Early warning method and system for surface collapse under dynamic influence of karst groundwater

Abstract

According to the surface collapse early warning method and system for karst groundwater dynamic influence provided by the invention, the analysis indication is obtained, and the corresponding target collapse intelligent analysis thread is automatically extracted from the collapse intelligent analysis thread library according to the target groundwater dynamic influence type included in the analysis indication; and determining an underground water dynamic influence analysis algorithm based on the target collapse intelligent analysis thread, and further generating collapse early warning data of the target karst matter in all analysis risk levels. A target collapse intelligent analysis thread is intelligently matched according to the target groundwater dynamic influence type, and the target collapse intelligent analysis thread is obtained based on groundwater dynamic influence factor configuration of the target groundwater dynamic influence type, so that accurate early warning is performed on groundwater dynamic influence characteristics of a specific groundwater dynamic influence type. Therefore, the target collapse intelligent analysis thread can accurately generate the underground water dynamic influence attribute of the target underground water dynamic influence type, and the early warning accuracy and reliability are ensured.

Description

Technical Field

[0001] The present application relates to the technical field of hidden danger early warning, and specifically, to a method and system for early warning of ground collapse influenced by karst groundwater dynamics. Background Art

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

[0003] At present, the research on the impact of groundwater on ground collapse has begun to take shape. There are still some defects in the specific analysis process. These defects lead to very inaccurate warning information when conducting collapse warning, which leads to great problems in governance or protection. Therefore, there is an urgent need for a way to improve ground collapse warning to improve the above technical problems. Summary of the invention

[0004] In order to improve the technical problems existing in the related technologies, the present application provides a method and system for early warning of ground collapse under the influence of karst groundwater dynamics.

[0005] In a first aspect, a method for early warning of ground collapse under the influence of karst groundwater dynamics is provided, the method comprising: 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; Output the collapse warning data of the target karst event at all analyzed risk levels.

[0006] In this application, the output of the 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.

[0007] It can be understood that, through a reliable risk level matrix, collapse warning data of the target karst event at all analyzed risk levels can be accurately output.

[0008] In this application, the output of the 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.

[0009] It can be understood that the accuracy of the collapse warning data outputted for the target karst event at all analyzed risk levels is guaranteed by the influence properties of the groundwater dynamics to be excavated.

[0010] In the present application, the target karst items include several target monitoring items; the groundwater dynamic impact analysis algorithm according to the a+1th analysis risk level generates the groundwater dynamic 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.

[0011] It can be understood that when using the groundwater dynamic impact analysis algorithm for the a+1th analysis risk level, the calculation data is optimized to reduce the errors generated in the calculation, so that the groundwater dynamic impact attributes of the target karst item at the a+1th analysis risk level and the collapse risk points of the target karst item at the a+1th analysis risk level can be accurately generated.

[0012] In the present application, the step of obtaining the target karst event at the collapse 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.

[0013] In the present application, the matching collapse risk points of the bth configuration 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.

[0014] It can be understood that, through the underground temperature information and state information, the collapse potential point of the target karst event at the a+1th analysis risk level can be accurately obtained.

[0015] In the present application, 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.

[0016] It can be understood that the performance of the groundwater dynamic impact analysis algorithm can be improved by matching the collapse risk points.

[0017] In this application, the matching collapse potential risk points of the b-th configuration risk level segment are processed using the collapse intelligent analysis thread to obtain the groundwater dynamic impact analysis algorithm of the target karst item in the b+1-th configuration 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; The configuration example also includes an algorithm screening possibility, which is generated after screening the groundwater dynamic impact analysis algorithm distribution.

[0018] It can be understood that when the collapse intelligent analysis thread is used to process the matching collapse potential risk points in the bth configuration risk level segment, the problem of inaccurate processing is improved, so that the groundwater dynamic impact analysis algorithm of the target karst item in the b+1th configuration risk level segment can be accurately obtained.

[0019] In the present application, 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.

[0020] It can be understood that when the collapse intelligent analysis thread is optimized according to the received thread parameters, the problem of inaccurate optimization is improved, so that when the target collapse intelligent analysis thread is obtained, the performance of the target collapse intelligent analysis thread is stronger.

[0021] In this application, the groundwater dynamic influence coefficient is determined according to the matching collapse risk point of the b-th 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.

[0022] It can be understood that, when matching the collapse potential risk points according to 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.

[0023] In the present application, 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.

[0024] It can be understood that a more high-performance target collapse intelligent analysis thread can be trained through multiple configuration examples.

[0025] In a second aspect, a ground collapse early warning system influenced by karst groundwater dynamics is provided, comprising a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute it to implement the above method.

