A karst area foundation pit water inrush early warning system and method

Abstract

The invention discloses a karst area foundation pit water inrush early warning system and method, which relate to the field of artificial intelligence technology. The system monitors the groundwater dynamics, geological stress and soil moisture data of the karst area in real time through a data acquisition module, performs data cleaning, noise filtering and completion in a data processing module, and compensates for data loss using a multidimensional interpolation algorithm, thereby improving data integrity and accuracy. In a regional division and analysis module, the karst area is divided into independent karst sub-areas, and geological characteristic data is monitored in real time. The model of a chain reaction early warning module is used to analyze the fracture extension trend and water level change, thereby realizing the prediction of the time and area of ​​water inrush, and reducing the system's dependence on historical data patterns. The feedback control module evaluates the joint risk index Tsyz, thereby realizing the early prevention of water inrush risks in adjacent areas, thereby ensuring the real-time response of the system and the safety of construction.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a karst area foundation pit water inrush early warning system and method. Background Art

[0002] The early warning system for water inrush in foundation pits in karst areas originates from the complexity of karst landforms and the risk of water inrush in foundation pit engineering. In the early days, karst water inrush problems were mostly predicted by manual monitoring and on-site experience. However, due to the heterogeneity of karst geology, the risk of water inrush is difficult to accurately predict, causing safety hazards and economic losses. With the development of sensor technology, geological detection technology and data analysis methods, the early warning system for water inrush in foundation pits in karst areas has gradually entered engineering applications. The system integrates multiple monitoring methods such as geological radar, groundwater monitoring, and stress sensors, and through real-time data analysis and model establishment, it realizes the prediction and early warning of water inrush risks, significantly improving construction safety.

[0003] 1. Insufficient data quality: The geological conditions in karst areas are complex and changeable, and monitoring data may be noisy or incomplete. Artificial intelligence algorithms rely heavily on high-quality and accurate data. Missing or inaccurate data will lead to biased prediction results and reduce the reliability of the early warning system.

[0004] 2. Over-reliance on data patterns: AI systems usually rely on historical data and the recognition of existing patterns, and are difficult to handle completely novel or extreme water inrush situations. For example, the system may not be able to identify the risk of water inrush caused by unknown karst structures, resulting in the failure of early warning.

[0005] 3. Lack of real-time performance: Due to the complexity of the karst environment, artificial intelligence models need to process a large amount of real-time data and perform highly complex analysis and calculations, which may lead to insufficient system response speed and inability to issue timely warnings before water inrush occurs. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a karst area foundation pit water inrush warning system and method, which solves the technical problems of insufficient data quality, over-reliance on data models and insufficient real-time performance in the background technology;

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a karst area foundation pit water inrush warning system, including a data acquisition module, a data processing module, a regional division and analysis module, a chain reaction warning module and a feedback control module;

[0008] The data acquisition module is used to monitor and collect groundwater dynamics-related data, geological stress-related data and soil moisture content-related data in the karst area in real time during foundation pit excavation and construction, and to construct a multi-dimensional environmental data set;

[0009] The data processing module is used to pre-process the multi-dimensional environmental data set, including data cleaning, noise filtering and completion processing, using a multi-dimensional interpolation algorithm to compensate for missing data, and performing data fusion according to karst geological characteristics, and finally performing dimensionless processing;

[0010] The regional division and analysis module is used to divide the monitored karst area into multiple independent karst sub-areas according to the geological structure of the karst area and number them, monitor the karst sub-areas in real time and extract the geological characteristic data in each karst sub-area respectively; calculate the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx in the current karst sub-area by extracting the geological characteristic data; finally, fit the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx to calculate the water inrush risk index Tfxz of the current karst sub-area;

[0011] The chain reaction warning module is used to comprehensively evaluate the water inrush risk index Tfxz of each karst sub-region according to the number of the karst sub-region, and to perform linkage analysis on the rock fracture extension trend and groundwater level change through a chain reaction model to calculate the joint risk index Tsyz;

[0012] The feedback control module is used to set the early warning threshold Q, and compare and analyze the associated risk index Tsyz with the early warning threshold Q, divide the water inrush risk level of the current karst sub-area, and take corresponding control measures; then the early warning information is displayed in real time through the user interface, and the user takes preventive measures for the water inrush risk of the next karst sub-area based on the water inrush risk level of the current karst sub-area.

[0013] Preferably, during the excavation and construction of the foundation pit, the data acquisition module deploys a variety of sensor devices according to the geological conditions of the karst area, including groundwater level monitoring sensors, geological stress monitors and soil moisture sensors; and at the same time, the sensor deployment points cover key areas around the foundation pit;

[0014] Among them, the multidimensional environmental data set includes groundwater flow velocity Glsl, soil moisture content Hshl and rock mass stress Rtys.

