A method and system for early warning of ground collapse affected by karst groundwater dynamics

Abstract

A ground subsidence early warning method and system affected by karst groundwater dynamics provided by the present application obtains analysis instructions, automatically extracts corresponding target collapse intelligent analysis threads from a collapse intelligent analysis thread library according to the types of target groundwater dynamic impacts included in the analysis instructions, determines a groundwater dynamic impact analysis algorithm based on the target collapse intelligent analysis threads, and further generates collapse early warning data of a target karst event at all analysis risk levels. The target collapse intelligent analysis threads are intelligently matched according to the types of target groundwater dynamic impacts. Since the target collapse intelligent analysis threads are configured based on the groundwater dynamic impact elements of the target groundwater dynamic impact types and accurately warn of the groundwater dynamic impact characteristics of specific groundwater dynamic impact types, the target collapse intelligent analysis threads can accurately generate the groundwater dynamic impact attributes of the target groundwater dynamic impact types, ensuring the accuracy and reliability of the early warning.

Description

Technical Field

[0001] This application relates to the technical field of hidden danger warning, and more specifically, to a ground subsidence warning method and system affected by karst groundwater dynamics. Background Art

[0002] The causes of ground subsidence mainly include natural factors and human activities. Natural factors such as earthquakes, rainfall, groundwater erosion, etc., while human activities such as over-exploitation, pipeline leakage, and groundwater extraction will also exacerbate the risk of ground subsidence. Ground subsidence not only damages houses and infrastructure, leading to problems such as house collapse and road interruption, but may also cause casualties and affect the normal operation of water conservancy facilities. In addition, ground subsidence will also damage land resources, affect agricultural production, and even trigger a series of environmental problems.

[0003] Currently, the research on the impact of groundwater on ground subsidence has taken initial shape, but there are still some deficiencies in the specific analysis process. These deficiencies lead to very inaccurate warning information during subsidence warning, which causes great problems in governance or protection. Therefore, there is an urgent need for a method to improve ground subsidence warning to solve the above technical problems. Summary of the Invention

[0004] To improve the technical problems existing in the related art, this application provides a ground subsidence warning method and system affected by karst groundwater dynamics.

[0005] In a first aspect, a ground subsidence warning method affected by karst groundwater dynamics is provided. The method includes:

[0006] Obtaining an analysis instruction for a target karst matter, where the analysis instruction includes the type of target groundwater dynamic impact;

[0007] Extracting a target collapse intelligent analysis thread belonging to the type of target groundwater dynamic impact from a collapse intelligent analysis thread library; the target collapse intelligent analysis thread is a collapse intelligent analysis thread configured according to target groundwater dynamic impact elements belonging to the type of target groundwater dynamic impact;

[0008] Using the target collapse intelligent analysis thread to process the collapse hidden danger points of the target karst matter at the a-th analysis risk level, and obtaining a groundwater dynamic impact analysis algorithm of the target karst matter at the (a + 1)-th analysis risk level, where the groundwater dynamic impact analysis algorithm is used to represent the groundwater dynamic impact executed by the target karst matter, and a is an integer greater than 0;

[0009] Generate the groundwater dynamic impact attribute of the target karst event at the (a + 1)-th analysis risk level and the potential collapse points of the target karst event at the (a + 1)-th analysis risk level according to the groundwater dynamic impact analysis algorithm for the (a + 1)-th analysis risk level.

[0010] Output the collapse warning data of the target karst event at all analysis risk levels.

[0011] In this application, the output of the collapse warning data of the target karst event at all analysis risk levels includes:

[0012] Integrate the collapse warning data at all analysis risk levels into a risk level matrix in the arrangement order of the generated risk levels from small to large according to the directory, and store the risk level matrix in the database; the types of groundwater dynamic impacts of the risk level matrix belong to the target groundwater dynamic impact types.

[0013] When a hidden danger excavation instruction covering the target groundwater dynamic impact type is received, extract the risk level matrix from the database in response to the hidden danger excavation instruction for excavation.

[0014] It can be understood that through a reliable risk level matrix, the collapse warning data of the target karst event at all analysis risk levels can be accurately output.

[0015] In this application, the output of the collapse warning data of the target karst event at all analysis risk levels includes:

[0016] Evaluate and process the collapse warning data of the target karst event at all analysis risk levels according to the set evaluation target values respectively, and load the key content recognition unit into the groundwater dynamic impact attribute after the evaluation and processing to obtain the groundwater dynamic impact attribute to be mined; the groundwater dynamic impact attribute to be mined includes several element description contents.

[0017] Mine the several element description contents in the recognition box, and each element description content in the several element description contents is displayed after meeting the specified mining requirements; the specified mining requirements are determined according to the set evaluation target values.

[0018] It can be understood that through the groundwater dynamic impact attribute to be mined, the accuracy of the output of the collapse warning data of the target karst event at all analysis risk levels is guaranteed.

[0019] In this application, the target karst matter includes a number of target monitoring matters; the generation of the groundwater dynamic influence attribute of the target karst matter at the (a + 1)-th analysis risk level and the collapse potential points of the target karst matter at the (a + 1)-th analysis risk level according to the groundwater dynamic influence analysis algorithm of the (a + 1)-th analysis risk level includes:

[0020] Determine the hydrodynamic values of each target monitoring matter in the target karst matter according to the collapse potential points at the a-th analysis risk level and the groundwater dynamic influence analysis algorithm at the (a + 1)-th analysis risk level;

[0021] Analyze each target monitoring matter of the target karst matter within the (a + 1)-th analysis risk level according to the hydrodynamic values to generate the groundwater dynamic influence attribute of the target karst matter in the (b + 1)-th configuration risk level segment;

[0022] After the analysis of the target karst matter is terminated, obtain the collapse potential points of the target karst matter at the (a + 1)-th analysis risk level.

[0023] It can be understood that when using the groundwater dynamic influence analysis algorithm at the (a + 1)-th analysis risk level, the calculation data is optimized, reducing the errors generated in the calculation, so as to accurately generate the groundwater dynamic influence attribute of the target karst matter at the (a + 1)-th analysis risk level and the collapse potential points of the target karst matter at the (a + 1)-th analysis risk level.

[0024] In this application, the obtaining of the collapse potential points of the target karst matter at the (a + 1)-th analysis risk level includes:

[0025] Obtain the underground temperature information and state information of the target karst matter; the underground temperature information is used to represent the assumed underground temperature where the target karst matter is located; the state information is used to represent the seepage information and deformation information of the target karst matter;

[0026] Generate the collapse potential points of the target karst matter at the (a + 1)-th analysis risk level according to the state information and the underground temperature information.

[0027] In this application, the matching collapse potential points in the b-th configuration risk level segment include: the first collapse potential points of the target karst matter and the second collapse potential points of the reference karst matter in the target groundwater dynamic influence factors;

[0028] The first collapse potential points include the collapse potential points of the target karst matter in the b-th configuration risk level segment and the collapse potential points of the target karst matter in the historical configuration risk level segment;

[0029] The second collapse risk point includes the collapse risk points of the reference karst matters in the reference configuration risk level section; the reference configuration risk level section is determined by the b-th configuration risk level section and the set configuration risk value;

[0030] The collapse risk points include seepage information, deformation information, and fracture structure.

