the deep soil layer area are obtained and a geological structure distribution model is generated based on it to determine the fracture distribution pattern and permeability parameter distribution; The fracture distribution pattern was extracted from the geological structure distribution model, and the slurry flow rate distribution was simulated to obtain the spatial variation data of the flow rate. If the fracture connectivity in the spatial variation data of flow rate is higher than a preset threshold, the main flow path is calculated by combining the fracture distribution pattern, and the flow path prediction result is generated. Based on the flow path prediction results, water content ratio data is obtained, and based on the water content ratio data, water saturation is analyzed and the spatial distribution of water content ratio is determined. High water content regions are extracted from the spatial distribution of water content proportions. If the permeability parameter distribution is higher than the preset threshold, an initial allocation scheme to enhance the diffusion effect is generated; The slurry volume distribution is optimized based on the initial distribution scheme, flow path prediction results, and water saturation. The final slurry volume distribution scheme is then generated by combining the optimized slurry volume distribution scheme with the geological structure distribution model.

[0012] Preferably, geological data is obtained from shallow soil layers, and the stress distribution of the soil layers is simulated based on the geological data of the shallow soil layers to determine whether there is an overload risk; if the overload risk exists, the slurry volume and injection rate of the overloaded area are extracted from the simulation results to determine the high-risk area.

[0013] Preferably, the relationship between the injection rate and soil stress in high-risk areas is predicted to obtain the adjusted injection rate; The curing time span of the slurry is obtained based on the adjusted injection speed, and the curing effect is predicted to obtain the optimized curing time. The grouting scheme is generated from the optimized injection speed and curing time, and the allocation scheme is integrated to obtain comprehensive grouting parameters; The grouting process was simulated using comprehensive grouting parameters to determine whether there was still an overload risk in the shallow soil layer, and the verification results were obtained. If the verification results show no risk of overload, the final grouting scheme will be output. Based on the final grouting scheme, an optimized scheme was determined.

[0014] Preferably, the mixing uniformity data during the grouting process is collected to obtain the original dataset; the mixing uniformity index is extracted from the original dataset and the uniformity deviation is calculated to determine the deviation value; If the deviation exceeds the preset threshold, the adjustment response mechanism is triggered to generate a parameter adjustment command; the control parameters in the grouting scheme are updated according to the parameter adjustment command to obtain the updated scheme configuration; The updated scheme is used to control the grouting equipment and collect new mixing uniformity data in real time to obtain real-time feedback data. By analyzing the uniformity change trend from real-time feedback data and predicting the effect of parameter adjustment based on the uniformity change trend, and obtaining effective prediction results, the parameters of the grouting scheme are iteratively optimized to obtain the optimized scheme configuration.

[0015] The working principle and beneficial effects of this invention are as follows: A segmented variable frequency grouting control method for cement-soil mixing piles includes: obtaining a distribution map of soil physical properties based on real-time stratum data acquired from borehole locations; The distribution map of soil physical properties is used to divide different stratigraphic regions and determine the range of permeability coefficient and compression modulus values ​​for each region. If the permeability coefficient is higher than the preset threshold, the grouting pressure value is increased to match the requirements of the sandy soil layer, and the adjusted grouting pressure sequence is obtained by combining the pressure maintenance level. Based on the adjusted grouting pressure sequence and combined with pile length data, the distribution gradient of grout injection volume is determined, and the grout flow rate distribution is further determined by combining the injection time sequence. Based on the slurry flow rate distribution, fracture pattern, and water content information, a slurry volume distribution scheme based on enhanced diffusion in deep soil layers is generated. Assess whether there is a risk of shallow soil overload in the slurry volume distribution scheme; If there is an overload risk, reduce the injection speed in the corresponding area and determine the optimized grouting scheme; Based on the optimized grouting scheme, the mixing uniformity index is monitored during construction. Based on the monitoring data, combined with the monitoring feedback cycle and adjustment response mechanism, real-time feedback data is obtained to iteratively adjust parameters and obtain the final variable frequency grouting control command.

[0016] In this invention, obtaining a distribution map of soil physical properties based on real-time stratum data acquired from borehole locations includes: collecting real-time stratum data from borehole locations to obtain an original dataset containing rock hardness and soil particle size; Next, noise and outliers were removed from the original dataset to obtain cleaned stratigraphic data; rock hardness distribution and soil particle size distribution were then extracted from the cleaned stratigraphic data. Based on the rock hardness distribution and soil particle size distribution, the soil layers are divided into layers to obtain layer information; Based on the layer information and depth distribution characteristics, a model of the hardness variation trend of the formation is constructed, and continuous hardness distribution data is obtained using this model. Furthermore, calculations are performed on the hardness distribution data and soil particle size distribution to generate preliminary physical property distribution data; If the spatial distribution pattern of the preliminary physical property distribution data deviates from the hardness variation trend by more than a preset threshold, the distribution data is adjusted to obtain optimized physical property distribution data. The optimized physical property distribution data is fused with depth distribution features to generate the final soil physical property distribution map.