[0015] Preferably, the data processing module automatically identifies extreme values ​​and removes them by using an anomaly detection algorithm in the data cleaning process; wherein the multidimensional interpolation algorithm generates supplementary data based on the spatial and temporal correlation of adjacent data points and the structural characteristics of karst geology; finally, the multidimensional environmental data from different monitoring points and different sensors are fused and processed, and the data fusion process uses weighted averaging and data correlation analysis methods.

[0016] Preferably, the regional division and analysis module includes a karst sub-region division unit, a geological feature extraction unit and a water inrush risk calculation unit;

[0017] The karst sub-region division unit is used to divide the karst area in the monitoring area into several independent karst sub-regions, and number each karst sub-region, specifically YR1, YR2...YRn, where n represents the total number of karst sub-regions; in the division process, the distribution of geological faults, groundwater flow paths and distribution characteristics of cave structures in the karst area are comprehensively considered to enable each karst sub-region to be independently monitored and data analyzed.

[0018] Preferably, the geological feature extraction unit is used to collect geological feature data in each karst sub-area one by one according to the number and calculate them one by one; real-time monitoring and collection are carried out through geological radar and flow velocity sensor to obtain rock fracture factor Dlyz, groundwater flow velocity Swlz, groundwater pressure Dxls, rock porosity Ykzl, rock thickness Ychd, rock gap width Rjjk and cave volume Krz, and after pre-processing and dimensionless processing, a geological feature data set is constructed;

[0019] The geological characteristic data set and the multidimensional environmental data set are extracted and calculated by the following formulas to obtain the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx in the karst sub-area:

[0020] ;

[0021] .

[0022] Preferably, the water inrush risk calculation unit is used to fit the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx, and calculate the water inrush risk index Tfxz of the karst sub-region. The specific formula is as follows:

[0023] .

[0024] Preferably, the chain reaction warning module includes a risk assessment unit and a linkage prediction unit;

[0025] The risk assessment unit compares and evaluates the water inrush risk index Tfxz of the current karst sub-region by presetting the water inrush risk threshold W. The specific assessment content is as follows:

[0026] If the water inrush risk index Tfxz ≥ the water inrush risk threshold W, it means that there is an abnormal water inrush risk in the current karst sub-area, and then further analysis is performed on the current karst sub-area;

[0027] If the water inrush risk index Tfxz < water inrush risk threshold W, it means that the current karst sub-area is normal. The system marks the current karst sub-area and performs water inrush risk analysis on the next karst sub-area according to the numbering sequence.

[0028] Preferably, the linkage prediction unit is used to construct and use a chain reaction model to analyze the extension direction of rock fractures, the rock fracture speed and the fluctuation frequency of groundwater level changes, and to extract rock and groundwater level related data after real-time acquisition and dimensionless processing, and to obtain the joint risk index Tsyz of the karst sub-area currently experiencing abnormal water inrush risk by calculating the following formula:

[0029] ;

[0030] Where, the rock fracture extension angle Ydjd in the rock formation and groundwater level correlation data, the rock fracture extension rate Ydsl in the rock formation and groundwater level correlation data, the groundwater level rise rate Dsws in the rock formation and groundwater level correlation data, and the groundwater fluctuation frequency Dsbd in the rock formation and groundwater level correlation data;

[0031] is a natural logarithmic function, which is used to convert the groundwater level rise rate Dsws into a linear change; is a numerical constant used to index the impact of groundwater fluctuation frequency Dsbd.

[0032] Preferably, the feedback control module is used to evaluate the joint risk index Tsyz and the warning threshold Q, and classify the water inrush risk level of the current karst sub-area. The specific evaluation content is as follows:

[0033] When the joint risk index Tsyz ≥ the warning threshold Q, the current karst sub-area is determined to be the first risk area, indicating that the current karst sub-area not only has the risk of water inrush, but is also about to trigger a chain reaction in other karst sub-areas. At this time, the warning signal is immediately triggered; preventive measures are taken in advance for the next adjacent karst sub-area. This includes setting up additional monitoring points in adjacent areas and increasing the monitoring frequency by 10% on the basis of the original monitoring frequency to prevent the spread and spread of water inrush risks;

[0034] When the associated risk index Tsyz < warning threshold Q, the current karst sub-area is judged as the second lowest risk area and the current monitoring mode is maintained, but water inrush response measures are taken at this time, including injecting quick-setting slurry at the water inrush point, forming a sealing layer at the rock fracture, and local drainage measures.

