A deep foundation pit excavation construction control method and system near sensitive buildings

Abstract

The present invention discloses a method and system for controlling deep foundation pit excavation construction near sensitive buildings, which relates to the technical field related to foundation pit construction. The method includes: conducting risk assessment and analysis based on deep foundation pit excavation targets, sensitive building data, soil geological data, and groundwater data; constructing a support excavation construction strategy library; activating channels and analyzing strategies for deep foundation pit engineering risk heat maps; executing deep foundation pit excavation construction based on target engineering construction strategy plans, and monitoring multi-source construction monitoring data streams in real time through a sensing Internet of Things network; and dynamically optimizing and controlling the target engineering construction strategy plans and excavation construction. The method solves the technical problems existing in the prior art of deep foundation pit construction risk warning lags, difficulty adapting to complex environments and sensitive building protection requirements, resulting in poor safety and construction efficiency of deep foundation pit projects. The method achieves the technical effects of accurately predicting construction risks, adjusting construction parameters in real time, and improving project safety and construction efficiency.

Description

Technical Field

[0001] The present application relates to the technical field related to foundation pit construction engineering, and specifically to a method and system for controlling the excavation construction of a deep foundation pit adjacent to sensitive buildings. Background Art

[0002] With the acceleration of urbanization, the demand for underground space development is growing. Deep foundation pit engineering is widely used in projects such as high-rise buildings, subway tunnels, and underground utility corridors. However, deep foundation pit excavation in complex environments near sensitive structures (such as historic buildings, old residential buildings, subway tunnels, hospitals, and schools) can easily cause deformation of the surrounding soil, building settlement, and even structural damage, posing serious challenges to project safety and the stability of the surrounding environment. Traditional deep foundation pit construction control relies on empirical design and static monitoring, which is difficult to cope with complex geological conditions, dynamic construction disturbances, and the high-precision protection requirements for sensitive buildings. Especially in soft soils, high water levels, or highly permeable strata, foundation pit excavation can lead to sudden changes in support structure stress, groundwater level fluctuations, and soil stress redistribution, which can induce risks such as deformation of the retaining structure, surface settlement, or building tilt. Furthermore, existing construction strategies often lack intelligent fusion and analysis of multi-source data (such as geological conditions, building status, and real-time monitoring data), resulting in delayed risk warnings, passive control measures, and difficulty in achieving refined construction control.

[0003] Therefore, in the current relevant technologies, there are technical problems such as delayed risk warning in deep foundation pit construction, difficulty in adapting to complex environments and the protection needs of sensitive buildings, resulting in poor safety and construction efficiency of deep foundation pit projects. Summary of the Invention

[0004] This application provides a deep foundation pit excavation construction control method and system near sensitive buildings, which solves the technical problems in the existing technology such as the delayed early warning of deep foundation pit construction risks, difficulty in adapting to complex environments and the protection needs of sensitive buildings, resulting in poor safety and construction efficiency of deep foundation pit projects. It achieves the technical effects of accurately predicting construction risks, adjusting construction parameters in real time, and improving project safety and construction efficiency.

[0005] The present application provides a method for controlling deep foundation pit excavation construction adjacent to sensitive buildings, the method comprising: performing risk assessment analysis based on deep foundation pit excavation targets, sensitive building data, soil geological data, and groundwater data in a target construction area to generate a deep foundation pit engineering risk heat map; constructing a support excavation construction strategy library, the support excavation construction strategy library comprising a general excavation construction strategy channel and a personalized excavation construction strategy channel; performing channel activation and strategy analysis on the deep foundation pit engineering risk heat map based on the general excavation construction strategy channel and the personalized excavation construction strategy channel to determine a target engineering construction strategy scheme; executing deep foundation pit excavation construction based on the target engineering construction strategy scheme, deploying a perception Internet of Things network in the target construction area, and monitoring multi-source construction monitoring data streams during excavation construction in real time through the perception Internet of Things network; and dynamically optimizing and regulating the target engineering construction strategy scheme and controlling excavation construction based on the multi-source construction monitoring data streams.

[0006] In a possible implementation, the method for controlling construction of deep foundation pit excavation near sensitive buildings further performs the following processing: performing key analysis on the deep foundation pit excavation target to obtain a deep foundation pit design parameter set; performing finite element coupling analysis based on the deep foundation pit design parameter set, sensitive building data, soil geological data, and groundwater data to establish a finite element model of a deep foundation pit project; presetting a working condition load simulation combination, applying the working condition load simulation combination to the deep foundation pit project finite element model to perform construction working condition simulation to obtain a deep foundation pit construction simulation parameter set; performing risk assessment analysis based on the deep foundation pit construction simulation parameter set to obtain a deep foundation pit project risk heat map.

[0007] In a possible implementation, the method for controlling deep foundation pit excavation construction near sensitive buildings further performs the following processing: constructing a deep foundation pit risk index system, using the deep foundation pit risk index system to perform risk assessment on the deep foundation pit construction simulation parameter set, and obtaining a risk index distribution parameter set; dividing the deep foundation pit risk index system into risk thresholds according to the deep foundation pit construction safety standard, and obtaining a risk index-grading color mapping rule; using the risk index-grading color mapping rule to map and transform the risk index distribution parameter set, and generate a risk index sub-thermal map set; superimposing the risk index sub-thermal map set and marking the building risk areas, and obtaining the deep foundation pit project risk heat map.

[0008] In a possible implementation, the method for controlling deep foundation pit excavation construction near sensitive buildings further performs the following processing: excavating and obtaining a deep foundation pit excavation construction dataset, wherein the deep foundation pit excavation construction dataset includes engineering risk heat map data and corresponding foundation pit support structure data and excavation construction strategy data; extracting a deep foundation pit associated feature set for obtaining the engineering risk heat map data; clustering and identifying the deep foundation pit excavation construction dataset according to the deep foundation pit associated feature set to obtain a general excavation construction strategy channel and a personalized excavation construction strategy channel; and constructing the support excavation construction strategy library based on the general excavation construction strategy channel and the personalized excavation construction strategy channel.

[0009] In a possible implementation, the method for controlling deep foundation pit excavation construction near sensitive buildings further performs the following processing: performing influence evaluation and key feature screening on each associated feature in the deep foundation pit associated feature set to determine a deep foundation pit feature dimension set; performing feature clustering analysis on the deep foundation pit excavation construction data set according to the deep foundation pit feature dimension set to obtain multiple excavation construction data clusters; dividing and identifying the multiple excavation construction data clusters to obtain a general excavation construction strategy channel and a personalized excavation construction strategy channel.