[0017] In this invention, firstly, data on rock hardness and soil particle size at the borehole location are obtained, a distribution map of soil physical properties is generated, and after dividing the strata region, the range of permeability coefficient and compression modulus is determined. By dynamically adjusting the grouting pressure, the dynamic matching of the requirements of sandy soil layers can be achieved. Based on the injection time series, the grout flow rate distribution is determined, enabling intelligent control of grouting pressure in high permeability areas; By incorporating the distribution of fissures and the water content ratio, the volume distribution of grout is optimized, and the risk of overload in shallow soil layers is monitored in real time, thereby achieving the goal of dynamically reducing the injection rate. By optimizing the curing time span, the grouting scheme can be improved to control the grouting pressure in deep soil layers greater than 30 meters. By monitoring the mixing uniformity of the slurry and dynamically adjusting the slurry injection parameters based on periodic iterations; This effectively improves grouting efficiency while ensuring ground stability during construction, thereby enhancing construction quality and safety.

[0018] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0019] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0021] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0022] according to Figure 1 As shown, this embodiment of the invention provides a segmented variable frequency grouting control method for cement-soil mixing piles, including: obtaining a distribution map of soil physical properties based on real-time stratum data obtained from borehole locations; The distribution map of soil physical properties is used to divide different stratigraphic regions and determine the range of permeability coefficient and compression modulus values ​​for each region. If the permeability coefficient is higher than the preset threshold, the grouting pressure value is increased to match the requirements of the sandy soil layer, and the adjusted grouting pressure sequence is obtained by combining the pressure maintenance level. Based on the adjusted grouting pressure sequence and combined with pile length data, the distribution gradient of grout injection volume is determined, and the grout flow rate distribution is further determined by combining the injection time sequence. Based on the slurry flow rate distribution, fracture pattern, and water content information, a slurry volume distribution scheme based on enhanced diffusion in deep soil layers is generated. Assess whether there is a risk of shallow soil overload in the slurry volume distribution scheme; If there is an overload risk, reduce the injection speed in the corresponding area and determine the optimized grouting scheme; Based on the optimized grouting scheme, the mixing uniformity index is monitored during construction. Based on the monitoring data, combined with the monitoring feedback cycle and adjustment response mechanism, real-time feedback data is obtained to iteratively adjust parameters and obtain the final variable frequency grouting control command.

[0023] In this invention, obtaining a distribution map of soil physical properties based on real-time stratum data acquired from borehole locations includes: collecting real-time stratum data from borehole locations to obtain an original dataset containing rock hardness and soil particle size; Next, noise and outliers were removed from the original dataset to obtain cleaned stratigraphic data; rock hardness distribution and soil particle size distribution were then extracted from the cleaned stratigraphic data. Based on the rock hardness distribution and soil particle size distribution, the soil layers are divided into layers to obtain layer information; Based on the layer information and depth distribution characteristics, a model of the hardness variation trend of the formation is constructed, and continuous hardness distribution data is obtained using this model. Furthermore, calculations are performed on the hardness distribution data and soil particle size distribution to generate preliminary physical property distribution data; If the spatial distribution pattern of the preliminary physical property distribution data deviates from the hardness variation trend by more than a preset threshold, the distribution data is adjusted to obtain optimized physical property distribution data. The optimized physical property distribution data is fused with depth distribution features to generate the final soil physical property distribution map.

[0024] In this invention, firstly, data on rock hardness and soil particle size at the borehole location are obtained, a distribution map of soil physical properties is generated, and after dividing the strata region, the range of permeability coefficient and compression modulus is determined. By dynamically adjusting the grouting pressure, the dynamic matching of the requirements of sandy soil layers can be achieved. Based on the injection time series, the grout flow rate distribution is determined, enabling intelligent control of grouting pressure in high permeability areas; By incorporating the distribution of fissures and the water content ratio, the volume distribution of grout is optimized, and the risk of overload in shallow soil layers is monitored in real time, thereby achieving the goal of dynamically reducing the injection rate. By optimizing the curing time span, the grouting scheme can be improved to control the grouting pressure in deep soil layers greater than 30 meters. By monitoring the mixing uniformity of the slurry and dynamically adjusting the slurry injection parameters based on periodic iterations; This effectively improves grouting efficiency while ensuring ground stability during construction, thereby enhancing construction quality and safety.

[0025] In one embodiment, real-time formation data is obtained from the borehole location, and the rock hardness and soil particle size of the bottom layer data are analyzed to generate a distribution map of soil physical properties. The distribution map of soil physical properties is analyzed and different strata regions are divided. The range of permeability coefficient and the range of compression modulus value of each region are determined. If the permeability coefficient range exceeds the preset threshold, the grouting pressure value is adjusted according to the requirements of sandy soil layer to generate an adjusted grouting pressure sequence. The depth gradient is calculated based on the adjusted grouting pressure sequence and pile length data, and the grout flow rate distribution is generated by combining the injection time sequence. When in deep soil layers, a grout volume distribution scheme to enhance diffusion is generated based on the grout flow rate distribution, crack distribution pattern, and water content ratio. If there is an overload risk in the shallow soil layer in the grout volume distribution scheme, the injection speed in the corresponding area is reduced according to the curing time span to generate an optimized grouting scheme. The mixing uniformity index is monitored based on the optimized grouting scheme, and the feedback cycle is iterated. Based on the iteration results, parameters are adjusted to generate real-time feedback data.