[0026] The embodiment of the present application provides a method and system for warning ground collapse under the influence of karst groundwater dynamics. By obtaining an analysis indication, the corresponding target collapse intelligent analysis thread can be automatically extracted from the collapse intelligent analysis thread library according to the target groundwater dynamics influence type included in the analysis indication, and the groundwater dynamics influence analysis algorithm is determined based on the target collapse intelligent analysis thread, thereby generating collapse warning data for the target karst matter at all analysis risk levels. The above scheme intelligently matches the target collapse intelligent analysis thread according to the target groundwater dynamics influence type. Since the target collapse intelligent analysis thread is obtained based on the groundwater dynamics influence element configuration of the target groundwater dynamics influence type, the groundwater dynamics influence characteristics of a specific groundwater dynamics influence type are accurately warned. Therefore, the target collapse intelligent analysis thread can accurately generate the groundwater dynamics influence attributes of the target groundwater dynamics influence type, ensuring the accuracy and reliability of the warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 A flow chart of a ground collapse early warning method affected by karst groundwater dynamics provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to better understand the above technical scheme, the technical scheme of the present application is 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 scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0030] See also Figure 1 , shows a ground collapse early warning method affected by karst groundwater dynamics, which may include the technical solutions described in the following steps S201-S205.

[0031] S201, obtaining analysis instructions for target karst matters.

[0032] For example, the analysis instruction can be understood as an analysis command, which is a command directly issued by the data terminal.

[0033] The target karst event refers to the karst event that will affect the properties of groundwater dynamics. The target karst event can be a rock mass (especially a fractured rock mass) in a certain geological environment. The temperature, stress and groundwater flow of the rock mass are the three main factors that affect the geological environment. The three factors are interconnected, interact and restrict each other, forming a complex coupling effect between 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, thermal action can generate thermal stress in the rock mass and cause changes in the elastic modulus, Poisson's ratio and other solid properties of the rock mass. At the same time, the change in groundwater density caused by temperature change affects the flow of groundwater. The flow of groundwater can diffuse heat faster through conduction or convection, and the presence of groundwater in the rock mass can change the stress of the rock mass. Due to the influence of deformation, the rock mass will generate a certain amount of heat dissipation inside, and mechanical deformation will also partially affect the changes in solid thermal properties. At the same time, the mechanical effect has a relatively obvious influence on the porosity or the permeability of the fluid.

[0034] S202, extracting a target collapse intelligent analysis thread belonging to a target groundwater dynamic impact category from a collapse intelligent analysis thread library.

[0035] 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 a thread suitable for analyzing the current scenario, and the analysis of this thread is the most accurate one.

[0036] The collapse intelligent analysis thread library is used to store configured collapse intelligent analysis threads belonging to different types of groundwater dynamic influence. Each collapse intelligent analysis thread is configured using groundwater dynamic influence elements of a certain type of groundwater dynamic influence.

