Geological disaster prevention and control sampling and monitoring methods, systems, equipment and storage media

Abstract

This invention discloses a sampling and monitoring method, system, equipment, and storage medium for geological disaster prevention and control, relating to the field of geological disaster monitoring and prevention technology. The method includes generating a cross-depth adaptive discrimination time window based on cross-correlation extreme values ​​and weighted combinations, and obtaining the time window length; generating event markers within the depth based on hierarchical thresholds within the cross-depth adaptive discrimination time window; obtaining inter-layer scores based on the consistency of change directions between adjacent depths; outputting a set of trigger layers when the scores meet the criteria and the number of trigger layers is greater than or equal to a set value; adding a layer above and below the trigger layer set to form a target depth set; determining the event window based on the maximum value of the adaptive discrimination time window and a proportional coefficient; performing stratified sampling and archiving at the target depth; and using chained hashing to bind lag parameters, inter-layer scores, and window parameters to the monitoring sequence. The method described in this invention achieves accurate identification and reliable evidence storage of geological disaster monitoring events through adaptive time windows and inter-layer consistency discrimination.

Description

Technical Field

[0001] This invention relates to the field of geological disaster monitoring and prevention technology, specifically to geological disaster prevention sampling and monitoring methods, systems, equipment, and storage media. Background Technology

[0002] Geological disasters occur frequently in mountainous, hilly, and complex terrain areas, with landslides, collapses, and debris flows being the most common types. With the advancement of infrastructure construction and the expansion of residential areas, higher demands are placed on real-time monitoring and scientific sampling of these disasters. Early monitoring relied mainly on single devices such as rain gauges, water level gauges, and inclinometers, resulting in mostly independent data sequences that were difficult to reveal dynamic processes deep within the strata. As research in geomechanics and groundwater dynamics deepened, the academic community gradually recognized the causal relationship between rainfall infiltration, pore water pressure rise, and stratum displacement. This led to the emergence of multi-source monitoring, using joint analysis of rainfall, pore water pressure, and displacement to infer disaster evolution. In recent years, the application of distributed fiber optic sensing, wireless sensor networks, and high-precision displacement gauges has enabled continuous data acquisition at different depths and locations, significantly improving spatial and temporal resolution. Time series analysis methods such as cross-correlation, wavelet analysis, and spectral analysis have been introduced to determine the hysteresis relationships between different physical quantities, thereby exploring the dynamic connection between rainfall and deep responses.

[0003] Current research still faces several limitations in practical applications. While the dynamic relationship between rainfall, pore water pressure, and displacement has received attention, the quantification methods for lag relationships are relatively simplistic, often relying on fixed time windows or empirical values. This lacks a discriminative mechanism that can be adjusted to varying geological conditions and seasonal environmental changes. Data acquired by multi-depth sensors is typically processed independently, making it difficult to establish consistent judgments across layers. This can easily lead to misjudgments due to local disturbances or anomalous signals, affecting the reliability of triggering events. Traditional methods mostly employ single-point threshold judgments; once a local sensor is interfered with, inaccurate alarms may be triggered, reducing data accuracy. Regarding reliability, existing automated devices can collect data when an event occurs, but their coupling with monitoring and discrimination is insufficient. The sampling results are difficult to correspond one-to-one with the monitoring sequence at the parameter level. Data archiving usually relies on timestamps and original data files, failing to systematically bind core information such as lag parameters, inter-layer discrimination indicators, and window parameters. This leads to difficulties in tracing back to the disaster and studying the mechanism. Faced with massive amounts of time-series data, how to extract key features while ensuring real-time performance and form a verifiable logical closed loop in the sampling and archiving process remains a core challenge in geological disaster prevention and monitoring technology. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is: in existing geological disaster monitoring and sampling methods, the time window is fixed and there is a lack of cross-depth lag adaptive mechanism, resulting in insufficient discrimination accuracy; multi-depth data is processed independently, lacking inter-layer consistency determination, and triggering reliability is low; the sampling and triggering logic is not sufficiently coupled, and it is difficult to correspond samples with monitoring sequences; the problem is how to form a unified processing framework between cross-depth monitoring, event triggering, target depth sampling and sequence archiving.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a sampling and monitoring method for geological disaster prevention, comprising: generating a cross-depth adaptive discrimination time window based on cross-correlation extreme values ​​and weighted combinations, and obtaining the time window length; generating intra-depth event markers within the cross-depth adaptive discrimination time window according to hierarchical thresholds, obtaining inter-layer scores based on the consistency of change directions of adjacent depths, and outputting a set of trigger layers when the scores meet the standard and the number of trigger layers is greater than or equal to a set value; adding one layer above and below the trigger layer set to form a target depth set, determining the event window according to the maximum value of the adaptive discrimination time window and the proportional coefficient, performing stratified sampling and sealing at the target depth, and using chained hashing to bind hysteresis parameters, inter-layer scores, and window parameters to the monitoring sequence.

