Geological disaster multi-source monitoring data intelligent analysis method and system

Abstract

The present application is suitable for the technical field of geological disaster monitoring and risk assessment, and provides a geological disaster multi-source monitoring data intelligent analysis method and system, the method comprises: when there is an evolution characteristic, a correction factor is generated according to the difference between the underground water dynamic strength index corresponding to the turning point and the underground water dynamic strength index of the target slope unit; the geological disaster risk assessment result generated for the target slope unit is obtained, and the geological disaster risk assessment result is corrected based on the correction factor. The present application breaks through the limitation of the prior art which only judges based on single blasting compliance or the current monitoring state by introducing a high-frequency blasting working condition recognition mechanism and combining the collaborative evolution analysis of the underground water dynamic strength index, the soil layer compressibility parameter and the maintenance strength index.

Description

Technical Field

[0001] This invention relates to the field of geological disaster monitoring and risk assessment technology, and in particular to an intelligent analysis method and system for multi-source monitoring data of geological disasters. Background Technology

[0002] In current engineering practice, geological hazard risk assessment is typically based on the geological conditions, geometric morphology, engineering disturbance information, and multi-source monitoring data of the slope unit. This assessment is used to determine the stability of the slope under given working conditions and to provide a basis for prevention and control decisions. In slope engineering involving blasting operations, related technologies often focus on controlling the parameters of a single blast and real-time monitoring of the blasting process. As long as indicators such as blasting vibration and displacement are within the allowable range specified in the standards, it is generally believed that this type of blasting operation will not have an adverse impact on slope stability.

[0003] However, in actual engineering projects, blasting operations are often carried out in multiple, phased phases. Although each blast is within a reasonable control range, its cumulative disturbance effect can still have long-term impacts on groundwater-bearing soil and underground engineering structures. Existing technologies do not pay sufficient attention to this type of cumulative effect, making it difficult to effectively identify it before obvious anomalies appear in slope units. Furthermore, the differences in maintenance needs generated by underground engineering structures during long-term operation are often regarded as operational management factors and are not systematically incorporated into the geological hazard risk assessment system, resulting in risk assessment results that fail to reflect the potential risk evolution trends in later operational phases.

[0004] Therefore, there is an urgent need for a geological hazard risk analysis method that can comprehensively consider the differences in high-frequency blasting conditions, groundwater dynamics, and underground engineering structure maintenance requirements, in order to improve the foresight and engineering applicability of risk assessment results. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent analysis method and system for multi-source monitoring data of geological disasters, aiming to solve the problems mentioned in the background art.

[0006] This invention is implemented as follows: an intelligent analysis method for multi-source monitoring data of geological disasters, the method comprising: When it is identified that the target slope unit is under high-frequency blasting conditions and there are underground engineering structures distributed within the target slope unit, the historical engineering database is retrieved, and several samples that are consistent with the target slope unit in terms of geological conditions and blasting conditions, but have different groundwater dynamic intensity indicators are selected. Calculate the maintenance strength index and soil compressibility parameters for each sample; Determine whether the following evolutionary characteristics exist in the sample: as the groundwater dynamic intensity index increases, the soil compressibility parameter and the maintenance strength index both show an evolutionary trend from a stable state to an enhanced state, and the difference between the groundwater dynamic intensity index corresponding to the inflection point of the two evolutionary trends is within the preset range. When evolutionary characteristics exist, a correction factor is generated based on the difference between the groundwater dynamic intensity index corresponding to the inflection point and the groundwater dynamic intensity index of the target slope unit. Obtain the geological hazard risk assessment results generated for the target slope unit, and revise the geological hazard risk assessment results based on the correction factor.

[0007] As a further limitation of the technical solution of the present invention, the high-frequency blasting condition refers to a number of blasting operations occurring within a preset time window. For each blasting operation, the blasting disturbance intensity value is determined, and the blasting disturbance intensity values ​​of each blasting operation within the preset time window are accumulated or weighted to obtain a cumulative blasting disturbance intensity value. When the cumulative blasting disturbance intensity value exceeds a preset threshold, it is determined to be a high-frequency blasting condition.

[0008] As a further limitation of the technical solution of the present invention, the geological conditions being consistent with the target slope unit specifically means that the slope unit corresponding to the sample and the target slope unit are the same or within a preset error range in terms of stratigraphic structure type, distribution of rock and soil physical and mechanical parameters, and underground engineering structure type and spatial distribution characteristics.

[0009] As a further limitation of the technical solution of the present invention, the calculation process of the groundwater dynamic intensity index includes: obtaining groundwater level change data, pore water pressure change data and groundwater seepage data corresponding to the slope unit, calculating the groundwater level change amplitude index, pore water pressure change rate index and seepage intensity index respectively, and weighting and combining the indexes to obtain the groundwater dynamic intensity index.

[0010] As a further limitation of the technical solution of the present invention, the process of obtaining the maintenance intensity index includes: parsing the sample, extracting the underground engineering structure maintenance frequency data, maintenance operation type data and maintenance input data, and calculating the maintenance intensity index based on the above three types of data.

[0011] As a further limitation of the technical solution of the present invention, the process of obtaining the soil compressibility parameter includes: obtaining soil settlement monitoring data of the slope area of ​​the sample, determining the effective stress change data of the soil corresponding to the underground engineering structure based on the soil settlement monitoring data, and then calculating the compressibility parameter characterizing the compressibility properties of the soil layer based on the effective stress change data.