[0031] It can be understood that through the underground temperature information and the state information, the collapse risk points of the target karst matter at the a + 1-th analysis risk level can be accurately obtained.

[0032] In this application, the method further includes:

[0033] Identifying the collapse risk points generated after applying the groundwater dynamic influence analysis algorithm for the b + 1-th configuration risk level section to the target karst matter, to obtain the matching collapse risk points of the target karst matter at the b + 1-th configuration risk level section;

[0034] If the thread parameters sent by the data processing terminal are not received within the b + 1-th configuration risk level section, then using the collapse intelligent analysis thread to process the matching collapse risk points of the b + 1-th configuration risk level section, to obtain the groundwater dynamic influence analysis algorithm for the target karst matter at the b + 2-th configuration risk level section.

[0035] It can be understood that by matching the collapse risk points, the performance of the groundwater dynamic influence analysis algorithm is improved.

[0036] In this application, using the collapse intelligent analysis thread to process the matching collapse risk points of the b-th configuration risk level section, to obtain the groundwater dynamic influence analysis algorithm for the target karst matter at the b + 1-th configuration risk level section, includes:

[0037] According to the matching collapse risk points of the target karst matter at the b-th configuration risk level section and the collapse intelligent analysis thread, determining the groundwater dynamic influence expectation and the groundwater dynamic influence function value of the target karst matter;

[0038] According to the groundwater dynamic influence function value and the groundwater dynamic influence expectation, determining the distribution of the groundwater dynamic influence analysis algorithm for the target karst matter at the b + 1-th configuration risk level section;

[0039] Performing a screening process on the distribution of the groundwater dynamic influence analysis algorithm for the b + 1-th configuration risk level section, to obtain the groundwater dynamic influence analysis algorithm for the target karst matter at the b + 1-th configuration risk level section;

[0040] Among them, the configuration example further includes an algorithm screening possibility, which is generated after screening the distribution of the groundwater dynamic influence analysis algorithm.

[0041] It can be understood that when using the collapse intelligent analysis thread to process the matching collapse hidden danger points in the b-th configuration risk level segment, the problem of inaccurate processing is improved, so that the groundwater dynamic influence analysis algorithm of the target karst matter in the (b + 1)-th configuration risk level segment can be accurately obtained.

[0042] In this application, optimizing the collapse intelligent analysis thread according to the received thread parameters to obtain the target collapse intelligent analysis thread includes:

[0043] Updating the thread parameters of the collapse intelligent analysis thread according to the received thread parameters. The updated collapse intelligent analysis thread is used to generate a new configuration example, and the new configuration example is used to continue updating the thread parameters of the collapse intelligent analysis thread;

[0044] When the updated collapse intelligent analysis thread meets the thread convergence requirement, the updated collapse intelligent analysis thread is determined as the target collapse intelligent analysis thread.

[0045] It can be understood that when optimizing the collapse intelligent analysis thread according to the received thread parameters, the problem of inaccurate optimization is improved, so that the performance of the target collapse intelligent analysis thread is stronger when obtaining the target collapse intelligent analysis thread.

[0046] In this application, determining the groundwater dynamic influence coefficient according to the matching collapse hidden danger points in the b-th configuration risk level segment includes:

[0047] Performing discrete processing on the collapse hidden danger points of the target karst matter in the b-th configuration risk level segment and the collapse hidden danger points of the reference karst matter of the target groundwater dynamic influence factor in the b-th configuration risk level segment to obtain discrete information;

[0048] Determining the discrete features corresponding to the discrete information; the discrete features include seepage information discrete features, deformation information discrete features, and fracture structure discrete features;

[0049] Performing weighted summation on the seepage information discrete features, the deformation information discrete features, and the fracture structure discrete features to obtain the groundwater dynamic influence coefficient of the target karst matter.

[0050] It can be understood that when according to the matching collapse hidden danger points in the b-th configuration risk level segment, the problem of inaccurate discrete information is improved, so that the groundwater dynamic influence coefficient can be determined more accurately.

[0051] In this application, the method further includes:

[0052] Obtaining a configuration example, where the configuration example includes matching collapse hazard points in the b-th configuration risk level segment, a groundwater dynamic influence analysis algorithm for the (b + 1)-th configuration risk level segment, a groundwater dynamic influence coefficient, and matching collapse hazard points in the (b + 1)-th configuration risk level segment; where b is an integer greater than 0; the groundwater dynamic influence analysis algorithm for the (b + 1)-th configuration risk level segment is obtained by processing the matching collapse hazard points in the b-th configuration risk level segment using a collapse intelligent analysis thread; the groundwater dynamic influence coefficient is determined according to the matching collapse hazard points in the b-th configuration risk level segment; the matching collapse hazard points in the (b + 1)-th configuration risk level segment are identified after applying the groundwater dynamic influence analysis algorithm for the (b + 1)-th configuration risk level segment to the target karst event;

[0053] Sending the configuration example to a data processing terminal so that the data processing terminal configures the collapse intelligent analysis thread stored in the data processing terminal according to the configuration example;

[0054] Receiving the thread parameters of the configured collapse intelligent analysis thread sent by the data processing terminal, and optimizing the collapse intelligent analysis thread according to the received thread parameters to obtain the target collapse intelligent analysis thread.

[0055] It can be understood that a more high-performance target collapse intelligent analysis thread can be trained through multiple configuration examples.

[0056] In a second aspect, a ground collapse warning system for karst groundwater dynamic influence is provided, including a processor and a memory that communicate with each other, and the processor is configured to read and execute a computer program from the memory to implement the above method.

[0057] A ground subsidence early warning method and system affected by karst groundwater dynamics provided by an embodiment of the present application can obtain an analysis instruction, automatically extract a corresponding target collapse intelligent analysis thread from a collapse intelligent analysis thread library according to the type of target groundwater dynamic impact included in the analysis instruction, determine a groundwater dynamic impact analysis algorithm based on the target collapse intelligent analysis thread, and then generate collapse early warning data of a target karst event at all analysis risk levels. In the above solution, the target collapse intelligent analysis thread is intelligently matched according to the type of target groundwater dynamic impact. Since the target collapse intelligent analysis thread is configured based on the groundwater dynamic impact factors of the target groundwater dynamic impact type, accurate early warning of the groundwater dynamic impact characteristics of a specific groundwater dynamic impact type is performed. Therefore, the target collapse intelligent analysis thread can accurately generate the groundwater dynamic impact attributes of the target groundwater dynamic impact type, ensuring the accuracy and reliability of the early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required for the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0059] Figure 1 It is a flowchart of a ground subsidence early warning method affected by karst groundwater dynamics provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] In order to better understand the above technical solutions, the technical solutions of the present application will be described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solutions of the present application, rather than limitations on the technical solutions of the present application. Without conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0061] Please refer to Figure 1 , which shows a ground subsidence early warning method affected by karst groundwater dynamics. The method may include the technical solutions described in the following steps S201-S205.

[0062] S201, obtain an analysis instruction for a target karst event.

[0063] Exemplarily, the analysis instruction can be understood as an analysis command. This is a command directly issued by a data terminal.