[0026] In this embodiment, real-time stratigraphic data is acquired, and based on the rock hardness and soil particle size analysis of the stratigraphic data, a distribution map of soil physical properties is generated. The distribution map of soil physical properties was analyzed to divide different stratigraphic regions and determine the range of permeability coefficient and compression modulus value for each region; If the permeability coefficient exceeds the preset threshold, the grouting pressure value is adjusted according to the requirements of the sandy soil layer to generate an adjusted grouting pressure sequence. The depth gradient is calculated based on the adjusted grouting pressure sequence and pile length data, and the grout flow rate distribution is generated by combining the injection time sequence. Based on the distribution of grout flow rate, the distribution pattern of fractures in deep soil layers, and the water content ratio, a weighted average method is used to generate a grout volume distribution scheme that enhances diffusion. If there is an overload risk in the shallow soil layer in the grout volume distribution scheme, the injection speed in the corresponding area will be reduced according to the curing time span to generate an optimized grouting scheme.

[0027] Based on the optimized grouting scheme, the mixing uniformity index is monitored, and the grouting parameters are adjusted according to the real-time feedback data to generate real-time feedback data.

[0028] More specifically, when the sensor array acquires real-time formation data from the borehole location, it uses multi-point resistivity sensors and acoustic sensors to collect resistivity and acoustic velocity at 0.5-meter intervals within the working range of the cement-soil mixing pile. The weighted average algorithm was used to calculate the rock hardness distribution and soil particle size analysis. A distribution map of soil physical properties was obtained; the weights were resistivity 0.4, acoustic velocity 0.3, hardness 0.2, and particle size 0.1, generating a two-dimensional distribution map. Based on the distribution map, the K-means clustering algorithm was used to divide the soil into four stratigraphic regions using resistivity, acoustic velocity, hardness, and particle size as features, and the permeability coefficient and compression modulus of each region were calculated. If the permeability coefficient of a certain area is higher than the threshold, for example, the permeability coefficient of a sandy soil layer is 2.5 cm / s, while the threshold is 1 cm / s, then the increase in grouting pressure is calculated using the formula, where is the grouting pressure, = 2.0, and is the permeability coefficient. The initial pressure of 10 MPa is adjusted to 15 MPa, and a grouting pressure sequence is generated by combining the pressure maintenance level. The depth gradient is calculated using linear interpolation based on the adjusted grouting pressure sequence and pile length data to obtain the pressure gradient value. The grout flow rate distribution is calculated using a time series recorded once per minute. For deep soil layers (depth > 20 meters), considering the distribution pattern of fractures and water content, a slurry volume distribution scheme to enhance diffusion was calculated using a diffusion equation, yielding the distribution ratio in each region. The diffusion equation is: [Equation omitted for brevity], where the diffusion coefficient D = 0.01 m² / s; Next, the bearing capacity threshold analysis was performed on the grout volume of shallow soil layers with a depth of less than 10 meters; If overload occurs, reduce the injection speed and incorporate the curing time span, adjusting the grouting scheme through multiple iterations; Furthermore, the uniformity of mixing is detected using ultrasonic testing, and the grouting parameters are iteratively adjusted based on real-time feedback data to ensure the uniformity of grouting.

[0029] In one embodiment, the soil physical property data is standardized to generate a property dataset in a uniform format. Grouping the characteristic datasets yields preliminary stratigraphic region division results. If the boundaries of the regions in the division results are unclear, spatial analysis can be used to adjust the boundaries and determine clear stratigraphic regions. The statistical range of permeability coefficient for each region is calculated based on the stratigraphic region to obtain the permeability coefficient distribution; the statistical range of compressibility modulus for each region is calculated based on the stratigraphic region to obtain the compressibility modulus value range. The distribution of permeability coefficient and the range of compressibility modulus are superimposed to generate a comprehensive physical property distribution map of the stratigraphic region; the significance of the property distribution of each region is verified by the comprehensive physical property distribution map, and the final stratigraphic region division and property value range are determined. The soil physical property data is based on real-time geological data obtained from borehole locations.

[0030] In this embodiment, soil physical property data is obtained through standardization processing to generate a property dataset in a unified format; The characteristic dataset is grouped to obtain preliminary stratigraphic region division results; If the boundaries of the regions in the delineation results are unclear, spatial analysis can be used to adjust the boundaries and determine clear stratigraphic regions. The permeability coefficient statistical range of each region is calculated based on the stratigraphic region to obtain the permeability coefficient distribution. Calculate the statistical range of the compressibility modulus for each region based on the stratigraphic region to obtain the compressibility modulus value range; A comprehensive physical property distribution map of the formation region is generated by superimposing the permeability coefficient distribution and the compression modulus value range; The comprehensive physical property distribution map is calculated to verify the significance of the property distribution in each region and to determine the final stratigraphic region division and property value range.

[0031] More specifically, when using cluster analysis algorithms to divide different stratigraphic regions and determine the range of permeability coefficients and compression modulus values ​​based on the distribution map of soil physical properties, the soil physical property data is first processed through a geographic information system, which includes the permeability coefficients and compression moduli of several sampling points. Read the coordinates of multiple sampling points and their attribute values ​​including permeability coefficient and compressibility modulus; normalize the permeability coefficient and compressibility modulus to the range of mean and standard deviation to avoid the influence of dimensional differences on the clustering results.

[0032] Next, the K-means clustering algorithm was used to calculate the distance from each point to the cluster center using Euclidean distance and iteratively optimized until the sum of squares within the cluster converged, resulting in the analysis results for three stratigraphic regions. These results included several points in the stratigraphic region with the initial number of clusters, the range of permeability coefficients, and the range of compressibility moduli. The silhouette coefficient was used to evaluate the clustering quality and to verify the results. The coefficients for each stratigraphic region were calculated. If the coefficients for a stratigraphic region were greater than the preset values, it indicated that the clustering effect was good, and the regional division results were obtained.