[0007] As a preferred embodiment of the geological disaster prevention and control sampling and monitoring method described in this invention, the cross-depth adaptive discrimination time window is represented as follows: Cross-depth hysteresis modeling is represented as: , The adaptive discrimination time window is represented as: , in, Indicates the first The lag between the depth of rain and pore water pressure is measured in time (s). Indicates the first The lag between pore water pressure at depth and displacement is measured in time (s). Representing rainfall sequence and the Deep pore water pressure sequence The cross-correlation function, Indicates the first Deep pore water pressure sequence and displacement sequence The cross-correlation function, Indicates the monitoring depth number. Indicates the first Depth-adaptive discrimination time window, in units of time (s). The station weight is a dimensionless weight determined by the geological structure, soil permeability, and historical data characteristics of the current monitoring point. The seasonal weights are dimensionless weights determined by regional seasonal rainfall, temperature, and hydrological conditions. This represents the minimum value of the adaptive discrimination time window, used to constrain abnormally small results, and is expressed in seconds (s). This represents the maximum value of the adaptive discrimination time window, used to constrain abnormally large results, and is expressed in seconds (s).

[0008] As a preferred embodiment of the geological disaster prevention and control sampling monitoring method described in this invention, the time window length is obtained by weighting the rain-to-pore water pressure lag and the pore water pressure-to-displacement lag according to the station weight and seasonal weight. When the weighted result is less than the minimum threshold, the current time window is set as the minimum threshold. When the weighted result exceeds the maximum threshold, the current time window is limited to the maximum threshold.

[0009] As a preferred embodiment of the geological disaster prevention and control sampling monitoring method of the present invention, the event marking within the depth includes: acquiring the pore water pressure change rate and displacement change rate within an adaptive discrimination time window, comparing them with a classification threshold, and generating event markers of different levels; the classification threshold includes a pre-trigger threshold, a trigger threshold, and a strong trigger threshold; the event marking includes: generating a pre-trigger event marker when either the pore water pressure change rate or the displacement change rate first exceeds the pre-trigger threshold; generating a trigger event marker when either the pore water pressure change rate or the displacement change rate exceeds the trigger threshold; generating a strong trigger event marker when either the pore water pressure change rate or the displacement change rate exceeds the strong trigger threshold; and using the highest-level event marker as the final marker for the current depth when different threshold conditions are met within the same time window.

[0010] As a preferred embodiment of the geological disaster prevention and control sampling and monitoring method described in this invention, the interlayer score is expressed as: , in, Indicates at time The inter-layer score, with a value range of [0,1]. Indicates at time The number of depths of the trigger layer set, Indicates the first The change in deep pore water pressure over adjacent sampling intervals. Indicates the first The change in pore water pressure in the adjacent upper layer at a certain depth. Indicates the first The changes in depth displacement and tilt angle between adjacent sampling intervals Indicates the first The amount of displacement and tilt change of the adjacent upper layer at depth This represents a sign function, with values ​​of +1, 0, and -1. This indicates the indicator function, which takes the value 1 when the condition is met and 0 when the condition is not met. Within the same time window, the direction of change of pore water pressure and displacement at adjacent depths are compared. If the direction of change is consistent, it is marked as co-directional. The inter-layer score is obtained by using the proportion of co-directional layers to the total number of triggering layers.