[0037] Among them, the collapse intelligent analysis thread specifically includes: the discrete fracture network model is based on the basic formula of water flow in a single fracture, and the equation is established by using the principle that the flow rate flowing into and out of each fracture intersection is equal, on the premise of clarifying the spatial orientation, gap width and other geometric parameters of each fracture. Then, the head value of each fracture intersection is obtained by solving the equation group. Wittke (1990) proposed a network line element method similar to the loop method in circuit analysis; Mao Changxi (1994) proposed a gap hydraulic network model similar to the pipe network problem in hydraulics; Wilson and Witherspoon (1974) simulated the fractures in the rock mass with triangular units or line units, and proposed two finite element techniques for simulating the water flow in a two-dimensional fracture network, and used examples to show that the water flow interference at the intersection of the fractures can be ignored, thus explaining the superiority and feasibility of using line units. For three-dimensional problems, Long JCS et al. (1985) first proposed a three-dimensional disk fracture network model and solved it using a hybrid analytical-numerical method; Nordqvist AW et al. (1992) also proposed a three-dimensional variable width fracture network model; Dershowitz (1985) proposed a three-dimensional polygonal fracture network model, and Wang Enzhi (1992) proposed a new fracture network numerical model based on the spatial distribution set function and numerical matrix of the fracture system, and proposed a corresponding solution to the problem of non-connected fracture networks. Wan Li et al. (1995) combined it with finite elements and further proposed a polygonal unit seepage model for three-dimensional fracture networks. The discrete fracture network model simulates each fracture in the rock mass fracture network system in detail and tries to obtain the real seepage state of each point in the fracture system. It obviously has the advantages of good simulation and high accuracy. However, when there are many cracks, the workload is quite large, especially for three-dimensional problems, which are difficult to achieve; in addition, due to the randomness of the crack distribution, it is also very difficult to establish a discrete real crack system. Therefore, except for simpler cases, the discrete fracture network model is currently difficult to be widely used in design engineering. In response to these problems, Wang Enzhi et al. (2001) divided the complex fracture system into three types: belt faults, surface cracks and tubular holes according to the development law of natural fracture systems and their permeability mechanisms. On the premise of ignoring the permeability of rock blocks, a three-dimensional fracture network seepage numerical model composed of tubular line units, slit surface units and belt units was established, and the rationality of the model was verified. This achievement has broadened the field of engineering application. Damage field-seepage field coupling model, based on Oda's (1986) parallel plate fluid mechanics equivalent model of two spring connections, Yang Yanyi et al. (1991) proposed a seepage-damage coupling analysis model for fractured rock mass. The fracture connectivity rate is analyzed by statistical analysis of on-site fractures and combined with computer analysis.The fracture system is equivalent to a continuous medium with anisotropic permeability and according to the self-consistent theory of microscopic damage mechanics, the permeability tensor and elastic flexibility tensor of the fractured rock mass are uniformly expressed by the geometric parameters of the fracture: normal vector, average opening, density, and radius. The damage fracture mechanics model is used to describe the damage evolution of various geometric parameters of the fracture under different stress states, so as to obtain the evolution equations of the permeability tensor and the elastic flexibility tensor. This model couples the seepage field and stress field of the 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 fluid diffusion energy superposition, an analytical expression of the seepage tensor of the fractured rock mass medium with unsteady seepage was established. Based on the Betti energy reciprocity quantification, Zheng Shaohe (1999) systematically studied the rock mass damage evolution mechanism under the combined action of stress field and seepage field, and established a three-dimensional water-containing fractured rock mass brittle elastic and elastoplastic fracture damage constitutive model and damage evolution equation considering the seepage pressure. Dual-medium fluid-solid coupling model: The dual-medium model regards the fractured rock mass as a superposition of a pore system and a fracture system. The fracture system serves as a channel for fluid seepage, while the rock system with good porosity stores fluid. Both systems are distributed throughout the rock mass. This theory requires the establishment of a seepage model in the fracture system and a seepage model in the porous medium, which are connected by flow exchange to form a coupled equation. Barenblatt (1960) first proposed the fracture-pore dual medium model. His main viewpoints are: (1) There are intersecting fracture systems and pore systems with poor conductivity but distributed throughout 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 rock block pore system are distributed throughout the entire area, forming two overlapping continua. Each point in the seepage field has two head values: the head value in the pore system and the head value in the fracture system; (3) The amount of water exchanged between pores and fractures is proportional to the head difference between them; (4) It is assumed that fractures and porous 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. The assumptions are: (1) A homogeneous, orthogonal, interconnected fracture system is developed in the rock mass, and the degree of anisotropy of the fractures is described by the spacing and width between the fractures; (2) The pore system contained in each rock block divided by the fractures is homogeneous and isotropic; (3) Water volume alternates between the two different systems, but the fluid can only flow to the well through the fractures, while the flow to the well in the pore system is ignored. Wang Enzhi (1998) developed a dual medium model based on the development law of the rock fracture system and its seepage characteristics, and proposed a dual fracture system seepage model.The model transforms the fracture network seepage theoretical model into a practical fracture rock seepage model that can describe the seepage mechanism of the primary and secondary fracture systems in detail, providing a new method for solving the fracture seepage analysis of rock mass in engineering. In addition, Wang Enzhi (2001) also proposed models such as "pipes instead of holes" and "fractures instead of wells" for seepage problems in water conservancy and hydropower projects, expanding the practicality of the model. The outstanding advantage of the dual medium model is that it considers the water alternation process between two different systems, but all these models have certain restrictions on the configuration and shape of the fracture system, which limits 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 viewpoint is: (1) It is believed that fractured rock mass consists of three systems: discrete main fracture network, branch fracture network and equivalent rock matrix. The main fracture network is mainly composed of geological structures (including faults) with large spatial distribution; the branch fracture network is composed of fractures or faults with smaller scale than the main fracture network; the rock matrix is ​​composed of smaller fractures and rock blocks, which are treated with equivalent porous continuous media; (2) In the main fracture network, the fluid movement along the fracture surface distribution direction is non-Darcy flow, and 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 exchange fluid conforms to Darcy's law. The movement law of the fluid in the branch fracture network and the rock matrix conforms to Darcy's law; (3) It is considered that the pore pressure is a continuous function of the coordinates of the spatial point and time, and the seepage continuity equations of the three systems and their mutual hydraulic connection equations are established respectively, and solved simultaneously. The advantage is that the fluid movement in the fracture surface distribution direction is considered to be the characteristic of non-Darcy flow, and the disadvantage is that the solution of the simultaneous equations is relatively complicated and the amount of calculation is large. Chai Junrui et al. (2000) also proposed a multi-fracture network seepage model, which divides 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. Finally, the four-level network is merged into two seepage systems: the connected fracture network and the equivalent continuous medium. The two seepage systems exchange water in the fracture walls of the connected fracture network, and the iterative method is used for joint solution. It can be seen that the equivalent-discrete coupling model takes into account the scale and density of the fractures, and divides the seepage area medium into two major systems: the discrete fracture network system and the equivalent continuous medium system, avoiding the huge workload caused by the discrete fracture network model to simulate each fracture, and ensuring the effectiveness of the equivalent continuous medium model. However, due to the existence of two types of systems, it is necessary to establish their own seepage continuity equations respectively, and consider the flow exchange between the two types of systems to solve the simultaneous equations, which makes the establishment and solution of the equations more complicated. When studying practical problems, multiple models or combined models can be adopted. The choice of model should be combined with the content of the study and the environmental medium in which it is located.