[0033] The regional division results are combined with geological survey data to generate GIS visualization layers, and the permeability coefficient and compression modulus range of each region are marked for engineering design reference; for example, stratigraphic region 1 is suitable for low permeability foundation construction, while stratigraphic regions 2 and 3 need to meet high drainage capacity; these analysis results are used to link with subsequent foundation stability analysis to form a complete technical chain.

[0034] In one embodiment, if the permeability coefficient exceeds a preset threshold, the difference between the permeability coefficient and the preset threshold is compared, and the adjustment coefficient is recalculated to obtain the initial pressure adjustment value. Based on the initial pressure adjustment value and the characteristic parameters of the sandy soil layer, a pressure adjustment sequence matching the soil layer characteristics is generated. The pressure change trend is obtained from the pressure adjustment sequence, and the stability condition is determined by combining the pressure maintenance level. If the stability condition is met, an optimized pressure sequence is obtained; If the pressure values ​​in the optimized pressure sequence exceed the safe range, the pressure sequence parameters are adjusted using the gradient descent algorithm to obtain a safe pressure sequence. The final grouting pressure sequence is generated based on the safety pressure sequence and the soil layer characteristic matching results. Key pressure points are extracted from the final grouting pressure sequence, and the sequence stability is judged. Based on the judgment result, the grouting execution sequence is obtained. A control signal sequence is generated based on the grouting execution sequence and output to the grouting equipment to complete pressure optimization control.

[0035] In this embodiment, if the permeability coefficient exceeds a preset threshold, an adjustment coefficient is calculated by comparing the difference between the permeability coefficient and the preset threshold to obtain the initial pressure adjustment value.

[0036] By combining the initial pressure adjustment value with the characteristic parameters of the sandy soil layer, a trend line is obtained to solve for the pressure adjustment sequence that matches the soil layer characteristics.

[0037] The pressure change trend is obtained from the pressure adjustment sequence, and the stability condition is judged by the level of the combined force to obtain the optimized pressure sequence. If the pressure values ​​in the optimized pressure sequence exceed the safe range, the pressure sequence parameters are adjusted using the gradient descent algorithm to obtain a safe pressure sequence.

[0038] The matching results of the soil layer characteristics in the safety pressure sequence are solved to generate the final grouting pressure sequence.

[0039] The key pressure points are extracted from the final grouting pressure sequence to determine the sequence stability, and the grouting execution sequence is obtained.

[0040] A control signal sequence is generated based on the grouting execution sequence, and the control signal sequence is output to the grouting equipment to complete the pressure optimization control.

[0041] More specifically, firstly, the permeability coefficient and preset threshold are obtained. If the permeability coefficient is detected to be higher than the threshold, the grouting pressure adjustment mechanism is triggered. That is, the permeability coefficient data of the sandy soil layer is collected in real time by the sensor to determine or trigger the grouting pressure adjustment.

[0042] The initial grouting pressure is calculated based on the empirical formula of permeability coefficient and grouting pressure. The demand of sandy soil layer is analyzed based on the initial grouting pressure. Combining the relationship between soil porosity and permeability coefficient, a modified formula is used to obtain the initial pressure value that matches the demand of soil layer. The formula for the initial grouting pressure is: (Unit: MPa); Permeability coefficient; The correction formula for the initial pressure value is as follows: , This is the initial grouting pressure.

[0043] To ensure pressure stability during the grouting process, this plan also includes: The target grouting maintenance pressure and the allowable error range are preset, and the adjusted grouting pressure sequence is calculated based on the target maintenance pressure and the allowable error range; Calculate the grouting pressure adjustment amount, and based on the grouting pressure adjustment amount, obtain the final grouting pressure sequence. The pressure values ​​corresponding to the final grouting pressure sequence are output to the grouting equipment controller to ensure that the grouting pressure is stabilized within the preset range, thereby meeting the grouting requirements of sandy soil layers.

[0044] The formula for adjusting the grouting pressure is as follows: , in, This is the proportionality coefficient, and its value ranges from [value missing]. ; The integral coefficient has a value of [value]. ; ; The current deviation is represented by a value of [value]. , The deviation from the previous time step is 0. The time step is set to a value. .

[0045] Based on the above values, the grouting pressure adjustment amount is calculated, and the final grouting pressure sequence is obtained through the calculated grouting pressure adjustment amount.

[0046] In one embodiment, the grouting pressure sequence and pile length data are obtained to calculate the depth gradient and obtain the depth change rate. The distribution of slurry injection volume is analyzed based on the depth change rate and injection time series to determine the injection volume distribution pattern; If the injection volume distribution pattern deviates from the preset uniform distribution threshold by more than a specified range, the grouting pressure sequence is adjusted by a weighted average method to obtain the adjusted pressure sequence.

[0047] The flow rate distribution of the slurry was calculated based on the adjusted pressure sequence and injection time sequence, and the flow rate distribution characteristics were obtained. The diffusion range of the slurry is divided into zones based on the flow rate distribution characteristics, and the zoning results of the diffusion range are determined. If there are anomalous zones in the diffusion range zoning results, the flow rate of the anomalous zones is recalculated using time series analysis to obtain the corrected flow rate distribution. Based on the corrected flow rate distribution and the depth change rate, an optimized sequence for slurry injection volume is generated, and the final optimized distribution is determined.

