Natural disaster monitoring information processing method and system

Abstract

The invention relates to the technical field of natural disaster monitoring information processing, and provides a natural disaster monitoring information processing method and system, and the method comprises the steps: receiving deep displacement data and accompanying physical information corresponding to the physical cause of the deep displacement data; performing trend judgment on the deep displacement data, judging whether the deep displacement data presents a specific growth trend or not, and obtaining a trend judgment result; when the trend judgment result shows that the deep displacement data presents a specific growth trend, judging a physical cause of the deep displacement data according to the accompanying physical information, and obtaining a credibility mark of the deep displacement data; if the credibility mark shows that the credibility is lower than the preset lower limit, the influence of the deep displacement data on risk assessment or parameter adjustment is limited or eliminated. The method has the effect of improving the accuracy and timeliness of natural disaster monitoring and early warning.

Description

Technical Field

[0001] The present invention relates to the technical field of natural disaster monitoring information processing, and in particular to a natural disaster monitoring information processing method and system. Background Art

[0002] In the field of natural disaster monitoring, advanced monitoring information processing methods and systems are often deployed to effectively prevent geological disasters. These systems utilize multi-source data fusion mechanisms to comprehensively analyze readings from various sensors, including surface displacement meters, deep displacement meters, and rain gauges, to assess geological stability and calculate a comprehensive risk index. During initial system configuration, deep displacement data is often given a higher priority due to its potential to indicate landslide precursors.

[0003] However, existing natural disaster monitoring information processing methods face a technical challenge. In practical applications, the external environment in the system deployment area may undergo non-catastrophic, slow but persistent changes. For example, incomplete leakage prevention in a small water diversion channel can cause channel water to slowly seep into deeper areas of the hillside over a long period of time, gradually changing the local soil physical properties and triggering extremely slow creep subsidence of the soil around the anchor point of the deep displacement meter. The deep displacement data generated by this creep subsidence is small, continuous, and stable, and is generally interpreted by the system as valid normal monitoring data. During the system's regular offline optimization and recalibration process, this "false precursor" deep displacement data exhibits a smooth and consistent growth trend, showing extremely high statistical significance in the learning dataset. This causes the learning mechanism to assign overwhelming weight to it, while significantly devaluing other real disaster signals. As a result, when a real disaster occurs, such as a short period of heavy rainfall triggering a hillside surface debris flow, the real emergency signal is "diluted" after weighted calculation, and the final output comprehensive risk index is far below the actual warning threshold. This prevents the system from issuing effective early warnings, thus missing the optimal opportunity for disaster prevention and mitigation. This phenomenon suggests that when faced with slow, non-catastrophic changes in the external environment, the data fusion weights of existing methods may drift, causing the system to fall into a "perception blind spot" of real risks due to its "logical self-consistency."

[0004] In view of the above problems, the existing technology is in urgent need of improvement. Summary of the Invention

[0005] This application discloses a natural disaster monitoring information processing method and system, which aims to solve the technical problem that in the existing natural disaster monitoring methods, when facing non-disaster slow changes in the external environment, the data fusion weights may "drift", causing the system to fall into a "perception blind spot" of real risks due to "logical self-consistency" and fail to issue effective warnings in a timely manner.

[0006] The technical solution of this application is as follows: In a first aspect, the present application discloses a method for processing natural disaster monitoring information. The method comprises: receiving deep displacement data and accompanying physical information corresponding to the physical causes of the deep displacement data; Perform trend judgment on the deep displacement data to determine whether the deep displacement data presents a specific growth trend and obtain a trend judgment result; When the trend judgment result indicates that the deep displacement data presents a specific growth trend, the physical cause of the deep displacement data is judged based on the accompanying physical information, and a credibility mark of the deep displacement data is obtained; If the confidence mark indicates that the confidence is lower than the preset lower limit, the influence of the deep displacement data on the risk assessment or parameter adjustment is limited or excluded.

[0007] Through this technical solution, this application introduces a mechanism for determining the credibility of deep displacement data. Specifically, when the data exhibits a specific growth trend, the credibility is marked by analyzing its physical causes. This effectively identifies and processes "false precursor" data caused by slow, non-catastrophic changes, preventing it from interfering with risk assessment and early warning. This overcomes the data weight "drift" problem in existing technologies and significantly improves the accuracy and timeliness of early warnings.

[0008] Furthermore, when the trend determination result indicates that the deep displacement data presents a specific growth trend, the steps of determining the physical cause of the deep displacement data based on the accompanying physical information and obtaining a credibility mark of the deep displacement data include: When the accompanying physical information does not clearly indicate the physical cause, and the regional ground stress monitoring data and regional microseismic activity monitoring data do not show abnormalities, obtain the electrochemical response data of the anchor point interface of the deep displacement meter; Based on the electrochemical response data, analyze whether microscopic chemical reactions related to the growth trend occur at the anchor point interface; If a microscopic chemical reaction related to the growth trend occurs at the anchor point interface, the deep displacement data is determined to be caused by the microscopic chemical reaction, and a credibility mark is marked for the deep displacement data, indicating that the credibility is lower than a preset lower limit.

[0009] Through this technical solution, this application provides a refined method for identifying the cause of abnormal growth in deep displacement data when conventional accompanying information is unclear. By introducing electrochemical response data to analyze the microchemical reactions at the anchor point interface, it is possible to accurately distinguish non-catastrophic displacements caused by equipment or environmental factors, thereby more accurately marking the credibility of the data, avoiding misjudgments, and further improving the intelligence and reliability of data processing.

[0010] More specifically, based on the electrochemical response data, the steps for analyzing whether a microscopic chemical reaction related to the growth trend occurs at the anchor point interface include: Extracting interface impedance parameters and / or redox peak parameters from electrochemical response data; Determine whether the interface impedance parameter shows a continuous downward trend, and / or whether the redox peak parameter shows a continuous increasing trend or a peak potential shift trend, and obtain a parameter determination result; If the parameter judgment result is yes, it is determined that a microscopic chemical reaction related to the growth trend occurs at the anchor point interface.

[0011] Through this technical solution, this application defines specific quantitative indicators and criteria for determining microchemical reactions using electrochemical response data, namely, the changing trends of interface impedance parameters and / or redox peak parameters. This makes the determination of microchemical reactions at the anchor point interface more objective, accurate, and actionable, providing a solid scientific basis for the credibility of deep displacement data.

[0012] In some preferred embodiments, the step of limiting or excluding the influence of deep displacement data on risk assessment or parameter adjustment includes: Obtaining credibility marks and cause marks corresponding to deep displacement data; Determine the risk level of deep displacement data based on the credibility mark and cause mark; Select the corresponding impact limitation strategy based on the risk level; According to the impact limitation strategy, the influence of deep displacement data on risk assessment or parameter adjustment is limited or excluded.

[0013] Through this technical solution, this application provides a risk level assessment and tiered restriction strategy based on data credibility and causes. This enables the system to adopt differentiated processing methods based on the actual reliability and potential impact of the data, avoiding blanket data exclusion. This maximizes the use of effective information while ensuring the accuracy of risk assessments, improving system flexibility and the refinement of decision-making.