[0035] A method for early warning of water inrush in foundation pits in karst areas, comprising the following steps:

[0036] Step 1: During the excavation and construction of the foundation pit, real-time monitoring and collection of groundwater dynamics data, geological stress data, and soil moisture data in the karst area are carried out, and a multi-dimensional environmental data set is constructed;

[0037] Step 2: Preprocess the multidimensional environmental data set, including data cleaning, noise filtering and completion processing, use multidimensional interpolation algorithm to compensate for the missing data, and perform data fusion according to karst geological characteristics, and finally perform dimensionless processing;

[0038] Step 3: According to the geological structure of the karst area, the monitored karst area is divided into multiple independent karst sub-areas and numbered, the karst sub-areas are monitored in real time, and the geological characteristic data in each karst sub-area are extracted respectively; the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx in the current karst sub-area are calculated by extracting the geological characteristic data; finally, the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx are fitted to calculate the water inrush risk index Tfxz of the current karst sub-area;

[0039] Step 4: Comprehensively evaluate the water inrush risk index Tfxz for each karst sub-region according to the number of the karst sub-region, and conduct linkage analysis on the rock fracture extension trend and groundwater level change through the chain reaction model to calculate the joint risk index Tsyz;

[0040] Step 5. Set the warning threshold Q, and compare and analyze the associated risk index Tsyz with the warning threshold Q, divide the water inrush risk level of the current karst sub-area, and take corresponding control measures; then display the warning information in real time through the user interface, and the user takes preventive measures for the water inrush risk of the next karst sub-area based on the water inrush risk level of the current karst sub-area.

[0041] The present invention provides a karst area foundation pit water inrush warning system and method, which has the following beneficial effects:

[0042] (1) The karst area foundation pit water inrush early warning system and method first effectively solves the problem of insufficient data quality. The data acquisition module monitors and collects groundwater dynamic related data, geological stress related data and soil moisture related data in the karst area in real time during the foundation pit excavation and construction process. The data cleaning, noise filtering and completion processing technologies are introduced into the data processing module. The multidimensional interpolation algorithm is used to compensate for the missing data, which effectively improves the integrity and accuracy of the data and ensures the data reliability of the early warning system. At the same time, the data fusion process performs weighted averaging and data correlation analysis based on the karst geological characteristics, ensuring the uniformity of multidimensional environmental data from different monitoring points, and providing high-quality data support for the accurate calculation of the water inrush risk index Tfxz.

[0043] (2) A karst area foundation pit water inrush warning system and method solves the problem of the system's over-reliance on historical data patterns. In the regional division and analysis module, the karst area is divided into multiple independent karst sub-areas and numbered. In each karst sub-area, geological characteristic data are monitored and extracted in real time, including rock formation fracture factor Dlyz, groundwater flow velocity Swlz, groundwater pressure Dxls, rock porosity Ykzl, rock formation thickness Ychd, rock formation gap width Rjjk and cave volume Krz. The hydraulic mutation coefficient Sltx and water inrush permeability coefficient Tssx are calculated to form a comprehensive water inrush risk index Tfxz. A chain reaction model is introduced into the chain reaction warning module. Through the linkage analysis of the rock formation fracture expansion trend and the groundwater level change, the time and area of ​​the current water inrush risk are comprehensively judged, so that the system has the ability to adapt to completely new or extreme water inrush situations. Even in unknown karst structures, the water inrush risk can be identified, reducing the system's dependence on existing models.

[0044] (3) This kind of karst area foundation pit water inrush early warning system and method improves the real-time response capability of the system. When the water inrush risk level is high, a chain reaction model is constructed through the linkage analysis and prediction unit in the feedback control module, and the joint risk index Tsyz is calculated in real time and compared with the early warning threshold Q to establish the water inrush risk level. Based on the current water inrush risk level, the user can take preventive measures in advance for the water inrush risk in the next karst sub-area, including increasing the monitoring frequency or setting up additional monitoring points, thereby effectively improving the early warning efficiency of the system, enabling it to issue early warnings and take corresponding response measures in time before water inrush occurs, thereby ensuring the safety of foundation pit construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of the framework structure of a karst area foundation pit water inrush warning system according to the present invention;

[0046] Figure 2 The present invention is a schematic diagram of the steps of a method for early warning of water inrush in foundation pits in karst areas. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Example 1

[0049] See also Figure 1, the present invention provides a karst area foundation pit water inrush warning system, including a data acquisition module, a data processing module, a regional division and analysis module, a chain reaction warning module and a feedback control module;

[0050] The data acquisition module is used to monitor and collect groundwater dynamics data, geological stress data and soil moisture data in the karst area in real time during foundation pit excavation and construction, and to construct a multi-dimensional environmental data set;