[0048] In this embodiment, the depth gradient is calculated from the obtained grouting pressure sequence and pile length data to obtain the depth change rate.

[0049] The distribution of slurry injection volume was analyzed based on the depth change rate and injection time series to determine the injection volume distribution pattern; If the deviation between the injection volume distribution pattern and the preset uniform distribution threshold exceeds the specified range, the grouting pressure sequence is adjusted by the weighted average method to obtain the adjusted pressure sequence. The flow rate distribution of the slurry was calculated based on the adjusted pressure sequence and injection time sequence, and the flow rate distribution characteristics were obtained. The slurry diffusion range is divided into zones by combining the K-means clustering algorithm with the flow rate distribution characteristics, and the zoning results are determined. If there are anomalous zones in the diffusion range zoning results, the flow rate of the anomalous zones is recalculated using time series analysis to obtain the corrected flow rate distribution. Based on the corrected flow rate distribution and the depth change rate, an optimized sequence for slurry injection volume is generated, and the final optimized distribution is determined.

[0050] More specifically, firstly, a pressure sequence and the corresponding depth of the pressure sequence are extracted from the construction data; then, pile length data and design depth interval data are obtained to generate a complete depth sequence; and finally, the missing pressure values ​​at depths equal to the pile length are calculated using linear interpolation to obtain a complete pressure sequence, i.e., the adjusted grouting pressure sequence. Next, the depth gradient is calculated using the finite difference method to obtain a sequence gradient that reflects the pressure variation trend with depth.

[0051] The injection volume sequence is obtained and its corresponding depth is calculated. The injection volume at the depth equal to the pile length is calculated by linear interpolation to obtain the injection volume sequence. The injection volume gradient is then calculated using the injection volume sequence. The injection volume gradient is used to express the distribution law of the injection volume as the depth increases, thus realizing the judgment of the grout injection volume distribution gradient.

[0052] Finally, the velocity distribution is obtained by calculating each value of the injection time series. That is, the grout flow rate is obtained by the injection time series, so as to analyze the flow velocity at different depths. For example, the flow velocity is low at a depth of 15 meters, which may be due to the high density of the stratum causing the grout flow to be obstructed. The grouting parameters need to be adjusted to optimize the construction efficiency.

[0053] In one embodiment, the geological structural characteristics of deep soil layers are obtained, and a geological structural distribution model is generated. Among them, the geological structure distribution model is used to determine the fracture distribution pattern and the distribution of permeability parameters; The fracture distribution pattern was extracted from the geological structure distribution model, and the slurry flow rate distribution was simulated to obtain the spatial variation data of the flow rate. If the fracture connectivity in the spatial variation data of flow rate is higher than a preset threshold, the main flow path is calculated by combining the fracture distribution pattern, and the flow path prediction result is generated. Based on the flow path prediction results, water content ratio data is obtained, and based on the water content ratio data, water saturation is analyzed and the spatial distribution of water content ratio is determined. High water content regions are extracted from the spatial distribution of water content proportions. If the permeability parameter distribution is higher than the preset threshold, an initial allocation scheme to enhance the diffusion effect is generated; The slurry volume distribution is optimized based on the initial distribution scheme, flow path prediction results, and water saturation. The final slurry volume distribution scheme is then generated by combining the optimized slurry volume distribution scheme with the geological structure distribution model.

[0054] In this embodiment, a geological structure distribution model is generated based on the acquired geological structure characteristics of the deep soil layer area, using three-dimensional geological modeling. Based on the geological structure distribution model, the distribution pattern of fractures and the distribution of permeability parameters are determined.

[0055] Extract the fracture distribution pattern from the geological structure distribution model; Simultaneously, the slurry flow rate distribution was simulated to obtain spatial variation data of the flow rate.

[0056] Further calculations were performed based on the spatial variation data of the flow rate. If the fissure connectivity is higher than the preset threshold, the main flow path is calculated using a seepage model based on the fissure distribution pattern, and the flow path prediction results are generated. Based on the flow path prediction results, the water content ratio data is obtained and the water saturation is analyzed to determine the spatial distribution of the water content ratio.

[0057] High water content regions are extracted from the spatial distribution of water content proportions. If the permeability parameter distribution is higher than the preset threshold, an initial allocation scheme to enhance the diffusion effect is generated; Based on the initial allocation scheme, the flow path prediction results and water saturation are combined to optimize the slurry volume allocation, resulting in an optimized allocation scheme.

[0058] By combining the allocation optimization scheme with the geological structure distribution model, the final slurry volume allocation scheme is generated.

[0059] More specifically, the distribution of slurry flow rate in deep soil layers is obtained through numerical simulation and data processing techniques. The slurry flow rate is calculated using Darcy's law to obtain the flow rate value of a single grid cell. The total flow rate distribution is obtained by accumulating several grid cells.

[0060] Next, based on ground-penetrating radar scanning data, 1000 discrete fracture network models were repeatedly generated using Monte Carlo simulation, and the slurry conductivity of each model was calculated to obtain the fracture enhancement flow rate ratio parameter. By pre-setting the water content distribution parameters and average parameters, and using the linear relationship model between water content and slurry diffusion, the diffusion volume value of a single grid is calculated.