[0014] Furthermore, the step of determining the risk level of the deep displacement data based on the credibility mark and the cause mark includes: Identify the corresponding physical parameters based on the credibility mark and the cause mark; Analyze the values ​​or changing trends of physical parameters; Determine whether the value or change trend of the physical parameter exceeds a preset threshold and obtain a physical parameter judgment result; If the physical parameter judgment result is yes, the risk level corresponding to the deep displacement data is adjusted and determined.

[0015] Through this technical solution, this application provides a method for dynamically adjusting risk levels by identifying physical parameters associated with credibility markers and causal markers and determining their values ​​or changing trends. This makes the determination of risk levels more objective and quantitative, enabling real-time adjustments based on actual monitoring data, making risk assessments more adaptable and accurate.

[0016] On the basis of the above, the steps of determining whether the value or change trend of the physical parameter exceeds a preset threshold and obtaining the physical parameter determination result include: Obtain regional environmental change data or geological background evolution data; Calculate the adaptive deviation of the preset threshold based on regional environmental change data or geological background evolution data; determining whether the adaptive deviation exceeds a predetermined range and obtaining a deviation determination result; If the deviation judgment result is yes, adjust the preset threshold; Based on the adjusted preset threshold, it is determined whether the value or change trend of the physical parameter exceeds the adjusted preset threshold.

[0017] Through this technical solution, this application introduces an adaptive adjustment mechanism for preset thresholds. By dynamically calculating and adjusting thresholds based on regional environmental changes or geological background evolution data, the risk assessment thresholds can be made more consistent with current conditions, avoiding misjudgments or missed detections caused by fixed thresholds. This significantly improves the system's environmental adaptability and the robustness of early warnings.

[0018] Preferably, the step of calculating the adaptive deviation of the preset threshold comprises: Analyze the correlation between regional environmental change data or geological background evolution data and historical threshold applicability data to obtain correlation information; According to the associated information, a mapping relationship of the associated information is constructed; Based on the current regional environmental change data or geological background evolution data and mapping relationships, the adaptive deviation of the preset threshold is calculated.

[0019] Through this technical solution, this application provides a method for calculating threshold adaptive deviation based on historical data and correlation analysis. This eliminates the need for simple empirical judgment in threshold adjustment and instead enables intelligent adjustments based on data-driven and historical patterns. This improves the accuracy and scientific nature of threshold adjustments and further optimizes the precision of risk assessment.

[0020] Furthermore, the steps of analyzing the correlation between the regional environmental change data or the geological background evolution data and the historical threshold applicability data to obtain correlation information include: Perform data quality processing on regional environmental change data or geological background evolution data; data quality processing includes interpolation of missing data based on the temporal and spatial correlation of the data, adaptive filtering of noisy data, and feature alignment and normalization of heterogeneous data; Based on the regional environmental change data or geological background evolution data that have been processed for data quality, combined with historical threshold applicability data, multivariate statistical analysis and nonlinear regression analysis are performed to identify the associations between the data and obtain correlation information.

[0021] Through its technical solution, this application emphasizes the importance of data quality processing and advanced analytical methods in establishing data associations. By interpolating, filtering, aligning, and normalizing the raw data, combined with multivariate statistical and nonlinear regression analysis, it is possible to more accurately and comprehensively explore the deep connections between environmental and geological data and historical thresholds, providing a more reliable basis for adaptive threshold adjustment.

[0022] In one embodiment, the step of constructing a mapping relationship of the association information according to the association information includes: Obtaining preset physical association rules or experience lookup tables; According to the preset physical association rules or empirical lookup table, the regional environmental change data or geological background evolution data are mapped to the adaptive deviation of the preset threshold value to construct the mapping relationship of the associated information.

[0023] Through the technical solution, this application provides a practical method for constructing association information mapping relationships, namely, through preset physical association rules or empirical lookup tables. This makes the calculation process of threshold adaptive deviation more efficient and controllable, and can be based on both theoretical models and long-term practical experience, improving the deployability and practicality of the system.

[0024] In a second aspect, the present application further discloses a natural disaster monitoring information processing system for performing natural disaster monitoring information processing, comprising: a displacement data receiving module, configured to receive deep displacement data and accompanying physical information corresponding to the physical causes of the deep displacement data; The displacement trend judgment module is used to perform trend judgment on the deep displacement data, determine whether the deep displacement data presents a specific growth trend, and obtain a trend judgment result; A credibility marking module is used to determine the physical cause of the deep displacement data based on accompanying physical information when the trend judgment result indicates that the deep displacement data presents a specific growth trend, and obtain a credibility mark for the deep displacement data; The impact limiting execution module is used to limit or exclude the impact of the deep displacement data on risk assessment or parameter adjustment if the credibility mark indicates that the credibility is lower than a preset lower limit.

[0025] Through a technical solution, this application provides a system entity capable of implementing the aforementioned natural disaster monitoring information processing method. Through a modular design, the system integrates key functions such as data reception, trend assessment, credibility marking, and impact limitation, providing hardware and software support for practical applications. This makes the aforementioned method possible, effectively resolving the issues of data misjudgment and warning failure in existing technologies.

[0026] Beneficial effects The natural disaster monitoring information processing method disclosed in this application proposes an innovative solution to the problem in the prior art that deep displacement data may produce "false precursors" due to slow changes in non-disaster factors, resulting in an imbalance in the system's weight distribution of real disaster signals, thereby affecting the accuracy of early warnings. This method introduces a mechanism to determine the credibility of deep displacement data. When receiving deep displacement data and judging that it presents a specific growth trend, it no longer simply regards it as a disaster precursor. Instead, it further determines the true physical cause of the deep displacement data based on the accompanying physical information corresponding to its physical cause, and accordingly obtains a credibility mark for the data. When the credibility mark indicates that the data credibility is lower than the preset lower limit, the system can intelligently limit or eliminate the impact of the deep displacement data on risk assessment or parameter adjustment.