[0038] It can be seen that there is a projection relationship between the collapse intelligent analysis threads and the groundwater dynamic impact types stored in the collapse intelligent analysis thread library. Therefore, according to the target groundwater dynamic impact type included in the analysis indication for the target karst matter, a 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 element 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 indication, and among all the groundwater dynamic impact types included in the collapse intelligent analysis thread library, it is the groundwater dynamic impact type that matches the input target groundwater dynamic impact type.

[0039] Here, the configured collapse intelligent analysis thread is matched with the groundwater dynamic impact type. Although several collapse intelligent analysis threads are configured through the groundwater dynamic impact elements of different groundwater dynamic impact types in the configuration stage, rather than generalizing a collapse intelligent analysis thread for all groundwater dynamic impact types, in the application stage of the collapse intelligent analysis thread, it is not necessary to input the target groundwater dynamic impact element for processing. Instead, the collapse intelligent analysis thread is directly matched with the groundwater dynamic impact type, and the target analysis algorithm of the target groundwater dynamic impact type is used to obtain the groundwater dynamic impact attribute, thereby effectively reducing the data processing amount of the target collapse intelligent analysis thread.

[0040] S203, using the target collapse intelligent analysis thread, the collapse risk point of the target karst item at the ath analysis risk level is processed to obtain the groundwater dynamic impact analysis algorithm of the target karst item at the a+1th analysis risk level, where a is an integer greater than 0.

[0041] Exemplarily, the potential collapse points are places where collapse may occur and are numerically processed and divided into 0-10, where 0 means that collapse is impossible and 10 means that collapse is bound to occur.

[0042] The ath analysis risk level belongs to the current analysis risk level of the target karst event, and the analysis risk level can characterize the risk level of the simulated groundwater dynamic impact of the target karst event.

[0043] In a possible implementation embodiment, the initial collapse risk point information of the target karst item in the ath risk level segment can be identified. The initial collapse risk point information is the most initial data of the target karst item under the current analysis risk level. Then, based on the initial collapse risk point information, the collapse risk point at the current analysis risk level can be determined, and the collapse risk point can be organized into a data format that conforms to the target collapse intelligent analysis thread processing.

[0044] Optionally, the initial collapse potential risk point information may include information such as the current collapse potential risk point, underground temperature parameters, and the current simulated risk level.

[0045] Optionally, the target karst item includes several collapse risk points of the ath analysis risk level in the collapse risk point, and the collapse risk point can be characterized by fracture structure, seepage information, and deformation information, and is a multi-dimensional vector.

[0046] In another embodiment, the initial collapse risk point information may also include groundwater dynamic influence factors of the target groundwater dynamic influence type, and 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 risk points of the target karst items at each analysis risk level.

[0047] That is to say, the collapse potential risk points of the ath analysis risk level may include fracture structure, seepage information, and deformation information.

[0048] The groundwater dynamics impact analysis algorithm is used to represent the groundwater dynamics impact of the target karst project execution.

[0049] S204, based on the groundwater dynamics impact analysis algorithm of the a+1th analysis risk level, generate the groundwater dynamics impact attributes of the target karst item at the a+1th analysis risk level, and the collapse risk points of the target karst item at the a+1th analysis risk level.