[0061] The slurry volume distribution scheme is generated through iterative optimization, with the objective function being to minimize the variance of slurry distribution unevenness. Specifically, it uses a genetic algorithm to set the population size and number of iterations to obtain the optimal allocation scheme; In this embodiment, the fissure region is allocated as follows: 30% of the slurry is distributed evenly in the non-fissure region based on the optimal allocation scheme.

[0062] In this invention, the flow rate of the fracture enhancement significantly affects the distribution efficiency, and the slurry diffuses faster in areas with high water content. The optimized scheme reduces the distribution variance by 10%, thus verifying the effectiveness of the scheme.

[0063] In this invention, the formula for slurry flow rate is: ,in Take the permeability coefficient as , The unit area is 1 square meter. These are the hydraulic gradient parameters.

[0064] In one embodiment, geological data is obtained from shallow soil layers, and the stress distribution of the soil layers is simulated based on the geological data of the shallow soil layers to determine whether there is an overload risk; if the overload risk exists, the slurry volume and injection rate of the overloaded area are extracted from the simulation results to determine the high-risk area.

[0065] The relationship between injection rate and soil stress in high-risk areas was predicted to obtain the adjusted injection rate. The curing time span of the slurry is obtained based on the adjusted injection speed, and the curing effect is predicted to obtain the optimized curing time. The grouting scheme is generated from the optimized injection speed and curing time, and the allocation scheme is integrated to obtain comprehensive grouting parameters; The grouting process was simulated using comprehensive grouting parameters to determine whether there was still an overload risk in the shallow soil layer, and the verification results were obtained. If the verification results show no risk of overload, the final grouting scheme will be output. Based on the final grouting scheme, an optimized scheme was determined.

[0066] In this embodiment, geological data of shallow soil layers are obtained to simulate and analyze the stress distribution of the soil layers, and to determine whether there is a risk of overload. If there is an overload risk, the slurry volume and injection rate of the overloaded area are extracted from the simulation results, and based on this, the high-risk area is determined. The relationship between injection rate and soil stress in high-risk areas is predicted, and the adjusted injection rate is obtained based on the prediction results. Based on the adjusted injection speed, the curing time span of the slurry is obtained, and time series analysis is used to predict the curing effect and determine the optimized curing time.

[0067] The grouting scheme is generated from the optimized injection speed and curing time. Then, the allocation scheme is integrated to obtain the comprehensive grouting parameters.

[0068] The grouting process was simulated based on comprehensive grouting parameters to determine whether there was still an overload risk in the shallow soil layer. Based on the overload risk, the verification results were obtained. If the verification results show no risk of overload, the final grouting scheme will be output, and the optimization scheme will be determined.

[0069] More specifically, firstly, the bearing capacity of shallow soil layers is assessed using geological survey data and finite element analysis; based on the grouting pressure distribution model, the soil stress at the injection point is calculated. If the soil stress at the injection point is lower than the allowable stress, it indicates no overload risk; if the stress in a local area exceeds the limit, it is determined that there is an overload risk.

[0070] The formula for calculating the soil stress at the injection point is: where is the grouting pressure and is the radius from the injection point.

[0071] For overloaded areas, the injection rate is adjusted. Specifically, the flow rate is recalculated based on fluid dynamics equations and the pressure is reduced to below the preset value. At the same time, based on the grout curing time, grouting is carried out in stages, with a pause for several hours after each stage of injection of a preset amount, to achieve the purpose of real-time monitoring of soil stress changes and ensure that the stress does not exceed the preset value.

[0072] The fluid dynamics equation is: ; where is the slurry density, is the pipe cross-sectional area, and is the flow velocity.

[0073] Furthermore, the optimized strategy includes grouting parameters such as injection volume per minute, single injection volume, and pause injection time; the above optimized strategy is executed cyclically until the total amount reaches the design value; stress data is monitored and recorded in real time, and soil stability is predicted through time series analysis to ensure that the grouting process is safe and controllable, thereby achieving the goal of generating an optimized grouting scheme by automatically adjusting all parameters.

[0074] In one embodiment, mixing uniformity data during the grouting process is collected to obtain a raw dataset; mixing uniformity index is extracted from the raw dataset and uniformity deviation is calculated to determine the deviation value; If the deviation exceeds the preset threshold, the adjustment response mechanism is triggered to generate a parameter adjustment command; the control parameters in the grouting scheme are updated according to the parameter adjustment command to obtain the updated scheme configuration; The updated scheme is used to control the grouting equipment and collect new mixing uniformity data in real time to obtain real-time feedback data. By analyzing the uniformity change trend from real-time feedback data and predicting the effect of parameter adjustment based on the uniformity change trend, and obtaining effective prediction results, the parameters of the grouting scheme are iteratively optimized to obtain the optimized scheme configuration.

[0075] In this embodiment, the mixing uniformity data during the grouting process is collected by sensors and stored in a database to obtain the original dataset; The uniformity index of the mixture is extracted from the original dataset, and the uniformity deviation is calculated based on statistical analysis to determine the deviation value; If the deviation value exceeds the preset threshold, the adjustment response mechanism is triggered, generating a parameter adjustment command.

[0076] The control parameters in the grouting scheme are updated using parameter adjustment commands to obtain the updated scheme configuration.

[0077] The updated scheme is used to configure and control the grouting equipment for grouting operations.