[0027] Through the above technical solution, the present application can effectively identify and filter out deep displacement data with "false precursor" characteristics caused by non-disaster factors (such as slow creep and sinking of anchor points due to leakage in small water diversion channels). This avoids such data from being given too high a weight during the system learning and calibration process, thereby preventing the real disaster signal from being "diluted" or masked. Therefore, when a real disaster occurs, the system can conduct risk assessment based on more accurate and reliable data and issue effective warnings in a timely manner, overcoming the shortcomings of existing methods that fall into "perception blind spots" due to "logical self-consistency", significantly improving the accuracy and timeliness of natural disaster monitoring and early warning, and providing more reliable technical support for disaster prevention and mitigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A flowchart of a method for processing natural disaster monitoring information in one embodiment of the present invention; Figure 2 This is one of the method flow charts of a natural disaster monitoring information processing method in another embodiment of the present invention; Figure 3 This is a second flow chart of a method for processing natural disaster monitoring information in another embodiment of the present invention; Figure 4 This is a third flow chart of a method for processing natural disaster monitoring information in another embodiment of the present invention; Figure 5 This is a fourth flow chart of a method for processing natural disaster monitoring information in another embodiment of the present invention; Figure 6 This is a fifth method flow chart of a natural disaster monitoring information processing method according to another embodiment of the present invention; Figure 7 This is a system block diagram of a natural disaster monitoring information processing system in another embodiment of the present invention; Description of reference numerals: 1. Natural disaster monitoring information processing system; 11. Displacement data receiving module; 12. Displacement trend judgment module; 13. Credibility marking module; 14. Impact limitation execution module. DETAILED DESCRIPTION

[0029] The technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0030] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0031] In the field of natural disaster monitoring, traditional information processing methods suffer from data fusion weight drift when faced with slow, non-disaster-related changes in the external environment. For example, non-disaster factors can cause deep displacement data to show a sustained upward trend, which the system mistakenly interprets as a valid precursor signal and, consequently, overweights during offline optimization and recalibration. This dilutes the weight of actual disaster signals, resulting in the system failing to issue effective warnings in a timely manner and missing the optimal opportunity for disaster prevention and mitigation.

[0032] In this regard, this application proposes a natural disaster monitoring information processing method, combining Figure 1 As shown, including: S1, receiving deep displacement data and accompanying physical information corresponding to the physical cause of the deep displacement data; S2, performing trend judgment on the deep displacement data to determine whether the deep displacement data presents a specific growth trend and obtaining a trend judgment result; S3, when the trend judgment result indicates that the deep displacement data presents a specific growth trend, the physical cause of the deep displacement data is judged based on the accompanying physical information, and a credibility mark of the deep displacement data is obtained; S4. If the credibility mark indicates that the credibility is lower than the preset lower limit, the influence of the deep displacement data on the risk assessment or parameter adjustment is limited or excluded.

[0033] In order to understand the technical solution of the present application more easily and clearly, the key terms and implementation environment involved are explained below. Deep displacement data refers to data obtained by sensors such as deep displacement meters, which reflects the displacement changes of deep structures in the stratum. It is usually used to monitor the deformation inside the geological body and is an important basis for judging the precursors of geological disasters such as landslides and collapses. Accompanying physical information refers to other physical quantity information obtained simultaneously or in association with the deep displacement data, which can reflect the physical cause of the deep displacement data. For example, it can be regional ground stress monitoring data, regional microseismic activity monitoring data, groundwater level data, soil moisture data, temperature data, etc. This information helps to comprehensively judge the real cause of the deep displacement change. Trend judgment refers to analyzing the law of change of deep displacement data over time to determine whether it presents a certain specific growth pattern, such as linear growth, accelerated growth or cyclical growth. A specific growth trend refers to a preset displacement growth pattern that may indicate abnormal changes in the geological body, such as continuous, nonlinear or accelerated displacement growth. A credibility mark is a label assigned after evaluating the reliability or validity of deep displacement data. This mark is used to indicate whether the data truly reflects the precursors of geological disasters or whether it is interfered with by non-disaster factors. The preset lower limit is the threshold used to judge the credibility of the data. When the credibility of the data is lower than this lower limit, it indicates that the data may be unreliable or not indicative. Risk assessment refers to the process of making a comprehensive judgment on the possibility, potential impact and severity of geological disasters based on monitoring data. Parameter adjustment refers to the dynamic modification of the warning thresholds, model parameters or data weights within the monitoring system based on monitoring data and risk assessment results to optimize the performance and accuracy of the system. The natural disaster monitoring information processing method of the present application is usually deployed at a monitoring station or data processing center in an area prone to geological disasters. This environment usually includes various types of geological sensors, data acquisition units, data transmission networks, and computing servers or cloud computing platforms for data processing and analysis. After being received, the data is preprocessed, analyzed and judged, and ultimately used to guide risk assessment and dynamic adjustment of system parameters.

[0034] Specifically, the various technical features of this application can be implemented in the following ways.

[0035] Regarding receiving deep displacement data and accompanying physical information corresponding to the physical causes of deep displacement data, deep displacement data can be collected in real time by deep displacement meters deployed deep within the formation and transmitted to a data processing center via a wired or wireless network. Accompanying physical information can be collected synchronously by other sensors and received together with the deep displacement data after aligning its timestamps. Alternatively, deep displacement data can be imported in batches from a historical database, which may have undergone preliminary cleaning and formatting. Accompanying physical information can be obtained from different data sources and received via data interfaces or file transfer. During the reception process, a data verification mechanism can be implemented to ensure data integrity and accuracy.

[0036] To determine whether deep displacement data exhibits a specific growth trend and obtain trend determination results, linear regression analysis can be used to determine the presence of a sustained growth trend. The slope of the displacement data over time is calculated and compared with a preset threshold to determine whether a sustained growth trend exists. For example, if the slope is consistently greater than a positive value, it is considered to exhibit a specific growth trend. As a preferred embodiment, the deep displacement data can be smoothed using a moving average or exponential smoothing method. The current data point can then be compared with the average or smoothed value of the previous time period to determine whether an accelerating growth trend exists. For example, if the smoothed displacement value increases over multiple consecutive periods, with the magnitude of the increase gradually increasing, it is considered to exhibit a specific growth trend. Furthermore, time series analysis models, such as the autoregressive integrated moving average model or the long short-term memory network, can be used to model and predict deep displacement data. By comparing the deviation between the actual displacement data and the model's predicted value and combining statistical significance testing, it can be determined whether an abnormal, sustained growth trend exists.

[0037] When the trend judgment result indicates that the deep displacement data exhibits a specific growth trend, the system determines the physical cause of the deep displacement data based on the accompanying physical information and obtains a credibility label for the deep displacement data. When the deep displacement data exhibits a specific growth trend, the system can first check whether the accompanying physical information contains clear hazard indicators, such as the presence of short-term heavy rainfall, significant regional ground stress anomalies, or high-frequency microseismic activity. If such clear indicators are present, the deep displacement data is labeled as highly credible. If no clear indicators are present, further analysis is required. As a preferred embodiment, a rule-based expert system can be constructed. When the deep displacement data exhibits a specific growth trend, the system infers the possible cause of the deep displacement based on preset physical association rules and the accompanying physical information. For example, if the deep displacement growth occurs simultaneously with a sustained, slow rise in the groundwater level and there are no other hazard signals, it may be inferred to be non-hazardous creep and labeled as having low credibility. Alternatively, machine learning classification models, such as support vector machines or decision trees, can be employed. The model learns the relationship between deep displacement data, accompanying physical information, and the actual physical causes in historical data. When new deep displacement data shows a specific growth trend, the corresponding accompanying physical information is input, and the model outputs a probability distribution of its physical cause and assigns a credibility label accordingly. For example, if the model determines that the probability of non-catastrophic creep is high, the credibility is marked as below a preset lower limit.

