A method for quantitatively evaluating risks of a water conservancy project spoil site and related products

Abstract

The present application relates to the technical field of water conservancy safety and risk management, and particularly relates to a water conservancy project spoil site risk quantitative evaluation method and related products, comprising obtaining multi-element attribute data of the spoil site, constructing a static risk evaluation index system, and calculating the static risk score of the spoil site; obtaining multi-period time-series deformation information; determining the dynamic weight of each monitoring period; constructing an adaptive dynamic weighting model to calculate the comprehensive risk score of the spoil site; and determining the risk grade of the spoil site according to the comprehensive risk score; the method provided by the present application breaks through the limitations of "static qualitative" and "single-period monitoring" in traditional spoil site risk evaluation through deep fusion and adaptive calculation of multi-source heterogeneous data, and can realize dynamic quantitative evaluation and time-series grading early warning of spoil site risk, has high scientificity, real-time performance and engineering application value, and can provide effective technical support for safety prevention and control and risk decision-making of water conservancy project spoil sites.

Description

Technical Field

[0001] This invention relates to the field of water conservancy project safety and risk management technology, specifically to a method for quantitative risk assessment of water conservancy project spoil disposal sites and related products, and particularly to a method for quantitative risk assessment of water conservancy project spoil disposal sites based on InSAR monitoring, dynamic and static fusion, multi-source value assignment and hierarchical combination weighting. Background Technology

[0002] During the construction of water conservancy projects, the complex topography and variable geological conditions along the route generate a large amount of excavated and filled waste. This waste is loosely structured, highly porous, and rapidly infiltrates with rainwater, often remaining in a state of incomplete consolidation. Therefore, the formation of numerous waste dumps is inevitable. These waste dumps are typically located in valleys, hillsides, or low-lying areas along the water conservancy project route, and the waste mainly includes excavated earth and rock, as well as construction debris. Over long periods of storage, these waste dumps are susceptible to various factors such as rainfall, geological conditions, permeability stability, and vegetation restoration, which can trigger secondary disasters such as landslides and debris flows. Once a risk event occurs, it not only threatens the safety of the water conservancy project itself but may also cause serious damage to the surrounding ecological environment and the lives and property of the people. Therefore, conducting risk assessments and classifications of waste dumps is a crucial foundation for achieving comprehensive safety management and risk decision support throughout the entire process of water conservancy projects.

[0003] Currently, research on risk assessment for spoil heaps, both domestically and internationally, is limited and suffers from two main shortcomings. Firstly, traditional risk assessment methods primarily rely on static indicator systems, such as topographic and geological conditions, rainfall characteristics, spoil heap structural stability, and protective engineering measures. While these methods can reflect the basic stability characteristics of spoil heaps, the results are static and one-time assessments, failing to reflect the temporal evolution of spoil heaps under dynamic influences such as rainfall and surface deformation. Secondly, although some existing studies have introduced quantitative models such as the Analytic Hierarchy Process (AHP), entropy weight method, and fuzzy comprehensive evaluation, they still exhibit strong subjectivity and simplisticity in weight assignment and risk level classification, making it difficult to achieve dynamic risk updates based on multi-source data fusion.

[0004] With the rapid development of remote sensing monitoring technology, the application of Synthetic Aperture Radar Interferometry (InSAR) in surface deformation monitoring has become increasingly mature. InSAR can achieve millimeter-level precision deformation monitoring at a regional scale, providing continuous and objective monitoring data for the dynamic stability of spoil heaps. However, most existing studies use InSAR monitoring results as independent deformation information, failing to integrate them with traditional static risk indicator systems, and lacking a comprehensive risk assessment framework that integrates dynamic and static data for spoil heaps. This results in risk assessment results that cannot simultaneously reflect the structural safety hazards and real-time deformation trends of spoil heaps, leading to problems such as response lag and insufficient early warning capabilities. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a quantitative risk assessment method and related products for water conservancy project spoil disposal sites. This method enables adaptive weighting and dynamic updating of risk indicators under multi-source information constraints, reflecting both the site's physical conditions and protection level, and timely capturing deformation and evolution trends. It achieves temporal quantification and scientific classification of risks, thereby providing more reliable technical support for the safety management and early warning decision-making of water conservancy project spoil disposal sites.

[0006] This invention is achieved through the following technical solution:

[0007] A method for quantitative risk assessment of spoil disposal sites in water conservancy projects, comprising:

[0008] Obtain multi-attribute data of the waste disposal site, construct a static risk assessment index system, and calculate the static risk score of the waste disposal site based on the static risk assessment index system.

[0009] Using InSAR technology to obtain multi-period temporal deformation information of spoil disposal sites;

[0010] Based on the time-series deformation information, the monitoring accuracy index and deformation characteristic index are extracted, the confidence level of InSAR monitoring data and the relative contribution of deformation in each monitoring period are calculated, and the dynamic weight of each monitoring period is determined accordingly.

[0011] An adaptive dynamic weighted model is constructed, and the static risk score is progressively integrated with the dynamic risk value of each monitoring period using the dynamic weights to calculate the comprehensive risk score of the spoil disposal site.

[0012] The risk level of the spoil disposal site is determined based on the comprehensive risk score.

[0013] Optionally, methods for calculating the static risk score of a spoil disposal site include:

[0014] A hierarchical indicator system comprising primary and secondary indicators is established, and the secondary indicators are subjected to numerical quantification, normalization, and directional transformation to obtain a standardized indicator system.

[0015] The weights of each primary indicator were calculated using the expert group decision-making analytic hierarchy process.

[0016] The objective weights of each secondary indicator are calculated using a combined weighting method, and the final weights of each secondary indicator are determined by combining the weights of the primary indicators.

[0017] Based on the standardized indicator system and the final weights of the secondary indicators, the static risk score of the spoil disposal site is calculated using a weighted average: ,in, For the first The static risk score of a waste disposal site This represents the total number of secondary indicators. For the first The first waste disposal site in the Standardized values ​​for each secondary indicator For the first The final weight of each secondary indicator.

[0018] Optionally, the weights of each primary indicator are calculated using the analytic hierarchy process (AHP) for expert group decision-making, including:

[0019] Determine the individual weights of each expert;

[0020] Obtain the judgment matrix of multiple experts comparing the primary indicators pairwise, and use the individual weights to perform a weighted geometric average of the judgment matrices of all experts to generate a group judgment matrix.

[0021] The group judgment matrix is ​​subjected to eigenvalue decomposition and consistency test to obtain the weight of each primary indicator.

[0022] Optionally, the objective weights of each secondary indicator are calculated using a combined weighting method, specifically as follows:

[0023] The entropy weight of each secondary indicator is calculated using the entropy weight method; the CRITIC weight of each secondary indicator is calculated using the CRITIC method.

[0024] The intra-group composite weights of the secondary indicators are obtained by linearly combining the entropy weights and the CRITIC weights through linear weighting.