[0010] In a possible implementation, the method for controlling deep foundation pit excavation construction near sensitive buildings further performs the following processing: sequentially performing feature identification and intra-cluster data statistics on the multiple excavation construction data clusters to obtain multiple intra-cluster associated data volumes; setting a common feature distribution threshold based on the multiple intra-cluster associated data volumes; and determining, dividing, and integrating the multiple excavation construction data clusters based on the common feature distribution threshold to construct a common excavation construction strategy channel and a personalized excavation construction strategy channel.

[0011] In a possible implementation, the method for controlling deep foundation pit excavation construction near sensitive buildings further performs the following processing: determining, dividing, and integrating the multiple excavation construction data clusters based on the general feature distribution threshold to obtain a general excavation construction strategy data set and a personalized excavation construction strategy data set; using a deep neural network structure to perform identification supervision training on the general excavation construction strategy data set and the personalized excavation construction strategy data set to construct the general excavation construction strategy channel and the personalized excavation construction strategy channel.

[0012] In a possible implementation, the deep foundation pit excavation construction control method near sensitive buildings also performs the following processing: channel matching and activation of the deep foundation pit engineering risk heat map based on the general excavation construction strategy channel and the personalized excavation construction strategy channel to obtain the target excavation construction strategy channel; and strategy analysis of the deep foundation pit engineering risk heat map using the target excavation construction strategy channel to determine the target engineering construction strategy plan.

[0013] In a possible implementation, the method for controlling deep foundation pit excavation construction near sensitive buildings further performs the following processing: using the deep foundation pit risk index system to conduct a risk assessment on the multi-source construction monitoring data stream to obtain deep foundation pit construction risk parameter information; dynamically optimizing and regulating the target project construction strategy scheme based on the deep foundation pit construction risk parameter information, and controlling deep foundation pit excavation construction through the regulated target project construction strategy scheme.

[0014] The present application also provides a deep foundation pit excavation construction control system adjacent to sensitive buildings, the system comprising: a risk assessment and analysis unit for performing risk assessment and analysis based on the deep foundation pit excavation targets, sensitive building data, soil geological data, and groundwater data of the target construction area, and generating a deep foundation pit engineering risk heat map; an excavation construction strategy library construction unit for constructing a support excavation construction strategy library, the support excavation construction strategy library comprising a general excavation construction strategy channel and a personalized excavation construction strategy channel; a construction strategy scheme determination unit for performing channel activation and strategy analysis on the deep foundation pit engineering risk heat map based on the general excavation construction strategy channel and the personalized excavation construction strategy channel, and determining the target engineering construction strategy scheme; a construction monitoring data stream perception unit for executing deep foundation pit excavation construction based on the target engineering construction strategy scheme, deploying a perception Internet of Things network in the target construction area, and monitoring multi-source construction monitoring data streams in real time during excavation construction through the perception Internet of Things network; and a dynamic optimization and control unit for dynamically optimizing and controlling the target engineering construction strategy scheme and controlling excavation construction based on the multi-source construction monitoring data stream.

[0015] This application proposes a method and system for controlling deep foundation pit excavation near sensitive buildings. The system conducts risk assessment and analysis based on deep foundation pit excavation targets, sensitive building data, soil geological data, and groundwater data. It also constructs a support excavation strategy library, activates channels, and analyzes strategies for deep foundation pit project risk heat maps. Deep foundation pit excavation is executed based on the target project construction strategy plan, and multi-source construction monitoring data streams are monitored in real time through a sensing IoT network. The system also dynamically optimizes and regulates the target project construction strategy plan, and controls excavation construction. This system addresses the existing technical issues of delayed deep foundation pit construction risk warnings, difficulty adapting to complex environments, and the need to protect sensitive buildings, resulting in poor safety and construction efficiency for deep foundation pit projects. It achieves the technical effect of accurately predicting construction risks, adjusting construction parameters in real time, and improving project safety and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0017] Figure 1 A flowchart of a method for controlling deep foundation pit excavation construction near sensitive buildings is provided in an embodiment of the present application.

[0018] Figure 2 A schematic structural diagram of a deep foundation pit excavation construction control system near sensitive buildings provided in an embodiment of the present application.

[0019] Explanation of the accompanying symbols: risk assessment and analysis unit 10, excavation construction strategy library construction unit 20, construction strategy solution determination unit 30, construction monitoring data flow perception unit 40, dynamic optimization and control unit 50. DETAILED DESCRIPTION

[0020] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0021] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be 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, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0023] The embodiment of the present application provides a method for controlling the excavation of a deep foundation pit near sensitive buildings, such as Figure 1 As shown, the method includes:

[0024] Step S100 , performing risk assessment analysis based on deep foundation pit excavation targets, sensitive building data, soil geological data, and groundwater data in the target construction area, and generating a deep foundation pit engineering risk heat map.

[0025] Preferably, in deep foundation pit excavation projects, deep foundation pit excavation targets, sensitive building data, soil geological data, and groundwater data are collected for the target construction area. Specifically, the deep foundation pit excavation targets for the target construction area clearly define the project's desired depth, shape, planar dimensions, and functional requirements. For example, for a foundation pit project for an underground multi-story parking garage, the excavation target may be to reach a depth of 15 meters underground, forming a regular rectangular space. Sensitive building data includes the building's location from the foundation pit, the building's structural type (e.g., brick-concrete, steel, reinforced concrete), age, and importance (e.g., whether it is a historical building or a residential building). For example, if there is a 50-year-old brick-concrete residential building next to the foundation pit, its structure is relatively fragile and will be more sensitive to vibration and deformation caused by the foundation pit excavation. Soil geological data includes soil types (such as sand, clay, and silt) at different depths within the target construction area, as well as particle size distribution, bearing capacity, and shear strength. For example, sand has good permeability but relatively low shear strength, making it prone to sand flow. Clay has high shear strength but poor permeability, potentially generating significant lateral earth pressure during excavation. Groundwater hydrological data includes groundwater levels, flow direction, water level fluctuations (such as seasonal variations), and water pressure. Excessively high water levels can cause piping at the bottom of the foundation pit or reduce the shear strength of the pit sidewalls, increasing the risk of pit collapse.