[0078] The grouting equipment performs the following operations: First, it collects new mixing uniformity data to obtain real-time feedback data; The uniformity change trend is analyzed from real-time feedback data, the effect of parameter adjustment is predicted using a random forest model, and the prediction result is judged based on the adjustment effect.

[0079] The parameters of the grouting scheme are iteratively optimized based on the prediction results to obtain the optimized scheme configuration.

[0080] More specifically, by monitoring the mixing uniformity index during construction using the optimized grouting scheme, and incorporating a monitoring feedback cycle and adjustment response mechanism to obtain real-time feedback data for iterative parameter adjustment, this can be achieved through the following specific implementation methods: First, monitor the uniformity of grout mixing, collect data on sound wave propagation speed and attenuation coefficient, and calculate the uniformity index value. If the uniformity index value of a certain area is lower than the threshold, a feedback cycle is triggered; Periodic data collection via IoT devices enables monitoring of the feedback cycle; Periodic data is transmitted to the cloud server using the MQTT protocol. Based on time series analysis algorithms, the uniformity variation trend of periodic data is predicted to obtain the predicted uniformity value within a preset future time period. If the predicted uniformity value is greater than the preset uniformity value, the adjustment response mechanism will be automatically activated. The response mechanism was adjusted, and the grouting parameters were optimized based on a genetic algorithm. By setting the initial population size and the number of iterations, the optimization objective is to minimize the uniformity deviation. The grouting pump pressure and grout mix ratio were adjusted and optimized using the adjusted uniformity deviation value.

[0081] Next, the uniformity index value was recalculated. If the adjusted uniformity index value is greater than the minimum uniformity deviation value, then the requirement is met. At this point, record the parameters and continue monitoring. If the requirements are still not met, continue iterative optimization until the target is achieved; This process forms a closed-loop control, ensuring stable grouting quality and improving construction efficiency by approximately 15% compared to traditional grouting.

[0082] The formula for calculating the uniformity index value is: Uniformity Index Value , in The region-average sound velocity, This refers to the velocity of a single-point sound wave.

[0083] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for controlling segmented variable frequency grouting in cement-soil mixing piles, characterized in that, include: A distribution map of soil physical properties is obtained based on real-time formation data acquired from borehole locations. The distribution map of soil physical properties is used to divide different stratigraphic regions and determine the range of permeability coefficient and compression modulus values ​​for each region. If the permeability coefficient is higher than the preset threshold, the grouting pressure value is increased to match the requirements of the sandy soil layer, and the adjusted grouting pressure sequence is obtained by combining the pressure maintenance level. Based on the adjusted grouting pressure sequence and combined with pile length data, the distribution gradient of grout injection volume is determined, and the grout flow rate distribution is further determined by combining the injection time sequence. Based on the slurry flow rate distribution, fracture pattern, and water content information, a slurry volume distribution scheme based on enhanced diffusion in deep soil layers is generated. Assess whether there is a risk of shallow soil overload in the slurry volume distribution scheme; If there is an overload risk, reduce the injection speed in the corresponding area and determine the optimized grouting scheme; Based on the optimized grouting scheme, the mixing uniformity index is monitored during construction. Based on the monitoring data, combined with the monitoring feedback cycle and adjustment response mechanism, real-time feedback data is obtained to iteratively adjust parameters and obtain the final variable frequency grouting control command.

2. The method for segmented variable frequency grouting control of cement-soil mixing piles as described in claim 1, characterized in that, Real-time formation data is obtained from the borehole location, and combined with rock hardness and soil particle size analysis to generate a distribution map of soil physical properties. The distribution map of soil physical properties is analyzed and different strata regions are divided. The range of permeability coefficient and the range of compression modulus value of each region are determined. If the permeability coefficient range exceeds the preset threshold, the grouting pressure value is adjusted according to the requirements of sandy soil layer to generate an adjusted grouting pressure sequence. The depth gradient is calculated based on the adjusted grouting pressure sequence and pile length data, and the grout flow rate distribution is generated by combining the injection time sequence. When in deep soil layers, a grout volume distribution scheme to enhance diffusion is generated based on the grout flow rate distribution, crack distribution pattern, and water content ratio. If there is an overload risk in the shallow soil layer in the grout volume distribution scheme, the injection speed in the corresponding area is reduced according to the curing time span to generate an optimized grouting scheme. The mixing uniformity index is monitored based on the optimized grouting scheme, and the feedback cycle is iterated. Based on the iteration results, parameters are adjusted to generate real-time feedback data.

3. The method for segmented variable frequency grouting control of cement-soil mixing piles as described in claim 2, characterized in that, The soil physical property data is standardized to generate a property dataset in a unified format; Grouping the characteristic datasets yields preliminary stratigraphic region division results. If the boundaries of the regions in the division results are unclear, spatial analysis can be used to adjust the boundaries and determine clear stratigraphic regions. The permeability coefficient statistical range of each region is calculated based on the stratigraphic region to obtain the permeability coefficient distribution. Calculate the statistical range of the compressibility modulus for each region based on the stratigraphic region to obtain the compressibility modulus value range; The distribution of permeability coefficient and the range of compressibility modulus are superimposed to generate a comprehensive physical property distribution map of the stratigraphic region; the significance of the property distribution of each region is verified by the comprehensive physical property distribution map, and the final stratigraphic region division and property value range are determined. The soil physical property data is based on real-time geological data obtained from borehole locations.