[0025] The final weight of each secondary indicator is obtained by multiplying the weight of the primary indicator by the corresponding group composite weight.

[0026] Optionally, the confidence level of the InSAR monitoring data is calculated, specifically including:

[0027] The coherence coefficient, average intensity, and root mean square error of the InSAR monitoring results are extracted as accuracy indicators; and the weight of each accuracy indicator is calculated using the entropy weight method.

[0028] Calculate the confidence level of the monitoring data based on the normalized values ​​of each accuracy indicator and their corresponding weights: ,in, For the first The confidence level of a waste disposal site , , The weights for coherence coefficient, average intensity, and root mean square error are... , , For the first Normalized values ​​of coherence coefficient, average intensity, and root mean square error for each spoil disposal site.

[0029] Optionally, the relative contribution of deformation for each monitoring period is calculated, specifically including:

[0030] The deformation amplitude for each period is calculated based on multi-period deformation data: ,in, For the first The magnitude of the deformation during the period For the first Deformation data for the period;

[0031] Calculate the relative contribution of deformation in each period: ,in, For the first The relative contribution of the period.

[0032] Optionally, the dynamic weights for each monitoring period are determined, specifically including:

[0033] Confidence levels for each monitoring period were determined using the entropy weight method. Relative contribution to deformation Conduct information content analysis to determine the weighting coefficients of the two. and ;

[0034] Construct an adaptive dynamic weighted model to calculate dynamic weights : .

[0035] Optionally, the static risk score and the dynamic risk value for each monitoring period are progressively integrated using the dynamic weights, specifically employing the following progressive formula:

[0036] Regarding the overall risk in Phase 1: ;

[0037] For the subsequent Overall risks during the period: ,in, For the first The overall risk score for the period, This is a static risk score. For the first Dynamic risk value over a period of time For the first Dynamic weights for each period.

[0038] Optionally, the risk level of the spoil disposal site can be determined, specifically including:

[0039] Based on Monte Carlo random sampling technology, the comprehensive risk score of the spoil disposal site is subjected to multiple random simulations to generate a probability distribution sample of the risk score.

[0040] Based on the characteristics of the probability distribution, quantiles are selected as the threshold for risk level classification;

[0041] The overall risk score of the waste disposal site is compared with the threshold to determine the risk level of the waste disposal site as low risk, medium risk, relatively high risk, or high risk.

[0042] A computer program product includes a computer program / instructions that, when executed by a processor, implement the quantitative risk assessment method for hydraulic engineering spoil disposal sites as described above.

[0043] Compared with the prior art, the present invention has the following features and beneficial effects:

[0044] This invention constructs a multidimensional static risk index evaluation system and establishes static risk scores using expert group decision hierarchy analysis and entropy weight-CRITIC combined weighting method. After introducing InSAR technology, an adaptive dynamic weighting model is established by constructing data confidence and relative contribution through solution accuracy index, and a comprehensive risk score is obtained after progressive fusion. Finally, the risk classification threshold is determined based on Monte Carlo random simulation.

[0045] Based on the engineering attributes and environmental characteristics of spoil disposal sites, this invention constructs a static risk assessment index system covering multiple dimensions such as topography, geology, hydrology, structure, and social environment. It adopts a hierarchical combination weighting method that combines the analytic hierarchy process of expert group decision-making with entropy weighting—CRITIC, which achieves a comprehensive balance between subjective experience and objective data. This ensures that the weight allocation is scientific and reasonable, and effectively avoids the bias caused by a single weighting method.

[0046] This invention introduces temporal InSAR technology to obtain multi-period temporal deformation information of waste disposal sites and constructs an adaptive dynamic weighted model driven by confidence level and deformation contribution. This enables the fusion analysis of static risk characteristics and dynamic monitoring data, which can quantitatively reflect the temporal evolution characteristics of waste disposal site risks, thereby improving the dynamic response capability and accuracy of risk assessment.

[0047] This invention employs Monte Carlo random sampling technology to simulate the uncertainty of comprehensive risk scores, and constructs a risk level threshold system based on quantiles. This overcomes the subjectivity of traditional experience-based classification methods, making the risk classification results more robust and statistically sound. It can be integrated with GIS, RS, and UAV aerial survey data to achieve visualized management, providing reliable technical support for the dynamic monitoring, graded management, and safety control of spoil disposal sites.

[0048] The method provided by this invention breaks through the limitations of "static qualitative" and "single-period monitoring" in traditional spoil disposal site risk assessment by deeply fusing and adaptively calculating multi-source heterogeneous data. It can realize dynamic quantitative assessment and time-series hierarchical early warning of spoil disposal site risks, and has high scientific, real-time and engineering application value. It can provide effective technical support for safety control and risk decision-making of spoil disposal sites in water conservancy projects. Attached Figure Description

[0049] The accompanying drawings illustrate exemplary embodiments of the present invention and, together with the description thereof, serve to explain the principles of the invention. These drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, but do not constitute a limitation on the embodiments of the present invention.

[0050] Figure 1 This is a flowchart illustrating a method for quantitative risk assessment of waste disposal sites in water conservancy projects according to the present invention.

[0051] Figure 2 This is a flowchart illustrating the evaluation method according to Embodiment 3 of the present invention.

[0052] Figure 3 These are drone images of selected typical spoil disposal sites according to Embodiment 3 of the present invention.

[0053] Figure 4 This is a first-phase InSAR monitoring deformation diagram of a typical spoil heap selected according to Embodiment 3 of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0055] It should also be noted that, for ease of description, only the parts relevant to the present invention are shown in the accompanying drawings.

[0056] Where there is no conflict, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0057] Example 1

[0058] like Figure 1 As shown in the figure, this embodiment provides a method for quantitatively evaluating the risks of waste disposal sites in water conservancy projects, which specifically includes the following steps:

[0059] Step S1: Obtain multi-attribute data of the waste disposal site, construct a static risk assessment index system, and calculate the static risk score of the waste disposal site based on the static risk assessment index system.

[0060] Multi-attribute data usually refers to basic attribute information in multiple dimensions such as topography and geology, hydrology and meteorology, engineering structure and environment of waste disposal sites, while static risk assessment index system refers to a set of pre-set standards used to measure the inherent safety hazards of waste disposal sites.

[0061] The static risk score represents the initial risk status of the spoil disposal site before significant deformation occurs.

[0062] Step S2: Use InSAR technology to obtain multi-period time-series deformation information of the spoil disposal site.

[0063] Using InSAR technology, the system can acquire deformation data of the waste disposal site surface at different points in time, forming a time series. InSAR is a remote sensing technology that uses radar satellite data to acquire minute surface deformations. It features all-weather operation and high precision, and can provide millimeter-level deformation information.

[0064] Temporal deformation information refers to a series of deformation observations arranged in chronological order, reflecting the displacement trend of the spoil heap over time.