[0050] The groundwater dynamic impact analysis algorithm of the a+1th analysis risk level is executed after the ath analysis risk level is terminated, and the data generated by the groundwater dynamic impact of the target karst event acts on the a+1th analysis risk level. That is, after the ath analysis risk level is terminated, the target karst event is analyzed in the a+1th analysis risk level according to the groundwater dynamic impact analysis algorithm generated by the collapse risk point of the ath analysis risk level, and the corresponding groundwater dynamic impact attribute can be generated. At this point, the target karst event completes the groundwater dynamic impact according to the groundwater dynamic impact analysis algorithm in the a+1th analysis risk level, and the deformation information and seepage information of the target karst event are changed, that is, the collapse risk point of the target karst event changes, so that the target karst event generates a new collapse risk point, that is, the collapse risk point of the a+1th analysis risk level.

[0051] In one embodiment, the specific implementation of step S204 may be as follows: 1) Determine the hydrodynamic value of each target monitoring item in the target karst item based on the collapse risk point of the ath analysis risk level and the groundwater dynamic impact analysis algorithm of the a+1th analysis risk level.

[0052] 2) Based on the hydrodynamic values, each target monitoring item of the target karst item is analyzed within the a+1th analysis risk level to generate the groundwater dynamic impact attributes of the target karst item in the b+1th configuration risk level segment.

[0053] According to the calculated torque, each rotatable joint can rotate according to the corresponding torque, and the rotation of each rotatable joint constitutes the analysis of the target karst event within the a+1th analysis risk level, thereby generating the groundwater dynamic impact attribute. Therefore, after the analysis of the target karst event is terminated, a new collapse risk point of the target karst event is generated and determined as the collapse risk point of the target karst event at the a+1th analysis risk level.

[0054] 3) After the analysis of the target karst event is terminated, the collapse potential point of the target karst event at the a+1th analysis risk level is obtained.

[0055] In one embodiment, the method of obtaining the collapse risk point of the target karst item at the a+1th analysis risk level includes: obtaining the underground temperature information and state information of the target karst item; generating the collapse risk point of the target karst item at the a+1th analysis risk level according to the state information and the underground temperature information. Among them, the underground temperature information is used to represent the proposed underground temperature of the target karst item; the state information is used to represent the seepage information and deformation information of the target karst item. The target karst item is analyzed according to the groundwater dynamic impact attribute generated by the groundwater dynamic impact analysis algorithm.

[0056] S205, outputting collapse warning data of the target karst event at all analyzed risk levels.

[0057] After the collapse risk point of the next analysis risk level is obtained from the collapse risk point of the current analysis risk level according to the target karst item, step S204 is repeated to generate collapse warning data of all analysis risk levels.

[0058] In a possible implementation example, an optional implementation of step S205 may be: The collapse warning data of all analyzed risk levels are arranged from small to large according to the risk level generated in the catalog, and integrated into a risk level matrix, and the risk level matrix is ​​stored in the database; the groundwater dynamic impact type of the risk level matrix belongs to the target groundwater dynamic impact type; when a hidden danger excavation instruction covering the target groundwater dynamic impact type is received, the risk level matrix is ​​extracted from the database for excavation in response to the hidden danger excavation instruction.

[0059] The corresponding directory of the groundwater dynamic impact attribute of each analysis risk level has a generated risk level. The generated risk level can sort the collapse warning data of all analysis risk levels according to the generated risk level of the directory to form a groundwater dynamic impact matrix with a sequential arrangement method. The groundwater dynamic impact matrix can be the groundwater dynamic impact of a continuous groundwater dynamic impact space. Afterwards, the risk level matrix can be stored in the database and marked as the target groundwater dynamic impact type, so that when a hidden danger excavation instruction covering the target groundwater dynamic impact type is received, the risk level matrix of the target groundwater dynamic impact type is extracted from the database in response to the hidden danger excavation instruction for excavation.

[0060] The risk level matrices processed by different target collapse intelligent analysis threads can be stored in the database. Each risk level matrix corresponds to a type of groundwater dynamic impact. Optionally, all risk level matrices in the database are risk level matrices generated by different target collapse intelligent analysis threads. When the user initiates a hidden danger excavation instruction, the risk level matrix corresponding to the type of groundwater dynamic impact included in the hidden danger excavation instruction can be extracted from the database for excavation.

[0061] In an alternative embodiment, the groundwater dynamic impact matrix corresponding to the target karst event is automatically generated by the target collapse intelligent analysis thread and stored in a database, and matched to the groundwater dynamic impact matrix of the corresponding groundwater dynamic impact type when hidden danger excavation is indicated. This makes it convenient to use the groundwater dynamic impact attributes to quickly and accurately find the groundwater dynamic impact matrix of the target groundwater dynamic impact type from the database for excavation.