[0011] As a preferred embodiment of the geological disaster prevention and control sampling monitoring method of the present invention, the event window includes: adding a pre-recording duration before the event time of the trigger layer set, and adding a post-recording duration obtained by multiplying the maximum value of the adaptive discrimination time window and the proportional coefficient after the event time, thereby constructing an event window containing the pre-recording duration and the post-recording duration.

[0012] As a preferred embodiment of the geological disaster prevention and control sampling and monitoring method of the present invention, the chain hash includes inputting the rain-to-pore water pressure lag, the pore water pressure-to-displacement lag, the interlayer score, the event window parameter, the monitoring depth code, the channel number and the window identifier into the hash operation in chronological order, and connecting the operation results one by one to form a chain structure.

[0013] Another objective of this invention is to provide a geological disaster prevention and control sampling and monitoring system, which solves the problems of fixed time windows, lack of interlayer constraints in trigger determination, and lack of parameterized correspondence between sampling data and monitoring sequences in current geological disaster monitoring systems through cross-depth adaptive discrimination time windows, interlayer consistency determination, and chain binding schemes for sampling and monitoring sequences.

[0014] As a preferred embodiment of the geological disaster prevention and control sampling monitoring system of the present invention, it includes: a time window construction module, a trigger determination module, and a sampling binding module; the time window construction module determines the rain-to-pore water pressure lag and the pore water pressure-to-displacement lag based on the cross-correlation extreme value, and generates a cross-depth adaptive discrimination time window by weighting according to the station weight and seasonal weight; the trigger determination module is used to generate in-depth event markers according to the graded threshold within the cross-depth adaptive discrimination time window, obtain inter-layer scores by the consistency of the change direction of adjacent depths, and output a set of trigger layers when the inter-layer scores meet the threshold and the number of trigger layers is greater than or equal to a set value; the sampling binding module is used to add a layer above and below the trigger layer set to form a target depth set, determine the event window according to the maximum value of the cross-depth adaptive discrimination time window and the proportional coefficient, and perform a hash operation at the target depth by inputting the rain-to-pore water pressure lag, the pore water pressure-to-displacement lag, the inter-layer score, the event window parameters, the monitoring depth code, the channel number, and the window identifier in chronological order, and connect the operation results one by one to form a chain structure.

[0015] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of a geological disaster prevention and control sampling and monitoring method.

[0016] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a geological disaster prevention and control sampling and monitoring method.

[0017] The beneficial effects of this invention are as follows: The geological disaster prevention and control sampling monitoring method provided by this invention generates a cross-depth adaptive discrimination time window by combining cross-correlation extreme values ​​and weighted combinations, realizing dynamic adjustment of the time window to match different geological conditions and seasonal environments; within the adaptive time window, hierarchical thresholds and inter-layer consistency judgment are introduced to realize multi-level constraints and spatial collaborative judgment of event triggering; a target depth set is formed by supplementing the upper and lower parts of the triggering layer set, and combined with event window scaling and chained hash binding, the integrated correspondence of sampling, window parameters and monitoring sequences is realized; this invention achieves better results in time window construction, event triggering judgment and sampling archiving association. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is an overall flowchart of the geological disaster prevention and control sampling and monitoring method provided in Embodiment 1 of the present invention. Detailed Implementation

[0020] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0021] Example 1, referring to Figure 1 As an embodiment of the present invention, a sampling and monitoring method for geological disaster prevention is provided, comprising: S1: Based on the cross-correlation extreme values, generate a cross-depth adaptive discrimination time window by weighted combination, and obtain the length of the time window.