[0026] Preferably, risk assessment analysis involves comprehensively analyzing multiple pieces of collected construction data, taking into account factors such as the distance between sensitive buildings and the foundation pit, soil properties, and the impact of groundwater, to assess the potential risks of deep foundation pit excavation. For example, if the foundation pit is close to sensitive buildings, the soil between them is weak (such as silty soil), and the groundwater level is high, the risk of building settlement and tilting during excavation is high, thus identifying the area as high-risk. Conversely, areas farther from the foundation pit, with hard soil and a low groundwater level, have relatively low risks. Based on the results of the risk assessment analysis, a deep foundation pit project risk heat map is generated, which uses different colors to indicate the degree of risk. Generally speaking, red represents high-risk areas, yellow represents medium-risk areas, and green represents low-risk areas. This heat map clearly displays the risk distribution of various locations around the foundation pit. Construction personnel can use this deep foundation pit project risk heat map to intuitively understand the risk status of various areas on the construction site, thereby formulating targeted construction strategies and implementing risk prevention and control measures.

[0027] Furthermore, step S100 also includes step S110, performing key analysis on the deep foundation pit excavation target to obtain a deep foundation pit design parameter set; step S120, performing finite element coupling analysis based on the deep foundation pit design parameter set, sensitive building data, soil geological data and groundwater data to establish a deep foundation pit engineering finite element model; step S130, presetting a working condition load simulation combination, applying the working condition load simulation combination to the deep foundation pit engineering finite element model to perform construction working condition simulation, and obtaining a deep foundation pit construction simulation parameter set; step S140, performing risk assessment analysis based on the deep foundation pit construction simulation parameter set to obtain a deep foundation pit engineering risk heat map.

[0028] Preferably, a key analysis of the excavation target is performed to clarify the spatial shape and scale requirements of the foundation pit, and then the deep foundation pit design parameter set is determined, which may include but is not limited to the slope of the foundation pit side slope, the type of support structure (such as pile anchor support, soil nail wall support, underground continuous wall support, etc.), the size of the support structure (such as the diameter, spacing, depth, etc. of the support piles), the elevation of the foundation pit bottom, and other parameters. Finite element coupled analysis is a numerical analysis method that comprehensively considers the interactions of multiple physical fields (such as mechanical fields and fluid force fields). In deep foundation pit projects, the mechanical behavior of the soil (such as deformation and stress) is coupled with groundwater seepage. For example, groundwater seepage can change the effective stress of the soil, thereby affecting its strength and deformation characteristics. The deep foundation pit design parameter set, sensitive building data (including building location, structural stiffness, and foundation form), soil geological data (such as mechanical parameters such as the elastic modulus, Poisson's ratio, internal friction angle, and cohesion of the soil layer, as well as geometric parameters such as the distribution range and thickness of different soil layers), and groundwater data (such as groundwater permeability and water level) are then input into the finite element analysis component. By dividing the finite element mesh, the entire deep foundation pit project area is discretized into numerous small units. A finite element model of the deep foundation pit project is established, which can simulate the mechanical and seepage interactions between the soil, support structure, and surrounding environment during the deep foundation pit construction process.

[0029] Preferably, a variety of working condition load simulation combinations are set according to the actual construction plan and expected conditions, such as different depth stages of foundation pit excavation, different stages of support structure construction, load changes of surrounding buildings, etc., and then the preset working condition load simulation combinations are input into the finite element model of the deep foundation pit project to simulate the construction conditions, and the simulation result parameters are obtained through numerical calculation, that is, the mechanical response and deformation of the deep foundation pit under different construction conditions are simulated, which may include the displacement and stress distribution of the soil, the internal force (such as bending moment, shear force, axial force, etc.) and displacement of the support structure, and the change of the seepage field of groundwater, etc. The simulation result parameter combination forms a deep foundation pit construction simulation parameter set, which reflects the performance of the deep foundation pit under various possible construction conditions. Then, based on the deep foundation pit construction simulation parameter set, the team assessed and analyzed the potential risks faced by the deep foundation pit project. For example, the risk of soil collapse surrounding the foundation pit was assessed based on the magnitude and rate of soil displacement; the risk of support structure instability was assessed based on the internal forces and deformations of the support structure; and the risk of seepage damage such as piping and sand flow in the foundation pit was assessed based on changes in the groundwater seepage field. Furthermore, the risk of damage to surrounding buildings, such as tilting and cracking due to uneven foundation settlement, was assessed. Finally, the results of the risk assessment analysis were visually displayed as a heat map, resulting in a final deep foundation pit project risk heat map.

[0030] Furthermore, step S140 also includes step S141, constructing a deep foundation pit risk index system, using the deep foundation pit risk index system to perform risk assessment on the deep foundation pit construction simulation parameter set, and obtaining a risk index distribution parameter set; step S142, dividing the deep foundation pit risk index system into risk thresholds according to the deep foundation pit construction safety standard, and obtaining a risk index-grading color mapping rule; step S143, using the risk index-grading color mapping rule to map and convert the risk index distribution parameter set, and generate a risk index sub-thermal map set; step S144, superimposing the risk index sub-thermal map set and marking the building risk area to obtain the deep foundation pit project risk heat map.

[0031] Preferably, a deep foundation pit risk index system is constructed based on indicators such as horizontal displacement of soil, vertical settlement, internal forces of support structures (such as bending moment, axial force, shear force), groundwater level changes, settlement and inclination rate of surrounding buildings, etc., to quantify and evaluate the risks of deep foundation pit construction. For example, horizontal displacement of soil can reflect the stability of the soil on the side walls of the foundation pit, and the internal forces of the support structure can reflect the stress conditions of the support structure. The deep foundation pit risk index system is then used to conduct risk assessment on the deep foundation pit construction simulation parameter set. Specifically, the data in the construction simulation parameter set is compared and calculated with the various indicators in the risk index system through numerical analysis to obtain the distribution of each risk indicator at different positions and different construction stages of the deep foundation pit, forming a risk indicator distribution parameter set. For example, the horizontal displacement value of the soil around the foundation pit at each node, that is, the distribution parameter of the soil horizontal displacement risk index, is obtained through finite element analysis.

[0032] Preferably, according to the deep foundation pit construction safety standards (based on a large amount of engineering practice experience and theoretical research), a risk threshold is set for each risk indicator in the deep foundation pit risk index system, and according to the risk threshold, each risk indicator is divided into different risk levels, such as low risk, medium risk, high risk, etc., and then a corresponding color is assigned to each risk level to form a risk indicator-graded color mapping rule. For example, low risk corresponds to green, medium risk corresponds to yellow, and high risk corresponds to red, and the risk level is then intuitively reflected through color. The risk indicator-graded color mapping rule is used to map and transform the data in the risk indicator distribution parameter set, that is, for each risk indicator, a risk indicator sub-thermal map is generated according to its distribution parameters and the corresponding mapping rules. For example, for the soil horizontal displacement risk indicator, a heat map showing the soil horizontal displacement risk level in the deep foundation pit area is generated according to its distribution parameters and color mapping rules. The example deep foundation pit risk indicator-graded color mapping data is shown in Table 1:

[0033] Table 1 Deep foundation pit risk index-graded color mapping data table

[0034]

[0035] Preferably, all risk indicator sub-thermal maps are finally superimposed, and the influence of each risk indicator is integrated to obtain a comprehensive risk distribution. Then, based on the relative position relationship between the building and the deep foundation pit and the sensitivity of the building itself, the building risk area is determined and marked on the superimposed heat map. Finally, a deep foundation pit engineering risk heat map is obtained, which can intuitively display the risk distribution of the entire deep foundation pit engineering area, including the risk levels of different parts such as soil, support structure and surrounding buildings. Construction personnel can take corresponding risk prevention and control measures according to the deep foundation pit engineering risk heat map.