[0065] Step S3: Extract monitoring accuracy indicators and deformation characteristic indicators based on the time-series deformation information, calculate the confidence level of InSAR monitoring data and the relative contribution of deformation in each monitoring period, and determine the dynamic weight of each monitoring period accordingly.

[0066] Confidence level represents the reliability of InSAR data; if a period of data has high noise and low accuracy, its confidence level will be low. Relative contribution of deformation represents the importance of deformation in that period within the overall change; if the deformation amplitude suddenly increases in a period, its relative contribution will be high.

[0067] The dynamic weight is calculated by combining confidence level and contribution level, which determines the proportion of the monitoring data in the final risk score.

[0068] Step S4: Construct an adaptive dynamic weighted model, and use the dynamic weights to progressively integrate the static risk score with the dynamic risk value of each monitoring period to calculate the comprehensive risk score of the spoil disposal site.

[0069] Progressive fusion means that the current comprehensive risk score is calculated by dynamically weighting the "risk status at the previous moment" and the "dynamic monitoring value at the current moment", ensuring that the risk assessment results can be updated in real time and have historical continuity.

[0070] Step S5: Determine the risk level of the spoil disposal site based on the comprehensive risk score.

[0071] The calculated comprehensive risk score is mapped to a specific risk level (such as high, medium, or low) according to a preset standard, so that project managers can make intuitive judgments and decisions.

[0072] This embodiment achieves dynamic quantification of waste disposal site risk through five steps: first, establishing a static risk benchmark (S1); second, capturing temporal deformation using InSAR technology (S2); then, generating adaptive dynamic weights by analyzing data confidence and deformation contribution (S3); subsequently, progressively fusing the static benchmark and dynamic changes using this weight model (S4); and finally, quantifying and outputting the risk level (S5). This embodiment overcomes the problem of disconnect between static indicators and dynamic monitoring data in traditional assessments, achieving accurate and real-time risk assessment results by considering both monitoring data quality and deformation significance.

[0073] Example 2

[0074] This embodiment provides a detailed description of the method in Embodiment 1.

[0075] Step S1: Obtain multi-attribute data of the spoil disposal site and construct a static risk assessment index system. The static risk assessment index system shall include at least the spoil disposal site's own attributes, regional geological environment, hydrological and meteorological conditions, engineering structural characteristics, and social environmental impacts.

[0076] The static risk score of the spoil disposal site is calculated based on the static risk assessment index system, which includes the following four sub-steps: S11, S12, S13 and S14. Each sub-step is explained in detail.

[0077] S11. Establish a hierarchical indicator system containing primary and secondary indicators, and perform numerical quantification, normalization, and directional transformation on the secondary indicators to obtain a standardized indicator system. This can be broken down into the following steps:

[0078] S111. Based on the risk characteristics of the spoil disposal site, the various indicators in the static risk assessment index system are classified according to their attributes and mechanisms of action, and divided into primary indicators such as spoil disposal site attributes, topographic features, engineering structural features, social environmental impact, regional geological environmental conditions, hydrology and meteorology, and stability calculation results. These primary indicators are further subdivided into multiple secondary indicators.

[0079] S112. Transform qualitative indicators in the secondary indicators into calculable quantitative values. For example, stratigraphic lithology, site suitability, and land use type are transformed into numerical indicators through expert scoring or grade assignment to ensure comparability between different indicators.

[0080] S113. Using the maximum and minimum value normalization method, each indicator is scaled to the [0,1] interval to eliminate the differences in dimensions and magnitudes between different indicators.

[0081] S114. Based on normalization, positive and negative indicators are converted to a directional standard, which is then unified as "the larger the value, the higher the risk", to ensure the consistency of indicator direction.

[0082] S115. Systematically integrate the pre-processed primary and secondary indicators to form a standardized indicator system with a complete structure, clear hierarchy, and data that can be directly used for subsequent weight calculation and risk assessment.

[0083] S12. The weights of each primary indicator are calculated using the analytic hierarchy process (AHP) of expert group decision-making; this can be broken down into the following steps:

[0084] S121. Determine the individual weight of each expert based on factors such as their educational background, professional title, research field, and years of work experience.

[0085] S122. Obtain the judgment matrix of pairwise comparison of primary indicators by multiple experts. That is, collect the scoring results of pairwise comparison of each primary indicator by multiple experts to form the corresponding judgment matrix. Each matrix element reflects the importance ratio between two primary indicators.

[0086] S123. Calculate the weighted geometric mean of the judgment matrices of all experts using the individual weights to generate a group judgment matrix; ,in, For the first The elements of the expert's judgment matrix This is the expert's own weight.

[0087] S124. Perform eigenvalue decomposition on the group judgment matrix, extract the eigenvector corresponding to the largest eigenvalue, and normalize it into a weight vector to obtain the relative weight of each first-level indicator.

[0088] S125. Perform a consistency test on the group judgment matrix and calculate the consistency ratio CR. If If so, the result is credible; if If so, the expert judgment matrix or expert weights need to be readjusted until the consistency requirement is met.

[0089] S13. Calculate the objective weights of each secondary indicator using the combined weighting method, and determine the final weights of each secondary indicator by combining them with the weights of the primary indicators; this can be broken down into the following steps:

[0090] S131. Calculate the entropy weight of each secondary indicator using the entropy weight method; based on the standardized matrix, calculate the information entropy of each secondary indicator to obtain the entropy redundancy. Then, determine the entropy weight of each indicator according to the magnitude of the redundancy, reflecting the information content and discriminative power of the indicator.

[0091] S132. Calculate the CRITIC weights of each secondary indicator using the CRITIC method; calculate the standard deviation of each indicator and the correlation coefficient between indicators to assess the comparative strength and conflict of the indicators; obtain the CRITIC weights by combining variance and conflict to reflect the differential contributions between indicators.

[0092] S133. The entropy weight and the CRITIC weight are linearly combined using linear weighting to obtain the intra-group composite weight of the secondary index. This is done according to preset parameters (such as...). The entropy weights and CRITIC weights are linearly combined to obtain the intra-group composite weights: The weights of the secondary indicators under each primary indicator are normalized to ensure that the sum of the weights within the group is 1.

[0093] S134. Multiply the weight of the primary indicator by the corresponding intra-group composite weight to obtain the final weight of each secondary indicator. This results in a secondary indicator weight system that reflects both objective data characteristics and the hierarchical structure of the primary indicators.

[0094] S14. Based on the standardized indicator system and the final weights of the secondary indicators, the static risk score of the spoil disposal site is calculated using a weighted average: ,in, For the first The static risk score of a waste disposal site This represents the total number of secondary indicators. For the first The first waste disposal site in the Standardized values ​​for each secondary indicator For the first The final weight of each secondary indicator.