[0064] The target karst matter refers to the karst matter for which the groundwater dynamic influence attributes are to be generated. The target karst matter can be a rock mass (especially a fractured rock mass) in a certain geological environment. The temperature, stress of the rock mass, and the flow of groundwater are three main factors affecting the geological environment. The three are interconnected, interact with each other, and restrict each other, forming a complex coupling effect among the seepage field, stress field, and temperature field in the fractured rock mass. Therefore, it is very necessary to analyze the THM coupling mechanism of the fractured rock mass. Simply put, in the temperature field, seepage field, and stress field, the thermal action can generate thermal stress in the rock mass and cause changes in the solid properties such as the elastic modulus and Poisson's ratio of the rock mass. At the same time, due to the change in groundwater density caused by temperature change, the flow of groundwater is affected. The flow of groundwater can make the heat spread faster through conduction or convection, and the presence of groundwater in the rock mass can change the stress state of the rock mass. The rock mass will generate a certain amount of heat dissipation inside due to deformation, and the mechanical deformation will also partially affect the change of the solid thermal properties. At the same time, the mechanical effect also has a relatively obvious impact on the porosity or the permeability of the fluid.

[0065] S202, extract the target collapse intelligent analysis thread belonging to the target groundwater dynamic influence type from the collapse intelligent analysis thread library.

[0066] Exemplarily, there are multiple threads in the collapse intelligent analysis thread library, and there are different analysis threads for different scenarios. The target collapse intelligent analysis thread is the thread suitable for analyzing the current scenario, and the analysis of this thread is the most accurate one.

[0067] The collapse intelligent analysis thread library is used to store the configured collapse intelligent analysis threads belonging to different groundwater dynamic influence types, and each collapse intelligent analysis thread is configured using the groundwater dynamic influence elements of a certain groundwater dynamic influence type.

[0068] Among them, the collapse intelligent analysis thread specifically includes: The discrete fracture network model is based on clarifying the geometric parameters such as the spatial orientation and aperture of each fracture. Based on the basic formula of water flow in a single fracture, equations are established using the principle that the flow rates into and out of each fracture intersection point are equal. Then, the head values of each fracture intersection point are obtained by solving the system of equations. Wittke (1990) proposed a network line element method similar to the loop method in circuit analysis; Mao Changxi (1994) proposed a crack hydraulic network model similar to the pipe network problem in hydraulics; Wilson and Witherspoon (1974) respectively simulated fractures in rock masses with triangular elements or line elements and proposed two finite element techniques for simulating two-dimensional fracture network water flow. Numerical examples showed that the water flow interference at fracture intersection points can be ignored, thus clarifying the superiority and feasibility of using line elements. For three-dimensional problems, Long J.C.S et al. (1985) first proposed a three-dimensional disc fracture network model and solved it using a hybrid analytical-numerical method; Nordqvist A.W et al. (1992) also proposed a three-dimensional variable aperture fracture network model; Dershowitz (1985) proposed a three-dimensional polygonal fracture network model. Wang Enzhi (1992) proposed a new fracture network numerical model based on expressing the spatial distribution set function and numerical matrix of the fracture system vividly, and proposed corresponding solutions for the problem of non-connected fracture networks. Wan Li et al. (1995) combined it with the finite element method and further proposed a polygonal element seepage model for three-dimensional fracture networks. The discrete fracture network model specifically simulates each fracture in the rock mass fracture network system and attempts to obtain the true seepage state at each point in the fracture system. Obviously, it has advantages such as good simulation and high accuracy. However, when there are many fractures, the workload is quite large, especially for three-dimensional problems, which is very difficult to achieve; in addition, due to the randomness of fracture distribution, it is also very difficult to establish a discrete real fracture system. Therefore, except for relatively simple cases, at present, the discrete fracture network model is difficult to be widely applied in design engineering. To address these problems, Wang Enzhi et al. (2001) divided the complex fracture system into three major types: zonal faults, planar fractures, and tubular holes according to the development law and seepage mechanism of the natural fracture system. On the premise of ignoring the permeability of rock blocks, a three-dimensional fracture network seepage numerical model composed of tubular line elements, slit-like surface elements, and zonal body elements was established, and the rationality of the model was verified. This achievement broadened the engineering application field. The damage field-seepage field coupling model is based on the equivalent hydrodynamics model of two parallel plates connected by two springs by Oda (1986). Yang Yanyi et al. (1991) proposed a seepage-damage coupling analysis model for fractured rock masses. The fracture connectivity rate is analyzed by field fracture statistics and combined with computer analysis.The fracture system is equivalent to a continuous medium with anisotropic permeability. According to the self-consistent theory of meso-damage mechanics, the permeability tensor and elastic compliance tensor of fractured rock mass are uniformly expressed by the geometric parameters of fractures: normal vector, average aperture, density, and radius. The damage evolution of each geometric parameter of fractures under different stress states is described by using the damage fracture mechanics model, so as to obtain the evolution equations of the permeability tensor and elastic compliance tensor. This model couples the seepage field and stress field of fractured rock mass through the deformation damage field. On this basis, Zhu Zhende et al. (1999) proposed a coupling model of unsteady seepage field and damage field. Starting from the principle of superposition of fluid diffusion energy, an analytical expression of the seepage tensor of fractured rock mass medium for unsteady seepage is established. Zheng Shaohe (1999) systematically studied the damage evolution mechanism of rock mass under the combined action of stress field and seepage field based on Betti energy reciprocity theorem, and established a brittle-elastic and elastoplastic fracture damage constitutive model and damage evolution equation of three-dimensional water-bearing fractured rock mass considering seepage pressure. Dual-medium fluid-solid coupling model: The dual-medium model regards fractured rock mass as the superposition of pore system and fracture system. The fracture system serves as the channel for fluid seepage, while the rock block system with good pore properties stores fluid. Both systems are distributed throughout the rock mass. This theory needs to establish the seepage model in the fracture system and the seepage model in the porous medium respectively. The two are linked by flow rate exchange to form a coupled equation. Barenblatt of the Soviet Union (1960) first proposed the fracture-pore dual-medium model. His main viewpoints are: (1) There are intersecting and connected fracture systems and pore systems with poor conductivity but distributed throughout the rock mass in the rock mass. The former is the channel for seepage, while the latter is the space for storing fluid; (2) The fracture system and the pore system of rock blocks are distributed throughout the whole area, forming two overlapping continua. At each point in the seepage field, there are two water head values - the water head value in the pore system and the water head value in the fracture system; (3) The amount of water exchanged between pores and fractures is proportional to the water head difference between them; (4) It is assumed that both fractures and pore rock blocks are homogeneous and isotropic. Warren and Root (1963) proposed a theory similar to Barenblatt's when studying the seepage of fractured oil and gas reservoirs, and added new restrictions on the geometric characteristics and seepage characteristics of the fracture system. Their assumptions are: (1) There is a homogeneous, orthogonal, and interconnected fracture system developed in the rock mass, and the anisotropy degree of fractures is described by the spacing and width between fractures; (2) The pore systems contained in each rock block divided by fractures are homogeneous and isotropic; (3) Water alternation occurs between the two different systems, but the fluid can only flow to the well through fractures, and the flow to the well in the pore system is ignored. Wang Enzhi (1998) developed the dual-medium model according to the development law and seepage characteristics of the rock mass fracture system, and proposed the seepage model of dual fracture system.The model transforms the fracture network seepage theory model into a practical fracture rock mass seepage model that can describe in detail the seepage mechanism of the primary and secondary fracture systems, providing a new method for solving the analysis of rock mass fracture seepage in engineering. In addition, Wang Enzhi (2001) also proposed models such as "replacing holes with pipes" and "replacing wells with fractures" for seepage problems in water conservancy and hydropower projects, expanding the practicality of the models. The outstanding advantage of the dual-medium model is that it considers the water exchange process between two different systems, but all these models have certain restrictions on the configuration and shape of the fracture system, limiting the application of these models. Multi-medium fluid-solid coupling model: Du Guanglin et al. (2000) proposed a multi-fracture network seepage model based on the concept of dual-medium. The basic viewpoints are as follows: (1) It is considered that the fractured rock mass consists of three systems: a discrete main fracture network, a branch fracture network, and an equivalent rock matrix. The main fracture network is mainly composed of large-scale geological structures (including faults) distributed in space; the branch fracture network is composed of fractures or faults with smaller scales compared to the main fracture network; the rock matrix is composed of fractures and rock blocks with even smaller scales and is treated as an equivalent porous continuous medium; (2) In the main fracture network, the fluid movement along the direction of the fracture surface is non-Darcy flow. The fluid inflow or outflow on the two fracture surfaces of the main fracture network is the hydraulic connection between the main fracture network and the other two systems, and the movement law of the exchanged fluid conforms to Darcy's law. The movement laws of the fluid in the branch fracture network and the rock matrix conform to Darcy's law; (3) It is considered that the pore pressure is a continuous function of the spatial point coordinates and time. The seepage continuity equations of the three systems and their mutual hydraulic connection equations are established respectively and solved simultaneously. The advantage is that it considers the characteristic that the fluid movement along the direction of the fracture surface is non-Darcy flow, and the disadvantage is that the simultaneous equations are relatively complex to solve and the calculation amount is large. Chai Junrui et al. (2000) also proposed a multi-fracture network seepage model, classifying various fractures and pores in the rock mass into four levels according to scale and permeability, namely the first-level real fracture network, the second-level random fracture network, the third-level equivalent continuous medium system, and the fourth-level continuous medium system. And finally, the four-level network is merged into two seepage systems: a connected fracture network and an equivalent continuous medium. The two seepage systems exchange water volume on the fracture walls of the connected fracture network, and an iterative method is used for joint solution. It can be seen that the equivalent-discrete coupling model considers the scale and density of fractures, divides the medium in the seepage area into two major systems: a discrete fracture network system and an equivalent continuous medium, avoiding the huge workload caused by simulating each fracture in the discrete fracture network model and ensuring the effectiveness of the equivalent continuous medium model. However, due to the existence of two systems, it is necessary to establish their respective seepage continuity equations separately, consider the flow exchange between the two systems, and solve the simultaneous equations, making the establishment and solution of the equations relatively complex. For the study of practical problems, multiple models or combined models can be adopted, and the selection of the model should be combined with the content being studied and the environmental medium in which it is located.