[0036] Step S200: constructing a support excavation construction strategy library, wherein the support excavation construction strategy library includes a general excavation construction strategy channel and a personalized excavation construction strategy channel.

[0037] Step S200 further includes step S210, mining and obtaining a deep foundation pit excavation construction data set, wherein the deep foundation pit excavation construction data set includes engineering risk heat map data and corresponding foundation pit support structure data and excavation construction strategy data; step S220, extracting a deep foundation pit associated feature set of the engineering risk heat map data; step S230, clustering and identifying the deep foundation pit excavation construction data set according to the deep foundation pit associated feature set to obtain a general excavation construction strategy channel and a personalized excavation construction strategy channel; step S240, constructing the support excavation construction strategy library based on the general excavation construction strategy channel and the personalized excavation construction strategy channel.

[0038] Preferably, engineering risk thermodynamic map data is collected from risk assessment, construction process monitoring, and related numerical simulation of deep foundation pit projects, including risk level information for different construction stages and different areas; detailed information on foundation pit support structures is collected, including support structure types (such as pile-anchor support, soil nail wall support, underground continuous wall support, etc.), dimensions (such as the diameter, spacing, and depth of support piles, the length and spacing of soil nails, etc.), material properties (such as concrete strength grade, steel type, etc.), and construction quality inspection data of support structures (such as pile body integrity inspection results, etc.); strategy data during the excavation construction process is collected, which may include excavation sequence (whether to excavate the middle first and then the surrounding areas or to excavate in layers and sections, etc.), excavation depth control strategy, support construction timing selection (whether to support immediately after excavating to a certain depth or to support after reaching a certain depth, etc.), groundwater control methods (such as dewatering, water-stop curtains, etc.), and emergency response measures, etc.; the engineering risk thermodynamic map data are combined with the corresponding foundation pit support structure data and excavation construction strategy data to obtain a deep foundation pit excavation construction dataset.

[0039] Preferably, the engineering risk heat map data is analyzed to explore the correlation characteristics of deep foundation pit excavation construction, and obtain the deep foundation pit correlation feature set of the engineering risk heat map data, which may include the spatial distribution pattern of risk areas (such as which part of the foundation pit the high-risk areas are mainly concentrated in), the change pattern of risk level with construction progress, the correspondence between areas of different risk levels and the location and type of foundation pit support structures, etc. For example, after each excavation to a certain depth, the risk level of a certain area around the foundation pit increases, or the risk level of the area near a certain type of support structure is relatively low.

[0040] Preferably, the deep foundation pit excavation construction data set is clustered and identified based on the extracted deep foundation pit correlation feature set, and data with similar correlation features are classified to form a general excavation construction strategy channel and a personalized excavation construction strategy channel. The general excavation construction strategy channel includes a data set that has similar risk characteristics and support structure characteristics in most cases and can adopt relatively general excavation construction strategies. For example, for foundation pit projects with good soil conditions, relatively open surrounding environments, and common pile-anchor support structures, the corresponding excavation strategies (such as layered and segmented excavation, controlling the excavation depth of each layer, etc.) are summarized into the general excavation construction strategy channel; the personalized excavation construction strategy channel includes a data set with special risk characteristics, special support structure requirements, or complex surrounding environmental conditions, and adopts personalized excavation construction strategies. For example, for foundation pit projects near important ancient buildings, complex geological conditions (such as the presence of weak interlayers, etc.), and support structures using new materials or special forms, the corresponding excavation strategies (such as using special support parameters, stricter groundwater control measures, etc.) are summarized into the personalized excavation construction strategy channel. Finally, the general excavation construction strategy channel and the personalized excavation construction strategy channel are integrated to obtain the support excavation construction strategy library, which includes excavation construction strategies for different types of foundation pit projects.

[0041] Furthermore, step S230 also includes step S231, performing influence evaluation and key feature screening on each associated feature in the deep foundation pit associated feature set to determine the deep foundation pit feature dimension set; step S232, performing feature clustering analysis on the deep foundation pit excavation construction data set according to the deep foundation pit feature dimension set to obtain multiple excavation construction data clusters; step S233, dividing and identifying the multiple excavation construction data clusters to obtain a general excavation construction strategy channel and a personalized excavation construction strategy channel.

[0042] Preferably, an impact assessment is performed on each associated feature in the deep foundation pit associated feature set to determine the importance of each feature to the deep foundation pit construction risk and construction strategy selection. Specifically, the impact of each associated feature (such as soil horizontal displacement, support structure type, etc.) on the construction risk (such as foundation pit collapse risk, surrounding building damage risk, etc.) is quantified through correlation analysis, regression analysis or feature importance scoring in random forest. Then, based on the results of the impact assessment, the key features that have a greater impact on the construction risk and construction strategy are screened out. For example, if the soil horizontal displacement and groundwater level change have a high impact on the foundation pit collapse risk, while other features (such as a minor parameter of the support structure) have a low impact, the soil horizontal displacement and groundwater level change are determined as key features and used as the main analysis dimensions to determine the deep foundation pit feature dimension set. Then, based on the determined deep foundation pit feature dimension set, a feature clustering analysis was performed on the deep foundation pit excavation construction dataset. That is, similar data objects were divided into the same cluster, and the data objects between different clusters were quite different. For example, based on the similarity of key features such as soil horizontal displacement and groundwater level changes, the deep foundation pit excavation construction dataset was divided into multiple excavation construction data clusters. The data in one cluster showed larger soil horizontal displacement and higher groundwater level changes, while the data in another cluster showed smaller soil horizontal displacement and lower groundwater level changes.

