CN-122022374-A - Soft foundation in-situ solidification construction optimization method and system based on Beidou 3D positioning

CN122022374ACN 122022374 ACN122022374 ACN 122022374ACN-122022374-A

Abstract

The invention relates to the technical field of soft foundation treatment engineering, in particular to a soft foundation on-site curing construction optimization method and system based on Beidou 3D positioning. The method comprises the steps of obtaining high-precision 3D position coordinates through positioning optimization of multi-source data fusion, extracting soil solidification potential characteristics by using machine-learned geological characteristics, obtaining optimized construction parameter characteristics according to construction parameter recommendation of case reasoning, obtaining construction priority area characteristics through construction area division of multi-feature fusion based on the high-precision 3D position coordinates, the soil solidification potential characteristics and the optimized construction parameter characteristics, obtaining material and mechanical cooperative characteristics according to cooperative optimization of a control theory by combining material effective coverage area characteristics and mechanical operation efficiency characteristics, and dynamically planning a construction plan by combining influence characteristics of a fusion environment on a solidification process, the construction priority area characteristics and the material and mechanical cooperative characteristics. The invention realizes the precision, the intellectualization and the high efficiency of the soft foundation construction process.

Inventors

LIU YANGQING
CHEN WEIWEI
YANG CHAO
TONG LIHONG
LIU WEIPING
TAO JINGLIN
ZHANG ZHIHAI
CHEN JUN
TAN HAIPING
HE QIANG
QU JIANGYI
NIE PING
GU LONG

Assignees

江西省交通投资集团有限责任公司
江西省公路桥梁工程有限公司
江西省交通工程集团有限公司
江西昌贤高速公路有限公司
江西省嘉和工程咨询监理有限公司
华东交通大学
南昌大学

Dates

Publication Date: 20260512
Application Date: 20260302

Claims (10)

1. The soft foundation on-site solidification construction optimization method based on Beidou 3D positioning is characterized by comprising the following steps of: Positioning optimization through multi-source data fusion is carried out, and high-precision 3D position coordinates are obtained; extracting soil solidification potential characteristics by using machine-learned geological characteristics, and obtaining optimized construction parameter characteristics according to construction parameter recommendation of case reasoning; Based on the high-precision 3D position coordinates, the soil solidification potential characteristics and the optimized construction parameter characteristics, the construction priority area characteristics are obtained through the construction area division of multi-characteristic fusion; Combining the effective coverage characteristic and the mechanical operation efficiency characteristic of the material, and obtaining the cooperative characteristic of the material and the machinery according to cooperative optimization of a control theory; and (3) fusing the influence characteristics of the environment on the curing process, the construction priority region characteristics and the material and machinery cooperative characteristics, and dynamically planning a construction plan.
2. The soft foundation on-site solidification construction optimization method based on Beidou 3D positioning according to claim 1, wherein the positioning optimization through multi-source data fusion obtains high-precision 3D position coordinates, and the method comprises the following steps: Primary hierarchical fusion of Beidou and IMU data is realized through Kalman filtering; and introducing DEM digital elevation data of the construction area, and carrying out elevation consistency check on the positioning result after layering fusion to obtain high-precision 3D position coordinates.
3. The soft foundation in-situ solidification construction optimization method based on Beidou 3D positioning according to claim 1, wherein the extraction of soil solidification potential features by using machine learning geological features comprises the following steps: Constructing a structured association data set of the 3D position and the multidimensional geological parameter; performing feature primary selection and correlation analysis based on the structured association data set to obtain core geological features; and training an integrated learning model by utilizing the core geological features and extracting the soil solidification potential features.
4. The soft foundation on-site solidification construction optimization method based on Beidou 3D positioning according to claim 1, wherein the construction parameter recommendation based on case reasoning obtains optimized construction parameter characteristics, and the method comprises the following steps: distributing differential weights for the search keywords, and searching historical matching cases in the structured case library according to the weight distribution results; And correcting case parameters in the history matching case, and extracting the characteristics of the optimized construction parameters from the correction result.
5. The soft foundation on-site solidification construction optimization method based on Beidou 3D positioning according to claim 1, wherein the construction priority region features are obtained through multi-feature fusion construction region division based on high-precision 3D position coordinates, soil solidification potential features and optimized construction parameter features, and the method comprises the following steps: Taking the 3D position coordinates of the construction area as a space reference, fusing the soil solidification potential characteristics and the optimized construction parameter characteristics, and constructing an area division evaluation index system; and dividing the construction area into different priorities through cluster analysis based on the area division evaluation index system, and extracting the construction priority area characteristics.
6. The soft foundation on-site solidification construction optimization method based on Beidou 3D positioning according to claim 1, wherein the material and machinery cooperative characteristics are obtained according to cooperative optimization of a control theory by combining effective coverage characteristics and mechanical operation efficiency characteristics of the material, and the method comprises the following steps: constructing a cooperative optimization model of material diffusion and mechanical operation based on a model predictive control theory; And combining the effective coverage characteristic of the material, the mechanical operation efficiency characteristic and the collaborative optimization model, and realizing the dynamic collaboration of mechanical operation and material diffusion by rolling optimization and feedback correction to obtain the material and mechanical collaborative characteristic.
7. The soft foundation cure-in-place construction optimization method based on Beidou 3D positioning according to claim 6, wherein the material effective coverage characteristics are obtained through hydrodynamic material diffusion optimization, comprising the following steps: constructing a diffusion model of the curing agent in soft foundation soil based on a porous medium seepage theory in fluid dynamics; and combining the soil pore structure, the rheological property of the curing agent and the operation parameters of the construction machine, simulating the diffusion process of the curing agent by using the diffusion model, and optimizing the operation parameters to ensure that the material effectively covers the target area so as to obtain the characteristic of the effective coverage of the material.
8. The soft foundation cure-in-place construction optimization method based on Beidou 3D positioning according to claim 6, wherein the mechanical operation efficiency characteristics are obtained through reinforcement learning mechanical behavior optimization, comprising the following steps: Constructing a mechanical operation optimization system according to a Q-learning algorithm; and taking the operation parameters of the construction machine as actions, taking the construction efficiency and the construction quality as reward signals, and obtaining the mechanical operation efficiency characteristics by continuously and iteratively learning and optimizing the mechanical operation behaviors.
9. The soft foundation on-site curing construction optimization method based on Beidou 3D positioning according to claim 1, wherein the method for dynamically planning a construction plan by means of influence features of the fusion environment on a curing process, construction priority region features and material and machinery cooperative features comprises the following steps: dividing the construction process into a plurality of decision stages according to time stages based on a dynamic planning theory; Constructing a state transfer equation for optimizing a construction plan by taking construction priority region characteristics, material and mechanical cooperative characteristics and environmental influence characteristics of each stage as decision basis; And solving the dynamic planning construction plan through an inverse sequence based on the optimization target.
10. The soft foundation on-site curing construction optimization system based on Beidou 3D positioning is characterized by comprising input equipment, output equipment, a processor and a memory, wherein the input equipment, the output equipment, the processor and the memory are connected with each other, and the memory comprises program instructions for executing the soft foundation on-site curing construction optimization method based on Beidou 3D positioning according to any one of claims 1-9.