[0012] As a further limitation of the technical solution of this invention, when evolutionary characteristics exist, the step of generating a correction factor based on the difference between the groundwater dynamic intensity index corresponding to the turning point and the groundwater dynamic intensity index of the target slope unit includes: When the evolutionary characteristics are confirmed, the groundwater dynamic intensity index corresponding to the target slope unit is calculated, and it is determined whether it is greater than the average groundwater dynamic intensity index corresponding to the turning point of the two evolutionary trends. When the groundwater dynamic intensity index corresponding to the target slope unit is not greater than the average value, the geological hazard risk assessment result remains unchanged. When the groundwater dynamic intensity index corresponding to the target slope unit is greater than the average value, a correction factor is generated based on the proportion by which the groundwater dynamic intensity index exceeds the average value.

[0013] As a further limitation of the technical solution of this embodiment of the invention, when using a correction factor to correct the geological disaster risk assessment results, a preset correction model is retrieved, wherein the correction model is: ; in, This refers to the revised geological hazard risk assessment results. This refers to the original geological hazard risk assessment results. This refers to the maximum upper limit of the geological hazard risk assessment results. This refers to the groundwater dynamic intensity index corresponding to the target slope unit. This refers to the average value. This refers to the percentage by which the groundwater dynamic intensity index exceeds the average value. This refers to the preset control correction strength coefficient, and it satisfies... Greater than 0, This refers to the correction factor.

[0014] As a further limitation of the technical solution of the present invention, after correcting the geological disaster risk assessment results based on the correction factor and obtaining the optimized geological disaster risk assessment results, the optimized geological disaster risk assessment results are applied to the geological disaster prevention and control decision of the target slope unit.

[0015] A multi-source monitoring data intelligent analysis system for geological disasters, the system comprising: The sample screening module is used to retrieve historical engineering databases when the target slope unit is identified to be under high-frequency blasting conditions and underground engineering structures are distributed within the target slope unit. It then selects several samples that are consistent with the target slope unit in terms of geological conditions and blasting conditions, but differ in groundwater dynamic intensity indicators. The parameter calculation module is used to calculate the maintenance strength index and soil compressibility parameters for each sample. The evolution feature identification module is used to determine whether the following evolution features exist in the sample: as the groundwater dynamic intensity index increases, the soil compressibility parameter and the maintenance strength index both show an evolution trend of changing from a stable state to an improved state, and the difference between the groundwater dynamic intensity index corresponding to the turning point of the two evolution trends is within a preset range. The correction factor generation module is used to generate a correction factor based on the difference between the groundwater dynamic intensity index corresponding to the inflection point and the groundwater dynamic intensity index of the target slope unit when evolutionary characteristics exist. The risk assessment correction module is used to obtain the geological hazard risk assessment results generated for the target slope unit and correct the geological hazard risk assessment results based on the correction factor.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention overcomes the limitations of existing technologies that rely solely on the compliance of a single blasting operation or the current monitoring status by introducing a high-frequency blasting condition identification mechanism and combining it with the synergistic evolution analysis of groundwater dynamic intensity indicators, soil compressibility parameters, and maintenance strength indicators. By extracting similar samples from historical engineering databases and identifying key turning points, it achieves a forward-looking characterization of the cumulative effects of groundwater dynamics and its coupling relationship with the maintenance needs of underground engineering structures. Based on this, a risk correction model based on difference ratios is constructed, enabling geological hazard risk assessment results to reflect potential long-term evolution trends and serve as a basis for optimizing prevention and control decisions. This invention balances engineering rationality and implementation feasibility, effectively improving the accuracy of risk assessment and proactive prevention and control capabilities of slope units during construction and operation phases. Attached Figure Description

[0017] Figure 1 A flowchart of the method provided in the embodiments of the present invention; Figure 2 A flowchart illustrating the formulation of a correction factor in the method provided in this embodiment of the invention; Figure 3 The application architecture diagram of the system provided in the embodiments of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0020] Specifically, an intelligent analysis method for multi-source monitoring data of geological disasters includes the following steps: Step S100: When it is identified that the target slope unit is under high-frequency blasting conditions and there are underground engineering structures distributed within the target slope unit, the historical engineering database is retrieved, and several samples that are consistent with the target slope unit in terms of geological conditions and blasting conditions, but have different groundwater dynamic intensity indicators are selected.

[0021] The high-frequency blasting condition refers to a series of blasting operations occurring within a preset time window. The blasting disturbance intensity value is determined for each blasting operation, and the blasting disturbance intensity values ​​of each blasting operation within the preset time window are accumulated or weighted to obtain a cumulative blasting disturbance intensity value. When the cumulative blasting disturbance intensity value exceeds a preset threshold, it is determined to be a high-frequency blasting condition.

[0022] The phrase "consistent with the target slope unit in geological conditions" specifically means that the slope unit corresponding to the sample and the target slope unit are the same in terms of stratigraphic structure type, distribution of geotechnical physical and mechanical parameters, and underground engineering structure type and spatial distribution characteristics, or are within a preset error range.

[0023] The calculation process of the groundwater dynamic intensity index includes: obtaining groundwater level change data, pore water pressure change data and groundwater seepage data corresponding to the slope unit; calculating the groundwater level change amplitude index, pore water pressure change rate index and seepage intensity index respectively; and weighting and combining the above indices to obtain the groundwater dynamic intensity index.