[0043] Preferably, multiple excavation construction data clusters are divided and identified, and based on the characteristics and patterns of the data within the clusters, a general excavation construction strategy channel and a personalized excavation construction strategy channel are constructed based on a deep neural network. The construction scenarios corresponding to the data within the clusters of the general excavation construction strategy channel are likely to occur in most cases and can be addressed by relatively mature and general construction strategies. For example, if the data within a cluster has good soil conditions, a relatively simple surrounding environment, and a conventional support structure type, it is classified into the general excavation construction strategy channel, and its corresponding construction strategy is a standardized general excavation strategy that has been verified by a large number of practices. The data within the clusters of the personalized excavation construction strategy channel have special feature combinations, and the corresponding construction scenarios are relatively complex or special, requiring personalized construction strategies to be formulated according to the specific circumstances. For example, if the data within a cluster has complex soil conditions (such as weak interlayers), is surrounded by important sensitive buildings, and uses a new type of support structure, it is classified into the personalized excavation construction strategy channel, and its corresponding construction strategy needs to be customized according to these special conditions, such as using special support parameters, groundwater control measures, and deformation monitoring plans.

[0044] Furthermore, step S232 also includes step a, performing feature identification and intra-cluster data statistics on the multiple excavation construction data clusters in turn to obtain multiple intra-cluster associated data volumes; step b, setting a general feature distribution threshold based on the multiple intra-cluster associated data volumes; and step c, judging, dividing, and integrating the multiple excavation construction data clusters based on the general feature distribution threshold to construct a general excavation construction strategy channel and a personalized excavation construction strategy channel.

[0045] Preferably, feature identification is performed on each cluster of excavation construction data in turn. That is, based on the results of cluster analysis, the common features of each cluster on the deep foundation pit feature dimension set are determined, and the data within each cluster are statistically analyzed to obtain the amount of intra-cluster associated data, including the number of data samples contained in each cluster and the data distribution on each feature dimension. For example, the number of data samples in a cluster is counted, as well as statistical indicators such as the average, maximum, and minimum values ​​of the soil horizontal displacement feature. The amount of intra-cluster associated data of each cluster is then analyzed to understand the distribution of the number of data samples in different clusters. A common feature distribution threshold is then set to distinguish which clusters have common features (i.e., feature combinations that may appear in most data) and which clusters have more special features (i.e., feature combinations that only appear in a small number of data). For example, a certain proportion of the number of data samples to the total number of data samples (e.g., 80%) can be used as the common feature distribution threshold. Clusters exceeding this proportion are considered common feature clusters, and clusters below this proportion are considered special feature clusters.

[0046] Preferably, multiple excavation and construction data clusters are divided based on a common feature distribution threshold. For clusters where the amount of associated data exceeds the common feature distribution threshold, a general excavation and construction strategy channel is constructed using a deep neural network. The corresponding feature combination is likely to occur in most cases, and the corresponding construction strategy is used as the common strategy. For clusters where the amount of associated data is below the common feature distribution threshold, a personalized excavation and construction strategy channel is constructed using a deep neural network. The corresponding feature combination is relatively unique and requires a personalized construction strategy. Finally, the divided clusters are integrated to construct a general excavation and construction strategy channel and a personalized excavation and construction strategy channel. Based on the specific characteristics of different deep foundation pit projects, appropriate construction strategies can be selected from the corresponding channels to improve construction efficiency and safety.

[0047] Furthermore, step c also includes step c1, judging, dividing and integrating the multiple excavation construction data clusters based on the general feature distribution threshold to obtain a general excavation construction strategy data set and a personalized excavation construction strategy data set; step c2, using a deep neural network structure to perform identification supervision training on the general excavation construction strategy data set and the personalized excavation construction strategy data set to construct the general excavation construction strategy channel and the personalized excavation construction strategy channel.

[0048] Preferably, multiple excavation construction data clusters are determined and divided according to the common feature distribution threshold, and the common and personalized construction strategy clusters are distinguished. The divided clusters are integrated to obtain a common excavation construction strategy data set and a personalized excavation construction strategy data set. The common excavation construction strategy data set contains construction strategy-related data with universal applicability, while the personalized excavation construction strategy data set contains construction strategy-related data that requires special processing. The general excavation construction strategy dataset and the personalized excavation construction strategy dataset were then preprocessed separately, including data cleaning (removing noise data and outliers), feature selection (selecting features that are important for distinguishing construction strategies), and data labeling (identifying each data sample as belonging to its category, i.e., general or personalized construction strategy). A deep neural network model consisting of an input layer, multiple hidden layers, and an output layer was designed. The input layer receives the preprocessed construction strategy data feature vector, the hidden layer extracts complex patterns and feature combinations in the data through nonlinear transformation, and the output layer classifies the data samples into general or personalized construction strategy categories. The general excavation construction strategy dataset and the personalized excavation construction strategy dataset were then used for supervised training of the deep neural network. During the training process, the network calculates the output results through forward propagation, compares them with the true labels, and calculates the loss function (such as cross-entropy loss). The network weight parameters are then adjusted through the backpropagation algorithm to continuously optimize the network performance, enabling the network to accurately classify the construction strategy data. After supervised training, the deep neural network model can accurately classify new excavation construction data, thereby constructing a general excavation construction strategy channel and a personalized excavation construction strategy channel.

[0049] Step S300: activating channels and performing strategy analysis on the deep foundation pit engineering risk heat map based on the general excavation construction strategy channel and the personalized excavation construction strategy channel to determine a target engineering construction strategy plan.

[0050] Preferably, the risk characteristics (such as risk area distribution, risk level, etc.) in the risk heat map of the deep foundation pit project are matched with the characteristics of the general excavation construction strategy channel and the personalized excavation construction strategy channel. For example, the risk heat map shows that there is a high risk in a certain area around the foundation pit, and the general excavation construction strategy channel may contain strategy characteristics applicable to general high-risk areas, and the personalized excavation construction strategy channel may contain strategy characteristics applicable to special high-risk situations (such as proximity to important buildings, special geological conditions, etc.); then, based on the matching results, the channel that best matches the characteristics of the current deep foundation pit project risk heat map is activated. If the characteristics of the risk heat map match the characteristics of the general excavation construction strategy channel, the general channel is activated; if there are personalized characteristics (such as complex surrounding environment, special geological conditions, etc.), the personalized channel is activated. Then, construction strategy plans suitable for the current deep foundation pit project are extracted from the activated channels. Specifically, for the general excavation construction strategy channel, standardized construction strategies that have been verified by a large number of practices are extracted, such as common layered and segmented excavation, conventional construction parameters of support structures, etc.; for the personalized excavation construction strategy channel, customized construction strategies for special risk situations are extracted, such as the use of special support structure forms, stricter groundwater control measures, special protection measures for surrounding buildings, etc.; finally, the extracted construction strategy plans are integrated to form a target project construction strategy plan, including excavation sequence, support structure construction parameters, groundwater control methods, deformation monitoring plans, emergency plans and other aspects.