Description

Soft foundation in-situ solidification construction optimization method and system based on Beidou 3D positioning Technical Field The invention relates to the technical field of soft foundation treatment engineering, in particular to a soft foundation on-site curing construction optimization method and system based on Beidou 3D positioning. Background The soft foundation on-site solidification technology has become one of the main technologies for soft foundation treatment in infrastructure construction such as road engineering, ports and docks, airport roads and the like by virtue of the core advantages of construction convenience, cost economy, low environmental disturbance and the like. However, the existing soft foundation on-site curing construction technology system still has a plurality of core technical bottlenecks, which restrict the stability of construction quality and the improvement of engineering efficiency, and the concrete manifestation is as follows: The positioning accuracy limitation is remarkable, the traditional construction positioning scheme is mostly dependent on a single Beidou positioning mode or a Beidou and reference station differential positioning framework, key influencing factors such as vibration interference, positioning deviation caused by topography fluctuation of a soft foundation area and the like in the operation process of the construction machinery are not fully considered, so that the quality defects such as insufficient spatial positioning accuracy of the operation end (such as a curing agent injection head and a stirring drill bit) of the construction machinery, easy curing agent injection deviation and stirring depth control misalignment are easily induced. The geological feature characterization and solidification potential evaluation system is simplified, the soil solidification potential evaluation is usually carried out only based on a single geological parameter (such as soil moisture content) or an empirical fitting formula in the prior art, the synergistic action mechanism and the spatial heterogeneity characteristics among multiple geological parameters are not fully considered, accurate matching of construction parameters and geological conditions of different spatial points is difficult to achieve, and problems of blindness of a construction scheme, waste of solidified materials and the like are easily caused. The material diffusion and mechanical operation cooperative regulation capability is weak, the determination of the curing agent injection parameters is dependent on engineering experience values, the spatial heterogeneity of the soil pore structure and the rheological property of the curing agent are not combined to carry out targeted optimization design, the mechanical operation parameters (such as the operation speed and the stirring rotation speed) are in an immobilized setting mode, and the real-time matching with the curing agent diffusion dynamic process is not formed, so that the phenomena of uneven space distribution, insufficient effective coverage, excessive material consumption and the like of the curing agent are easily caused. The dynamic response and quantitative evaluation capability of the environmental factors are insufficient, namely the prior art adopts a static analysis mode (such as only considering the temperature condition of the construction day) or a qualitative description mode for evaluating the environmental influence, the quantitative characterization of the dynamic evolution rule of the environmental factors and the accurate measurement and calculation of the influence weight cannot be realized, and adverse interference of sudden environmental changes such as temperature fluctuation, rainfall and the like on the curing reaction process and the curing effect is difficult to be effectively caused. The construction plan dynamic suitability is lacking, the priority of the construction area is divided by adopting a simple terrain division or homogenization division strategy, the core characteristics such as soil solidification potential, construction parameter suitability and the like are not fused, the construction plan is mainly formulated by static planning, uncertainty factors such as environment dynamic change, resource constraint fluctuation and the like are not fully brought into play, and engineering risks such as construction period delay, engineering cost hyperbranched and the like are easily caused. In summary, the existing soft foundation on-site curing construction technology still has significant technical bottlenecks in the core links of positioning precision, feature characterization, cooperative regulation, environmental response, plan adaptation and the like, and development of a soft foundation on-site curing construction whole-flow optimization method capable of realizing high-precision positioning, accurate feature extraction, dynamic cooperative optimization and adaptive plan adjustment is needed to