[0038] Regarding limiting or excluding the impact of deep displacement data on risk assessment or parameter adjustment if the credibility flag indicates that the credibility is below a preset lower limit, when the credibility flag of deep displacement data indicates that its credibility is below the preset lower limit, the weight of this deep displacement data in the calculation of the comprehensive risk index can be reduced during the risk assessment process. For example, its weight can be reduced from 0.4 to 0.1, thereby reducing its contribution to the final risk assessment result. As a preferred embodiment, deep displacement data with a credibility below the preset lower limit can be completely excluded from the dataset used for system parameter adjustment. This means that this data will not participate in subsequent machine learning model training or optimization processes, avoiding its negative impact on the system's adaptive capabilities. In addition, deep displacement data with a credibility below the preset lower limit can be specially marked and stored separately. When conducting risk assessment, the system will prioritize high-confidence data and only refer to low-confidence data when necessary, but will apply strict correction factors or manual review processes to it.

[0039] Optional, combined Figure 2 As shown, when the trend judgment result indicates that the deep displacement data presents a specific growth trend, the step of determining the physical cause of the deep displacement data based on the accompanying physical information and obtaining a credibility mark of the deep displacement data includes: S31, when the accompanying physical information does not clearly indicate a physical cause, and the regional ground stress monitoring data and the regional microseismic activity monitoring data do not show abnormalities, obtain electrochemical response data of the anchor point interface of the deep displacement meter; S32, based on the electrochemical response data, analyze whether microchemical reactions related to the growth trend occur at the anchor point interface; S33, if a microscopic chemical reaction related to the growth trend occurs at the anchor point interface, it is determined that the deep displacement data is caused by the microscopic chemical reaction, and a credibility mark is marked on the deep displacement data, indicating that the credibility is lower than a preset lower limit.

[0040] The anchor point interface of a deep displacement meter refers to the fixed point connecting the meter to the surrounding rock, soil, or structure. Its stability directly impacts the accuracy of the displacement data. Electrochemical response data can be understood as real-time monitoring of changes in the electrochemical properties of the interface material, such as impedance, potential, and current, by deploying electrochemical sensors at the anchor point interface. Changes in these parameters can reflect whether microchemical reactions such as corrosion, dissolution, and material fatigue are occurring at the anchor point interface. Furthermore, after acquiring the electrochemical response data, it is necessary to analyze whether microchemical reactions related to the growth trend are occurring at the anchor point interface based on this data. Microchemical reactions here refer to chemical processes that may cause the anchor point to loosen, fail, or degrade the sensor's performance, such as electrochemical corrosion of metal anchors or aging and decomposition of polymer encapsulation materials. These reactions can cause the deep displacement meter's measurement baseline to drift, resulting in the displacement data appearing to be an untrue geological displacement growth trend. Therefore, if the analysis results indicate that microchemical reactions related to the growth trend of the deep displacement data are indeed occurring at the anchor point interface, it can be determined that the deep displacement data is not caused by actual geological displacement but rather by microchemical reactions at the anchor point interface. In this case, to ensure the accuracy of subsequent risk assessments or parameter adjustments, the deep displacement data will be marked as having a credibility lower than a preset lower limit. This preset lower limit can be set based on the actual application scenario and the requirements for data reliability. For example, if the data is below a certain threshold, it is considered unreliable.

[0041] In some preferred embodiments, a specific example is provided below for illustration. Assume that in a geological disaster monitoring area, a deep displacement meter continuously monitors that its deep displacement data shows a slow but continuous growth trend. However, the accompanying physical information such as rainfall, temperature, groundwater level, etc. monitored during the same period are all within the normal range, and the ground stress monitoring data and regional microseismic activity monitoring data in the area do not show any abnormal fluctuations. In this case, if we only rely on traditional macroscopic accompanying physical information, it will be difficult to determine whether the growth of the deep displacement data is real geological creep or caused by other factors.

[0042] At this point, the solution of the present application is activated. The system first determines that the accompanying physical information does not clearly indicate the physical cause, and neither the regional ground stress monitoring data nor the regional microseismic activity monitoring data show any abnormalities. Subsequently, the system obtains the electrochemical response data of the deep displacement meter anchor point interface. By analyzing these electrochemical response data, for example, the interface impedance parameters and redox peak parameters are extracted. If it is found that the interface impedance parameters show a continuous downward trend, and / or the redox peak parameters show a continuous increasing trend or a peak potential shift trend, this indicates that microscopic chemical reactions such as corrosion or material degradation may be occurring at the anchor point interface.

[0043] Based on this analysis, the system determined that the deep displacement data was not caused by actual geological displacement, but rather by microscopic chemical reactions at the anchor point interface. Therefore, the deep displacement data was flagged as having a credibility below a preset lower limit. This effectively identified the abnormal data and limited its impact on risk assessments or parameter adjustments, avoiding misjudgments due to sensor issues and ensuring the accuracy and reliability of the monitoring system.

[0044] Optional, combined Figure 3 As shown, the steps of analyzing whether a microscopic chemical reaction related to the growth trend occurs at the anchor point interface based on the electrochemical response data in S32 include: S321, extracting interface impedance parameters and / or redox peak parameters from the electrochemical response data; S322, determining whether the interface impedance parameter shows a continuous downward trend, and / or whether the redox peak parameter shows a continuous increasing trend or a peak potential shift trend, and obtaining a parameter determination result; S323, if the parameter judgment result is yes, it is determined that a microscopic chemical reaction related to the growth trend occurs at the anchor point interface.

[0045] Specifically, electrochemical response data can be acquired by placing an electrochemical sensor at the anchor interface of the depth displacement meter. This sensor can monitor the electrochemical state at the anchor interface in real time or periodically. Interfacial impedance parameters, such as interfacial resistance and capacitance, are measured using electrochemical impedance spectroscopy (EIS). They reflect charge transfer resistance, diffusion resistance, and double-layer properties at the interface. Redox peak parameters, typically obtained using techniques such as cyclic voltammetry (CV) or linear sweep voltammetry (LSV), include oxidation peak current, reduction peak current, and peak potential. These parameters are closely related to the type, rate, and reversibility of the redox reaction occurring at the interface. In practical applications, when microscopic chemical reactions occur at the anchor interface, such as corrosion, dissolution, or the formation of new compounds, the electrochemical properties of the anchor interface can change significantly. For example, corrosion often leads to a decrease in interfacial impedance because the formation of corrosion products or metal dissolution increases the conductivity or changes the structure of the interface. Furthermore, the occurrence of certain redox reactions can increase the redox peak current or shift the peak potential, indicating the formation of new electrochemically active species or a change in the reaction pathway. Therefore, by monitoring the changing trends of these parameters, it is possible to effectively determine whether microchemical reactions related to the growth trend of deep displacement are occurring at the anchor point interface.

[0046] Optional, combined Figure 4 As shown, the steps in S4 to limit or exclude the influence of deep displacement data on risk assessment or parameter adjustment include: S41, obtaining a credibility mark and a cause mark corresponding to the deep displacement data; S42, determining the risk level of the deep displacement data based on the credibility mark and the cause mark; S43, select the corresponding impact limitation strategy based on the risk level; S44. Limit or exclude the impact of deep displacement data on risk assessment or parameter adjustment according to the impact limitation strategy.