[0051] Furthermore, step S300 also includes step S310, performing channel matching and activation on the deep foundation pit engineering risk heat map based on the general excavation construction strategy channel and the personalized excavation construction strategy channel to obtain the target excavation construction strategy channel; step S320, using the target excavation construction strategy channel to perform strategy analysis on the deep foundation pit engineering risk heat map to determine the target engineering construction strategy plan.

[0052] Preferably, a comprehensive analysis is first conducted on the risk heat map of the deep foundation pit project to identify risk areas, risk level distribution, and risk change trends. For example, the risk heat map may show that there is a high risk in a certain area around the foundation pit, and the risk area is close to important buildings. The characteristics of the risk heat map are then compared and matched with the characteristics of the general excavation construction strategy channel and the personalized excavation construction strategy channel. For example, if the characteristics of the risk heat map meet the characteristics of the personalized channel, the personalized excavation construction strategy channel is activated; otherwise, the general excavation construction strategy channel is activated, and finally the target excavation construction strategy channel is obtained. Then, the construction strategy corresponding to the risk heat map characteristics is extracted from the activated target excavation construction strategy channel. For example, the personalized excavation construction strategy channel is activated to extract special support parameters, excavation sequence adjustment strategies, and groundwater control measures for areas close to important buildings. Finally, the extracted construction strategies are integrated to form a target project construction strategy plan, clarifying the excavation method, support structure construction details, monitoring plan, and emergency plan. For example, the plan stipulates the use of a layered and segmented excavation method, and timely support is applied after each layer is excavated. Monitoring points are encrypted in areas close to buildings. If the deformation exceeds the warning value, the emergency plan is immediately activated, thereby ensuring that an accurate construction strategy plan is quickly determined to ensure construction safety and quality.

[0053] Step S400: execute deep foundation pit excavation construction based on the target engineering construction strategy plan, deploy a sensing Internet of Things network in the target construction area, and monitor multi-source construction monitoring data streams during the excavation construction in real time through the sensing Internet of Things network.

[0054] Preferably, the excavation of the deep foundation pit is started according to the determined target project construction strategy plan. During the construction process, the construction process and quality control standards in the plan are strictly followed. For example, for the construction of the support structure, the size, material quality and construction process parameters of the support structure are controlled to ensure that the construction quality of the support structure meets the design requirements; a perception Internet of Things network is deployed in the target construction area, including the installation of various sensors to monitor key data in the construction process in real time, for example, soil pressure sensors are installed to monitor the pressure changes of the soil around the foundation pit, displacement sensors are used to monitor the displacement of the support structure and surrounding buildings, and water level sensors are used to monitor changes in the groundwater level; and multiple sensors are connected through Internet of Things technology to form a perception Internet of Things network, which can transmit the data collected by the sensors to the construction monitoring system in real time. For example, wireless communication modules (such as 5G or NB-IoT) are used to transmit sensor data to a cloud server or a local monitoring center. By sensing the Internet of Things network, multi-source construction monitoring data streams during excavation construction are monitored in real time, including soil deformation data, internal force data of support structures, groundwater level data, settlement and inclination data of surrounding buildings, etc., and real-time multi-source construction monitoring data are analyzed to timely understand the changes in the status of the foundation pit and the surrounding environment during construction. For example, if the deformation of the support structure exceeds the warning value, an alarm can be issued immediately. Construction personnel can adjust the construction strategy according to the real-time data and take corresponding measures (such as strengthening support, adjusting the excavation sequence, etc.) to ensure construction safety, realize refined management and dynamic control of the deep foundation pit excavation construction process, and improve construction safety and efficiency.

[0055] Step S500: Dynamically optimize and regulate the target project construction strategy and excavation construction control based on the multi-source construction monitoring data stream.

[0056] Step S500 further includes step S510, using the deep foundation pit risk index system to perform risk assessment on the multi-source construction monitoring data stream to obtain deep foundation pit construction risk parameter information; step S520, dynamically optimizing and regulating the target project construction strategy scheme based on the deep foundation pit construction risk parameter information, and controlling deep foundation pit excavation construction through the regulated target project construction strategy scheme.

[0057] Preferably, a deep foundation pit risk index system is used to conduct risk assessment on the collected multi-source construction monitoring data, that is, the monitoring data is compared with each indicator in the risk index system, and the parameter information corresponding to each risk indicator is calculated. For example, based on the soil horizontal displacement monitoring data, the risk parameter value of the soil horizontal displacement is calculated to determine whether it is close to or exceeds the set risk threshold; through risk assessment, deep foundation pit construction risk parameter information is obtained, including the risk level of each risk indicator (such as low risk, medium risk, high risk), the size of the risk value, and the risk change trend. Then, the risk parameter information of deep foundation pit construction is analyzed to find out the risk points and potential problems in the construction process. For example, the analysis found that the vertical settlement risk parameter value of the soil at the bottom of the foundation pit is high, and there may be a risk of uplift at the bottom of the foundation pit. Then, based on the risk parameter information, the construction strategy of the target project is dynamically optimized and regulated, including adjusting the excavation sequence, support parameters, groundwater control measures, etc. The optimized construction strategy is then applied to the deep foundation pit excavation construction control, and the multi-source construction monitoring data stream during the construction process is continuously monitored in real time through the sensing Internet of Things network. Risk assessment and optimization regulation are performed again to form a closed-loop dynamic optimization mechanism to achieve precise control of deep foundation pit excavation construction, and then respond to various risk changes in the deep foundation pit excavation construction process in a timely manner, thereby ensuring construction safety and quality while improving construction efficiency.

[0058] In the above, refer to Figure 1 A method for controlling deep foundation pit excavation near sensitive buildings according to an embodiment of the present invention is described in detail. Figure 2 A deep foundation pit excavation construction control system near sensitive buildings according to an embodiment of the present invention is described.

[0059] According to an embodiment of the present invention, a deep foundation pit excavation construction control system near sensitive buildings is used to solve the technical problems existing in the prior art, such as the lag in deep foundation pit construction risk warning, the difficulty in adapting to complex environments and the protection needs of sensitive buildings, which leads to poor safety and construction efficiency of deep foundation pit projects. It achieves the technical effects of accurately predicting construction risks, adjusting construction parameters in real time, and improving project safety and construction efficiency. Figure 2 As shown, a deep foundation pit excavation construction control system near sensitive buildings includes: a risk assessment and analysis unit 10, an excavation construction strategy library construction unit 20, a construction strategy solution determination unit 30, a construction monitoring data flow perception unit 40, and a dynamic optimization and control unit 50.