[0024] In this embodiment of the invention, the target slope unit can be a slope monitoring unit divided according to engineering management and monitoring layout. Specifically, it can be an open-pit mine bench slope unit, a highway or railway cutting slope unit, a reservoir bank slope unit for water conservancy and hydropower projects, an artificial slope unit formed by foundation pit or site excavation, and slope units set up by zone in mountain slope treatment projects. The slope unit usually has a clear spatial boundary and is equipped with multi-source monitoring points for displacement, settlement, groundwater level, pore water pressure, seepage, etc., so as to form a multi-source monitoring data set that can be used for evaluation and comparison.

[0025] The underground engineering structures mentioned can include underground pipelines, underground utility tunnels, drainage culverts, seepage interception / drainage facilities, tunnels or roadways, foundations of underground structures, and their ancillary structures. Due to factors such as the extension of urban and transportation infrastructure, the layout of mining and transportation systems, and the need for drainage and protection in slope stabilization projects, many slope units often have such underground engineering structures below or near them. This phenomenon is quite common in road slopes, reservoir bank slopes, mine slopes, and excavated slopes around towns. Underground engineering structures usually have operation and maintenance requirements during the service life of the slope unit, and their maintenance and repair records can reflect the degree of long-term environmental and engineering disturbances.

[0026] In current engineering practice, blasting is a common procedure for certain types of slope units. Blasting can be used for earthwork excavation, step formation, slope shaping, unstable rock treatment, foundation pit or road cut excavation, and creating working faces for subsequent support construction. Generally, to avoid adverse effects on slope stability and surrounding facilities, blasting operations are subject to strict parameter constraints and are monitored. Monitoring typically includes vibration, displacement, settlement, cracks, groundwater level, and pore water pressure. When monitoring results indicate that the blasting operation is within a reasonable control range, current technology often assumes it will not trigger geological hazard risks, and therefore, no hazard warnings or additional risk mitigation measures are implemented in the routine process.

[0027] However, those skilled in the art recognize that even if each blasting operation is within a reasonable control range, repeated blasting operations or increased cumulative disturbance within a certain time window can still have a gradual and cumulative impact on the soil, especially for soils containing groundwater. Related studies show that such soils can maintain an acceptable deformation response under a certain level of disturbance, but due to their structural and stress path characteristics, they often have a tolerance threshold. When the accumulated disturbance exceeds this threshold, the soil compressibility deteriorates, manifested as a significant increase in soil compressibility parameters from relative stability. It should be noted that this increase in soil compressibility parameters may still be within the engineering allowable range in some cases, meaning it does not immediately lead to excessive deformation or significant anomalies in the slope unit. Therefore, it is not often systematically considered as a key forward-looking indicator in existing technologies.

[0028] Furthermore, those skilled in the art have discovered that, even with similar slope types and engineering conditions, the maintenance requirements for underground engineering structures in slope units with underground structures can still differ. These differences are often small and are traditionally attributed to variations in operation and management or the impact of unforeseen events. Through comparative analysis of multi-source monitoring data and records of underground engineering structure operation and maintenance, those skilled in the art have found a correlation between the maintenance intensity indicators of underground engineering structures and the aforementioned changes in soil compressibility parameters caused by blasting disturbance, exhibiting a coupling relationship under certain conditions. Existing technologies struggle to proactively identify this coupling relationship before significant anomalies appear in the slope unit, and also find it difficult to optimize relevant parameters (such as geological hazard risk assessment results) in advance through quantitative correction mechanisms during the slope unit construction or remediation plan development stage, thereby reducing the probability or magnitude of subsequent increases in maintenance intensity.

[0029] In this embodiment of the invention, the geological hazard risk assessment result is a comprehensive assessment output commonly used in the prior art. It can be one or a combination of risk level, stability evaluation value, reinforcement requirement value, resource input recommendation value, or safety margin result. It is used to characterize the risk status of the target slope unit under given working conditions and to provide a basis for prevention and control decisions. The prior art is usually based on monitoring data, geological conditions, engineering disturbance information, etc., but it lacks a directly implementable quantitative correction mechanism for the "cumulative effect that may still exist within the reasonable range of blasting" and its coupling relationship with the maintenance needs of underground engineering structures. This makes it difficult for the assessment results to reflect the potential trend of future maintenance intensity improvement.

[0030] Based on the above understanding, this embodiment of the invention introduces the identification of high-frequency blasting conditions in step S100. The purpose of identifying high-frequency blasting conditions is to distinguish between the "transient rationality" of a single blast and the "cumulative disturbance" of multiple blasts, avoiding the neglect of the long-term impact of cumulative disturbance on aquifers and underground engineering structures based solely on the compliance of a single blast. The calculation method for the high-frequency blasting conditions can be to determine the blasting disturbance intensity value for each blasting operation within a preset time window, and then accumulate or weighted accumulate the values ​​to obtain the cumulative blasting disturbance intensity value. Whether the cumulative blasting disturbance intensity value exceeds a preset threshold is used as the judgment condition. The blasting disturbance intensity value can be determined based on the charge amount, single-shot energy, peak vibration velocity, vibration acceleration, dominant frequency energy, or equivalent energy index, etc.; the weighted accumulation can be weighted according to the blasting distance, medium attenuation conditions, and the relative position of the operation location and the underground engineering structure, etc. In addition to the above methods, a comprehensive scoring method consisting of blasting event counts and energy indicators can also be used, as well as statistical characteristics of blasting vibration monitoring sequences (such as root mean square and quantile characteristics) as a cumulative disturbance characterization method, or an exponential decay accumulation method for blasting disturbance intensity values ​​can be used to reflect the working condition characteristics of "stronger impact of near-term blasting" within the time window.