[0095] Step S2: Obtain multi-phase temporal deformation information of the spoil heap using InSAR technology; To obtain the deformation rate and temporal deformation characteristics of the spoil heap during long-term operation, multi-temporal remote sensing monitoring is carried out using Small Baseline Set InSAR (SBAS-InSAR) technology to obtain high spatiotemporal resolution surface deformation information, which can be decomposed into the following steps:

[0096] S21. Collect multi-source data required for InSAR monitoring, including multi-temporal SAR image data, digital elevation model (DEM), atmospheric delay correction data, and satellite precise orbit data.

[0097] S22. Based on SBAS-InSAR technology, multi-temporal SAR image data is processed, including steps such as interferogram generation, phase unwrapping, temporal inversion and geocoding, to obtain information such as temporal deformation information, cumulative deformation, annual average deformation rate and accuracy evaluation index of each spoil disposal site.

[0098] Step S3: Based on the time-series deformation information, extract monitoring accuracy indicators and deformation characteristic indicators, calculate the confidence level of the InSAR monitoring data and the relative contribution of deformation in each monitoring period, and determine the dynamic weight of each monitoring period accordingly. This step, based on the acquisition of time-series deformation information and accuracy evaluation indicators of the spoil heap, constructs a dynamic risk assessment model based on confidence level, deformation amplitude, and adaptive weighting to achieve the fusion analysis of dynamic and static information. It can be decomposed into the following steps:

[0099] S31. Calculate the confidence level of the InSAR monitoring data, specifically including:

[0100] S311. Extract the coherence coefficient, mean intensity, and root mean square error (RMSE) of the SBAS-InSAR monitoring results as accuracy indicators; and use the entropy weight method to calculate the weight of each accuracy indicator.

[0101] S312. Calculate the confidence level of the monitoring data based on the normalized values ​​of each accuracy indicator and their corresponding weights: ,in, For the first The confidence level of a waste disposal site , , The weights for coherence coefficient, average intensity, and root mean square error are... , , For the first Normalized values ​​of coherence coefficient, average intensity, and root mean square error for each spoil disposal site.

[0102] S32. Calculate the relative contribution of deformation for each monitoring period, specifically including:

[0103] S321, Based on multi-period deformation data Calculate the deformation amplitude for each period: ,in, For the first The magnitude of the deformation during the period For the first Deformation data for the period;

[0104] S322. Calculate the relative contribution of deformation in each period: ,in, For the first The relative contribution of each monitoring time series describes the dominant role of each monitoring time series in the overall deformation process.

[0105] S33. Determine the dynamic weights for each monitoring period, specifically including:

[0106] S331. Utilizing the entropy weight method to determine the confidence level for each monitoring period. Relative contribution to deformation Conduct information content analysis to determine the weighting coefficients of the two. and Based on this, an adaptive dynamic weighted model is established.

[0107] S332. Construct an adaptive dynamic weighted model and calculate dynamic weights. : This reflects the dynamic risk importance of each monitoring period.

[0108] Step S4: Construct an adaptive dynamic weighted model, and use the dynamic weights to progressively integrate the static risk score with the dynamic risk value of each monitoring period to calculate the comprehensive risk score of the spoil disposal site.

[0109] S411, Static risk score As a benchmark risk, based on dynamic weights for each monitoring period The static risk assessment results are dynamically and progressively integrated, specifically using the following progressive formula:

[0110] S412. Regarding the overall risk in Phase 1: Subsequently, a progressive approach was adopted to integrate the subsequent phases.

[0111] S413, Regarding the subsequent... Overall risks during the period: ,in, For the first The comprehensive risk score for a period is the dimensionless value of the deformation variable after normalization. This is a static risk score. For the first Dynamic risk value over a period of time For the first Dynamic weights for each period.

[0112] Step S5: Determine the risk level of the spoil disposal site based on the comprehensive risk score, specifically including:

[0113] S51. Based on Monte Carlo random sampling technology, the comprehensive risk score of the spoil disposal site is simulated multiple times to generate a probability distribution sample of the risk score; by generating a probability distribution sample of the risk score and fitting its probability density function, the uncertainty of the evaluation results can be quantified and the statistical reliability of risk classification can be improved.

[0114] S52. Select quantiles (such as 10%, 60%, 90%) as the dividing points for risk level classification based on probability distribution characteristics; use the quantile values ​​corresponding to the comprehensive risk score as the thresholds for risk levels to form a classification standard.

[0115] S53. Compare the comprehensive risk score of the waste disposal site with the threshold to determine the risk level of the waste disposal site as low risk, medium risk, relatively high risk or high risk.

[0116] Example 3

[0117] like Figure 2 As shown in the figure, this embodiment provides a specific example to illustrate a method for quantitatively evaluating the risks of waste disposal sites in water conservancy projects.

[0118] like Figure 3 As shown, a certain irrigation district project is a Class I, large-scale (1) water conservancy project developed for agricultural irrigation and urban and rural domestic and industrial water supply. It includes canal systems and pumping works. The canal system comprises 17 canals, including main canals, branch canals, sub-branch canals, filling canals, and tributary canals, with a total length of 383.81 km. The main project involved 18,583,600 m³ of earthwork excavation (natural volume, the same below), of which: earthwork excavation was 3,257,100 m³, rock excavation was 6,554,500 m³, and rock excavation was 8,772,100 m³; earthwork backfilling and embankment utilization amounted to 3,827,900 m³; after earthwork balance, the project generated 14,755,700 m³ of waste (21,244,300 m³ of loose material). During the construction drawing stage, a total of 175 waste disposal sites were set up for centralized storage of project waste. Considering the characteristics of each spoil disposal site and the results of on-site investigations, 18 typical spoil disposal sites were ultimately selected as embodiments of the present invention. The specific steps are as follows:

[0119] S1. Collect and obtain multi-dimensional attribute data related to 18 typical waste disposal sites, and establish a static risk assessment index system for waste disposal sites consisting of 37 attribute information.

[0120] The 37 initial static risk assessment indicators are as follows: spoil disposal site type, slope, aspect, distance from river, geological hazard core density, multi-year average annual rainfall, topographic relief, bottom width of retaining wall, top width of retaining wall, height of retaining wall, stability calculation value (rainfall condition), stability calculation value (normal condition), environmental sensitive points, stratum occurrence, stratum lithology, interception and drainage measures, flood discharge measures, catchment area, peak ground acceleration, seismic intensity, ground elevation, flood standard for slope interception and drainage projects, farmland ratio, land occupation type, strongly weathered zone, weakly weathered zone, land occupation area, spoil capacity, maximum pile height, spoil disposal site grade, site suitability, earthwork ratio, rockwork ratio, retaining wall anti-sliding stability (normal condition), retaining wall anti-sliding stability (rainfall condition), retaining wall anti-overturning stability (normal condition), and retaining wall anti-overturning stability (rainfall condition).

[0121] S11. Preprocessing operations such as classification, numerical quantification, and positive / negative standardization are performed on the 37 initial indicators to form a final evaluation indicator system composed of multiple primary and secondary indicators; this includes the following steps:

[0122] S111. Based on the risk characteristics of the spoil disposal site, the various indicators in the initial evaluation index system are classified according to their attributes and mechanisms of action, and divided into seven primary indicators: spoil disposal site attributes, topographic features, engineering structural features, social environmental impact, regional geological environmental conditions, hydrology and meteorology, and stability calculation results. These primary indicators are further subdivided into multiple secondary indicators.