[0022] Rainfall sensors, pore water pressure gauges, and displacement sensors were deployed in the geological disaster monitoring area to collect rainfall sequences 101, pore water pressure sequences at different monitoring depths 102, and displacement sequences 103. The collected data were timestamped and stored under a unified time reference, and a cross-correlation function 104 between the rainfall sequences and the pore water pressure sequences was generated, expressed as: , The cross-correlation function between the pore water pressure sequence and the displacement sequence is expressed as: , in, Representing rainfall sequence and the Deep pore water pressure sequence The cross-correlation function characterizes the hysteresis relationship between rainfall and pore pressure response, identifying the response delay from rain to pore pressure. Indicates the first Deep pore water pressure sequence and displacement sequence The cross-correlation function describes the hysteretic effect of pore pressure changes on displacement. This represents the time delay variable, indicating the shift when comparing two sequences on the time axis. Its value range is typically set to... , This represents the lower limit of the delay time, i.e., the minimum allowable lag value when performing cross-correlation calculations. This represents the upper limit of the delay time, i.e., the maximum lag range for cross-correlation calculations. Represents the discrete sampling time of a time series. This indicates the starting time of the cross-correlation calculation, i.e., the beginning of a monitoring cycle. This indicates the end time of the cross-correlation calculation, i.e., the end of a monitoring cycle. Cross-correlation analysis is performed on the rainfall sequence and the pore water pressure sequence, and the maximum value position is extracted within the set delay time interval to obtain the lag from rainfall to pore pressure. Further cross-correlation analysis is performed on the pore water pressure sequence and the displacement sequence, and the maximum value position is extracted within the same interval to obtain the lag from pore pressure to displacement. Then, based on the geological structure, soil permeability and historical monitoring characteristics of the monitoring points, the station weight is set to 105, and the seasonal weight is set to 106 in combination with regional seasonal rainfall, temperature and hydrological conditions. The two types of lags are weighted and combined and limited to the preset minimum and maximum value interval to generate a cross-depth adaptive discrimination time window.

[0023] It should be noted that the cross-depth adaptive discrimination time window 10 is represented as follows: Cross-depth hysteresis modeling is represented as: , The adaptive discrimination time window is represented as: , in, Indicates the first The lag between the depth of rain and pore water pressure is measured in time (s). Indicates the first The lag between pore water pressure at depth and displacement is measured in time (s). Representing rainfall sequence and the Deep pore water pressure sequence The cross-correlation function characterizes the hysteresis relationship between rainfall and pore pressure response, identifying the response delay from rain to pore pressure. Indicates the first Deep pore water pressure sequence and displacement sequence The cross-correlation function describes the hysteretic effect of pore pressure changes on displacement. Indicates the monitoring depth number. Indicates the first Depth-adaptive discrimination time window, in units of time (s). The station weight is a dimensionless weight determined by the geological structure, soil permeability, and historical data characteristics of the current monitoring point. The seasonal weights are dimensionless weights determined by regional seasonal rainfall, temperature, and hydrological conditions. This represents the minimum value of the adaptive discrimination time window, used to constrain abnormally small results, and is expressed in seconds (s). This represents the maximum value of the adaptive discrimination time window, used to constrain abnormally large results, and is expressed in seconds (s).

[0024] It should be noted that the time window length is obtained by weighting the rain-to-pore water pressure lag and the pore water pressure-to-displacement lag according to the station weight and seasonal weight. When the weighted result is less than the minimum threshold, the current time window is set as the minimum threshold. When the weighted result exceeds the maximum threshold, the current time window is limited to the maximum threshold.

[0025] It should also be noted that by introducing dual time-series parameters of rain-to-pore water pressure lag and pore water pressure-to-displacement lag, a cross-depth adaptive discrimination time window is formed under the combined effect of site weight and seasonal weight, and the multi-source response link is incorporated into a unified time-domain framework, breaking through the limitations of a single threshold or fixed time window that cannot adapt to complex geological conditions.