[0051] The data processing module is used to pre-process the multi-dimensional environmental data set, including data cleaning, noise filtering and completion processing, using multi-dimensional interpolation algorithm to compensate for the missing data, and data fusion according to karst geological characteristics, and finally dimensionless processing;

[0052] The regional division and analysis module is used to divide the monitored karst area into multiple independent karst sub-areas according to the geological structure of the karst area and number them, monitor the karst sub-areas in real time and extract the geological characteristic data in each karst sub-area respectively; calculate the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx in the current karst sub-area by extracting the geological characteristic data; finally, fit the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx to calculate the water inrush risk index Tfxz of the current karst sub-area;

[0053] The chain reaction warning module is used to comprehensively evaluate the water inrush risk index Tfxz for each karst sub-area according to the number of the karst sub-area, and to link the rock fracture extension trend with the groundwater level change through the chain reaction model to calculate the joint risk index Tsyz;

[0054] The feedback control module is used to set the early warning threshold Q, and compare and analyze the associated risk index Tsyz with the early warning threshold Q, divide the water inrush risk level of the current karst sub-area, and take corresponding control measures; then the early warning information is displayed in real time through the user interface, and the user takes preventive measures for the water inrush risk of the next karst sub-area based on the water inrush risk level of the current karst sub-area.

[0055] In this embodiment, the data acquisition module can monitor and collect groundwater dynamics-related data, geological stress-related data and soil moisture-related data in the karst area in real time during foundation pit excavation and construction, construct a multidimensional environmental data set, and ensure the real-time and accuracy of the data from the source; the data processing module can pre-process the multidimensional environmental data set, including data cleaning, noise filtering and completion processing, use a multidimensional interpolation algorithm to compensate for data missing, and perform data fusion and dimensionless processing according to the karst geological characteristics to ensure the high quality and consistency of subsequent data analysis; the regional division and analysis module can divide the monitoring area into multiple independent karst sub-areas according to the complexity of the geological structure of the karst area and number them, monitor the geological characteristic data of each sub-area in real time, and calculate the hydraulic mutation coefficient Sltx and The water inrush permeability coefficient Tssx is fitted to the water inrush risk index Tfxz to achieve accurate quantification of the water inrush risk in each sub-area; the chain reaction warning module comprehensively evaluates the number of each karst sub-area and the water inrush risk index Tfxz, and uses the chain reaction model to analyze the rock fracture expansion trend and groundwater level changes, thereby obtaining the joint risk index Tsyz, which significantly improves the system's prediction accuracy of water inrush risk; the feedback control module sets the warning threshold Q, compares and analyzes the joint risk index Tsyz with the warning threshold Q, divides the water inrush risk level and takes corresponding control measures, and displays the warning information in real time through the user interface. The user can take preventive measures for the next karst sub-area in advance based on the water inrush risk level of the current karst sub-area, effectively improving the real-time performance and prevention and control effect of the water inrush warning system.

[0056] Example 2

[0057] During the excavation and construction of the foundation pit, the data acquisition module deploys a variety of sensor equipment according to the geological conditions of the karst area, including groundwater level monitoring sensors, geological stress monitors, and soil moisture sensors; at the same time, the sensor deployment points cover the key areas around the foundation pit;

[0058] Among them, the multidimensional environmental data set includes groundwater flow velocity Glsl, soil moisture content Hshl and rock mass stress Rtys.

[0059] The data processing module automatically identifies and removes extreme values ​​by using anomaly detection algorithms in the data cleaning process; the multidimensional interpolation algorithm generates supplementary data based on the spatial and temporal correlations between adjacent data points and the structural characteristics of karst geology; finally, the multidimensional environmental data from different monitoring points and different sensors are fused and processed. The data fusion process uses weighted averaging and data correlation analysis methods to comprehensively analyze data from different sources and construct a unified geological change model for the karst area.

[0060] In this embodiment, the data acquisition module and the data processing module realize the key data protection and processing optimization of the karst area foundation pit water inrush warning system; the data acquisition module reasonably deploys a variety of sensor equipment according to the geological conditions of the karst area during the foundation pit excavation and construction process, including groundwater level monitoring sensors, geological stress monitors and soil moisture sensors, effectively covering the key areas around the foundation pit to ensure the comprehensiveness and accuracy of the collected data; among them, the multidimensional environmental data set includes groundwater flow rate Glsl, soil moisture content Hshl and rock mass stress Rtys, which provides a reliable data basis for subsequent water inrush risk analysis; the data processing module is based on the data cleaning An anomaly detection algorithm is introduced in the process to automatically identify and eliminate extreme values ​​to ensure the purity and consistency of the data; a multidimensional interpolation algorithm is used to compensate for the missing data, and high-precision supplementary data is generated based on the spatial and temporal correlation of adjacent data points and the characteristics of the karst geological structure; finally, the multidimensional environmental data from different monitoring points and sensors are integrated through weighted averaging and data correlation analysis to construct a unified geological change model for the karst area, providing high-quality data support and a comprehensive geological dynamic view for the water inrush warning system; these measures enhance the system's sensitivity to changes in water inrush risks and ensure the system's real-time and accuracy.