[0047] Among them, obtaining the credibility mark and cause mark corresponding to the deep displacement data means that after the system receives the deep displacement data, it will perform preliminary processing on it to obtain the reliability assessment result of the data (i.e., credibility mark) and the potential cause of its specific growth trend (i.e., cause mark). The credibility mark can indicate whether the deep displacement data is reliable. For example, a credibility lower than the preset lower limit may indicate that the data is abnormal or interfered with by non-geological factors. The cause mark is used to identify the physical cause of the growth trend of the deep displacement data, for example, whether it is caused by geological tectonic activity, groundwater changes, temperature effects or other non-geological factors (such as equipment failure, environmental interference). These marks are usually generated after the deep displacement data is received and preliminarily judged.

[0048] Furthermore, the risk level of the deep displacement data is determined based on the credibility mark and the causal mark. This step aims to comprehensively consider the reliability of the data and its physical causes, and to quantitatively assess the potential risks that may be caused by the deep displacement data. For example, if the deep displacement data is marked as low credibility and the causal mark points to non-geological factors, its risk level may be assessed as low, indicating that its reference value for the actual geological hazard risk assessment is limited. Conversely, if the data credibility is high and the causal mark points to potential geological hazard precursors, its risk level may be assessed as high. The determination of the risk level can be based on preset rules, expert knowledge systems or machine learning models to map the credibility mark and causal mark to different risk levels, such as "high risk", "medium risk", "low risk" or "invalid data".

[0049] On this basis, the corresponding impact limitation strategy is selected according to the risk level. Different risk levels require different handling methods to avoid unreliable or misleading data from negatively impacting subsequent risk assessments and parameter adjustments. For example, for deep displacement data that is assessed as "invalid data", a strategy of completely excluding its impact can be adopted; for "low-risk" data, a strategy of limiting its weight or using it only as a reference can be adopted; for "medium-risk" data, further verification or correction may be required before use; and for "high-risk" data, even if its credibility is questionable, it may need to be handled with caution to prevent underreporting of real risks. Impact limitation strategies can include data exclusion, data demotion, data correction, data isolation, or using it only for auxiliary reference.

[0050] Ultimately, according to the impact limitation strategy, the impact of deep displacement data on risk assessment or parameter adjustment is limited or excluded. This means that according to the risk level determined previously and the strategy selected, the system will intervene in the role of deep displacement data in subsequent risk assessment model input, warning threshold adjustment, geological parameter calibration and other links. For example, if the strategy is "data exclusion", the deep displacement data will not be included in the risk assessment calculation; if the strategy is "data de-weighting", the data will be given a lower weight in the calculation; if the strategy is "data isolation", the data may be stored and analyzed separately, and not mixed with mainstream data. This move is intended to ensure the accuracy of risk assessment and the effectiveness of parameter adjustment, and to avoid misjudgment or improper operation due to erroneous or unreliable data.

[0051] Optionally, the step of determining the risk level of the deep displacement data based on the credibility mark and the cause mark may include the following operations: Identify the corresponding physical parameters based on the credibility mark and the cause mark; Analyze the values ​​or changing trends of physical parameters; Determine whether the value or change trend of the physical parameter exceeds a preset threshold and obtain a physical parameter judgment result; If the physical parameter judgment result is yes, the risk level corresponding to the deep displacement data is adjusted and determined.

[0052] Identifying the corresponding physical parameters means that, based on the credibility markers of the deep displacement data and the identified physical cause markers, the system can intelligently select the physical parameters most relevant to the current monitoring scenario and potential risk type. For example, if the deep displacement data is marked as caused by groundwater activity, physical parameters such as pore water pressure and groundwater level changes may be identified; if the cause marker points to geological tectonic activity, parameters such as ground stress and microseismic activity frequency may be identified. These physical parameters are selected to provide deeper physical insights into deep displacement phenomena.

[0053] Furthermore, analyzing the values ​​or trends of physical parameters involves continuously monitoring the identified physical parameters and conducting in-depth analysis of their current values ​​and trends over time. This can include evaluating the absolute value, rate of change, acceleration, and linkage with other related parameters. For example, a sustained increase in pore water pressure or a rapid accumulation of ground stress could indicate a potential risk.

[0054] Therefore, determining whether a physical parameter's value or trend exceeds a preset threshold involves comparing the analyzed value or trend with pre-set safety thresholds. These thresholds are determined based on historical data, geological models, or expert experience, and serve to delineate between normal fluctuations and abnormal changes. When a physical parameter's value or trend exceeds these thresholds, a positive physical parameter determination is obtained, indicating a potential escalation of risk.

[0055] If the physical parameters determine an abnormality, the risk level corresponding to the deep displacement data is adjusted and determined. This means that if a physical parameter indicates an abnormality, even if the deep displacement data itself is less reliable or the cause is unclear, the system will adjust the risk level indicated by the deep displacement data upward or determine it more accurately based on the changes in these more physically meaningful parameters to reflect the actual risk situation.

[0056] In some of the above-mentioned embodiments of the present application, when determining the risk level of deep displacement data based on the credibility mark and the cause mark, it is necessary to judge whether the value or change trend of the physical parameter exceeds the preset threshold. However, in actual applications, the occurrence and evolution of natural disasters are often affected by various dynamic factors such as regional environmental changes and geological background evolution. If the preset threshold remains fixed, it may not accurately reflect the current actual geological environment status, resulting in a decrease in the accuracy of risk assessment, and may even lead to misjudgment or omission. In this regard, the present application further proposes a method for optimizing the judgment of whether the physical parameter exceeds the threshold, aiming to improve the accuracy and adaptability of risk assessment by dynamically adjusting the preset threshold.

[0057] Optionally, the step of determining whether the value or change trend of the physical parameter exceeds a preset threshold and obtaining the physical parameter determination result includes: Obtain regional environmental change data or geological background evolution data; Calculate the adaptive deviation of the preset threshold based on regional environmental change data or geological background evolution data; determining whether the adaptive deviation exceeds a predetermined range and obtaining a deviation determination result; If the deviation judgment result is yes, adjust the preset threshold; Based on the adjusted preset threshold, it is determined whether the value or change trend of the physical parameter exceeds the adjusted preset threshold.

[0058] Specifically, obtaining regional environmental change data or geological background evolution data refers to collecting dynamic information on external environmental factors and the internal state of the geological body related to the monitoring area. Among them, regional environmental change data can include but are not limited to meteorological and hydrological data such as rainfall, temperature, groundwater level, river flow, soil moisture, as well as data on human activities such as engineering construction and reservoir water storage and release. Geological background evolution data can include data such as changes in ground stress, microseismic activity, surface deformation, and changes in groundwater chemical composition. These data reflect long-term or short-term changes in the internal state of the geological body. The acquisition of these data can be achieved through various channels such as various sensors deployed in the monitoring area, remote sensing technology, historical data records, and geological exploration reports. Its purpose is to provide comprehensive background information for subsequent threshold adaptive adjustments.