[0069] It can be seen that there is a projection relationship between the collapse intelligent analysis threads stored in the collapse intelligent analysis thread library and the types of groundwater dynamic impacts. Therefore, according to the target groundwater dynamic impact type included in the analysis instruction for the target karst matter, the target collapse intelligent analysis thread belonging to the target groundwater dynamic impact type can be extracted from several collapse intelligent analysis threads included in the collapse intelligent analysis thread library. The target collapse intelligent analysis thread is a collapse intelligent analysis thread configured according to the target groundwater dynamic impact elements belonging to the target groundwater dynamic impact type. The target groundwater dynamic impact type here is the target groundwater dynamic impact type in the analysis instruction, and it is the groundwater dynamic impact type that matches the input target groundwater dynamic impact type among all the groundwater dynamic impact types included in the collapse intelligent analysis thread library.

[0070] Here, the configured collapse intelligent analysis threads are corresponding to the types of groundwater dynamic impacts. Although several collapse intelligent analysis threads are configured through the groundwater dynamic impact elements of different types of groundwater dynamic impacts during the configuration stage, rather than generalizing a collapse intelligent analysis thread for all types of groundwater dynamic impacts, during the application stage of the collapse intelligent analysis thread, it is not necessary to input the target groundwater dynamic impact elements for processing. Instead, the collapse intelligent analysis threads can be directly corresponding to the types of groundwater dynamic impacts, and the groundwater dynamic impact attributes can be obtained by using the target analysis algorithm of the target groundwater dynamic impact type, thereby effectively reducing the data processing volume of the target collapse intelligent analysis thread.

[0071] S203. Use the target collapse intelligent analysis thread to process the collapse hidden danger points of the target karst matter at the a-th analysis risk level, and obtain the groundwater dynamic impact analysis algorithm of the target karst matter at the (a + 1)-th analysis risk level, where a is an integer greater than 0.

[0072] Exemplarily, the collapse hidden danger points are numerically processed for the places where collapse may occur, divided into 0 - 10. 0 means that collapse is impossible to occur, and 10 means that collapse is certain to occur.

[0073] The a-th analysis risk level belongs to the current analysis risk level of the target karst matter, and the analysis risk level can represent the risk level of simulating the groundwater dynamic impact of the target karst matter.

[0074] In a possible embodiment, the initial collapse hidden danger point information of the target karst matter at the a-th risk level segment can be identified. The initial collapse hidden danger point information is the most initial data of the target karst matter at the current analysis risk level. Then, according to the initial collapse hidden danger point information, the collapse hidden danger points at the current analysis risk level can be determined, and the collapse hidden danger points are sorted into a data format that conforms to the processing of the target collapse intelligent analysis thread.

[0075] Optionally, the initial collapse hidden danger point information may include information such as the current collapse hidden danger point, underground temperature parameters, and the current simulated risk level.

[0076] Optionally, the collapse hidden danger points of the target karst matter at the a-th analysis risk level include several collapse hidden danger points of the analysis risk level. The role collapse hidden danger points can be characterized by fracture structures, seepage information, and deformation information, and are multi-dimensional vectors.

[0077] In another embodiment, the initial collapse hidden danger point information may further include groundwater dynamic influence factors of the target groundwater dynamic influence type. The relevant parameter information of the groundwater dynamic influence factors of the target groundwater dynamic influence type is similar to the content included in the role collapse hidden danger points of the target karst matter at each analysis risk level.

[0078] That is to say, the collapse hidden danger points at the a-th analysis risk level may include fracture structures, seepage information, and deformation information.

[0079] The groundwater dynamic influence analysis algorithm is used to represent the groundwater dynamic influence executed by the target karst matter.

[0080] S204. According to the groundwater dynamic influence analysis algorithm at the (a + 1)-th analysis risk level, generate the groundwater dynamic influence attributes of the target karst matter at the (a + 1)-th analysis risk level, and the collapse hidden danger points of the target karst matter at the (a + 1)-th analysis risk level.