[0061] Example 3

[0062] The regional division and analysis module includes a karst sub-region division unit, a geological feature extraction unit, and a water inrush risk calculation unit;

[0063] The karst sub-region division unit is used to divide the karst area in the monitoring area into several independent karst sub-regions, and each karst sub-region is numbered and identified, specifically YR1, YR2...YRn, where n represents the total number of karst sub-regions; in the division process, the distribution of geological faults, groundwater flow paths and distribution characteristics of cave structures in the karst area are comprehensively considered to enable each karst sub-region to be independently monitored and data analyzed.

[0064] The geological feature extraction unit is used to collect geological feature data in each karst sub-area one by one according to the number and calculate them one by one; through real-time monitoring and collection by geological radar and flow velocity sensor, the rock fracture factor Dlyz, groundwater pressure Dxls, rock porosity Ykzl, rock thickness Ychd, rock gap width Rjjk and cave volume Krz are obtained, and after pre-processing and dimensionless processing, a geological feature data set is constructed;

[0065] The geological characteristic data set and the multidimensional environmental data set are extracted and calculated by the following formulas to obtain the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx in the karst sub-area:

[0066] ;

[0067] .

[0068] The water inrush risk calculation unit is used to fit the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx, and calculate the water inrush risk index Tfxz of the karst sub-region. The specific formula is as follows:

[0069] .

[0070] In this embodiment, the regional division and analysis module realizes the precise monitoring and independent analysis of the risk of water inrush in foundation pits in karst areas through the karst sub-region division unit, the geological feature extraction unit and the water inrush risk calculation unit; the karst sub-region division unit divides the karst area in the monitoring area into several independent karst sub-regions and numbers them to ensure independent monitoring and data analysis of each area, comprehensively considering the distribution of geological faults, groundwater flow paths and karst cave structure distribution characteristics, so that the monitoring system covers all important water inrush risk points; the geological feature extraction unit collects key data such as rock fracture factor Dlyz, groundwater pressure Dxls, rock porosity Ykzl, rock thickness Ychd, rock gap width Rjjk and cave volume Krz in real time through equipment such as geological radar and flow velocity sensor; the rock fracture factor Dlyz reflects the degree of fracture and the structural fragility of the risk area; the groundwater pressure Dxls directly affects the water penetration and water inrush dynamics; the rock porosity Ykzl and the rock layer thickness Ychd characterize the permeability and pressure bearing capacity of the rock layer; the rock layer gap width Rjjk and the cave volume Krz jointly describe the size of the water flow channel in the rock layer and the possibility of water inrush; the geological characteristic data set is combined with the multidimensional environmental data set to calculate the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx to comprehensively evaluate the hydrodynamic factors and permeability of rock formation water inrush; the water inrush risk calculation unit further fits the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx, and finally obtains the water inrush risk index Tfxz of the karst sub-area, providing a more accurate regional risk assessment basis for the water inrush warning system.

[0071] Example 4

[0072] The chain reaction warning module includes a risk assessment unit and a linkage prediction unit;

[0073] The risk assessment unit compares and evaluates the water inrush risk index Tfxz of the current karst sub-area by presetting the water inrush risk threshold W. The specific assessment contents are as follows:

[0074] If the water inrush risk index Tfxz ≥ the water inrush risk threshold W, it means that there is an abnormal water inrush risk in the current karst sub-area, and then further analysis is performed on the current karst sub-area;

[0075] If the water inrush risk index Tfxz < water inrush risk threshold W, it means that the current karst sub-area is normal. The system marks the current karst sub-area and performs water inrush risk analysis on the next karst sub-area according to the numbering sequence.

[0076] The linkage prediction unit is used to construct and use the chain reaction model to analyze the extension direction of rock fractures, the speed of rock fractures and the fluctuation frequency of groundwater level changes, and to collect rock and groundwater level related data in real time and perform dimensionless processing, extract rock and groundwater level related data, and calculate through the following formula to obtain the joint risk index Tsyz of the karst sub-area with abnormal water inrush risk:

[0077] ;

[0078] Where, the rock fracture extension angle Ydjd in the rock formation and groundwater level correlation data, the rock fracture extension rate Ydsl in the rock formation and groundwater level correlation data, the groundwater level rise rate Dsws in the rock formation and groundwater level correlation data, and the groundwater fluctuation frequency Dsbd in the rock formation and groundwater level correlation data;

[0079] is a natural logarithmic function, which is used to convert the groundwater level rise rate Dsws into a linear change; is a numerical constant used to index the impact of groundwater fluctuation frequency Dsbd.