[0031] Step S100 further retrieves historical engineering databases. These databases can originate from existing engineering project monitoring system archives, construction and operation management systems, geological survey databases, underground engineering structure and facility ledgers and maintenance systems, and engineering archives within relevant management units or enterprises. All of these data sources fall within the data formats available in the existing engineering management system. The historical engineering database may include at least: slope unit division and spatial extent information, stratigraphic structure type and geotechnical physical and mechanical parameter distribution information, underground engineering structure type and spatial distribution characteristics information, blasting operation records and blasting disturbance intensity information, groundwater level change data, pore water pressure change data, groundwater seepage data, soil settlement or deformation monitoring data, underground engineering structure operation and maintenance records, and data that can be used to calculate maintenance intensity indicators. The aggregation of these data provides a foundation for subsequent sample screening, correlation analysis, and threshold identification.

[0032] The reason for setting up the sample screening process in step S100 is that, under the premise of "consistent geological conditions and blasting conditions," this embodiment of the invention focuses on the impact of differences in groundwater dynamic intensity indicators on the coupled changes of soil compressibility parameters and maintenance strength indicators. By screening samples that are consistent with the target slope unit in terms of geological conditions and blasting conditions, the interference of stratigraphic differences, structural differences, and blasting differences on the results can be eliminated as much as possible, so that the main differences between samples are concentrated in the groundwater dynamic intensity indicators, thereby improving the reliability of subsequent evolution feature identification.

[0033] In addition to "consistent geological conditions" and "consistent blasting conditions," sample screening can further introduce several constraints to improve comparability within the same category. These include: the relative distance between the underground engineering structure and the blasting operation area being within a preset range; the burial depth of the underground engineering structure being within a preset range; the geometric parameters of the slope unit (slope height, slope angle, and graded step parameters) being within a preset range; the layout of monitoring points and data integrity meeting preset requirements; and external environmental conditions (such as rainfall periods or drainage conditions) being within a preset range. Setting relatively strict screening conditions aims to reduce the influence of irrelevant variables on soil compressibility parameters and maintenance strength indicators, avoiding misjudging occasional operational factors as systematic differences caused by groundwater dynamic strength indicators. The preset error range allows for unavoidable measurement errors and natural dispersion of parameters in actual engineering practice, ensuring comparability within the same category without causing insufficient effective samples due to excessive stringency.

[0034] The calculation process of the groundwater dynamic intensity index in step S100 is used to incorporate the groundwater state into the analysis system in the form of quantifiable indicators. In this embodiment of the invention, after obtaining the groundwater level change data, pore water pressure change data, and groundwater seepage data corresponding to the slope unit, the groundwater level change amplitude index, pore water pressure change rate index, and seepage intensity index are calculated respectively, and the groundwater dynamic intensity index is obtained by weighted combination of each index. The above weighting can adopt a linear weighting method, and the weights can be determined by the correlation strength of historical samples, set by expert experience, or obtained by model training. In addition to linear weighting, normalization followed by weighted geometric mean, principal component analysis to obtain comprehensive factors, threshold-based piecewise combination, or groundwater level, pore water pressure, and seepage characteristics can be used to form a feature vector and output the groundwater dynamic intensity index through a mapping function to adapt to different engineering monitoring systems and data quality conditions. All of the above methods can realize the transformation of groundwater dynamic state into a unified quantitative index for sample comparison and threshold identification.

[0035] Furthermore, the intelligent analysis method for multi-source monitoring data of geological disasters also includes the following steps: Step S200: Calculate the maintenance strength index and soil compressibility parameters corresponding to each sample.

[0036] Step S300: Determine whether the following evolutionary characteristics exist in the sample: As the groundwater dynamic intensity index increases, the soil compressibility parameter and maintenance strength index both show an evolutionary trend from a stable state to an improved state, and the difference between the groundwater dynamic intensity index corresponding to the inflection point of the two evolutionary trends is within a preset range.

[0037] The process of obtaining the maintenance intensity index includes: parsing the sample, extracting the underground engineering structure inspection frequency data, maintenance operation type data, and maintenance input data, and calculating the maintenance intensity index based on the above three types of data.

[0038] The process of obtaining the soil compressibility parameters includes: obtaining soil settlement monitoring data of the slope area of ​​the sample, determining the effective stress change data of the soil corresponding to the underground engineering structure based on the soil settlement monitoring data, and then calculating the compressibility parameters characterizing the soil compressibility based on the effective stress change data.

[0039] In this embodiment of the invention, step S200 is used to further quantify and analyze the samples obtained in step S100. The purpose is to transform the information in the samples that reflects the operating status of underground engineering structures and the mechanical response of soil into comparable and calculable index forms, thereby providing a data basis for the identification of subsequent evolutionary characteristics.