[0123] S112. The 18 typical spoil heaps selected all correspond to Level 4, with peak ground acceleration of 0.05g, seismic intensity of Level VI, and flood standards for slope drainage works of 10-minute short-duration rainfall (a 5-year return period). All sites have drainage measures in place, and none are located near drinking water sources, nature reserves, scenic spots, or other environmentally sensitive areas. Considering that the above six indicators cannot provide effective information for distinguishing risk differences, they are considered invalid. Therefore, to avoid data redundancy and improve the discriminative power of the evaluation system, this embodiment removes six initial indicators with identical attribute values, retaining the remaining 31 indicators as secondary indicators for subsequent risk assessment, ensuring that the standardized indicator system has higher independence and information contribution. The seven primary indicators and the corresponding 31 secondary indicators are as follows:

[0124] (1) The inherent properties of the slag dump

[0125] ① Waste Disposal Type: Waste disposal sites are categorized and assigned values ​​based on their type. Channel-type sites are assigned a value of 1, while slope-type sites are assigned a value of 0. Channel-type waste disposal sites are often located within water catchment channels, resulting in concentrated water collection, large runoff / flood peaks, and numerous potential triggers such as debris flows / blockages and dam failures. Once instability occurs, the deposits spread rapidly downstream along the channel, leading to stronger externalities. Slope-type waste disposal sites are generally located on watersheds or side slopes. Although they have slope stability issues, their runoff organization is weak, and downstream connectivity is relatively low.

[0126] ② Land area: The larger the land area of ​​the waste disposal site, the greater the potential risk range.

[0127] ③ Waste disposal capacity: The larger the waste disposal site, the higher the risk after instability.

[0128] ④ Maximum stacking height: The greater the stacking height of the waste disposal site, the worse the stability and the higher the risk.

[0129] ⑤ Site suitability: The suitability of the spoil disposal site is quantified numerically, with a value of 2 for relatively suitable and 1 for poorly suitable. Therefore, the larger the index value, the lower the risk.

[0130] ⑥ Earthwork ratio: The more loose the waste soil, the higher the earthwork ratio, and the higher the risk.

[0131] ⑦ Stone ratio: Stone waste has higher stability, and the higher the stone ratio, the lower the risk.

[0132] (2) Topographic and geomorphological features

[0133] ① Slope: The greater the slope of the spoil disposal site, the higher the risk.

[0134] ② Ground elevation: The higher the ground elevation of the spoil disposal site, the less susceptible it is to floods and river erosion, and the better the drainage conditions, resulting in lower risks.

[0135] ③ Terrain undulation: The greater the terrain undulation of the spoil disposal site, the higher the risk.

[0136] (3) Structural features of the project

[0137] ① Width of the bottom of the retaining wall: The wider the bottom of the retaining wall, the higher the stability and the lower the risk.

[0138] ② Width of the top of the retaining wall: The wider the top of the retaining wall, the higher the stability and the lower the risk.

[0139] ③ Height of the retaining wall: The higher the retaining wall, the higher the stability and the lower the risk.

[0140] ④ Flood Discharge Measures: The flood discharge measures at the spoil heap are quantified numerically, with a value of 1 for having flood discharge measures and 0 for not having them. Therefore, having flood discharge measures results in lower risk compared to not having them.

[0141] (4) Social environmental impact

[0142] ① Farmland ratio: The higher the proportion of farmland, the greater the impact if the spoil disposal site becomes unstable. Therefore, the risk is higher.

[0143] ②Land Use Type: Each type of land use at the spoil heap is assigned a score, which is then weighted and averaged. The specific scores are: arable land 5, forest land 4, grassland 3, orchard 3, residential land 5, industrial and mining storage land 2, water area and water conservancy facilities land 5, transportation land 4, and other land 1. Therefore, a higher overall score for each land use type at the spoil heap indicates a higher risk.

[0144] ③ Environmentally Sensitive Points: The number of environmentally sensitive points within 1 km downstream of each spoil disposal site was counted. These points were then graded and scored, with the score used as the new indicator. A higher score indicates a lower risk level.

[0145] Table 1 Environmental Sensitive Point Scoring Table

[0146]

[0147] (5) Regional geological environment conditions

[0148] ① Rock strata dip angle: The greater the dip angle of the rock strata, the more prone it is to instability and the higher the risk.

[0149] ② Stratigraphic Lithology: Stratigraphic lithology is classified and quantified numerically using the "degree of weakness". For example, the sandstone of the Lower Cretaceous Bailong Formation (K1b) is assigned a value of 1, and the sandstone interbedded with silty mudstone of the Lower Cretaceous Bailong Formation (K1b) is assigned a value of 2. Therefore, the higher the stratigraphic lithology score, the lower the risk.

[0150] ③ Strongly weathered zone: The greater the thickness of the strongly weathered zone, the greater the risk.

[0151] ④ Weakly weathered zone: The greater the thickness of the weakly weathered zone, the greater the risk.

[0152] ⑤ Geological Hazard Core Density: Collect historical geological hazard data for the region, including landslides, collapses, and debris flows. Using GIS software, perform core density analysis and extract the core density value for the location of the spoil disposal site. The higher the geological hazard core density, the greater the risk of the spoil disposal site.

[0153] (6) Hydrology and Meteorology

[0154] ①Catchment area: A large catchment area and sufficient water source in the upper and middle reaches of the spoil heap are essential conditions for the formation of debris flow. The larger the catchment area, the higher the risk of rainwater erosion and the higher the risk of the spoil heap.

[0155] ② Distance from the river: Collect DEM data within the area, extract river network vector data using hydrological analysis tools in GIS software, and perform buffer analysis at intervals of 250m, 500m, 1000m, and 2000m to obtain the distance of the spoil disposal site from the river. The farther the spoil disposal site is from the river, the lower the risk.

[0156] ③ Multi-year average annual rainfall: The scouring and softening effects of rainfall on the spoil heap often trigger landslides. Vector data from rainfall stations within the region were collected and interpolated to extract the multi-year average annual rainfall at the spoil heap location. Higher rainfall generally indicates a higher risk.

[0157] (7) Stability calculation results

[0158] ① Stability calculation value (normal operating conditions): The higher the stability calculation value of the spoil disposal site under normal operating conditions, the higher the stability and the lower the risk.

[0159] ②Stability calculation value (rainfall condition): The higher the stability calculation value of the spoil disposal site under rainfall condition, the higher the stability and the lower the risk.

[0160] ③ Retaining wall anti-slip stability (normal working conditions): The higher the calculated value of the retaining wall anti-slip stability under normal working conditions, the higher the stability and the lower the risk.