[0080] In this embodiment, the chain reaction warning module realizes accurate analysis and dynamic prediction of the water inrush risk in the karst sub-region through the risk assessment unit and the linkage prediction unit; the risk assessment unit compares and evaluates the water inrush risk index Tfxz of the current karst sub-region based on the preset water inrush risk threshold Q. When the water inrush risk index Tfxz is greater than or equal to the water inrush risk threshold W, the region is marked as an abnormal water inrush risk region and further analyzed. Otherwise, the system continues to evaluate the next karst sub-region; the linkage prediction unit uses the chain reaction model to analyze the extension direction of rock fractures, the extension rate of rock fractures and the frequency of groundwater level fluctuations, so as to predict the time and region of water inrush risk, collect rock formation and groundwater level related data in real time and perform dimensionless processing to ensure the analysis The standardization and accuracy of the process; among them, the rock fracture extension angle Ydjd is used to evaluate the directionality and expansion trend of the rock fracture, providing a basis for the prediction of the water inrush path; the rock fracture extension rate Ydsl indicates the changing speed of the fracture, providing a basis for the prediction of the time of water inrush; the groundwater level rise rate Dsws reflects the rapid change of the water level, which is converted into a linear change through the natural logarithmic function, and can enhance the impact of water level changes on the risk of water inrush; the groundwater fluctuation frequency Dsbd reflects the periodic fluctuation of the water level, and after exponential processing using digital constants, the significant impact of water level fluctuations is enhanced, so as to finally calculate the overall joint risk index Tsyz of the foundation pit, which significantly enhances the timeliness and reliability of the water inrush risk warning and provides a scientific decision-making basis for foundation pit construction.

[0081] Example 5

[0082] The feedback control module is used to evaluate the joint risk index Tsyz and the warning threshold Q, and to classify the water inrush risk level of the current karst sub-area. The specific evaluation contents are as follows:

[0083] When the joint risk index Tsyz ≥ the warning threshold Q, the current karst sub-area is determined to be the first risk area, indicating that the current karst sub-area not only has the risk of water inrush, but is also about to trigger a chain reaction in other karst sub-areas. At this time, the warning signal is immediately triggered; preventive measures are taken in advance for the next adjacent karst sub-area. This includes setting up additional monitoring points in adjacent areas and increasing the monitoring frequency by 10% on the basis of the original monitoring frequency to prevent the spread and spread of water inrush risks;

[0084] When the associated risk index Tsyz < warning threshold Q, the current karst sub-area is judged as the second lowest risk area and the current monitoring mode is maintained, but water inrush response measures are taken at this time, including injecting quick-setting slurry at the water inrush point, forming a sealing layer at the rock fracture, and local drainage measures.

[0085] In this embodiment, by real-time evaluation of the associated risk index Tsyz and the warning threshold Q, the water inrush risk level of the current karst sub-region is accurately divided, ensuring rapid response and dynamic adjustment of the water inrush risk; when the associated risk index Tsyz is greater than or equal to the warning threshold Q, the current karst sub-region is determined as the first risk region, indicating that the region not only has the risk of water inrush but also may trigger a chain reaction, and the warning signal is immediately triggered. At the same time, preventive measures are taken for adjacent karst sub-regions, additional monitoring points are deployed in adjacent areas, and the monitoring frequency is increased by 10% to prevent the spread of water inrush risk; when the associated risk index Tsyz is less than the warning threshold Q, the current karst sub-region is determined as the second lowest risk region, the system maintains the current monitoring mode but takes water inrush response measures, and effectively mitigates the water inrush risk by injecting quick-setting slurry at the water inrush point, forming a sealing layer at the rock fracture, and taking local drainage measures; the comprehensive application of various measures not only ensures the immediacy of risk control, but also provides a flexible response strategy for regional water inrush prevention.