[0040] In step S200, the calculation of the maintenance intensity index is based on the operation and maintenance information of the underground engineering structure recorded in the sample. Specifically, by analyzing the maintenance frequency data of the underground engineering structure corresponding to the sample, the frequency of human intervention required due to environmental or engineering disturbances within a certain time scale can be reflected; by analyzing the maintenance operation type data, different maintenance levels such as daily inspection, routine maintenance, reinforcement and repair, or emergency treatment can be distinguished, thereby reflecting the severity or complexity of maintenance behavior; by analyzing the maintenance input data, the scale of human, material, and financial resources consumed to maintain the normal operation of the underground engineering structure can be reflected. Based on the above three types of data, the maintenance intensity index, which characterizes the strength of the maintenance demand of the underground engineering structure, can be calculated by using methods such as weighted summation, normalized comprehensive scoring, or mapping functions. In addition to the above methods, in other embodiments, information such as the duration of maintenance operations, the number of components involved in maintenance, and the recovery effect after maintenance can also be included in the calculation, or the maintenance intensity index can be obtained by statistically analyzing the density of maintenance events per unit time, so as to adapt to the data format under different engineering management systems.

[0041] Simultaneously, in step S200, the compressibility parameter of the soil layer corresponding to each sample also needs to be calculated. This parameter is used to characterize the compressive deformation characteristics of the soil in the slope area corresponding to the sample under the influence of engineering disturbance and groundwater. In this embodiment of the invention, by acquiring soil settlement monitoring data of the slope area corresponding to the sample, and determining the effective stress change data of the soil corresponding to the underground engineering structure based on the soil settlement monitoring data, the compressibility parameter characterizing the compressibility of the soil layer is calculated based on the effective stress change data. The effective stress change data can reflect the evolution of the stress state of the soil under the combined influence of its own weight, blasting disturbance, and groundwater dynamics, while the compressibility parameter is used to quantitatively describe the deformation sensitivity of the soil under this stress path. In addition to using settlement data to invert the effective stress change, in other embodiments, the soil compressibility parameter can also be calculated or corrected by combining soil void ratio change, deformation modulus change, in-situ test results, or laboratory test parameters to enhance its applicability to different types of soil.

[0042] Step S300 is the core verification step in this embodiment of the invention. Its function is to identify whether there are engineering-significant evolutionary features from historical samples, thereby verifying whether there is a consistent nonlinear response relationship between the groundwater dynamic intensity index and the soil compressibility parameter and maintenance strength index. In step S300, by analyzing the samples according to the magnitude of the groundwater dynamic intensity index, it is determined whether there is an evolutionary trend in which the soil compressibility parameter and maintenance strength index both show a change from a stable state to an improved state as the groundwater dynamic intensity index increases. The above evolutionary trend reflects the fact that in the range where the groundwater dynamic effect is weak, the soil compressibility parameter does not change significantly, and the maintenance requirements of underground engineering structures remain at a relatively stable level; however, when the groundwater dynamic effect increases to a certain extent, the soil compressibility parameter begins to increase significantly, and at the same time, the maintenance strength index of underground engineering structures also increases accordingly, showing a synchronous change characteristic.

[0043] The requirement that both soil compressibility parameters and maintenance strength indices simultaneously exhibit the aforementioned evolutionary trends is to avoid misjudgments based solely on changes in a single indicator. If only soil compressibility parameters change, while maintenance strength indices do not show a corresponding change, it may indicate that the change is still within the acceptable range for the project and has not yet had a substantial impact on the operation of the underground engineering structure. Conversely, if maintenance strength indices change while soil compressibility parameters do not show a significant change, it may be more due to operational management factors or incidental events, rather than a systemic evolution caused by the coupling of groundwater dynamics and blasting disturbance. Therefore, requiring both to simultaneously exhibit an evolutionary trend from stability to improvement helps identify truly engineering-significant coupled response characteristics.

[0044] Furthermore, in step S300, it is determined whether the difference in groundwater dynamic intensity indices corresponding to the inflection points of the two evolution trends is within a preset range. This setting is significant in verifying whether the deterioration of soil compressibility parameters and the improvement of maintenance strength occur under similar groundwater dynamic conditions. If the difference in groundwater dynamic intensity indices corresponding to the two inflection points is too large, it indicates that their changes may be controlled by different dominant factors, and their correlation is weak, making them unsuitable as a basis for subsequent correction factors. However, when the difference in groundwater dynamic intensity indices corresponding to the two inflection points is within the preset range, it indicates that the soil mechanical response and the maintenance requirements of underground engineering structures change synergistically under similar groundwater dynamic intensity levels, reflecting a stable and usable coupling relationship between the two from an engineering perspective. This constraint can filter out accidental samples, improving the reliability and foresight of subsequent correction factor generation and geological hazard risk assessment result correction.

[0045] Furthermore, the intelligent analysis method for multi-source monitoring data of geological disasters also includes the following steps: Step S400: When evolutionary characteristics exist, a correction factor is generated based on the difference between the groundwater dynamic intensity index corresponding to the inflection point and the groundwater dynamic intensity index of the target slope unit.

[0046] Step S500: Obtain the geological hazard risk assessment results generated for the target slope unit, and correct the geological hazard risk assessment results based on the correction factor. After correcting the geological hazard risk assessment results based on the correction factor and obtaining the optimized geological hazard risk assessment results, apply the optimized geological hazard risk assessment results to the geological hazard prevention and control decision-making of the target slope unit.

[0047] Specifically, Figure 2 A flowchart for formulating the correction factor is shown.