[0059] Furthermore, the adaptive deviation of the preset threshold is calculated based on regional environmental change data or geological background evolution data. The adaptive deviation refers to the difference between the preset threshold and the actual required threshold under the current regional environmental and geological background. This calculation can be based on historical data, statistical models, machine learning algorithms, or expert experience rules. For example, by analyzing the relationship between historical environmental changes and threshold effectiveness, a mapping model can be established. When new environmental data is input, the model can output a recommended threshold adjustment amount, namely the adaptive deviation.

[0060] Next, a determination is made as to whether the adaptive deviation exceeds a predetermined range. The predetermined range refers to the allowable threshold adjustment range. For example, a percentage range (such as ±10%) or an absolute value range can be set. This step is intended to avoid over-adjusting the threshold and ensure the rationality and stability of the adjustment. If the deviation is too large, it may mean that the current environmental changes are unusually drastic or there is a problem with the calculation model. In this case, manual intervention or deeper analysis may be required. If the deviation judgment result is yes, that is, the adaptive deviation exceeds the predetermined range, the preset threshold is adjusted. The adjustment method can be simple addition and subtraction operations or more complex nonlinear adjustments to make the threshold more suitable for the current actual situation.

[0061] Finally, based on the adjusted preset threshold, a determination is made as to whether the value or trend of the physical parameter exceeds the adjusted preset threshold. After the threshold is dynamically adjusted, the physical parameters corresponding to the deep displacement data (such as displacement rate and acceleration) are compared with this new, more adaptable threshold. This step ensures that the risk assessment is based on a dynamic threshold that matches the current environmental and geological conditions, thereby improving the accuracy and reliability of the assessment.

[0062] In some preferred embodiments, a specific example is provided below. Assume that in a landslide monitoring area, a deep displacement meter continuously monitors small displacement increases. According to traditional fixed preset thresholds, these displacement data may not yet reach the critical value that triggers an early warning. However, the solution of the present application further obtains regional environmental change data in the area. For example, monitoring of recent continuous heavy rainfall has led to a significant increase in soil moisture content. At the same time, geological background evolution data, such as regional microseismic activity monitoring, indicates that internal stress in the strata is accumulating. Based on these rainfall and microseismic activity data, the system calculates an adaptive deviation from the preset displacement threshold. For example, through historical data analysis, it is known that under conditions of continuous heavy rainfall and stress accumulation, the critical displacement threshold for landslides will significantly decrease. If the calculated adaptive deviation indicates that the current threshold should be lowered, and the deviation is within a predetermined range, the system adjusts the original preset threshold. At this point, even if the deep displacement data has not yet reached the original fixed threshold, it may have exceeded the adjusted, lower adaptive threshold. As a result, the system can promptly determine that the risk level corresponding to the deep displacement data has increased, and trigger corresponding warnings or take restrictive measures, thereby effectively avoiding the underestimation of potential risks caused by environmental changes and significantly improving the timeliness and accuracy of warnings.

[0063] Optionally, the step of calculating the adaptive deviation of the preset threshold includes: Analyze the correlation between regional environmental change data or geological background evolution data and historical threshold applicability data to obtain correlation information; According to the associated information, a mapping relationship of the associated information is constructed; Based on the current regional environmental change data or geological background evolution data and mapping relationships, the adaptive deviation of the preset threshold is calculated.

[0064] Analyzing the correlation between regional environmental change data or geological background evolution data and historical threshold applicability data aims to identify key factors influencing threshold applicability and their interrelationships. This historical threshold applicability data can include past cases where the pre-set thresholds proved effective or ineffective under different environmental or geological conditions, along with the corresponding environmental or geological parameters. Analysis of this data can reveal patterns in threshold performance under different scenarios.

[0065] Furthermore, based on the obtained correlation information, a mapping relationship of the correlation information can be constructed. This mapping relationship can be a mathematical model, a lookup table, a set of rules, or a machine learning model. Its purpose is to convert the current regional environmental change data or geological background evolution data into a prediction or calculation of the adaptive deviation from the preset threshold value through the identified correlation. For example, if the correlation information indicates that an increase in a certain environmental parameter will cause the threshold to be adjusted downward, the mapping relationship will reflect this adjustment rule.

[0066] Ultimately, based on the currently acquired regional environmental change data or geological background evolution data, combined with the established mapping relationship, the adaptive deviation of the preset threshold can be calculated. This deviation quantifies the difference between the preset threshold and the actual requirements under the current environmental or geological conditions, providing a quantitative basis for subsequent threshold adjustments.

[0067] In some of the above-mentioned embodiments of the present application, it is proposed to obtain correlation information by analyzing the correlation between regional environmental change data or geological background evolution data and historical threshold applicability data. However, in actual applications, the original regional environmental change data or geological background evolution data may have problems such as missing, noisy or heterogeneous. If the correlation analysis is performed directly, it may lead to insufficient accuracy of the correlation information, thereby affecting the calculation accuracy of the preset threshold adaptability deviation.

[0068] Optional, combined Figure 5 As shown, the steps of analyzing the correlation between regional environmental change data or geological background evolution data and historical threshold applicability data to obtain correlation information include: A1. Data quality processing is performed on regional environmental change data or geological background evolution data. Data quality processing includes interpolation of missing data based on the temporal and spatial correlation of the data, adaptive filtering of noisy data, and feature alignment and normalization of heterogeneous data. A2, based on the regional environmental change data or geological background evolution data that have been processed for data quality, combined with historical threshold applicability data, conduct multivariate statistical analysis and nonlinear regression analysis to identify the association between the data and obtain correlation information.

[0069] Specifically, data quality processing refers to the preprocessing of original regional environmental change data or geological background evolution data to improve the integrity, accuracy and consistency of the data. Among them, interpolation of missing data based on the temporal and spatial correlation of the data means that when there are gaps in the monitoring data, the continuity or correlation of the data in time and space is used to estimate and fill in the missing values. For example, linear interpolation, spline interpolation, Kriging interpolation or machine learning-based methods can be used for interpolation. The purpose is to ensure the integrity of the data sequence and avoid affecting subsequent analysis due to discontinuous data. Adaptive filtering of noisy data refers to the use of algorithms that can dynamically adjust the filtering parameters according to the characteristics of the data for random interference, sensor errors or environmental noise that may occur during the monitoring process. For example, Kalman filtering, wavelet denoising or adaptive median filtering can be used. The purpose is to remove invalid information in the data and improve the signal-to-noise ratio of the data. Feature alignment and normalization of heterogeneous data means making data comparable by unifying the data representation and dimensions when the data comes from different sensors, different measurement standards, or different data formats. For example, feature alignment can be performed using min-max normalization, Z-score standardization, or principal component analysis (PCA). The purpose is to eliminate heterogeneity between data and ensure the effectiveness of joint analysis of different types of data.