[0081] The groundwater dynamic influence analysis algorithm at the (a + 1)-th analysis risk level is executed after the termination of the a-th analysis risk level, and the data generated by the target karst matter executing this groundwater dynamic influence acts on the (a + 1)-th analysis risk level. That is, after the termination of the a-th analysis risk level, the groundwater dynamic influence analysis algorithm generated by the target karst matter according to the collapse hidden danger points at the a-th analysis risk level is analyzed within the (a + 1)-th analysis risk level, and the corresponding groundwater dynamic influence attributes can be generated. Thus, the target karst matter completes the groundwater dynamic influence according to the groundwater dynamic influence analysis algorithm within the (a + 1)-th analysis risk level, and the deformation information and the seepage information and other information of the target karst matter have all changed. That is, the collapse hidden danger points of the target karst matter have changed, so that the target karst matter generates new collapse hidden danger points, that is, the collapse hidden danger points at the (a + 1)-th analysis risk level.

[0082] In one embodiment, the specific implementation manner of step S204 may be as follows in steps 1) to 3):

[0083] 1) Determine the hydrodynamic values of each target monitoring matter in the target karst matter according to the collapse hidden danger points at the a-th analysis risk level and the groundwater dynamic influence analysis algorithm at the (a + 1)-th analysis risk level.

[0084] 2) According to the hydrodynamic value, analyze each target monitoring item of the target karst event within the (a + 1)-th analysis risk level to generate the groundwater dynamic influence attribute of the target karst event within the (b + 1)-th configuration risk level segment.

[0085] According to the calculated torque, each rotatable joint can rotate according to the corresponding torque. The rotation of each rotatable joint constitutes the analysis of the target karst event within the (a + 1)-th analysis risk level, and then generates the groundwater dynamic influence attribute. Thus, after the analysis of the target karst event is terminated, a new collapse hazard point of the target karst event is generated and determined as the collapse hazard point of the target karst event within the (a + 1)-th analysis risk level.

[0086] 3) After the analysis of the target karst event is terminated, obtain the collapse hazard point of the target karst event within the (a + 1)-th analysis risk level.

[0087] In one embodiment, the method for obtaining the collapse hazard point of the target karst event within the (a + 1)-th analysis risk level includes: obtaining the underground temperature information and state information of the target karst event; generating the collapse hazard point of the target karst event within the (a + 1)-th analysis risk level according to the state information and the underground temperature information. Among them, the underground temperature information is used to represent the assumed underground temperature where the target karst event is located; the state information is used to represent the seepage information and deformation information of the target karst event. The target karst event is analyzed according to the groundwater dynamic influence attribute analysis algorithm.

[0088] S205, output the collapse warning data of the target karst event at all analysis risk levels.

[0089] After obtaining the collapse hazard point of the next analysis risk level based on the collapse hazard point of the target karst event at the current analysis risk level, repeat step S204 to generate the collapse warning data at all analysis risk levels.

[0090] In a possible embodiment, an alternative implementation of step S205 may be:

[0091] Arrange the collapse warning data at all analysis risk levels in ascending order of the generated risk level of the directory to form a risk level matrix, and store the risk level matrix in the database; the groundwater dynamic influence types of the risk level matrix belong to the target groundwater dynamic influence types; when receiving a hazard excavation instruction covering the target groundwater dynamic influence types, extract the risk level matrix from the database in response to the hazard excavation instruction for excavation.

[0092] For each groundwater dynamic impact attribute of the analysis risk level, the corresponding directory has a generated risk level. Based on this generated risk level, the collapse warning data of all analysis risk levels can be sorted to form a groundwater dynamic impact matrix with a sequential arrangement method. This groundwater dynamic impact matrix can be the groundwater dynamic impact of a continuous groundwater dynamic impact space. Subsequently, the risk level matrix can be stored in the database and marked as the target groundwater dynamic impact type correspondingly, so that when a hidden danger mining instruction covering the target groundwater dynamic impact type is received, the risk level matrix of the target groundwater dynamic impact type can be extracted from the database in response to this hidden danger mining instruction for mining.

[0093] The risk level matrices obtained by different target collapse intelligent analysis threads can all be stored in the database. Each risk level matrix corresponds to a type of groundwater dynamic impact. Optionally, all the risk level matrices in the database are risk level matrices generated by different target collapse intelligent analysis threads. When a user issues a hidden danger mining instruction, the risk level matrix corresponding to the type of groundwater dynamic impact included in the hidden danger mining instruction can be extracted from the database for mining.

[0094] In an alternative embodiment, the groundwater dynamic impact matrix corresponding to the target karst matter automatically generated by the target collapse intelligent analysis thread is stored in the database, and the groundwater dynamic impact matrix of the corresponding groundwater dynamic impact type is matched during the hidden danger mining instruction. This can facilitate quickly and accurately finding the groundwater dynamic impact matrix of the target groundwater dynamic impact type from the database for mining using the groundwater dynamic impact attribute.

[0095] In another embodiment, an alternative implementation manner of step S205 can also be:

[0096] The target karst matter is respectively evaluated and processed for the collapse warning data of all analysis risk levels according to the set evaluation target value, and the key content recognition unit is loaded into the groundwater dynamic impact attribute after the evaluation and processing to obtain the groundwater dynamic impact attribute to be mined; the groundwater dynamic impact attribute to be mined includes several element description contents; several element description contents are mined in the recognition frame, and each element description content among the several element description contents is displayed after meeting the specified mining requirements; the specified mining requirements are determined according to the set evaluation target value.

[0097] In a possible embodiment, after the collapse warning data of the target karst matter at all analysis risk levels is generated, the groundwater dynamic impact attribute can be evaluated, skinned, and then directly mined in the recognition frame.

[0098] The solution provided by the embodiments of the present application can obtain an analysis instruction, and according to the types of target groundwater dynamic impacts included in the analysis instruction, automatically and intelligently extract the corresponding target collapse intelligent analysis threads from the collapse intelligent analysis thread library. Since the target collapse intelligent analysis threads are configured based on the groundwater dynamic impact elements of a type of groundwater dynamic impact, and accurately warn of the groundwater dynamic impact characteristics of a specific type of groundwater dynamic impact, the target collapse intelligent analysis threads can accurately generate the groundwater dynamic impact attributes of the target type of groundwater dynamic impact.

[0099] S501. Obtain the matching collapse hidden danger points of the target karst matter in the b-th configured risk level segment; where b is an integer greater than 0.

[0100] The b-th configured risk level segment is the risk level segment currently being configured. The matching collapse hidden danger points of the b-th configured risk level segment may include the collapse hidden danger points of one or several previous historical configured risk level segments. It can be understood that if it is the first configured risk level segment, then the matching collapse hidden danger points only include the collapse hidden danger points of the target karst matter in the first configured risk level segment. These matching collapse hidden danger points are used to configure the groundwater dynamic impact analysis algorithm of the target karst matter, and may include the data of the target karst matter itself and / or the data of the reference karst matter in the target groundwater dynamic impact elements.

[0101] In a possible embodiment, the matching collapse hidden danger points of the b-th configured risk level segment include: the first collapse hidden danger point of the target karst matter, and the second collapse hidden danger point of the reference karst matter in the target groundwater dynamic impact elements; the first collapse hidden danger point includes the collapse hidden danger points of the target karst matter in the b-th configured risk level segment and the collapse hidden danger points of the target karst matter in the historical configured risk level segments; the second collapse hidden danger point includes the collapse hidden danger points of the reference karst matter in the reference configured risk level segment; the reference configured risk level segment is determined by the b-th configured risk level segment and the set configured risk level value; the collapse hidden danger points include seepage information, deformation information, and fracture structure.