4. The method for segmented variable frequency grouting control of cement-soil mixing piles as described in claim 3, characterized in that, If the permeability coefficient exceeds the preset threshold, the difference between the permeability coefficient and the preset threshold is compared, the adjustment coefficient is recalculated, and the initial pressure adjustment value is obtained. Based on the initial pressure adjustment value and the characteristic parameters of the sandy soil layer, a pressure adjustment sequence matching the soil layer characteristics is generated. The pressure change trend is obtained from the pressure adjustment sequence, and the stability condition is determined by combining the pressure maintenance level. If the stability condition is met, the optimized pressure sequence is obtained. If the pressure values ​​in the optimized pressure sequence exceed the safe range, the pressure sequence parameters are adjusted using the gradient descent algorithm to obtain a safe pressure sequence. The final grouting pressure sequence is generated based on the safety pressure sequence and the soil layer characteristic matching results. Key pressure points are extracted from the final grouting pressure sequence to determine the sequence stability, and the grouting execution sequence is obtained. A control signal sequence is generated based on the grouting execution sequence and output to the grouting equipment to complete pressure optimization control.

5. The method for segmented variable frequency grouting control of cement-soil mixing piles as described in claim 4, characterized in that, The grouting pressure sequence and pile length data are obtained to calculate the depth gradient and obtain the depth change rate; the grout injection volume distribution is analyzed based on the depth change rate and injection time sequence to determine the injection volume distribution pattern. If the injection volume distribution pattern deviates from the preset uniform distribution threshold by more than a specified range, the grouting pressure sequence is adjusted by a weighted average method to obtain the adjusted pressure sequence.

6. The method for segmented variable frequency grouting control of cement-soil mixing piles as described in claim 5, characterized in that, The flow rate distribution of the slurry was calculated based on the adjusted pressure sequence and injection time sequence, and the flow rate distribution characteristics were obtained. The diffusion range of the slurry is divided into zones based on the flow rate distribution characteristics, and the zoning results of the diffusion range are determined. If there are anomalous zones in the diffusion range zoning results, the flow rate of the anomalous zones is recalculated using time series analysis to obtain the corrected flow rate distribution. Based on the corrected flow rate distribution and the depth change rate, an optimized sequence for slurry injection volume is generated, and the final optimized distribution is determined.

7. The method for segmented variable frequency grouting control of cement-soil mixing piles as described in claim 6, characterized in that, The geological structure characteristics of deep soil layers are obtained and a geological structure distribution model is generated based on them to determine the fracture distribution pattern and permeability parameter distribution. The fracture distribution pattern was extracted from the geological structure distribution model, and the slurry flow rate distribution was simulated to obtain the spatial variation data of the flow rate. If the fracture connectivity in the spatial variation data of flow rate is higher than a preset threshold, the main flow path is calculated by combining the fracture distribution pattern, and the flow path prediction result is generated. Based on the flow path prediction results, water content ratio data is obtained, and based on the water content ratio data, water saturation is analyzed and the spatial distribution of water content ratio is determined. High water content regions are extracted from the spatial distribution of water content proportions. If the permeability parameter distribution is higher than the preset threshold, an initial allocation scheme to enhance the diffusion effect is generated; The slurry volume distribution is optimized based on the initial distribution scheme, flow path prediction results, and water saturation. The final slurry volume distribution scheme is then generated by combining the optimized slurry volume distribution scheme with the geological structure distribution model.

8. The method for segmented variable frequency grouting control of cement-soil mixing piles as described in claim 7, characterized in that, Geological data is obtained from shallow soil layers. Based on this geological data, the stress distribution of the soil layers is simulated to determine whether there is an overload risk. If an overload risk exists, the slurry volume and injection rate of the overloaded area are extracted from the simulation results to determine the high-risk area.

9. The method for segmented variable frequency grouting control of cement-soil mixing piles as described in claim 8, characterized in that, The relationship between injection rate and soil stress in high-risk areas was predicted to obtain the adjusted injection rate. The curing time span of the slurry is obtained based on the adjusted injection speed, and the curing effect is predicted to obtain the optimized curing time. The grouting scheme is generated from the optimized injection speed and curing time, and the allocation scheme is integrated to obtain comprehensive grouting parameters; The grouting process was simulated using comprehensive grouting parameters to determine whether there was still an overload risk in the shallow soil layer, and the verification results were obtained. If the verification results show no risk of overload, the final grouting scheme will be output. Based on the final grouting scheme, an optimized scheme was determined.

10. The method for segmented variable frequency grouting control of cement-soil mixing piles as described in claim 9, characterized in that, Collect mixing uniformity data during the grouting process to obtain the raw dataset; extract the mixing uniformity index from the raw dataset and calculate the uniformity deviation to determine the deviation value; If the deviation exceeds the preset threshold, the adjustment response mechanism is triggered to generate a parameter adjustment command; the control parameters in the grouting scheme are updated according to the parameter adjustment command to obtain the updated scheme configuration; The updated scheme is used to control the grouting equipment and collect new mixing uniformity data in real time to obtain real-time feedback data. By analyzing the uniformity change trend from real-time feedback data and predicting the effect of parameter adjustment based on the uniformity change trend, and obtaining effective prediction results, the parameters of the grouting scheme are iteratively optimized to obtain the optimized scheme configuration.

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