[0026] S2: Generate event markers within the depth based on the hierarchical threshold within the cross-depth adaptive discrimination time window, obtain inter-layer scores based on the consistency of the change direction of adjacent depths, and output the set of trigger layers when the score meets the standard and the number of trigger layers is greater than or equal to the set value.

[0027] Furthermore, the depth-based event marking includes acquiring the pore water pressure change rate 201 and displacement change rate 202 within an adaptive discrimination time window, comparing them with a grading threshold, and generating event markers of different levels. The grading thresholds include a pre-trigger threshold, a trigger threshold, and a strong trigger threshold. The event marking includes generating a pre-trigger event marker when either the pore water pressure change rate or the displacement change rate first exceeds the pre-trigger threshold; generating a trigger event marker when either the pore water pressure change rate or the displacement change rate exceeds the trigger threshold; generating a strong trigger event marker when either the pore water pressure change rate or the displacement change rate exceeds the strong trigger threshold; and using the highest-level event marker as the final marker 204 for the current depth when different threshold conditions are met within the same time window.

[0028] Within the adaptive discrimination time window, the event rate within the depth is expressed as: , The maximum value of the pore pressure rate and the displacement rate is taken as the triggering criterion, expressed as: , The grading threshold of 203 is expressed as: , in, Indicates at time The rate of change of pore water pressure, Indicates the first The deep pore water pressure sequence at time The value of , This indicates the sampling time interval for the current monitoring period. Indicates at time The rate of displacement change, Indicates the first Depth displacement sequence at time The value of , Indicates at time No. The event intensity at depth is defined as the maximum of the absolute values ​​of the rate of change of pore water pressure and the rate of change of displacement. Indicates at time Hierarchical event markers, The strong triggering threshold is derived statistically from the distribution of extreme velocity values ​​before instability in disaster cases, combined with the joint response of the high-intensity rainfall-pore water pressure-displacement linkage. This indicates the trigger threshold, which is determined through retrospective analysis based on typical rate levels prior to landslides or slope deformation in historical monitoring sequences. The pre-trigger threshold is determined by selecting the upper limit of background noise, based on the background fluctuation statistics of pore water pressure rate and displacement rate under long-term disaster-free conditions in the monitoring area.

[0029] After obtaining the event marker, an interlayer consistency analysis is performed on the triggering situation across the depth range. Based on the triggering layer set, all depth points within the triggering layer set are selected, and the direction of change of pore water pressure and displacement between adjacent depths is compared one by one. When adjacent depths are consistent in both the direction of change of pore water pressure and displacement, they are determined to be in the same direction and consistent. If the directions are inconsistent, they are recorded as inconsistent. By statistically analyzing the proportion of in the same direction and consistent relationship in all triggering layers, the interlayer consistency score is obtained.

[0030] It should be noted that the trigger layer set is represented as: , in, Indicates at time The set of triggering layers, index Indicates "triggered". Indicates the monitoring depth number. Indicates the first The depth-adaptive discrimination time window is measured in seconds.

[0031] It should be noted that the inter-layer score of 20 is represented as: , in, Indicates at time The inter-layer score, with a value range of [0,1]. Indicates at time The number of depths of the trigger layer set, Indicates the first The change in deep pore water pressure over adjacent sampling intervals. Indicates the first The change in pore water pressure in the adjacent upper layer at a certain depth. Indicates the first The changes in depth displacement and tilt angle between adjacent sampling intervals Indicates the first The amount of displacement and tilt change of the adjacent upper layer at depth This represents a sign function, with values ​​of +1, 0, and -1. This indicates the indicator function, which takes the value 1 when the condition is met and 0 when the condition is not met. Within the same time window, the direction of change of pore water pressure and displacement at adjacent depths are compared. If the direction of change is consistent, it is marked as co-directional. The inter-layer score is obtained by using the proportion of co-directional layers to the total number of triggering layers.