[0102] Specifically, the target groundwater dynamic impact elements are given fixed animation elements, belonging to a type of target groundwater dynamic impact, and here the target type of groundwater dynamic impact is any one of multiple types of groundwater dynamic impacts. The task of configuring the collapse intelligent analysis threads is to make the target karst matter with dynamic simulation in the groundwater dynamic impact simulation engine try to track the target groundwater dynamic impact elements as much as possible, and use the groundwater dynamic impact analysis algorithm to analyze whether the groundwater dynamic impact attributes generated by the target karst matter conform to the dynamic characteristics.

[0103] The groundwater dynamic impact on the reference karst matters among the target groundwater dynamic impact factors is the groundwater dynamic impact tracked by the target karst matters. Therefore, the reference karst matters also have potential collapse points, which are specifically represented by the following second potential collapse points. The first potential collapse point among the matching potential collapse points is the potential collapse point directly matching the target karst matters. The first potential collapse point includes not only the potential collapse points of the target karst matters in the current configured risk level segment, that is, the potential collapse points in the bth configured risk level segment, but also the potential collapse points in the historical risk level segments. The historical configured risk level segments are extracted based on the bth configured risk level segment. For example, the potential collapse points in the previous configured risk level segment (the (b - 1)th configured risk level segment) and the potential collapse points in the second previous configured risk level segment (the (b - 2)th configured risk level segment).

[0104] The second potential collapse point is the potential collapse point directly matching the reference karst matters. The potential collapse points of the reference karst matters in the reference configured risk level segment in the second potential collapse point include several future configured risk level segments determined based on the bth configured risk level segment. Among them, the set configured risk level values are, for example, numerical values such as 1, 2, 3, etc. For example, the next configured risk level segment (i.e., the (b + 1)th configured risk level segment) and the next two configured risk level segments (i.e., the (b + 2)th configured risk level segment) based on the current configured risk level segment. Whether it is the potential collapse point of the target karst matters or the potential collapse point of the karst matters in the target groundwater dynamic impact factors, it covers seepage information, deformation information, and fracture structure. Determining the potential collapse point in the target groundwater dynamic impact factors as a reference can enable the target karst matters to know the rotation angles of each joint in the next configured risk level segment based on the potential collapse points in the current configured risk level segment and the historical configured risk level segments, so as to better perform tracking configuration.

[0105] S502. Use the collapse intelligent analysis thread to process the matching potential collapse points in the bth configured risk level segment, obtain the groundwater dynamic impact analysis algorithm of the target karst matters in the (b + 1)th configured risk level segment, and determine the groundwater dynamic impact coefficient based on the matching potential collapse points in the bth configured risk level segment.

[0106] In the embodiment of the present application, the collapse intelligent analysis thread may include an algorithm network configured based on a deep reinforcement learning algorithm. The algorithm network may specifically be a basic neural network, such as a convolutional neural network, a residual neural network, a fully connected neural network, etc.

[0107] After identifying the matching data of the target karst matters in the bth configured risk level segment, the collapse intelligent analysis thread processes the matching potential collapse points in the bth configured risk level segment to obtain the groundwater dynamic impact analysis algorithm applied to the (b + 1)th configured risk level segment.

[0108] In one embodiment, the steps of processing the b-th configured risk level segment by using the collapse intelligent analysis thread may include: determining the expected groundwater dynamic influence and the groundwater dynamic influence function value of the target karst event according to the matching collapse hidden danger points of the target karst event in the b-th configured risk level segment and the collapse intelligent analysis thread; determining the distribution of the groundwater dynamic influence analysis algorithm of the target karst event in the (b + 1)-th configured risk level segment according to the groundwater dynamic influence function value and the expected groundwater dynamic influence; performing a screening process on the distribution of the groundwater dynamic influence analysis algorithm in the (b + 1)-th configured risk level segment to obtain the groundwater dynamic influence analysis algorithm of the target karst event in the (b + 1)-th configured risk level segment.

[0109] In one embodiment, the method for determining the groundwater dynamic influence coefficient according to the matching collapse hidden danger points in the b-th configured risk level segment may be: performing discrete processing on the collapse hidden danger points of the target karst event in the b-th configured risk level segment and the collapse hidden danger points of the reference karst event of the target groundwater dynamic influence factor in the b-th configured risk level segment to obtain discrete information; determining the discrete features corresponding to the discrete information; the discrete features include seepage information discrete features, deformation information discrete features, and fracture structure discrete features; performing weighted summation on the seepage information discrete features, deformation information discrete features, and fracture structure discrete features to obtain the groundwater dynamic influence coefficient of the target karst event.

[0110] Here, the calculation of the groundwater dynamic influence coefficient introduces the target groundwater dynamic influence factor. Based on the target groundwater dynamic influence factor, the trajectory error of the posture in the target karst event and the target groundwater dynamic influence factor can be converted into the groundwater dynamic influence coefficient. Specifically, referring to the collapse hidden danger points including seepage information, deformation information, and fracture structure introduced in the foregoing step S501, discrete calculation can be performed on the collapse hidden danger points of the target karst event and the reference karst event of the target groundwater dynamic influence factor. For example, subtracting the seepage information of the target karst event in the b-th configured risk level segment from the seepage information of the target groundwater dynamic influence factor in the b-th configured risk level segment, and the same for the corresponding deformation information and fracture structure, to obtain various corresponding discrete information. In this way, according to the discrete information, discrete features can be further determined, such as directly determining the discrete information as the discrete feature, or processing the discrete information.

[0111] S503, integrating the matching collapse hidden danger points in the b-th configured risk level segment, the groundwater dynamic influence analysis algorithm in the (b + 1)-th configured risk level segment, the groundwater dynamic influence coefficient, and the matching collapse hidden danger points in the (b + 1)-th configured risk level segment into a configuration example.

[0112] The matching collapse hazard points of the target karst event in the b-th configured risk level segment, the groundwater dynamic influence analysis algorithm, the groundwater dynamic influence coefficient, and the matching collapse hazard points of the (b + 1)-th configured risk level segment output by the collapse intelligent analysis thread can be integrated into a configuration example, which will be used later to configure the collapse intelligent analysis thread to optimize the thread parameters. Among them, the matching collapse hazard points of the (b + 1)-th configured risk level segment are identified after applying the groundwater dynamic influence analysis algorithm of the (b + 1)-th configured risk level segment to the target karst event.

[0113] Optionally, the configuration example further includes the current simulation risk level, which is the risk level used for integration in the dynamic simulation. Through the current simulation risk level, the sequence of data generation included in the catalog collapse hazard points can be determined.

[0114] S504, Send the configuration example to the data processing terminal so that the data processing terminal configures the collapse intelligent analysis thread stored in the data processing terminal according to the configuration example.