[0086] Example 6

[0087] See also Figure 2 , a karst area foundation pit water inrush early warning method, comprising the following steps:

[0088] Step 1: During the excavation and construction of the foundation pit, real-time monitoring and collection of groundwater dynamics data, geological stress data, and soil moisture data in the karst area are carried out, and a multi-dimensional environmental data set is constructed;

[0089] Step 2: Preprocess the multidimensional environmental data set, including data cleaning, noise filtering and completion processing, use multidimensional interpolation algorithm to compensate for the missing data, and perform data fusion according to karst geological characteristics, and finally perform dimensionless processing;

[0090] Step 3: According to the geological structure of the karst area, the monitored karst area is divided into multiple independent karst sub-areas and numbered, the karst sub-areas are monitored in real time, and the geological characteristic data in each karst sub-area are extracted respectively; the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx in the current karst sub-area are calculated by extracting the geological characteristic data; finally, the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx are fitted to calculate the water inrush risk index Tfxz of the current karst sub-area;

[0091] Step 4: Comprehensively evaluate the water inrush risk index Tfxz for each karst sub-region according to the number of the karst sub-region, and conduct linkage analysis on the rock fracture extension trend and groundwater level change through the chain reaction model to calculate the joint risk index Tsyz;

[0092] Step 5. Set the warning threshold Q, and compare and analyze the associated risk index Tsyz with the warning threshold Q, divide the water inrush risk level of the current karst sub-area, and take corresponding control measures; then display the warning information in real time through the user interface, and the user takes preventive measures for the water inrush risk of the next karst sub-area based on the water inrush risk level of the current karst sub-area.

[0093] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A karst area foundation pit water inrush warning system, characterized by: It includes data acquisition module, data processing module, area division and analysis module, chain reaction warning module and feedback control module; The data acquisition module is used to monitor and collect groundwater dynamics-related data, geological stress-related data and soil moisture content-related data in the karst area in real time during foundation pit excavation and construction, and to construct a multi-dimensional environmental data set; The data processing module is used to pre-process the multi-dimensional environmental data set, including data cleaning, noise filtering and completion processing, using a multi-dimensional interpolation algorithm to compensate for missing data, and performing data fusion according to karst geological characteristics, and finally performing dimensionless processing; The regional division and analysis module is used to divide the monitored karst area into multiple independent karst sub-areas and number them according to the geological structure of the karst area, monitor the karst sub-areas in real time and extract the geological characteristic data in each karst sub-area respectively; calculate the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx in the current karst sub-area by extracting the geological characteristic data; finally, fit the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx, and calculate the water inrush risk index Tfxz of the current karst sub-area; The chain reaction warning module includes a risk assessment unit and a linkage prediction unit; it is used to conduct a comprehensive assessment of the water inrush risk index Tfxz for each karst sub-region according to the number of the karst sub-region, and to conduct a linkage analysis of the rock fracture extension trend and the groundwater level change through a chain reaction model to calculate the joint risk index Tsyz; The risk assessment unit compares and evaluates the water inrush risk index Tfxz of the current karst sub-region by presetting the water inrush risk threshold W. The specific assessment content is as follows: If the water inrush risk index Tfxz ≥ the water inrush risk threshold W, it means that there is an abnormal water inrush risk in the current karst sub-area, and then further analysis is performed on the current karst sub-area; If the water inrush risk index Tfxz < water inrush risk threshold W, it means that the current karst sub-area is normal. The system marks the current karst sub-area and performs water inrush risk analysis on the next karst sub-area according to the numbering sequence; The linkage prediction unit is used to construct and use a chain reaction model to analyze the extension direction of rock fractures, the rock fracture speed and the fluctuation frequency of groundwater level changes, collect rock formation and groundwater level related data in real time and perform dimensionless processing, extract rock formation and groundwater level related data, and calculate through the following formula to obtain the joint risk index Tsyz of the karst sub-area currently experiencing abnormal water inrush risk: Where, the rock fracture extension angle Ydjd in the rock formation and groundwater level correlation data, the rock fracture extension rate Ydsl in the rock formation and groundwater level correlation data, the groundwater level rise rate Dsws in the rock formation and groundwater level correlation data, and the groundwater fluctuation frequency Dsbd in the rock formation and groundwater level correlation data; ln is a natural logarithmic function used to convert the groundwater level rise rate Dsws into a linear change; e is a numerical constant used to index the impact of the groundwater fluctuation frequency Dsbd; The feedback control module is used to set the early warning threshold Q, and compare and analyze the associated risk index Tsyz with the early warning threshold Q, divide the water inrush risk level of the current karst sub-area, and take corresponding control measures; then the early warning information is displayed in real time through the user interface, and the user takes preventive measures for the water inrush risk of the next karst sub-area based on the water inrush risk level of the current karst sub-area.

2. The karst area foundation pit water inrush warning system according to claim 1 is characterized by: During the excavation and construction of the foundation pit, the data acquisition module deploys a variety of sensor devices according to the geological conditions of the karst area, including groundwater level monitoring sensors, geological stress monitors and soil moisture sensors; at the same time, the sensor deployment points cover the key areas around the foundation pit; Among them, the multidimensional environmental data set includes groundwater flow velocity Glsl, soil moisture content Hshl and rock mass stress Rtys.