[0048] Specifically, when evolutionary characteristics exist, generating a correction factor based on the difference between the groundwater dynamic intensity index corresponding to the inflection point and the groundwater dynamic intensity index of the target slope unit includes the following steps: Step S401: When the evolutionary characteristics are determined to exist, calculate the groundwater dynamic intensity index corresponding to the target slope unit and determine whether it is greater than the average groundwater dynamic intensity index corresponding to the turning point of the two evolutionary trends. Step S402: When the groundwater dynamic intensity index corresponding to the target slope unit is not greater than the average value, the geological hazard risk assessment result remains unchanged; Step S403: When the groundwater dynamic intensity index corresponding to the target slope unit is greater than the average value, a correction factor is generated based on the excess ratio of the groundwater dynamic intensity index compared to the average value.

[0049] When using correction factors to correct the geological hazard risk assessment results, a preset correction model is retrieved. The correction model is as follows: ; in, This refers to the revised geological hazard risk assessment results. This refers to the original geological hazard risk assessment results. This refers to the maximum upper limit of the geological hazard risk assessment results. This refers to the groundwater dynamic intensity index corresponding to the target slope unit. This refers to the average value. This refers to the percentage by which the groundwater dynamic intensity index exceeds the average value. This refers to the preset control correction strength coefficient, and it satisfies... Greater than 0, This refers to the correction factor.

[0050] In this embodiment of the invention, steps S400 and S500 constitute the core implementation process after completing the evolution feature verification in step S300. The purpose is to compare the current groundwater dynamic state of the target slope unit with the bearing threshold identified in historical samples, and make forward-looking corrections to the geological disaster risk assessment results accordingly, so that the assessment results can reflect potential long-term impact trends, rather than being limited to the current monitoring status.

[0051] In step S400, when the evolutionary characteristics are confirmed in the sample, the groundwater dynamic intensity index corresponding to the current target slope unit is further compared with the average groundwater dynamic intensity index corresponding to the turning points of the two evolutionary trends, and a correction factor is generated based on the difference between the two. Using the average groundwater dynamic intensity index corresponding to the turning points of the two evolutionary trends as a comparison benchmark has clear engineering and statistical significance. On the one hand, the turning point itself reflects the critical condition for the transformation of soil compressibility parameters and maintenance strength indices from a stable state to an enhanced state. This critical condition can be regarded as the bearing threshold of the slope unit and its underground engineering structure under long-term disturbance. On the other hand, considering that the two evolutionary trends originate from different indices, directly selecting either turning point as a benchmark may introduce bias. Using the average value can reduce the uncertainty caused by fluctuations in a single index while maintaining the significance of the threshold constraint, making the correction process more balanced and reasonable. In this way, over-correction can be avoided when the groundwater dynamic intensity index fluctuates slightly, and under-correction can also be prevented when the index significantly exceeds the bearing threshold, thereby improving the stability and engineering applicability of the correction results.

[0052] The basis for generating the correction factor based on the difference lies in the fact that, in this embodiment of the invention, step S300 has verified the synchronous nonlinear evolution relationship between the groundwater dynamic intensity index and the soil compressibility and maintenance strength indices. Based on this, the excess ratio of the groundwater dynamic intensity index relative to the average value at the inflection point can be considered as the degree to which the current working condition of the target slope unit deviates from the historical stable range. Using this excess ratio as the basis for generating the correction factor ensures that the correction magnitude is consistent with the potential risk growth trend, thereby achieving a gradual adjustment of the risk assessment results rather than a sudden adjustment. This correction method is highly compatible with the overall process of this invention, which extracts evolutionary features from historical samples and makes forward-looking judgments, and is conducive to gradually reflecting implicit long-term impacts in the assessment results.

[0053] In step S500, the geological hazard risk assessment results generated for the target slope unit are obtained, and the results are corrected based on the correction factor generated in step S400 to obtain an optimized geological hazard risk assessment result. The geological hazard risk assessment result can be one of the forms used in existing technologies, such as risk level values, stability evaluation values, risk indices, reinforcement requirement values, or resource input recommendations. The optimized result obtained after correction incorporates the cumulative effect of groundwater dynamic intensity and the evolution of maintenance requirements without changing the basic framework of the original assessment system, making the assessment results closer to the actual risk state that the slope unit may face in the subsequent operation phase. Subsequently, the optimized geological hazard risk assessment result is applied to the geological hazard prevention and control decisions of the target slope unit, for example, to determine whether to take reinforcement measures in advance, whether to adjust the maintenance strategy of underground engineering structures, whether to increase monitoring frequency, or to allocate more prevention and control resources, thereby realizing a shift from passive response to proactive prevention and control.

[0054] The correction model provided in this embodiment of the invention is intuitive and effective. It dynamically corrects the original geological hazard risk assessment results by introducing an adjustment term based on differences in groundwater dynamic intensity indicators. A maximum upper limit is set for the risk result, ensuring that the correction reflects both the upward trend of potential risk levels under enhanced groundwater dynamic intensity and avoids the risk assessment results losing their engineering rationality due to excessive correction. The calculation process of this correction model is clear, the parameters are well-defined, and it is easy to integrate with existing geological hazard risk assessment systems, facilitating understanding, application, and interpretation in engineering practice.

[0055] In addition to the above-mentioned correction methods, different forms of correction models can be adopted in other implementations according to actual engineering needs. For example, a segmented correction method can be used, with different correction intensity coefficients applied when the excess proportion of the groundwater dynamic intensity index falls within different intervals; a nonlinear mapping method can also be used, so that the correction amplitude changes relatively smoothly when approaching the tolerance threshold, and gradually increases when it significantly exceeds the tolerance threshold; or the calculation rules of the correction factor can be determined by function fitting or model training based on the correlation between the groundwater dynamic intensity index and the risk assessment results in historical samples, so as to adapt to the differentiated needs of risk sensitivity in different engineering scenarios.