[0060] The risk assessment and analysis unit 10 is used to perform risk assessment and analysis based on the deep foundation pit excavation targets, sensitive building data, soil geological data and groundwater data of the target construction area to generate a deep foundation pit project risk heat map; the excavation construction strategy library construction unit 20 is used to construct a support excavation construction strategy library, and the support excavation construction strategy library includes a general excavation construction strategy channel and a personalized excavation construction strategy channel; the construction strategy scheme determination unit 30 is used to activate the channel and analyze the strategy of the deep foundation pit project risk heat map based on the general excavation construction strategy channel and the personalized excavation construction strategy channel to determine the target project construction strategy scheme; the construction monitoring data stream perception unit 40 is used to execute deep foundation pit excavation construction based on the target project construction strategy scheme, deploy a perception Internet of Things network in the target construction area, and monitor the multi-source construction monitoring data stream in real time during the excavation construction through the perception Internet of Things network; the dynamic optimization and control unit 50 is used to dynamically optimize and control the target project construction strategy scheme and control the excavation construction based on the multi-source construction monitoring data stream.

[0061] The specific configuration of the risk assessment and analysis unit 10 will be described in detail below. The risk assessment and analysis unit 10 further includes: performing a key analysis of the deep foundation pit excavation target to obtain a deep foundation pit design parameter set; performing a finite element coupling analysis based on the deep foundation pit design parameter set, sensitive building data, soil geological data, and groundwater data to establish a deep foundation pit engineering finite element model; presetting a working condition load simulation combination, applying the working condition load simulation combination to the deep foundation pit engineering finite element model to perform construction condition simulation to obtain a deep foundation pit construction simulation parameter set; performing a risk assessment analysis based on the deep foundation pit construction simulation parameter set to obtain a deep foundation pit engineering risk heat map.

[0062] The specific configuration of the risk assessment and analysis unit 10 will be described in detail below. The risk assessment and analysis unit 10 further includes: constructing a deep foundation pit risk index system, using the deep foundation pit risk index system to perform risk assessment on the deep foundation pit construction simulation parameter set to obtain a risk index distribution parameter set; dividing the deep foundation pit risk index system into risk thresholds according to the deep foundation pit construction safety standard to obtain a risk index-grading color mapping rule; using the risk index-grading color mapping rule to map and transform the risk index distribution parameter set to generate a risk index sub-thermal map set; and superimposing the risk index sub-thermal map sets and marking the building risk areas to obtain the deep foundation pit project risk heat map.

[0063] The specific configuration of the excavation construction strategy library construction unit 20 will be described in detail below. The excavation construction strategy library construction unit 20 further includes: excavating and obtaining a deep foundation pit excavation construction dataset, wherein the deep foundation pit excavation construction dataset includes engineering risk heat map data, corresponding foundation pit support structure data, and excavation construction strategy data; extracting a deep foundation pit associated feature set from the engineering risk heat map data; clustering and identifying the deep foundation pit excavation construction dataset according to the deep foundation pit associated feature set to obtain a universal excavation construction strategy channel and a personalized excavation construction strategy channel; and constructing the support excavation construction strategy library based on the universal excavation construction strategy channel and the personalized excavation construction strategy channel.

[0064] The specific configuration of the excavation construction strategy library construction unit 20 will be described in detail below. The excavation construction strategy library construction unit 20 further includes: performing an impact assessment and key feature screening on each associated feature in the deep foundation pit associated feature set to determine a deep foundation pit feature dimension set; performing feature clustering analysis on the deep foundation pit excavation construction dataset according to the deep foundation pit feature dimension set to obtain multiple excavation construction data clusters; and dividing and identifying the multiple excavation construction data clusters to obtain universal excavation construction strategy channels and personalized excavation construction strategy channels.

[0065] The specific configuration of the excavation construction strategy library construction unit 20 will be described in detail below. The excavation construction strategy library construction unit 20 further includes: sequentially performing feature identification and intra-cluster data statistics on the multiple excavation construction data clusters to obtain the amount of correlated data within the multiple clusters; setting a common feature distribution threshold based on the amount of correlated data within the multiple clusters; and determining, dividing, and integrating the multiple excavation construction data clusters based on the common feature distribution threshold to construct a common excavation construction strategy channel and a personalized excavation construction strategy channel.

[0066] The specific configuration of the excavation construction strategy library construction unit 20 will be described in detail below. The excavation construction strategy library construction unit 20 further includes: determining, dividing, and integrating the multiple excavation construction data clusters based on the common feature distribution threshold to obtain a common excavation construction strategy dataset and a personalized excavation construction strategy dataset; and using a deep neural network structure to perform label supervision training on the common excavation construction strategy dataset and the personalized excavation construction strategy dataset to construct the common excavation construction strategy channel and the personalized excavation construction strategy channel.

[0067] The specific configuration of the construction strategy scheme determination unit 30 will be described in detail below. The construction strategy scheme determination unit 30 further includes: performing channel matching activation on the deep foundation pit project risk heat map based on the general excavation construction strategy channel and the personalized excavation construction strategy channel to obtain a target excavation construction strategy channel; and performing strategy analysis on the deep foundation pit project risk heat map using the target excavation construction strategy channel to determine the target project construction strategy scheme.

[0068] The specific configuration of the dynamic optimization and control unit 50 will be described in detail below. The dynamic optimization and control unit 50 further includes: using the deep foundation pit risk index system to perform a risk assessment on the multi-source construction monitoring data stream to obtain deep foundation pit construction risk parameter information; dynamically optimizing and controlling the target project construction strategy based on the deep foundation pit construction risk parameter information; and controlling deep foundation pit excavation construction using the optimized target project construction strategy.

[0069] A deep foundation pit excavation construction control system near sensitive buildings provided by an embodiment of the present invention can execute a deep foundation pit excavation construction control method near sensitive buildings provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the execution method.