[0161] ④ Retaining wall anti-slip stability (rainfall condition): The higher the calculated value of the retaining wall anti-slip stability under rainfall condition, the higher the stability and the lower the risk.

[0162] ⑤ Retaining wall overturning stability (normal working conditions): The larger the calculated value of the retaining wall's overturning stability under normal working conditions, the higher the stability and the lower the risk.

[0163] ⑥ Retaining wall overturning stability (rainfall condition): The higher the calculated value of the retaining wall's overturning stability under rainfall conditions, the higher the stability and the lower the risk.

[0164] S113. Using the maximum-minimum normalization method, the 31 secondary indicators are scaled to the range [0,1] to eliminate differences in dimensions and magnitudes between different indicators. The calculation formula is as follows: ,in, The original value of the indicator; This is the minimum value of the indicator across all samples; This represents the maximum value of the indicator across all samples. This is the normalized value of the index, ranging from [0,1].

[0165] S114. Based on normalization, the indicators are divided into positive and negative indicators according to their attributes, and their directions are converted to unify them into the standard that "the larger the value, the higher the risk".

[0166] For positive indicators, the normalized result is retained directly, i.e.: The 16 positive indicators are: slope, topographic relief, retaining wall height, farmland ratio, land occupation type, rock strata dip angle, strong weathering zone, weak weathering zone, geological disaster core density, catchment area, multi-year average annual rainfall, slag dump type, land occupation area, slag capacity, maximum pile height, and earthwork ratio.

[0167] For negative indicators, a reverse conversion is performed, that is: The 15 negative indicators are: ground elevation, bottom width of retaining wall, top width of retaining wall, flood drainage measures, environmentally sensitive points, stratum lithology, distance from river, site suitability, proportion of rock, stability calculation value (normal working condition), stability calculation value (rainfall working condition), retaining wall anti-sliding stability (normal working condition), retaining wall anti-sliding stability (rainfall working condition), retaining wall anti-overturning stability (normal working condition), and retaining wall anti-overturning stability (rainfall working condition).

[0168] S115. The 7 primary indicators and 31 secondary indicators that have completed preprocessing are systematically integrated to form a standardized indicator system with a complete structure, clear hierarchy, and data that can be directly used for subsequent weight calculation and static risk assessment.

[0169] S12. Using the expert group decision-making hierarchy analysis method, the importance of the seven primary indicators is systematically quantified, and the weight of each primary indicator is determined; this includes the following steps:

[0170] S121. For the experts participating in the evaluation, quantitative standards are set from aspects such as research field, professional title level, educational level and years of work experience. The expert scoring standards are shown in Table 2.

[0171] Table 2 Expert Scoring Criteria

[0172]

[0173] S122. The professional background and experience level of the 15 experts participating in the evaluation were scored. The scores of each item were added together to obtain the expert's comprehensive score, and the individual weight of each expert was obtained by normalization calculation (the comprehensive scores and weights of each expert are shown in Table 3). The introduction of expert weights can distinguish the influence of different expert opinions in group decision-making and avoid all expert opinions being treated as simply equal.

[0174] Table 3 Expert Overall Score and Weighting Table

[0175]

[0176] After determining the expert weights, the experts' judgments on the relative importance of each primary indicator were collected. A 1–9 scale was used to compare each primary indicator pairwise, and a judgment matrix was constructed. The elements in the matrix... Indicators relative to indicators The degree of importance is determined according to the scale shown in Table 4.

[0177] Table 4. Scale of the Analytic Hierarchy Process (AHP)

[0178] Scale value Meaning Explanation 1 This indicates that, compared to the previous indicator, indicator 𝑖 and indicator 𝑗 are of equal importance. 3 This indicates that, compared to the previous indicator, indicator 𝑖 is slightly more important than indicator 𝑗. 5 This indicates that, compared to the two indicators, indicator A is significantly more important than indicator B. 7 This indicates that, compared to the two indicators, indicator 𝑖 is significantly more important than indicator 𝑗. 9 This indicates that, compared to the two indicators, indicator 𝑖 is extremely important to indicator 𝑗. 2,4,6,8 Indicates the intermediate value between adjacent judgments. reciprocal If the importance of indicators A and B is 'a', then the ratio of the importance of indicators A and B is 1 / a.

[0179] S123. After obtaining the judgment matrices and individual weights of each expert, the weighted geometric mean method is used to integrate the judgment results of all experts to obtain the group judgment matrix. The calculation formula is as follows: ,in, For the first The elements of the expert's judgment matrix This is the expert's own weight.

[0180] S124. Perform eigenvalue decomposition on the integrated group judgment matrix, extract the eigenvector corresponding to the largest eigenvalue, and normalize it to obtain the relative weights of each first-level indicator, as shown in Table 5.

[0181] Table 5 Weights of Primary Indicators

[0182] Indicator Name Topographic features Hydrometeorology Engineering structural features Regional geological environment conditions Social environmental impact Self-attribute Stability calculation results Weight 0.1978 0.1549 0.1811 0.1766 0.0699 0.0859 0.1338

[0183] S125. Perform a consistency check on the group judgment matrix. The consistency index (CI) is calculated using the following formula: The formula for calculating the consistency ratio (CR) is: .like If so, the result is credible; if If so, the expert judgment matrix or expert weights need to be readjusted until the consistency test is passed.

[0184] The largest eigenvalue was calculated. , , , Therefore, the judgment matrix passes the consistency test, and the obtained first-level indicator weight results are reliable.

[0185] S13. Using the entropy weight method and the CRITIC method, calculate the objective weights of each secondary indicator, and then perform a linear combination of the results of the two methods to obtain the final weights of the secondary indicators. This includes the following steps:

[0186] S131. Based on the obtained standardized index matrix, calculate the information entropy and entropy redundancy for each of the 31 secondary indicators. The entropy weight of each secondary indicator is determined by the magnitude of the redundancy, reflecting the information content and variability of the indicators.

[0187] S132. Calculate the standard deviation and correlation coefficient matrix for each secondary indicator to obtain the contrast strength and conflict of each indicator.

[0188] S133, Using a linear weighting method (parameters) The entropy weight and CRITIC weight are fused to obtain the intra-group composite weight of each secondary indicator.

[0189] S134. The weights of the secondary indicators under each primary indicator have been normalized to ensure that the sum of the weights within a group is 1. The comprehensive weights of each secondary indicator are shown in Table 6.