[0056] To facilitate understanding of the overall implementation process of this invention, an example with specific numerical values ​​is provided below. Assume that, through the analysis in steps S100 to S300, a target slope unit identifies groundwater dynamic intensity indices corresponding to the turning points of two evolution trends as 80 and 90, respectively, with an average value of 85. Further monitoring reveals that the current groundwater dynamic intensity index of the target slope unit is 100. At this point, the current groundwater dynamic intensity index exceeds the average value by 15, and the excess ratio relative to the average value is 15 divided by 85, approximately 0.176. Assuming the control correction intensity coefficient is 0.5, the original geological hazard risk assessment result is 60, and the maximum upper limit of the geological hazard risk assessment result is set to 100, then according to the correction model, the original geological hazard risk assessment result is adjusted upwards to obtain a correction result greater than 60 and not exceeding 100. The revised results indicate that although the target slope unit has not yet shown significant instability or abnormalities, its groundwater dynamic state has exceeded the corresponding bearing threshold in historical samples. This increases the likelihood of subsequent soil compressibility deterioration and increased maintenance needs for underground engineering structures, thus providing an early risk indication basis for geological disaster prevention and control decisions.

[0057] In summary, this invention introduces high-frequency blasting condition identification in step S100, explores the evolutionary relationship between groundwater dynamic intensity index, soil compressibility parameter, and maintenance strength index in steps S200 and S300, and transforms this evolutionary relationship into an executable correction mechanism in steps S400 and S500. This effectively addresses the core research problem of "cumulative impacts may still exist within the reasonable range of blasting operations, and these cumulative impacts are coupled with the maintenance needs of underground engineering structures."

[0058] It is important to note that this invention incorporates maintenance intensity as a crucial analytical dimension, fully considering the varying maintenance requirements of underground engineering structures during the long-term operation of slope units. While changes in maintenance intensity are typically not a core indicator in conventional engineering management for geological hazard risk assessment, the maintenance intensity objectively reflects the operational status and environmental adaptability of underground engineering structures during service, particularly highlighting the comprehensive impact level experienced by slope units in later operational phases. Therefore, joint analysis of the maintenance intensity indicator with soil compressibility parameters and groundwater dynamic strength indicators helps identify potential risk evolution trends from a long-term operational perspective.

[0059] By introducing maintenance strength indicators and identifying their evolutionary characteristics in sync with soil compressibility parameters, this invention can reveal the potential impact of blasting disturbance and groundwater dynamic conditions on the overall operational status of slopes before significant anomalies appear in slope units. This overcomes the shortcomings of existing technologies that rely solely on current monitoring status for judgment, enabling geological hazard risk assessment results to not only reflect the immediate safety status but also the potential for subsequent maintenance needs and risk evolution. It has clear engineering basis and good practical application value.

[0060] In terms of application prospects, the method and system described in this invention can be widely applied to scenarios where blasting operations are common and underground engineering structures are prevalent, such as open-pit mine slopes, highway and railway slopes, reservoir bank slopes of water conservancy and hydropower projects, and excavated slopes around cities. By combining with existing monitoring systems, engineering management systems, and risk assessment systems, the foresight and decision support capabilities of risk assessment can be improved without significantly increasing hardware investment, demonstrating good prospects for widespread application.

[0061] Furthermore, Figure 3 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0062] In another preferred embodiment of the present invention, a multi-source monitoring data intelligent analysis system for geological disasters includes: The sample screening module 100 is used to retrieve historical engineering databases when it is identified that the target slope unit is under high-frequency blasting conditions and there are underground engineering structures distributed within the target slope unit. It then selects several samples that are consistent with the target slope unit in terms of geological conditions and blasting conditions, but have different groundwater dynamic intensity indicators. The parameter calculation module 200 is used to calculate the maintenance strength index and soil compressibility parameters corresponding to each sample. The evolution feature recognition module 300 is used to determine whether the following evolution features exist in the sample: as the groundwater dynamic intensity index increases, the soil compressibility parameter and the maintenance strength index both show an evolution trend of changing from a stable state to an improved state, and the difference between the groundwater dynamic intensity index corresponding to the turning point of the two evolution trends is within a preset range. The correction factor generation module 400 is used to generate a correction factor based on the difference between the groundwater dynamic intensity index corresponding to the inflection point and the groundwater dynamic intensity index of the target slope unit when evolutionary characteristics exist. The risk assessment correction module 500 is used to obtain the geological hazard risk assessment results generated for the target slope unit and correct the geological hazard risk assessment results based on the correction factor.

[0063] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0064] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0065] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0066] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0067] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent analysis of multi-source monitoring data of geological disasters, characterized in that, The method includes: When it is identified that the target slope unit is under high-frequency blasting conditions and there are underground engineering structures distributed within the target slope unit, the historical engineering database is retrieved, and several samples that are consistent with the target slope unit in terms of geological conditions and blasting conditions, but have different groundwater dynamic intensity indicators are selected. Calculate the maintenance strength index and soil compressibility parameters for each sample; Determine whether the following evolutionary characteristics exist in the sample: as the groundwater dynamic intensity index increases, the soil compressibility parameter and the maintenance strength index both show an evolutionary trend from a stable state to an enhanced state, and the difference between the groundwater dynamic intensity index corresponding to the inflection point of the two evolutionary trends is within the preset range. When evolutionary characteristics exist, a correction factor is generated based on the difference between the groundwater dynamic intensity index corresponding to the inflection point and the groundwater dynamic intensity index of the target slope unit. Obtain the geological hazard risk assessment results generated for the target slope unit, and revise the geological hazard risk assessment results based on the correction factor.