[0062] In another embodiment, an optional implementation of step S205 may also be: According to the set assessment target value, the collapse warning data of the target karst item at all analyzed 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 number of element description contents; a number of element description contents are excavated in the identification box, and each element description content in the number of element description contents is displayed after meeting the specified excavation requirements; the specified excavation requirements are determined according to the set assessment target value.

[0063] In a possible implementation example, after the collapse warning data of all analyzed risk levels are generated, the target karst items can be evaluated for groundwater dynamic impact properties and directly mined out in the identification frame after skinning.

[0064] The solution provided in the embodiment of the present application can automatically and intelligently extract the corresponding target collapse intelligent analysis thread from the collapse intelligent analysis thread library according to the target groundwater dynamic impact type included in the analysis indication by obtaining the analysis indication. Since the target collapse intelligent analysis thread is obtained based on the groundwater dynamic impact element configuration of a groundwater dynamic impact type, the groundwater dynamic impact characteristics of a specific groundwater dynamic impact type are accurately warned. Therefore, the target collapse intelligent analysis thread can accurately generate the groundwater dynamic impact attributes of the target groundwater dynamic impact type.

[0065] S501, obtaining the matching collapse potential risk point of the target karst item in the bth configured risk level segment; wherein b is an integer greater than 0.

[0066] The bth configured risk level segment is the risk level segment currently being configured. The matching collapse risk points of the bth configured risk level segment may include the collapse risk points of the previous or several historical configured risk level segments. It is understandable that if it is the first configured risk level segment, then the matching collapse risk points only include the collapse risk points of the target karst item of the first configured risk level segment. These matching collapse risk points are used to configure the groundwater dynamic impact analysis algorithm of the target karst item, and may include the data of the target karst item itself and / or the data of the reference karst item in the target groundwater dynamic impact factor.

[0067] In a possible implementation embodiment, the matching collapse risk points of the bth configured risk level segment include: the first collapse risk point of the target karst item, and the second collapse risk point of the reference karst item in the target groundwater dynamic 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 configured risk level segment; the reference configured risk level segment is determined by the bth configured risk level segment and the set configured risk level value; the collapse risk points include seepage information, deformation information and fracture structure.

[0068] Specifically, the target groundwater dynamics impact element is a given fixed animation element, which belongs to a target groundwater dynamics impact type, and the target groundwater dynamics impact type here is any one of multiple groundwater dynamics impact types. The task of the collapse intelligent analysis thread configuration is to make the target karst event with dynamic simulation in the groundwater dynamics impact simulation engine track the target groundwater dynamics impact element as much as possible, and use the groundwater dynamics impact analysis algorithm to analyze whether the groundwater dynamics impact attribute generated by the target karst event is consistent with the dynamic characteristics.

[0069] The groundwater dynamic impact made by the reference karst item in the target groundwater dynamic impact factor is the groundwater dynamic impact tracked by the target karst item. Therefore, the reference karst item also has a collapse risk point, which is specifically represented by the second collapse risk point described below. The first collapse risk point in the matched collapse risk point is a collapse risk point that directly matches the target karst item. The first collapse risk point includes not only the collapse risk point of the target karst item in the current configured risk level segment, that is, the collapse risk point of the bth configured risk level segment, but also the collapse risk point of the historical risk level segment. The historical configured risk level segment is extracted based on the bth configured risk level segment, such as the collapse risk point of the previous configured risk level segment (b-1th configured risk level segment) and the collapse risk point of the previous second configured risk level segment (b-2th configured risk level segment).

[0070] The second collapse risk point is a collapse risk point that directly matches the reference karst event. The collapse risk point of the reference karst event in the reference configuration risk level segment in the second collapse risk point includes several future configuration risk level segments determined based on the bth configuration risk level segment. Among them, the configuration risk level value is set to be, for example, 1, 2, 3, etc. For example, the next configuration risk level segment (i.e., the b+1th configuration risk level segment) based on the current configuration risk level segment, the next two configuration risk level segments (i.e., the b+2th configuration risk level segment), etc. Whether it is the collapse risk point of the target karst event or the collapse risk point of the karst event in the target groundwater dynamics influencing element, it covers seepage information, deformation information, and fracture structure. Determining the collapse risk point in the target groundwater dynamics influencing element as a reference can enable the target karst event to know the rotation angle of each joint of the next configuration risk level segment based on the collapse risk point of the current configuration risk level segment and the historical configuration risk level segment, so as to better track and configure.