[0190] Table 6. Comprehensive Weighting Table of Secondary Indicators

[0191]

[0192] Note: A1 represents the density of geological hazards; A2 represents the dip angle of rock strata; A3 represents the lithology of the strata; A4 represents the strongly weathered zone; A5 represents the weakly weathered zone; B1 represents the slope; B2 represents the ground elevation; B3 represents the topographic relief; C1 represents the width of the retaining wall base; C2 represents the width of the retaining wall top; C3 represents the height of the retaining wall; C4 represents flood control measures; D1 represents the average annual rainfall over many years; D2 represents the distance from the river; D3 represents the catchment area; E1 represents the land occupation type; E2 represents the proportion of farmland; E3 represents the environmental sensitivity. Points to note: F1 represents the retaining wall's overturning stability (normal working condition), F2 represents the stability calculation value (rainfall working condition), F3 represents the stability calculation value (normal working condition), F4 represents the retaining wall's sliding stability (normal working condition), F5 represents the retaining wall's sliding stability (rainfall working condition), F1=6 represents the retaining wall's overturning stability (rainfall working condition); G1 represents the slag dump type, G2 represents the land area, G3 represents the slag volume, G4 represents the maximum stockpile height, G5 represents the site suitability, G6 represents the earthwork ratio (%), and G7 represents the rockwork ratio (%).

[0193] S14. After calculating the weights of each primary and secondary indicator, a comprehensive weighted average is used to obtain the static risk scores for the 18 spoil disposal sites. ,in, For the first The static risk score of a waste disposal site This represents the total number of secondary indicators. For the first The first waste disposal site in the Standardized values ​​for each secondary indicator For the first The final weights of each secondary indicator are shown in Table 7. The static risk scores of each spoil disposal site are also shown in Table 7.

[0194] Table 7 Static Risk Score for Waste Disposal Sites

[0195]

[0196] S2. Obtaining time-series deformation information of spoil disposal sites based on SBAS-InSAR technology

[0197] Small Baseline Set Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology was used to monitor the deformation of the spoil heap in multiple time phases, obtaining the temporal deformation, cumulative deformation, annual average deformation rate, and corresponding accuracy indicators. This provides basic data support for subsequent dynamic and static fusion risk assessment, including the following steps:

[0198] S21. Collect Sentinel-1 SAR images of the area where the waste disposal site is located from March 26, 2024 to September 5, 2025, along with corresponding GACOS atmospheric correction data, POD precise orbit determination ephemeris data, and topographic data.

[0199] S22. Based on SBAS-InSAR technology, multi-temporal SAR data is processed, including steps such as interferogram generation, phase unwrapping and filtering, and geocoding, and finally information such as deformation rate, temporal deformation, cumulative deformation and accuracy evaluation index of each spoil disposal site is obtained.

[0200] S3. Dynamic risk assessment of spoil disposal sites integrating time-series InSAR monitoring.

[0201] Based on the acquisition of temporal deformation information and accuracy assessment indicators of the spoil disposal site, a dynamic risk assessment model based on confidence level, deformation amplitude, and adaptive weighting is constructed to achieve the fusion calculation of dynamic and static information and obtain the comprehensive risk score of the spoil disposal site. The model includes the following steps:

[0202] S311. Extract three accuracy indicators—root mean square error (RMSE), coherence coefficient, and mean intensity (meanpwr)—from the SBAS-InSAR monitoring results for each spoil disposal site. Calculate the weights of the three accuracy indicators using the entropy weight method: 0.107, 0.4316, and 0.4614, respectively. Calculate the confidence level based on the accuracy indicator values ​​and their corresponding weights to reflect the reliability of the InSAR monitoring results.

[0203] S312. The confidence level calculation formula is as follows: Where w1, w2, and w3 represent the weights of the coherence coefficient, average intensity, and root mean square error, respectively; , , These represent the normalized values ​​of coherence coefficient, average intensity, and root mean square error, respectively. The normalized values ​​and confidence scores of the accuracy indicators for each spoil disposal site are shown in Table 8.

[0204] Table 8. Calculation results of normalized values ​​and confidence levels of accuracy indicators for spoil disposal sites.

[0205]

[0206] S321, Based on multi-period deformation data The formula for calculating the magnitude of deformation is as follows: Specifically, such as Figure 4 As shown, taking March 26, 2024 as the starting date and a 6-month cycle, the deformation data for three periods—September 10, 2024, March 21, 2025, and September 5, 2025—were extracted from the 31-period time series deformation data (denoted as...). ) is used as a dynamic evaluation index, and the corresponding deformation amplitude (denoted as ) is calculated. The normalized values ​​and amplitudes of deformation variables for each spoil disposal site over three phases are shown in Table 9.

[0207] Table 9. Normalized values ​​and magnitudes of deformation variables for the three phases of the spoil disposal site.

[0208]

[0209] S322. Calculate the relative contribution of deformation in each period to describe the dominant role of each monitoring time series in the overall deformation process. The formula for calculating the relative contribution is: ,in, For the first The relative contribution of the period.

[0210] S331. The confidence level for each monitoring period is determined using the entropy weight method. Relative contribution to deformation Conduct information content analysis to determine the weighting coefficients of the two. and According to calculations, the first period: Issue 2 Issue 3: Based on this, an adaptive dynamic weighted model is established to calculate the dynamic weights for each period. This reflects the dynamic risk importance of each monitoring period. The relative contribution and dynamic weight of deformation in each period of the spoil heap are shown in Table 10.

[0211] Table 10. Relative Contribution and Dynamic Weight of Deformation in Different Stages at the Waste Disposal Site

[0212]

[0213] S411, Static risk score As a benchmark risk, based on dynamic weights for each monitoring period The static risk assessment results are dynamically and progressively integrated to calculate the comprehensive risk score of the waste disposal site.

[0214] Specifically, the overall risk for period 1 is first calculated using the following formula: Then, a progressive approach was used to merge the subsequent periods, resulting in: ;

[0215] ;

[0216] ...

[0217] ,in, For the first The comprehensive risk score for a period is the dimensionless value of the deformation variable after normalization. This is a static risk score. For the first Dynamic risk value over a period of time For the first The dynamic weight of the period, and The comprehensive risk scores for each spoil disposal site are shown in Table 11.

[0218] Table 11 Comprehensive Risk Score for Waste Disposal Sites

[0219]

[0220] S5. Comprehensive Risk Level Classification Based on Monte Carlo Random Sampling Technique

[0221] After calculating the integrated risk score based on dynamic and static factors, in order to quantify the uncertainty of risk and determine the risk level threshold, Monte Carlo random sampling technology was used to simulate and analyze the integrated risk score of each spoil disposal site, and a statistically significant risk level classification standard was established, including the following steps:

[0222] S51. Based on the comprehensive risk score of each spoil disposal site Based on this, it is assumed that the risk score follows a normal perturbation distribution within the standard deviation range. The number of random samplings is set to 10,000. A risk score sample sequence is generated using the Monte Carlo random sampling method. The probability density distribution function of the risk score for each spoil disposal site is calculated, and its mean and variance are statistically analyzed.

[0223] S52. Based on the risk score sample sequence obtained from Monte Carlo simulation, select typical quantiles (10%, 60%, 90%) as reference points for comprehensive risk level classification. Use the corresponding quantile values ​​as risk level thresholds to form a classification standard.