[0115] Identify the collapse hazard points generated after applying the groundwater dynamic influence analysis algorithm of the (b + 1)-th configured risk level segment to the target karst event to obtain the matching collapse hazard points of the target karst event in the (b + 1)-th configured risk level segment; if no thread parameters are received from the data processing terminal within the (b + 1)-th configured risk level segment, use the collapse intelligent analysis thread to process the matching collapse hazard points of the (b + 1)-th configured risk level segment to obtain the groundwater dynamic influence analysis algorithm of the target karst event in the (b + 2)-th configured risk level segment.

[0116] After obtaining the groundwater dynamic impact analysis algorithm for the (b + 1)-th configuration risk level segment, in the next immediately following configuration risk level segment, i.e., the (b + 1)-th configuration risk level segment, apply this groundwater dynamic impact analysis algorithm to obtain the matching collapse hazard points of the target karst event in the (b + 1)-th configuration risk level segment. It should be noted that this matching collapse hazard point is not the initial data after the target karst event applies the groundwater dynamic impact analysis algorithm, but is obtained after sorting and calculation. The specific process can refer to the process of obtaining the matching collapse hazard points in the b-th configuration risk level segment, which will not be elaborated here. After the b-th configuration risk level segment, the configuration example is sent to the data processing terminal. However, before inputting the matching collapse hazard points into the collapse intelligent analysis thread in the (b + 1)-th configuration risk level segment, the data sent by the data processing terminal has not been received yet, and there are no new thread parameters loaded into the collapse intelligent analysis thread for optimization. Then, the collapse intelligent analysis thread that processes the matching collapse hazard points in the b-th configuration risk level segment is still used to process the matching collapse hazard points in the (b + 1)-th configuration risk level segment. That is, the b-th configuration risk level segment and the (b + 1)-th configuration risk level segment use the same collapse intelligent analysis thread to calculate the new matching collapse hazard points, and obtain a new groundwater dynamic impact analysis algorithm, that is, the groundwater dynamic impact analysis algorithm applied to the (b + 2)-th configuration risk level segment. And based on the groundwater dynamic impact analysis algorithm in the (b + 2)-th configuration risk level segment, perform dynamic simulation calculations, that is, calculate the torque.

[0117] S505, receive the thread parameters of the configured collapse intelligent analysis thread sent by the data processing terminal, and optimize the collapse intelligent analysis thread according to the received thread parameters to obtain the target collapse intelligent analysis thread.

[0118] In a possible embodiment, the implementation steps of optimizing the collapse intelligent analysis thread according to the received thread parameters to obtain the target collapse intelligent analysis thread may include: updating the thread parameters of the collapse intelligent analysis thread according to the received thread parameters. The updated collapse intelligent analysis thread is used to generate a new configuration example, and the new configuration example is used to continue updating the thread parameters of the collapse intelligent analysis thread; when the updated collapse intelligent analysis thread meets the thread convergence requirement, determine the updated collapse intelligent analysis thread as the target collapse intelligent analysis thread.

[0119] After receiving the thread parameters sent by the data processing terminal, these thread parameters will be reloaded in the local subsidence intelligent analysis thread, that is, the original thread parameters will be replaced with the thread parameters sent by the data processing terminal to generate a new subsidence intelligent analysis thread. According to the new subsidence intelligent analysis thread, a groundwater dynamic impact analysis algorithm can be generated to identify the matching subsidence hazard points of the target karst event. Based on the matching subsidence hazard points, a configuration example is sorted out and sent to the data processing terminal. This process is repeated until the algorithm performance meeting the task requirements is obtained. Here, the task requirements can be that the groundwater dynamic impact coefficient reaches the reward threshold, and then the target subsidence intelligent analysis thread can be obtained. Since the target subsidence intelligent analysis thread is generated with reference to the target groundwater dynamic impact elements of the target groundwater dynamic impact type, it can well capture the groundwater dynamic impact characteristics of the target groundwater dynamic impact type, and then imitate the groundwater dynamic impact of this type to generate the corresponding groundwater dynamic impact attributes.

[0120] It should be noted here that according to the configuration steps of steps S501 - S505, several subsidence intelligent analysis threads can be configured. Each subsidence intelligent analysis thread belongs to a type of groundwater dynamic impact. When it is called, it can generate the subsidence intelligent analysis thread of this type of groundwater dynamic impact. This is caused by the different types of groundwater dynamic impact to which the groundwater dynamic impact elements referred to in the configuration belong. That is to say, when configuring the subsidence intelligent analysis thread by inputting the groundwater dynamic impact elements of a certain type of groundwater dynamic impact, the configured subsidence intelligent analysis thread belongs to this type of groundwater dynamic impact, and correspondingly, the groundwater dynamic impact attributes of this type of groundwater dynamic impact can be generated during the application stage.

[0121] On the above basis, a ground subsidence warning system for karst groundwater dynamic impact is shown, including a processor and a memory that communicate with each other. The processor is used to read and execute a computer program from the memory to implement the above method.

[0122] On the above basis, a computer-readable storage medium is also provided, and the computer program stored thereon implements the above method when running.

[0123] In summary, based on the above solution, by obtaining the analysis indication, the corresponding target collapse intelligent analysis thread can be automatically extracted from the collapse intelligent analysis thread library according to the type of target groundwater dynamic influence included in the analysis indication, and the groundwater dynamic influence analysis algorithm can be determined based on the target collapse intelligent analysis thread, and then the collapse warning data of the target karst event at all analysis risk levels can be generated. In the above solution, the target collapse intelligent analysis thread is intelligently matched according to the type of target groundwater dynamic influence. Since the target collapse intelligent analysis thread is configured based on the groundwater dynamic influence factors of the target groundwater dynamic influence type, and the groundwater dynamic influence characteristics of a specific groundwater dynamic influence type are accurately warned, the target collapse intelligent analysis thread can accurately generate the groundwater dynamic influence attributes of the target groundwater dynamic influence type, ensuring the accuracy and reliability of the warning.

[0124] It should be understood that the systems and their modules shown above can be implemented in various ways. For example, in some embodiments, the systems and their modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art can understand that the above methods and systems can be implemented using computer-executable instructions and / or included in processor control code, for example, such code is provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The systems and their modules of the present application can be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software implemented by various types of processors, or by a combination of the above hardware circuits and software (for example, firmware).

[0125] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced can be any one or several combinations of the above, or any other beneficial effects that may be obtained.

Claims

1. A ground collapse early warning method affected by karst groundwater dynamics, characterized in that: The method comprises: Obtaining analytical indications for target karst matters, the analytical indications including target groundwater dynamic impact categories; Extracting a target collapse intelligent analysis thread belonging to the target groundwater dynamics impact category from a collapse intelligent analysis thread library; the target collapse intelligent analysis thread is a collapse intelligent analysis thread configured according to a target groundwater dynamics impact element belonging to the target groundwater dynamics impact category; The target collapse intelligent analysis thread is used to process the collapse risk point of the target karst item at the ath analysis risk level, and obtain the groundwater dynamic impact analysis algorithm of the target karst item at the a+1th analysis risk level. The groundwater dynamic impact analysis algorithm is used to represent the groundwater dynamic impact of the target karst item, and a is an integer greater than 0; According to the groundwater dynamics impact analysis algorithm of the a+1th analysis risk level, the groundwater dynamics impact attributes of the target karst item at the a+1th analysis risk level and the collapse potential risk points of the target karst item at the a+1th analysis risk level are generated; Outputting collapse warning data of the target karst event at all analyzed risk levels; The target karst items include several target monitoring items; the groundwater dynamics impact analysis algorithm according to the a+1th analysis risk level generates the groundwater dynamics impact attributes of the target karst items at the a+1th analysis risk level, and the collapse risk points of the target karst items at the a+1th analysis risk level, including: Determine the hydrodynamic value of each target monitoring item in the target karst item according to the collapse potential risk point of the ath analysis risk level and the groundwater dynamic impact analysis algorithm of the a+1th analysis risk level; Analyzing each target monitoring item of the target karst item within the a+1th analysis risk level according to the hydrodynamic value to generate groundwater dynamic impact attributes of the target karst item within the b+1th configuration risk level segment; After the analysis of the target karst event is terminated, the collapse potential risk point of the target karst event at the a+1th analysis risk level is obtained.