2. The intelligent analysis method for multi-source monitoring data of geological disasters according to claim 1, characterized in that, The high-frequency blasting condition refers to a series of blasting operations occurring within a preset time window. The blasting disturbance intensity value is determined for each blasting operation, and the blasting disturbance intensity values ​​of each blasting operation within the preset time window are accumulated or weighted to obtain a cumulative blasting disturbance intensity value. When the cumulative blasting disturbance intensity value exceeds a preset threshold, it is determined to be a high-frequency blasting condition.

3. The intelligent analysis method for multi-source monitoring data of geological disasters according to claim 1, characterized in that, The phrase "consistent with the target slope unit in geological conditions" specifically means that the slope unit corresponding to the sample and the target slope unit are the same in terms of stratigraphic structure type, distribution of geotechnical physical and mechanical parameters, and underground engineering structure type and spatial distribution characteristics, or are within a preset error range.

4. The intelligent analysis method for multi-source monitoring data of geological disasters according to claim 1, characterized in that, The calculation process of the groundwater dynamic intensity index includes: obtaining groundwater level change data, pore water pressure change data and groundwater seepage data corresponding to the slope unit; calculating the groundwater level change amplitude index, pore water pressure change rate index and seepage intensity index respectively; and weighting and combining the above indices to obtain the groundwater dynamic intensity index.

5. The intelligent analysis method for multi-source monitoring data of geological disasters according to claim 1, characterized in that, The process of obtaining the maintenance intensity index includes: parsing the sample, extracting the underground engineering structure inspection frequency data, maintenance operation type data, and maintenance input data, and calculating the maintenance intensity index based on the above three types of data.

6. The intelligent analysis method for multi-source monitoring data of geological disasters according to claim 1, characterized in that, The process of obtaining the soil compressibility parameters includes: obtaining soil settlement monitoring data of the slope area of ​​the sample, determining the effective stress change data of the soil corresponding to the underground engineering structure based on the soil settlement monitoring data, and then calculating the compressibility parameters characterizing the soil compressibility based on the effective stress change data.

7. The intelligent analysis method for multi-source monitoring data of geological disasters according to claim 1, characterized in that, When evolutionary characteristics exist, the steps for generating a correction factor based on the difference between the groundwater dynamic intensity index corresponding to the inflection point and the groundwater dynamic intensity index of the target slope unit include: When the evolutionary characteristics are confirmed, the groundwater dynamic intensity index corresponding to the target slope unit is calculated, and it is determined whether it is greater than the average groundwater dynamic intensity index corresponding to the turning point of the two evolutionary trends. When the groundwater dynamic intensity index corresponding to the target slope unit is not greater than the average value, the geological hazard risk assessment result remains unchanged. When the groundwater dynamic intensity index corresponding to the target slope unit is greater than the average value, a correction factor is generated based on the proportion by which the groundwater dynamic intensity index exceeds the average value.

8. The intelligent analysis method for multi-source monitoring data of geological disasters according to claim 7, characterized in that, When using correction factors to correct the geological hazard risk assessment results, a preset correction model is retrieved. The correction model is as follows: ； in, This refers to the revised geological hazard risk assessment results. This refers to the original geological hazard risk assessment results. This refers to the maximum upper limit of the geological hazard risk assessment results. This refers to the groundwater dynamic intensity index corresponding to the target slope unit. This refers to the average value. This refers to the percentage by which the groundwater dynamic intensity index exceeds the average value. This refers to the preset control correction strength coefficient, and it satisfies... Greater than 0, This refers to the correction factor.

9. The intelligent analysis method for multi-source monitoring data of geological disasters according to claim 1, characterized in that, After correcting the geological hazard risk assessment results based on the correction factor and obtaining the optimized geological hazard risk assessment results, the optimized geological hazard risk assessment results are applied to the geological hazard prevention and control decision of the target slope unit.

10. An intelligent analysis system for multi-source monitoring data of geological disasters, characterized in that, The system includes: The sample screening module is used to retrieve historical engineering databases when the target slope unit is identified to be under high-frequency blasting conditions and underground engineering structures are distributed within the target slope unit. It then selects several samples that are consistent with the target slope unit in terms of geological conditions and blasting conditions, but differ in groundwater dynamic intensity indicators. The parameter calculation module is used to calculate the maintenance strength index and soil compressibility parameters for each sample. The evolution feature identification module is used to determine whether the following evolution features exist in the sample: as the groundwater dynamic intensity index increases, the soil compressibility parameter and the maintenance strength index both show an evolution trend of changing from a stable state to an improved state, and the difference between the groundwater dynamic intensity index corresponding to the turning point of the two evolution trends is within a preset range. The correction factor generation module is used to generate a correction factor based on the difference between the groundwater dynamic intensity index corresponding to the inflection point and the groundwater dynamic intensity index of the target slope unit when evolutionary characteristics exist. The risk assessment correction module is used to obtain the geological hazard risk assessment results generated for the target slope unit and correct the geological hazard risk assessment results based on the correction factor.

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