[0032] After determining the inter-layer consistency score, valid events are judged based on the size of the triggering layer set. When the inter-layer score reaches a preset value and the number of layers contained in the triggering layer set is greater than or equal to the minimum threshold, the triggering layer set is output as the final result 20' of valid events, represented as: , in, Indicates the time. Whether it is determined to be a valid event, Indicates at time The inter-layer score, with a value range of [0,1]. This represents the inter-layer consistency threshold, determined by statistical analysis of long-term observation data from multiple depths of the monitoring profile, to assess whether the inter-layer score is sufficiently high. This represents the minimum threshold for the number of triggering layers. Based on the statistical regularity of the change direction of pore water pressure and displacement sequence at adjacent depths, it determines whether the set of triggering layers has reached a sufficient size; if it is less than this threshold, it is returned to the time window for re-determination.

[0033] It should also be noted that by introducing a graded threshold within the cross-depth adaptive discrimination time window to generate event markers within the depth, and by combining the consistency of pore water pressure and displacement change direction at adjacent depths to calculate inter-layer scores, the transformation from single-point triggering to multi-depth collaborative discrimination is realized. Under complex geological conditions, the trigger layer set is accurately locked and a cross-depth discrimination link is formed.

[0034] S3: Add a layer above and below the trigger layer set to form a target depth set. Determine the event window according to the maximum value of the adaptive discrimination time window and the scaling factor. Perform layered sampling and sealing at the target depth. Use chained hashing to bind the hysteresis parameter, inter-layer score and window parameter to the monitoring sequence.

[0035] Furthermore, the event window includes adding a pre-recording duration before the event time of the trigger layer set, and adding a post-recording duration obtained by multiplying the maximum value of the adaptive discrimination time window and the scaling factor after the event time, thus constructing an event window containing the pre-recording duration and the post-recording duration.

[0036] It should be noted that chained hashing involves sequentially inputting the rain-to-pore water pressure lag, pore water pressure-to-displacement lag, interlayer score, event window parameters, monitoring depth code, channel number and window identifier into the hash operation, and then connecting the results one by one to form a chain structure.

[0037] After the trigger layer set is determined, a layer is added above and below the trigger layer set to form a target depth set, and an event window is constructed. The length of the event window is determined by selecting the maximum value of the adaptive discrimination time window of each layer in the target depth set and calculating it in combination with the scaling factor. The event window includes the previous recording duration and the subsequent recording duration, which are used to cover the entire process before and after the event is triggered.

[0038] It should be noted that the event window 301, which includes the pre-recording duration and the post-recording duration, is represented as follows: , in, This indicates the duration of the last record in the event window, used to determine the duration for which data needs to be saved after the event is triggered. This represents a scaling factor that adjusts the relationship between the post-event recording duration and the maximum window duration. It represents any depth point contained in the target depth set. This represents the final event window, with the time range extending from the pre-recorded duration before the event trigger to the post-recorded duration after the event trigger. Indicates the pre-recording duration before the event is triggered. Indicates the duration of the record after the event is triggered.

[0039] Within the event window, a summary of the monitoring sequence is generated. The monitoring sequence summary is obtained by hashing the pressure sequence, displacement sequence, and settlement sequence within the window to obtain a unique identifier. The sample unit data and the monitoring sequence summary are then input into the hash function layer by layer using a chain hashing method to form a chain hash structure.

[0040] It should also be noted that a chained hash 302 is represented as: , , , in, This represents the initial hash value, taken from the initialization vector (Ⅳ). Indicates the first The hash value at the layer depth represents the result generated through chained hash iterations. Indicates the first Individual sample unit data, including hysteresis parameters, inter-layer scores, depth information, and event window parameters. Indicates pore water pressure over time The observation sequence, Indicates the first The deep pore water pressure sequence at time The value of , Indicates the first Depth displacement sequence at time The value of , This indicates the number of layers contained in the target depth set; chained hashing must cover all target depths.