[0070] After data quality processing, multivariate statistical analysis and nonlinear regression analysis are performed based on the processed regional environmental change data or geological background evolution data, combined with historical threshold applicability data. Multivariate statistical analysis refers to the simultaneous consideration of the relationships between multiple variables. For example, correlation analysis, cluster analysis, factor analysis, or principal component analysis can be used. Its purpose is to reveal the potential association patterns between multiple environmental or geological factors and historical threshold applicability. Nonlinear regression analysis refers to the establishment of mathematical models for nonlinear relationships between variables. For example, polynomial regression, support vector regression (SVR), neural networks, or Gaussian process regression can be used. Its purpose is to capture complex nonlinear dependencies between data, thereby more accurately quantifying the impact of environmental or geological changes on threshold applicability.

[0071] In some preferred embodiments, a specific example is used for illustration. Assume that the present application is applied to natural disaster monitoring in a landslide-prone area. The regional environmental change data of the area include rainfall, temperature, soil moisture, etc., and the geological background evolution data include surface deformation, groundwater level change, etc. The historical threshold applicability data includes the environmental and geological parameter thresholds and their applicability assessments when landslide events occurred in the past in the area. When analyzing the association between regional environmental change data or geological background evolution data and historical threshold applicability data, these raw data are first subjected to data quality processing. For example, if there is missing rainfall data, it can be interpolated based on the data and time series pattern of adjacent monitoring points; if the surface deformation sensor data is subject to environmental electromagnetic interference and generates noise, an adaptive Kalman filter algorithm can be used to process it to remove the noise; if the geological background evolution data comes from different types of sensors (such as GNSS displacement meters and InSAR satellite images), feature alignment and normalization processing are required to eliminate the scale and format differences of different data sources. After data quality processing, these high-quality data are combined with historical threshold suitability data to conduct multivariate statistical analysis. For example, principal component analysis (PCA) is used to identify the main environmental and geological factors that influence landslide occurrence. Nonlinear regression analysis, such as support vector regression (SVR) or neural network models, is then used to establish complex nonlinear relationship models between these factors and historical threshold suitability. This allows for precise identification of associations between the data and the resulting correlation information used to calculate the adaptive deviation of the preset threshold. This allows for a more accurate assessment of the risk level under current environmental and geological conditions, and the adjustment of warning thresholds accordingly.

[0072] Optional, combined Figure 6 As shown, the steps of constructing a mapping relationship of the association information according to the association information include: B1, obtaining preset physical association rules or experience lookup table; B2, according to the preset physical association rules or experience lookup table, the regional environmental change data or geological background evolution data is mapped to the adaptive deviation of the preset threshold value to construct the mapping relationship of the association information.

[0073] Pre-set physical association rules can be understood as mathematical models or logical rules established based on physics principles, geomechanical models, or engineering experience. They describe the quantitative or qualitative relationship between regional environmental change data or geological background evolution data and the adaptive deviation of a preset threshold. For example, these rules can define how the deep displacement monitoring threshold should be adjusted under specific changes in temperature, humidity, or formation pressure. An empirical lookup table is a data table constructed through historical data analysis, expert experience, or simulation results. It contains the adaptive deviation values ​​of the preset threshold corresponding to different combinations of regional environmental change data or geological background evolution data. Upon receiving the current regional environmental change data or geological background evolution data, the system can quickly obtain the corresponding adaptive deviation through a table lookup. Mapping regional environmental change data or geological background evolution data to the adaptive deviation of the preset threshold refers to the process of converting or associating the input data (regional environmental change data or geological background evolution data) to the output data (the adaptive deviation of the preset threshold) through the aforementioned rules or lookup table. Its purpose is to dynamically and accurately adjust the monitoring threshold in complex and changing environments to better align it with actual conditions, thereby improving the accuracy of risk assessment.

[0074] The specific embodiment of the present application also discloses a natural disaster monitoring information processing system for performing natural disaster monitoring information processing, combined with Figure 7 As shown, the natural disaster monitoring information processing system 1 includes: The displacement data receiving module 11 is used to receive the deep displacement data and the accompanying physical information corresponding to the physical cause of the deep displacement data; The displacement trend judgment module 12 is used to perform trend judgment on the deep displacement data, determine whether the deep displacement data presents a specific growth trend, and obtain a trend judgment result; The credibility marking module 13 is used to determine the physical cause of the deep displacement data based on the accompanying physical information when the trend judgment result indicates that the deep displacement data presents a specific growth trend, and obtain a credibility mark of the deep displacement data; The impact limiting execution module 14 is configured to limit or exclude the impact of the deep displacement data on risk assessment or parameter adjustment if the credibility mark indicates that the credibility is lower than a preset lower limit.

[0075] The natural disaster monitoring information processing system proposed in this application is intended to solve the problem that when traditional existing methods face non-disaster slow changes in the external environment, the data fusion weights may "drift", causing the system to fall into a "perception blind spot" of real risks due to "logical self-consistency". Through a modular design, the system realizes the intelligent processing and credibility management of deep displacement data. Specifically, the displacement data receiving module is responsible for the comprehensive collection of data, the displacement trend judgment module performs preliminary screening of the data, the credibility marking module deeply analyzes the physical causes of the data and assigns credibility markings, and finally the impact limitation execution module performs intelligent regulation on the data according to the impact of the credibility marking. As a result, the system can effectively avoid the interference of "false precursor" data caused by non-disaster factors on the accuracy of the system warning, and significantly improve the reliability and timeliness of natural disaster monitoring and warning.

[0076] Specifically, the various technical features of this application can be implemented in the following ways.

[0077] Regarding the displacement data receiving module, this module can be configured to connect to sensors such as deep displacement meters via a wired or wireless communication interface to receive deep displacement data in real time. At the same time, the module can also receive accompanying physical information corresponding to the physical causes of deep displacement data from other monitoring devices or databases via a network interface or data bus, such as regional ground stress monitoring data, regional microseismic activity monitoring data, groundwater level data, etc. As an implementation method, the displacement data receiving module can be an independent hardware unit, such as a data acquisition card or embedded processor, which integrates the necessary communication protocol stack and data caching mechanism. As another implementation method, the module can be a software service deployed on a server or cloud computing platform, receiving data streams from different data sources through an application programming interface (API) or message queue.

[0078] Regarding the displacement trend judgment module, this module is configured to perform trend analysis on the deep displacement data received by the displacement data receiving module. The specific method for trend judgment of deep displacement data has been described in the above-mentioned embodiment and will not be repeated here. It should be emphasized that the displacement trend judgment module can be a dedicated signal processing unit, such as a digital signal processor (DSP) or field programmable gate array (FPGA), for efficiently executing complex trend analysis algorithms. Furthermore, this module can also be a software component running on a general-purpose processor, implementing the trend judgment function by calling a statistical analysis library or a machine learning framework.

[0079] In terms of the credibility marking module, the module is configured to judge the physical cause of the deep displacement data based on the accompanying physical information when the trend judgment result of the displacement trend judgment module indicates that the deep displacement data presents a specific growth trend, and obtain the credibility mark of the deep displacement data. The specific method of judging the physical cause of the deep displacement data and obtaining the credibility mark has been recorded in the above embodiment and will not be repeated here. It should be emphasized that the credibility marking module can be an expert system based on rule reasoning, which stores a series of preset physical association rules for inferring the cause of displacement based on the accompanying physical information. As a preferred embodiment, the module can be an inference engine integrated with a machine learning model, which learns the complex relationship between deep displacement data, accompanying physical information and real physical causes through offline training, so that new data can be classified and credibility evaluated in real time.