[0224] The three-phase comprehensive risk score of each spoil disposal site is compared with the threshold determined in S52 above to determine its final risk level. The comparison of the dynamic and static integrated comprehensive risk levels of each spoil disposal site is shown in Table 12.

[0225] Table 12 Comparison of Dynamic and Static Integrated Risk Levels of Waste Disposal Sites

[0226]

[0227] S53. To verify the rationality of the Monte Carlo classification, the empirical quantile method was further used to directly classify the comprehensive risk score of the first phase. The thresholds for the four risk levels determined by the empirical quantile method are as follows: Level I (low risk): R≤0.2752, Level II (medium risk): (0.2752, 0.4203], Level III (higher risk): (0.4203, 0.6377], and Level IV (high risk): R>0.6377. The results show that the comprehensive risk level distributions obtained by the two methods are largely consistent. Except for the differences in the comprehensive risk levels of the 8-3# and 3-9# spoil heaps, the risk levels of the other spoil heaps are the same. This indicates that the proposed Monte Carlo simulation method has good reliability and scientific validity.

[0228] Example 4

[0229] A computer program product includes a computer program / instructions that, when executed by a processor, implement the quantitative risk assessment method for hydraulic engineering spoil disposal sites as described above.

[0230] Computer program products include computer programs or instruction sets used to perform specific tasks or achieve specific functions. These programs or instructions are designed to be executed by a processor to implement a series of predefined steps or operations. The program product may be stored in various forms of computer storage media, such as memory, hard disks, solid-state drives, optical discs, or other forms of digital storage devices. It may exist in the form of compiled binary code or in the form of scripts or bytecode that can be executed by an interpreter. Through carefully designed algorithms and logical instructions, the program product enables the processor to process data in a specific order and manner, performing various functions such as data analysis, user interaction, and device control.

[0231] Those skilled in the art should understand that the above embodiments are merely for illustrating the present invention and are not intended to limit the scope of the invention. Those skilled in the art can make other changes or modifications based on the above invention, and these changes or modifications still fall within the scope of the present invention.

Claims

1. A method for quantitatively assessing the risks of spoil disposal sites in water conservancy projects, characterized in that, include: Obtain multi-attribute data of the waste disposal site, construct a static risk assessment index system, and calculate the static risk score of the waste disposal site based on the static risk assessment index system. Using InSAR technology to obtain multi-period temporal deformation information of spoil disposal sites; Based on the time-series deformation information, the monitoring accuracy index and deformation characteristic index are extracted, the confidence level of InSAR monitoring data and the relative contribution of deformation in each monitoring period are calculated, and the dynamic weight of each monitoring period is determined accordingly. An adaptive dynamic weighted model is constructed, and the static risk score is progressively integrated with the dynamic risk value of each monitoring period using the dynamic weights to calculate the comprehensive risk score of the spoil disposal site. The risk level of the spoil disposal site is determined based on the comprehensive risk score. The calculation of the confidence level of InSAR monitoring data specifically includes: The coherence coefficient, average intensity, and root mean square error of the InSAR monitoring results are extracted as accuracy indicators; and the weight of each accuracy indicator is calculated using the entropy weight method. Calculate the confidence level of the monitoring data based on the normalized values ​​of each accuracy indicator and their corresponding weights: ,in, For the first The confidence level of a waste disposal site , , The weights for coherence coefficient, average intensity, and root mean square error are... , , For the first Normalized values ​​of coherence coefficient, average intensity, and root mean square error for each spoil disposal site; The calculation of the relative contribution of deformation for each monitoring period includes: The deformation amplitude for each period is calculated based on multi-period deformation data: , ,in, For the first The magnitude of the deformation during the period For the first Deformation data for the period; Calculate the relative contribution of deformation in each period: ,in, For the first The relative contribution of the period; The determination of dynamic weights for each monitoring period specifically includes: Confidence levels for each monitoring period were determined using the entropy weight method. Relative contribution to deformation Conduct information content analysis to determine the weighting coefficients of the two. and ; Construct an adaptive dynamic weighted model to calculate dynamic weights : ; Specifically, the static risk score is progressively integrated with the dynamic risk value for each monitoring period using the dynamic weights, and the following progressive formula is adopted: Regarding the overall risk in Phase 1: ; For the subsequent Overall risks during the period: ,in, For the first The overall risk score for the period, This is a static risk score. For the first Dynamic risk value over a period of time For the first Dynamic weights for each period.

2. The method for quantitative risk assessment of water conservancy project spoil disposal sites according to claim 1, characterized in that, Methods for calculating the static risk score of a spoil disposal site include: A hierarchical indicator system comprising primary and secondary indicators is established, and the secondary indicators are subjected to numerical quantification, normalization, and directional transformation to obtain a standardized indicator system. The weights of each primary indicator were calculated using the expert group decision-making analytic hierarchy process. The objective weights of each secondary indicator are calculated using a combined weighting method, and the final weights of each secondary indicator are determined by combining the weights of the primary indicators. Based on the standardized indicator system and the final weights of the secondary indicators, the static risk score of the spoil disposal site is calculated using a weighted average: ,in, For the first The static risk score of a waste disposal site This represents the total number of secondary indicators. For the first The first waste disposal site in the Standardized values ​​for each secondary indicator For the first The final weight of each secondary indicator.

3. The method for quantitative risk assessment of water conservancy project spoil disposal sites according to claim 2, characterized in that, The weights of each primary indicator are calculated using the analytic hierarchy process (AHP) for expert group decision-making, including: Determine the individual weights of each expert; Obtain the judgment matrix of multiple experts comparing the primary indicators pairwise, and use the individual weights to perform a weighted geometric average of the judgment matrices of all experts to generate a group judgment matrix. The group judgment matrix is ​​subjected to eigenvalue decomposition and consistency test to obtain the weight of each primary indicator.

4. The method for quantitative risk assessment of water conservancy project spoil disposal sites according to claim 2, characterized in that, The objective weights of each secondary indicator are calculated using a combined weighting method, specifically as follows: The entropy weight of each secondary indicator is calculated using the entropy weight method; the CRITIC weight of each secondary indicator is calculated using the CRITIC method. The intra-group composite weights of the secondary indicators are obtained by linearly combining the entropy weights and the CRITIC weights through linear weighting. The final weight of each secondary indicator is obtained by multiplying the weight of the primary indicator by the corresponding group composite weight.

5. The method for quantitative risk assessment of water conservancy project spoil disposal sites according to claim 1, characterized in that, Determining the risk level of a spoil disposal site includes: Based on Monte Carlo random sampling technology, the comprehensive risk score of the spoil disposal site is subjected to multiple random simulations to generate a probability distribution sample of the risk score. Based on the characteristics of the probability distribution, quantiles are selected as the threshold for risk level classification; The overall risk score of the waste disposal site is compared with the threshold to determine the risk level of the waste disposal site as low risk, medium risk, relatively high risk, or high risk.

6. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the method for quantitative risk assessment of water conservancy project spoil disposal sites as described in any one of claims 1-5.

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