2. The method according to claim 1, characterized in that The output of collapse warning data of the target karst event at all analyzed risk levels includes: Integrate all the collapse warning data of the analyzed risk levels into a risk level matrix according to the arrangement of the generated risk levels of the catalog from small to large, and store the risk level matrix in the database; the groundwater dynamic impact category of the risk level matrix belongs to the target groundwater dynamic impact category; When a hidden danger excavation instruction covering the target groundwater dynamic impact category is received, the risk level matrix is ​​extracted from the database for excavation in response to the hidden danger excavation instruction.

3. The method according to claim 1, characterized in that The output of collapse warning data of the target karst event at all analyzed risk levels includes: According to the set evaluation target value, the collapse warning data of the target karst event at all analysis risk levels are evaluated and processed respectively, and the key content identification unit is loaded into the groundwater dynamic impact attribute after the evaluation and processing to obtain the groundwater dynamic impact attribute to be excavated; the groundwater dynamic impact attribute to be excavated includes a plurality of element description contents; The plurality of element description contents are mined in the identification box, and each element description content in the plurality of element description contents is displayed after meeting a specified mining requirement; the specified mining requirement is determined according to the set evaluation target value.

4. The method according to claim 1, characterized in that: The step of obtaining the target karst event at the collapse potential risk point of the a+1th analysis risk level includes: Obtaining underground temperature information and state information of the target karst event; the underground temperature information is used to indicate the proposed underground temperature of the target karst event; the state information is used to indicate the seepage information and deformation information of the target karst event; The collapse potential point of the target karst event at the a+1th analysis risk level is generated according to the state information and the underground temperature information.

5. The method according to claim 1, characterized in that The matching collapse risk points of the bth configured risk level segment include: the first collapse risk point of the target karst event, and the second collapse risk point of the reference karst event in the target groundwater dynamics influencing factor; The first collapse risk point includes the collapse risk point of the target karst item in the bth configured risk level segment and the collapse risk point of the target karst item in the historical configured risk level segment; The second collapse risk point includes the collapse risk point of the reference karst item in the reference configuration risk level segment; the reference configuration risk level segment is determined by the bth configuration risk level segment and the set configuration risk level value; The potential collapse points include seepage information, deformation information and crack structure; Wherein, the method further comprises: Identify the collapse risk points generated after the groundwater dynamics impact analysis algorithm of the b+1th configured risk level segment is applied to the target karst event, and obtain the matching collapse risk points of the target karst event in the b+1th configured risk level segment; If the thread parameters sent by the data processing terminal are not received within the b+1th configured risk level segment, the collapse intelligent analysis thread is used to process the matching collapse risk points of the b+1th configured risk level segment to obtain the groundwater dynamics impact analysis algorithm of the target karst item in the b+2th configured risk level segment.

6. The method according to claim 1, characterized in that The matching collapse potential points of the b-th configured risk level segment are processed by using the collapse intelligent analysis thread to obtain the groundwater dynamics impact analysis algorithm of the target karst item in the b+1-th configured risk level segment, including: Determine the expected groundwater dynamic impact and groundwater dynamic impact function value of the target karst event according to the matching collapse potential risk point of the target karst event in the bth configured risk level segment and the collapse intelligent analysis thread; Determine the groundwater dynamic impact analysis algorithm distribution of the target karst item in the b+1th configuration risk level segment according to the groundwater dynamic impact function value and the groundwater dynamic impact expectation; Screening the distribution of the groundwater dynamics impact analysis algorithm in the b+1th configuration risk level segment to obtain the groundwater dynamics impact analysis algorithm of the target karst item in the b+1th configuration risk level segment; Wherein, the configuration example also includes an algorithm screening possibility, wherein the screening possibility is generated after screening the groundwater dynamic impact analysis algorithm distribution; The step of optimizing the collapse intelligent analysis thread according to the received thread parameters to obtain the target collapse intelligent analysis thread includes: updating the thread parameters of the collapse intelligent analysis thread according to the received thread parameters, the updated collapse intelligent analysis thread is used to generate a new configuration example, and the new configuration example is used to continue updating the thread parameters of the collapse intelligent analysis thread; When the updated collapse intelligent analysis thread meets the thread convergence requirement, the updated collapse intelligent analysis thread is determined as the target collapse intelligent analysis thread.

7. The method according to claim 5, characterized in that The groundwater dynamic influence coefficient is determined according to the matching collapse potential risk point of the bth configuration risk level segment, including: Discretization is performed on the collapse risk point of the target karst item in the bth configured risk level segment and the collapse risk point of the reference karst item of the target groundwater dynamic influencing factor in the bth configured risk level segment to obtain discrete information; Determine discrete features corresponding to the discrete information; the discrete features include discrete features of seepage information, discrete features of deformation information, and discrete features of fracture structure; The discrete characteristics of the seepage information, the discrete characteristics of the deformation information and the discrete characteristics of the fracture structure are weighted and summed to obtain the groundwater dynamic influence coefficient of the target karst event.

8. The method according to claim 1, characterized in that The method further comprises: A configuration example is obtained, wherein the configuration example includes a matching collapse risk point of the b-th configured risk level segment, a groundwater dynamics impact analysis algorithm of the b+1-th configured risk level segment, a groundwater dynamics impact coefficient, and a matching collapse risk point of the b+1-th configured risk level segment; wherein b is an integer greater than 0; the groundwater dynamics impact analysis algorithm of the b+1-th configured risk level segment is obtained by processing the matching collapse risk point of the b-th configured risk level segment using a collapse intelligent analysis thread; the groundwater dynamics impact coefficient is determined based on the matching collapse risk point of the b-th configured risk level segment; the matching collapse risk point of the b+1-th configured risk level segment is identified after the groundwater dynamics impact analysis algorithm of the b+1-th configured risk level segment is applied to the target karst event; Sending the configuration example to a data processing terminal so that the data processing terminal configures the collapsed intelligent analysis thread stored in the data processing terminal according to the configuration example; The configured thread parameters of the collapse intelligent analysis thread sent by the data processing terminal are received, and the collapse intelligent analysis thread is optimized according to the received thread parameters to obtain the target collapse intelligent analysis thread.

9. A ground collapse early warning system affected by karst groundwater dynamics, characterized in that: The invention comprises a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the method according to any one of claims 1 to 8.

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