[0080] In terms of the impact limitation execution module, the module is configured to limit or exclude the impact of deep displacement data on risk assessment or parameter adjustment if the credibility mark obtained by the credibility mark module indicates that the credibility is lower than the preset lower limit. The specific method of limiting or excluding the impact of deep displacement data on risk assessment or parameter adjustment has been recorded in the above embodiment and will not be repeated here. It should be emphasized that the impact limitation execution module can be a policy management unit that dynamically adjusts the weight of deep displacement data in the risk assessment model based on the credibility mark, or removes it from the data set used for system parameter adjustment. For example, the module can be a software agent that intercepts and filters the data stream before it enters the risk assessment or parameter adjustment process to ensure that only high-credibility data is used for key decisions. In some implementations, the module can also trigger a manual review process to remind the operator to perform manual intervention and confirmation when the credibility of the data is in doubt.

[0081] The above are merely examples of the present application and are not intended to limit the scope of protection of the present application. Those skilled in the art will appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A natural disaster monitoring information processing method, characterized in that: include: receiving deep displacement data and accompanying physical information corresponding to a physical cause of the deep displacement data; Performing trend judgment on the deep displacement data to determine whether the deep displacement data presents a specific growth trend, and obtaining a trend judgment result; When the trend judgment result indicates that the deep displacement data presents a specific growth trend, judging the physical cause of the deep displacement data based on the accompanying physical information, and obtaining a credibility mark of the deep displacement data; If the credibility mark indicates that the credibility is lower than a preset lower limit, the influence of the deep displacement data on risk assessment or parameter adjustment is limited or excluded.

2. A natural disaster monitoring information processing method according to claim 1, characterized in that: When the trend judgment result indicates that the deep displacement data presents a specific growth trend, the step of judging the physical cause of the deep displacement data based on the accompanying physical information and obtaining a credibility mark of the deep displacement data includes: When the accompanying physical information does not clearly indicate a physical cause, and the regional ground stress monitoring data and the regional microseismic activity monitoring data do not show abnormalities, obtaining electrochemical response data of the anchor point interface of the deep displacement meter; analyzing, based on the electrochemical response data, whether a microscopic chemical reaction related to the growth trend occurs at the anchor point interface; If a microscopic chemical reaction related to a growth trend occurs at the anchor point interface, the deep displacement data is determined to be caused by the microscopic chemical reaction, and a credibility mark with a credibility lower than a preset lower limit is marked on the deep displacement data.

3. A natural disaster monitoring information processing method according to claim 2, characterized in that: The step of analyzing whether a microscopic chemical reaction related to the growth trend occurs at the anchor point interface based on the electrochemical response data comprises: extracting interface impedance parameters and / or redox peak parameters from the electrochemical response data; Determining whether the interface impedance parameter shows a continuous downward trend, and / or whether the redox peak parameter shows a continuous increasing trend or a peak potential shift trend, to obtain a parameter determination result; If the parameter judgment result is yes, it is determined that a microscopic chemical reaction related to the growth trend occurs at the anchor point interface.

4. A natural disaster monitoring information processing method according to claim 1, characterized in that: The step of limiting or excluding the influence of the deep displacement data on risk assessment or parameter adjustment includes: Obtaining a credibility mark and a cause mark corresponding to the deep displacement data; determining a risk level of the deep displacement data according to the credibility mark and the cause mark; Select the corresponding impact limitation strategy based on the risk level; According to the impact limitation strategy, the influence of the deep displacement data on risk assessment or parameter adjustment is limited or eliminated.

5. A natural disaster monitoring information processing method according to claim 4, characterized in that: The step of determining the risk level of the deep displacement data according to the credibility mark and the cause mark includes: identifying corresponding physical parameters according to the credibility mark and the cause mark; Analyzing the values ​​or changing trends of the physical parameters; Determine whether the value or change trend of the physical parameter exceeds a preset threshold, and obtain a physical parameter determination result; If the physical parameter judgment result is yes, the risk level corresponding to the deep displacement data is adjusted and determined.

6. A natural disaster monitoring information processing method according to claim 5, characterized in that: The step of determining whether the value or change trend of the physical parameter exceeds a preset threshold and obtaining a physical parameter determination result includes: Obtain regional environmental change data or geological background evolution data; Calculating the adaptive deviation of a preset threshold value based on the regional environmental change data or geological background evolution data; determining whether the adaptive deviation exceeds a predetermined range, and obtaining a deviation determination result; If the deviation judgment result is yes, adjusting the preset threshold; Based on the adjusted preset threshold, it is determined whether the value or change trend of the physical parameter exceeds the adjusted preset threshold.

7. A natural disaster monitoring information processing method according to claim 6, characterized in that: The step of calculating the adaptive deviation of the preset threshold comprises: Analyzing the correlation between the regional environmental change data or geological background evolution data and the historical threshold applicability data to obtain correlation information; Constructing a mapping relationship of the associated information according to the associated information; The adaptive deviation of the preset threshold is calculated based on the current regional environmental change data or geological background evolution data and the mapping relationship.

8. A natural disaster monitoring information processing method according to claim 7, characterized in that: The step of analyzing the correlation between the regional environmental change data or the geological background evolution data and the historical threshold applicability data to obtain correlation information includes: Performing data quality processing on the regional environmental change data or geological background evolution data; the data quality processing includes interpolating missing data based on the spatiotemporal correlation of the data, performing adaptive filtering on noisy data, and performing feature alignment and normalization on heterogeneous data; Based on the regional environmental change data or geological background evolution data that have undergone data quality processing, combined with historical threshold applicability data, multivariate statistical analysis and nonlinear regression analysis are performed to identify the associations between the data and obtain association information.

9. A natural disaster monitoring information processing method according to claim 7, characterized in that: The step of constructing a mapping relationship of the association information according to the association information includes: Obtaining preset physical association rules or experience lookup tables; According to a preset physical association rule or an empirical lookup table, the regional environmental change data or geological background evolution data is mapped to an adaptive deviation of a preset threshold value, and a mapping relationship of the association information is constructed.

10. A natural disaster monitoring information processing system for performing natural disaster monitoring information processing, characterized in that: include: a displacement data receiving module, configured to receive deep displacement data and accompanying physical information corresponding to the physical cause of the deep displacement data; a displacement trend judgment module, configured to perform trend judgment on the deep displacement data, determine whether the deep displacement data presents a specific growth trend, and obtain a trend judgment result; a credibility marking module, configured to, when the trend judgment result indicates that the deep displacement data presents a specific growth trend, determine the physical cause of the deep displacement data based on the accompanying physical information, and obtain a credibility marking of the deep displacement data; The impact limiting execution module is configured to limit or exclude the impact of the deep displacement data on risk assessment or parameter adjustment if the credibility mark indicates that the credibility is lower than a preset lower limit.

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