[0071] S502, using the collapse intelligent analysis thread to process the matching collapse risk points of the b-th configured risk level segment, obtain the groundwater dynamics impact analysis algorithm of the target karst item in the b+1-th configured risk level segment, and determine the groundwater dynamics impact coefficient based on the matching collapse risk points of the b-th configured risk level segment.

[0072] In an embodiment of the present application, the collapse intelligent analysis thread may include an algorithm network configured based on a deep reinforcement learning algorithm, and 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, and the like.

[0073] After identifying the matching data of the target karst item in the bth configured risk level segment, the collapse intelligent analysis thread processes the matching collapse potential points of the bth configured risk level segment to obtain the groundwater dynamic impact analysis algorithm applied to the b+1th configured risk level segment.

[0074] In one embodiment, the step of using the collapse intelligent analysis thread to process the bth configuration risk level segment may include: determining the expected groundwater dynamic impact and the groundwater dynamic impact function value of the target karst item based on the matching collapse risk point of the target karst item in the bth configuration risk level segment and the collapse intelligent analysis thread; determining the distribution of the groundwater dynamic impact analysis algorithm of the target karst item in the b+1th configuration risk level segment based on the groundwater dynamic impact function value and the expected groundwater dynamic impact; screening and processing the distribution of the groundwater dynamic impact analysis algorithm in the b+1th configuration risk level segment to obtain the groundwater dynamic impact analysis algorithm of the target karst item in the b+1th configuration risk level segment.

[0075] In one embodiment, the method for determining the groundwater dynamic influence coefficient based on the matching collapse risk point of the bth configured risk level segment can be: discretizing 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 influence factor in the bth configured risk level segment to obtain discrete information; determining the 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; weightedly summing the discrete features of seepage information, discrete features of deformation information and discrete features of fracture structure to obtain the groundwater dynamic influence coefficient of the target karst item.

[0076] The calculation of the groundwater dynamic influence coefficient here 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 matter and the target groundwater dynamic influence factor can be converted into the groundwater dynamic influence coefficient. Specifically, referring to the collapse risk point introduced in the aforementioned step S501, including seepage information, deformation information and fracture structure, the collapse risk point of the reference karst matter in the target karst matter and the target groundwater dynamic influence factor can be discretely calculated, for example, the seepage information of the target karst matter in the bth configuration risk level segment and the seepage information of the target groundwater dynamic influence factor in the bth configuration risk level segment are subtracted, and the corresponding deformation information and fracture structure are similar, and various corresponding discrete information are obtained. In this way, according to the discrete information, discrete features can be further determined, for example, the discrete information can be directly determined as discrete features, or the discrete information can be processed.

[0077] S503, integrating the matching collapse potential risk points of the b-th configuration risk level segment, the groundwater dynamic impact analysis algorithm of the b+1-th configuration risk level segment, the groundwater dynamic impact coefficient and the matching collapse potential risk points of the b+1-th configuration risk level segment into a configuration example.

[0078] The matching collapse risk points of the target karst event in the bth configured risk level segment, the groundwater dynamic impact analysis algorithm of the b+1th configured risk level segment output by the collapse intelligent analysis thread, the groundwater dynamic impact coefficient, and the matching collapse risk points of the b+1th configured risk level segment can be integrated into a configuration example, which will be used to configure the collapse intelligent analysis thread later to optimize the thread parameters. Among them, the matching collapse risk point of the b+1th configured risk level segment is identified after the groundwater dynamic impact analysis algorithm of the b+1th configured risk level segment is applied to the target karst event.

[0079] Optionally, the configuration example also includes a current simulation risk level, which is a risk level used for integration in dynamics simulation. The current simulation risk level can be used to list the order in which data included in the potential collapse point are generated.

[0080] S504: Send the configuration example to the 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.

[0081] 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 are identified to 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 matching collapse risk points of the b+1th configured risk level segment are processed using the collapse intelligent analysis thread to obtain the groundwater dynamics impact analysis algorithm of the target karst event in the b+2th configured risk level segment.