[0070] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0071] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A method for controlling the excavation of a deep foundation pit near sensitive buildings, characterized in that: The method comprises: Conduct risk assessment and analysis based on deep foundation pit excavation targets, sensitive building data, soil geological data, and groundwater data in the target construction area to generate a deep foundation pit engineering risk heat map; Constructing a support excavation construction strategy library, wherein the support excavation construction strategy library includes a general excavation construction strategy channel and a personalized excavation construction strategy channel; Based on the general excavation construction strategy channel and the personalized excavation construction strategy channel, the deep foundation pit project risk heat map is activated and strategy analyzed, and the risk characteristics in the deep foundation pit project risk heat map are matched with the characteristics of the general excavation construction strategy channel and the personalized excavation construction strategy channel to determine the target project construction strategy plan; Executing deep foundation pit excavation construction based on the target engineering construction strategy plan, deploying a sensing Internet of Things network in the target construction area, and monitoring multi-source construction monitoring data streams during the excavation construction in real time through the sensing Internet of Things network; Dynamically optimize and regulate the target project construction strategy and excavation construction control based on the multi-source construction monitoring data stream; The construction of the support excavation construction strategy library includes: Acquire a deep foundation pit excavation construction data set by mining, wherein the deep foundation pit excavation construction data set includes engineering risk heat map data, corresponding foundation pit support structure data, and excavation construction strategy data; Extracting a deep foundation pit associated feature set from the engineering risk heat map data; Clustering and labeling the deep foundation pit excavation construction data set according to the deep foundation pit associated feature set to obtain a general excavation construction strategy channel and a personalized excavation construction strategy channel; Based on the general excavation construction strategy channel and the personalized excavation construction strategy channel, the support excavation construction strategy library is constructed.

2. The method for controlling deep foundation pit excavation near sensitive buildings according to claim 1, characterized in that: The generating of the deep foundation pit engineering risk heat map includes: Performing a key analysis on the deep foundation pit excavation target to obtain a deep foundation pit design parameter set; Performing finite element coupling analysis based on the deep foundation pit design parameter set, sensitive building data, soil geological data, and groundwater data to establish a finite element model of the deep foundation pit engineering; Preset a working condition load simulation combination, apply the working condition load simulation combination to the deep foundation pit engineering finite element model to perform construction working condition simulation, and obtain a deep foundation pit construction simulation parameter set; A risk assessment analysis is performed based on the deep foundation pit construction simulation parameter set to obtain a risk heat map of the deep foundation pit project.

3. The method for controlling deep foundation pit excavation near sensitive buildings according to claim 2, characterized in that: The step of obtaining a risk heat map of a deep foundation pit project includes: Constructing a deep foundation pit risk index system, and using the deep foundation pit risk index system to perform risk assessment on the deep foundation pit construction simulation parameter set to obtain a risk index distribution parameter set; According to the deep foundation pit construction safety standard, the risk threshold of the deep foundation pit risk index system is divided to obtain the risk index-grading color mapping rule; The risk indicator distribution parameter set is mapped and converted using the risk indicator-grading color mapping rule to generate a risk indicator sub-heat map set; The risk indicator sub-thermal map sets are superimposed and building risk areas are marked to obtain the deep foundation pit engineering risk thermal map.

4. The method for controlling deep foundation pit excavation near sensitive buildings according to claim 1, characterized in that: The obtaining of the universal excavation construction strategy channel and the personalized excavation construction strategy channel includes: Performing impact assessment and key feature screening on each associated feature in the deep foundation pit associated feature set to determine a deep foundation pit feature dimension set; Performing feature cluster analysis on the deep foundation pit excavation construction data set according to the deep foundation pit feature dimension set to obtain multiple excavation construction data clusters; The multiple excavation construction data clusters are divided and identified to obtain a general excavation construction strategy channel and a personalized excavation construction strategy channel.

5. The method for controlling deep foundation pit excavation near sensitive buildings according to claim 4, characterized in that: The obtaining of the general excavation construction strategy channel and the personalized excavation construction strategy channel includes: performing feature identification and intra-cluster data statistics on the plurality of excavation construction data clusters in sequence to obtain a plurality of intra-cluster associated data volumes; Setting a common feature distribution threshold according to the amount of associated data within the plurality of clusters; The plurality of excavation construction data clusters are judged, divided and integrated based on the general feature distribution threshold, and a general excavation construction strategy channel and a personalized excavation construction strategy channel are constructed.

6. A method for controlling deep foundation pit excavation near sensitive buildings as claimed in claim 5, characterized in that: The construction of the universal excavation construction strategy channel and the personalized excavation construction strategy channel includes: Determining, dividing, and integrating the plurality of excavation construction data clusters based on the general feature distribution threshold to obtain a general excavation construction strategy data set and a personalized excavation construction strategy data set; A deep neural network structure is used to perform label supervision training on the general excavation construction strategy dataset and the personalized excavation construction strategy dataset to construct the general excavation construction strategy channel and the personalized excavation construction strategy channel.

7. The method for controlling deep foundation pit excavation near sensitive buildings according to claim 1, characterized in that: The determination of the target project construction strategy includes: Based on the general excavation construction strategy channel and the personalized excavation construction strategy channel, channel matching and activation are performed on the deep foundation pit engineering risk heat map to obtain a target excavation construction strategy channel; The target excavation construction strategy channel is used to perform strategy analysis on the risk heat map of the deep foundation pit project to determine the target project construction strategy plan.

8. The method for controlling deep foundation pit excavation near sensitive buildings as claimed in claim 3, characterized in that: The dynamically optimizing and regulating the target project construction strategy and controlling the excavation construction based on the multi-source construction monitoring data stream includes: Using the deep foundation pit risk indicator system to conduct risk assessment on the multi-source construction monitoring data stream to obtain deep foundation pit construction risk parameter information; The target engineering construction strategy is dynamically optimized and regulated based on the deep foundation pit construction risk parameter information, and deep foundation pit excavation construction is controlled by the regulated target engineering construction strategy.

9. A deep foundation pit excavation construction control system near sensitive buildings, characterized in that: The system is used to implement the deep foundation pit excavation construction control method adjacent to sensitive buildings according to any one of claims 1 to 8, and the system comprises: The risk assessment and analysis unit is used to conduct risk assessment and analysis based on the deep foundation pit excavation targets, sensitive building data, soil geological data, and groundwater data in the target construction area, and generate a risk heat map for the deep foundation pit project; An excavation construction strategy library building unit is used to build a support excavation construction strategy library, wherein the support excavation construction strategy library includes a general excavation construction strategy channel and a personalized excavation construction strategy channel; a construction strategy scheme determining unit, configured to perform channel activation and strategy analysis on the deep foundation pit engineering risk heat map based on the general excavation construction strategy channel and the personalized excavation construction strategy channel, match the risk characteristics in the deep foundation pit engineering risk heat map with the characteristics of the general excavation construction strategy channel and the personalized excavation construction strategy channel, and determine a target engineering construction strategy scheme; A construction monitoring data stream sensing unit is configured to execute deep foundation pit excavation construction based on the target project construction strategy, deploy a sensing Internet of Things network in the target construction area, and monitor multi-source construction monitoring data streams during the excavation construction in real time through the sensing Internet of Things network; A dynamic optimization and control unit is used to dynamically optimize and control the construction strategy of the target project and control the excavation construction based on the multi-source construction monitoring data stream.

Images