[0041] It should also be noted that by expanding the target depth set based on the trigger layer set, and combining the adaptive discrimination time window and chain hash mechanism to achieve unique binding and tamper-proof evidence storage of the layered sampling data, the depth coverage integrity of geological disaster monitoring events and the reliability of data traceability are ensured.

[0042] Example 2, an embodiment of the present invention, provides a geological disaster prevention and control sampling monitoring system, including a time window construction module, a trigger determination module, and a sampling binding module.

[0043] The time window construction module determines the lag from rain to pore water pressure and the lag from pore water pressure to displacement based on the cross-correlation extreme values, and generates a cross-depth adaptive discrimination time window by weighting the station weight and seasonal weight.

[0044] The trigger determination module is used to generate event markers within a depth based on a graded threshold within a cross-depth adaptive discrimination time window, obtain inter-layer scores by ensuring consistency of change directions between adjacent depths, and output a set of trigger layers when the inter-layer scores meet the threshold and the number of trigger layers is greater than or equal to a set value.

[0045] The sampling and binding module is used to add a layer above and below the trigger layer set to form a target depth set. The event window is determined based on the maximum value of the cross-depth adaptive discrimination time window and the proportional coefficient. At the target depth, a hash operation is performed on the rain-to-pore water pressure lag, pore water pressure-to-displacement lag, inter-layer score, event window parameters, monitoring depth code, channel number and window identifier input in chronological order. The operation results are then connected one by one to form a chain structure.

[0046] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0047] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0048] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0049] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0050] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A sampling and monitoring method for geological disaster prevention and control, characterized in that, include: Based on the cross-correlation extreme values, a cross-depth adaptive discrimination time window is generated by combining weights, and the length of the time window is obtained. Within the cross-depth adaptive discrimination time window, event markers within the depth are generated based on the hierarchical threshold. Inter-layer scores are obtained based on the consistency of the change direction of adjacent depths. When the score meets the standard and the number of triggered layers is greater than or equal to the set value, the set of triggered layers is output. A target depth set is formed by adding a layer above and below the trigger layer set. The event window is determined by the maximum value of the adaptive discrimination time window and the scaling factor. Layered sampling and sealing are performed at the target depth. Chained hashing is used to bind the hysteresis parameter, inter-layer score and window parameter to the monitoring sequence.

2. The geological disaster prevention and control sampling and monitoring method as described in claim 1, characterized in that: The cross-depth adaptive discrimination time window is represented as follows: Cross-depth hysteresis modeling is represented as: ， The adaptive discrimination time window is represented as: ， in, Indicates the first Deep rain lags behind pore water pressure. Indicates the first The pressure of deep pore water lags behind the displacement. Representing rainfall sequence and the Deep pore water pressure sequence The cross-correlation function, Indicates the first Deep pore water pressure sequence and displacement sequence The cross-correlation function, Indicates the monitoring depth number. Indicates the first Depth-adaptive discrimination time window The station weight is a dimensionless weight determined by the geological structure, soil permeability, and historical data characteristics of the current monitoring point. The seasonal weights are dimensionless weights determined by regional seasonal rainfall, temperature, and hydrological conditions. This represents the minimum value of the adaptive discrimination time window, used to constrain results that are exceptionally small. This represents the maximum value of the adaptive discrimination time window, used to constrain abnormally large results.

3. The geological disaster prevention and control sampling and monitoring method as described in claim 1 or 2, characterized in that: The time window length includes, The time window is obtained by weighting the lag from rain to pore water pressure and the lag from pore water pressure to displacement by station weight and seasonal weight. When the weighted result is less than the minimum threshold, the current time window is set to the minimum threshold. When the weighted result exceeds the maximum threshold, the current time window is limited to the maximum threshold.