[0082] After obtaining the groundwater dynamic impact analysis algorithm for the b+1th configuration risk level segment, the groundwater dynamic impact analysis algorithm is applied to the next configuration risk level segment, i.e., the b+1th configuration risk level segment, to obtain the matching collapse risk point of the target karst item in the b+1th configuration risk level segment. It should be noted that this matching collapse risk point is not the initial data after the groundwater dynamic impact analysis algorithm is applied to the target karst item, but is obtained after sorting and calculation. The specific process can be referred to the process of obtaining the matching collapse risk point of the bth configuration risk level segment, which will not be repeated here. After the bth configuration risk level segment, the configuration example is sent to the data processing terminal, but before the matching collapse risk point is input into the collapse intelligent analysis thread in the b+1th configuration risk level segment, the data sent by the data processing terminal has not been received, and the collapse intelligent analysis thread has no new thread parameters loaded for optimization. Then, the collapse intelligent analysis thread that processes the matching collapse risk point of the bth configuration risk level segment is still used to process the matching collapse risk point of the b+1th configuration risk level segment. That is, the bth configuration risk level segment and the b+1th configuration risk level segment use the same collapse intelligent analysis thread to calculate the new matching collapse potential risk point, and obtain a new groundwater dynamic impact analysis algorithm, that is, the groundwater dynamic impact analysis algorithm applied to the b+2th configuration risk level segment. And based on the groundwater dynamic impact analysis algorithm in the b+2th configuration risk level segment, a dynamic simulation calculation is performed, that is, the torque is calculated.

[0083] S505, receiving the configured thread parameters of the collapsed intelligent analysis thread sent by the data processing terminal, and optimizing the collapsed intelligent analysis thread according to the received thread parameters to obtain a target collapsed intelligent analysis thread.

[0084] In a possible implementation embodiment, the 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 to update the thread parameters of the collapse intelligent analysis thread; when the updated collapse intelligent analysis thread meets the thread convergence requirements, the updated collapse intelligent analysis thread is determined as the target collapse intelligent analysis thread.

[0085] After receiving the thread parameters sent by the data processing terminal, these thread parameters will be reloaded in the local collapse intelligent analysis thread, that is, the original thread parameters will be replaced with the thread parameters sent by the data processing terminal, and a new collapse intelligent analysis thread will be generated. According to the new collapse intelligent analysis thread, a groundwater dynamic impact analysis algorithm can be generated to identify the matching collapse risk points of the target karst event, and the configuration examples will be sorted out according to the matching collapse risk points and sent to the data processing terminal. This cycle is repeated until the algorithm performance that meets the task requirements is obtained. The task requirement here can be that the groundwater dynamic impact coefficient reaches the reward threshold, and the target collapse intelligent analysis thread can be obtained. Since the target collapse intelligent analysis thread is generated by referring to the target groundwater dynamic impact element 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 the groundwater dynamic impact type to generate the corresponding groundwater dynamic impact attributes.

[0086] It should be noted that, according to the configuration steps of steps S501-S505, several collapse intelligent analysis threads can be configured, each collapse intelligent analysis thread belongs to a groundwater dynamic impact type, and can generate collapse intelligent analysis threads of this groundwater dynamic impact type when called, and this is caused by the difference in groundwater dynamic impact types to which the groundwater dynamic impact elements referenced by the configuration belong. In other words, when configuring, the groundwater dynamic impact elements of a groundwater dynamic impact type are input to configure the collapse intelligent analysis thread, and the collapse intelligent analysis thread obtained by the configuration belongs to this groundwater dynamic impact type, and the groundwater dynamic impact attributes of this groundwater dynamic impact type can be generated in the application stage.

[0087] On the basis of the above, a ground collapse early warning system influenced by karst groundwater dynamics is shown, comprising a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute it to implement the above method.

[0088] Based on the above, a computer-readable storage medium is also provided, on which a computer program stored implements the above method when running.

[0089] In summary, based on the above scheme, 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 target groundwater dynamic impact type included in the analysis indication, and the groundwater dynamic impact analysis algorithm can be determined based on the target collapse intelligent analysis thread, thereby generating collapse warning data for the target karst matter at all analysis risk levels. The above scheme intelligently matches the target collapse intelligent analysis thread according to the target groundwater dynamic impact type. Since the target collapse intelligent analysis thread is obtained based on the groundwater dynamic impact element configuration of the target groundwater dynamic impact type, the groundwater dynamic impact characteristics of a specific groundwater dynamic impact type are accurately warned. 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 warning.

[0090] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its 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 a dedicated design hardware. Those skilled in the art will understand that the above methods and systems can be implemented using computer executable instructions and / or included in a processor control code, such as 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. Such code is provided on the 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 system and its modules of the present application can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software such as executed by various types of processors, and can also be implemented by a combination of the above hardware circuits and software (e.g., firmware).

[0091] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination 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; Output the collapse warning data of the target karst event at all analyzed risk levels.

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 any one of claims 1 to 3, characterized in that: 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 potential 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.

5. The method according to claim 4, 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.

6. 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.

7. 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.

8. The method according to claim 6, 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.

9. 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.

10. 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 9.

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