3. The karst area foundation pit water inrush warning system according to claim 1 is characterized by: The data processing module automatically identifies extreme values ​​and removes them by using an anomaly detection algorithm in the data cleaning process; wherein the multidimensional interpolation algorithm generates supplementary data based on the spatial and temporal correlation of adjacent data points and the structural characteristics of karst geology; finally, the multidimensional environmental data from different monitoring points and different sensors are fused and processed, and the data fusion process uses the weighted average and data correlation analysis methods.

4. The karst area foundation pit water inrush warning system according to claim 2 is characterized by: The regional division and analysis module includes a karst sub-region division unit, a geological feature extraction unit and a water inrush risk calculation unit; The karst sub-region division unit is used to divide the karst area in the monitoring area into several independent karst sub-regions, and number each karst sub-region, specifically YR1, YR2...YRn, where n represents the total number of karst sub-regions; in the division process, the distribution of geological faults, groundwater flow paths and distribution characteristics of cave structures in the karst area are comprehensively considered to enable each karst sub-region to be independently monitored and data analyzed.

5. The karst area foundation pit water inrush warning system according to claim 4 is characterized by: The geological feature extraction unit is used to collect geological feature data in each karst sub-area one by one according to the number and calculate them one by one; through real-time monitoring and collection by geological radar and flow velocity sensor, the rock fracture factor Dlyz, groundwater flow velocity Swlz, groundwater pressure Dxls, rock porosity Ykzl, rock thickness Ychd, rock gap width Rjjk and cave volume Krz are obtained and pre-processed and dimensionless processed to construct a geological feature data set; The geological characteristic data set and the multidimensional environmental data set are extracted, and the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx in the karst sub-area are calculated by the following formulas:

6. The karst area foundation pit water inrush warning system according to claim 5 is characterized by: The water inrush risk calculation unit is used to fit the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx, and calculate the water inrush risk index Tfxz of the karst sub-region. The specific formula is as follows:

7. The karst area foundation pit water inrush warning system according to claim 1 is characterized by: The feedback control module is used to evaluate the joint risk index Tsyz and the warning threshold Q, and classify the water inrush risk level of the current karst sub-area. The specific evaluation contents are as follows: When the joint risk index Tsyz ≥ the warning threshold Q, the current karst sub-area is determined to be the first risk area, indicating that the current karst sub-area not only has the risk of water inrush, but is also about to trigger a chain reaction in other karst sub-areas. At this time, the warning signal is immediately triggered; preventive measures are taken in advance for the next adjacent karst sub-area, including setting up additional monitoring points in the adjacent area and increasing the monitoring frequency by 10% on the basis of the original monitoring frequency, so as to prevent the spread and spread of the risk of water inrush; When the associated risk index Tsyz < warning threshold Q, the current karst sub-area is judged as the second lowest risk area and the current monitoring mode is maintained, but water inrush response measures are taken at this time, including injecting quick-setting slurry at the water inrush point, forming a sealing layer at the rock fracture, and local drainage measures.

8. A karst area foundation pit water inrush warning method, applied to a karst area foundation pit water inrush warning system according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1: During the excavation and construction of the foundation pit, real-time monitoring and collection of groundwater dynamics data, geological stress data, and soil moisture data in the karst area are carried out, and a multi-dimensional environmental data set is constructed; Step 2: Preprocess the multidimensional environmental data set, including data cleaning, noise filtering and completion processing, use multidimensional interpolation algorithm to compensate for the missing data, and perform data fusion according to karst geological characteristics, and finally perform dimensionless processing; Step 3: According to the geological structure of the karst area, the monitored karst area is divided into multiple independent karst sub-areas and numbered, the karst sub-areas are monitored in real time, and the geological characteristic data in each karst sub-area are extracted respectively; the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx in the current karst sub-area are calculated by extracting the geological characteristic data; finally, the hydraulic mutation coefficient Sltx and the water inrush permeability coefficient Tssx are fitted to calculate the water inrush risk index Tfxz in the current karst sub-area; Step 4: Comprehensively evaluate the water inrush risk index Tfxz for each karst sub-region according to the number of the karst sub-region, and conduct linkage analysis on the rock fracture extension trend and groundwater level change through the chain reaction model to calculate the joint risk index Tsyz; Step 5. Set the warning threshold Q, and compare and analyze the associated risk index Tsyz with the warning threshold Q, divide the water inrush risk level of the current karst sub-area, and take corresponding control measures; then display the warning information in real time through the user interface, and the user takes preventive measures for the water inrush risk of the next karst sub-area based on the water inrush risk level of the current karst sub-area.

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