4. The geological disaster prevention and control sampling and monitoring method as described in claim 3, characterized in that: The depth-in-event markers include, Within the adaptive discrimination time window, the rate of change of pore water pressure and the rate of change of displacement are acquired, compared with the classification threshold, and event markers of different levels are generated. The tiered thresholds include the pre-trigger threshold, the trigger threshold, and the strong trigger threshold; Event flags include generating a pre-triggered event flag when either the rate of change of pore water pressure or the rate of change of displacement first exceeds a pre-triggered threshold; A trigger event flag is generated when either the rate of change of pore water pressure or the rate of change of displacement exceeds the trigger threshold. A strong trigger event flag is generated when either the rate of change of pore water pressure or the rate of change of displacement exceeds the strong trigger threshold. When different threshold conditions are met within the same time window, the event marker of the highest level is used as the final marker for the current depth.

5. The geological disaster prevention and control sampling and monitoring method as described in any one of claims 1, 2, and 4, characterized in that: The interlayer score is represented as follows: ， in, Indicates at time The inter-layer score, with a value range of [0,1]. Indicates at time The number of depths of the trigger layer set, Indicates the first The change in deep pore water pressure over adjacent sampling intervals. Indicates the first The change in pore water pressure in the adjacent upper layer at a depth of [depth]. Indicates the first The changes in depth displacement and tilt angle between adjacent sampling intervals Indicates the first The amount of displacement and tilt change of the adjacent upper layer at depth This represents a sign function, with values ​​of +1, 0, and -1. This indicates the indicator function, which takes the value 1 when the condition is met and 0 when the condition is not met. Within the same time window, the direction of change of pore water pressure and displacement at adjacent depths are compared. If the direction of change is consistent, it is marked as co-directional. The inter-layer score is obtained by using the proportion of co-directional layers to the total number of triggering layers.

6. The geological disaster prevention and control sampling and monitoring method as described in claim 5, characterized in that: The event window includes, Add a pre-recording duration before the event time in the trigger layer set, and add a post-recording duration after the event time, which is obtained by multiplying the maximum value of the adaptive discrimination time window and the scaling factor, to construct an event window that includes the pre-recording duration and the post-recording duration.

7. The geological disaster prevention and control sampling and monitoring method according to any one of claims 1, 2, 4, and 6, characterized in that: The chain hash includes, The rain-to-pore water pressure lag, pore water pressure-to-displacement lag, interlayer score, event window parameters, monitoring depth code, channel number and window identifier are input into the hash operation in chronological order, and the operation results are connected one by one to form a chain structure.

8. A geological disaster prevention and control sampling and monitoring system, employing the geological disaster prevention and control sampling and monitoring method as described in any one of claims 1 to 7, characterized in that: Includes a time window construction module, a trigger determination module, and a sampling binding module; The time window construction module determines the lag from rain to pore water pressure and the lag from pore water pressure to displacement based on the cross-correlation extreme values, and generates a cross-depth adaptive discrimination time window by weighting the station weight and seasonal weight. The trigger determination module is used to generate event markers within the depth according to the hierarchical threshold within the cross-depth adaptive discrimination time window, obtain inter-layer scores by the consistency of the change direction of adjacent depths, and output the set of trigger layers when the inter-layer scores meet the threshold and the number of trigger layers is greater than or equal to the set value. The sampling binding module is used to add a layer above and below the trigger layer set to form a target depth set. The event window is determined based on the maximum value of the cross-depth adaptive discrimination time window and the proportional coefficient. At the target depth, a hash operation is performed on the rain to pore water pressure lag, pore water pressure to displacement lag, inter-layer score, event window parameters, monitoring depth code, channel number and window identifier input in chronological order. The operation results are then connected one by one to form a chain structure.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the geological disaster prevention and control sampling and monitoring method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the geological disaster prevention and control sampling and monitoring method as described in any one of claims 1 to 7.

Images