Power transmission corridor disaster monitoring multi-dimensional matrix construction method

Abstract

The invention provides a power transmission corridor disaster monitoring multi-dimensional matrix construction method, and relates to the field of disaster monitoring, and the method comprises the steps: building a chain pile-cross section-time three-dimensional main coordinate system with the center line of a power transmission line as a reference, and constructing a corridor multi-dimensional coordinate system; mapping the geographic observation points to corridor space coordinates through a space projection mapping relation; performing time resampling and covariance constrained spatial interpolation on the multi-source monitoring data to realize space-time alignment; constructing a fused disaster monitoring matrix, and embedding the uncertainty dimension in the matrix; dividing each corridor local unit in the disaster monitoring moment into a four-quadrant structure; modeling a coupling relationship among disaster species through a Copula function, and embedding a graph regularization constraint to obtain a multi-dimensional matrix; and performing slicing operation on the multi-dimensional matrix embedded with the disaster coupling relationship, extracting a local risk view, and outputting a risk cube with a confidence interval. According to the invention, the limitation of traditional two-dimensional geography or single disaster monitoring is broken through.

Description

A method for constructing a multi-dimensional matrix for disaster monitoring in power transmission corridors Technical Field

[0001] This application relates to the field of disaster monitoring, and in particular to a method for constructing a multi-dimensional matrix for disaster monitoring in power transmission corridors. Background Technology

[0002] Transmission line corridors are one of the most important spatial carriers of the power system, undertaking tasks such as long-distance power transmission, inter-regional interconnection, and the export of large-scale energy bases. With the increase in transmission voltage levels and the expansion of inter-regional power transmission scale, transmission corridors often cross land-sea boundaries, mountainous areas, river valleys, and densely populated areas, and are subject to multiple impacts from complex meteorological and geological disasters. In recent years, extreme weather events (such as severe convection, typhoons, rainstorms, icing, thunderstorms, and wildfires) have shown a trend of multiple disasters occurring simultaneously, with enhanced coupling and spatial concentration, making traditional single-disaster monitoring methods insufficient to meet the requirements of real-time and comprehensive monitoring.

[0003] Current disaster monitoring in power transmission corridors relies heavily on online sensing equipment, including micro-meteorological sensors, conductor vibration and icing monitoring devices, video recognition terminals, radar, and lightning location systems. While these monitoring methods provide high-precision data at the point-to-point level, they exhibit significant differences in sampling frequency, spatial distribution, data format, and time synchronization. Furthermore, disaster formation often involves the synergistic effects of multiple factors; for example, "low temperature + humidity + light wind" leads to icing, "strong wind + dryness + vegetation" triggers wildfires, and "heavy rain + slope + lithology" causes landslides. Therefore, how to organize these discrete factors within a unified spatiotemporal system under multi-source heterogeneous observation conditions to form a "multi-dimensional matrix" that reflects the evolutionary logic and coupling relationships of disasters has become the core issue for current disaster monitoring and intelligent identification in power transmission corridors. Summary of the Invention

[0004] The purpose of this invention is to provide a method for constructing a multi-dimensional matrix for disaster monitoring in power transmission corridors, in order to solve the problems of heterogeneous data, simple structure, fragmented disaster types, and inability to quantify risk uncertainty in existing disaster monitoring of power transmission corridors.

[0005] The above-mentioned objective of this application is achieved through the following technical solution, including the following steps: Step S1: Establish a three-dimensional principal coordinate system based on the centerline of the transmission line, link-cross section-time, and extend it to include disaster type, element, asset, scale, and uncertainty dimensions to construct a corridor-based multi-dimensional coordinate system; through spatial projection mapping, map geographical observation points to corridor spatial coordinates to achieve spatial alignment of multi-source monitoring data; Step S2: Perform time resampling and covariance-constrained spatial interpolation on the multi-source monitoring data to achieve spatiotemporal alignment; introduce an event-based coding strategy to map discrete disaster events into spatiotemporal pulse signals; construct a fused disaster monitoring matrix and embed the uncertainty dimension into the matrix; Step S3: Divide each corridor local unit in the disaster monitoring matrix into a four-quadrant structure; model the coupling relationship between disaster types using the Copula function, embed graph regularization constraints, and obtain a multi-dimensional matrix; Step S4: Perform slicing operations on the multi-dimensional matrix with embedded disaster coupling relationships to extract local risk views; calculate risk indicators through weighted fusion and propagate uncertainty to output a risk cube with confidence intervals.

[0006] An electronic device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to perform a method for constructing a multi-dimensional matrix for monitoring disasters in power transmission corridors.

[0007] A computer-readable storage medium storing instructions that, when executed, perform a method for constructing a multi-dimensional matrix for monitoring disasters in power transmission corridors.

[0008] The beneficial effects of the technical solution provided in this application are: 1. Establishing a unified spatial basis: By using the corridor coordinate embedding method, meteorological and monitoring data from different sources and at different scales are unified onto the geometric main line of the power transmission channel, thus constructing a data structure that combines physical consistency and algorithm friendliness, providing a solid foundation for subsequent intelligent analysis.

[0009] 2. Achieve high-precision assimilation of multi-source heterogeneous data: Through hierarchical resampling, joint interpolation and event-based coding, realize the unified matrixing of multimodal information such as high-frequency monitoring data, meteorological reanalysis data and video recognition results, so as to comprehensively depict the spatiotemporal state of the power transmission corridor.

[0010] 3. Embedding disaster coupling and uncertainty information: This invention introduces correlation modeling and uncertainty quantification between disaster types during the matrix construction stage, realizes the structural expression of disaster chain triggering relationship, and improves the model's sensitivity and robustness to extreme parallel events.

[0011] 4. Supporting intelligent identification and proactive prevention and control of disaster chains: Through multi-dimensional matrix slicing and dynamic calculation, indicators such as disaster intensity, exposure, vulnerability, importance and comprehensive risk can be generated, enabling quantifiable assessment and traceable interpretation of disasters, and providing technical support for dynamic risk early warning, maintenance scheduling and resilience planning of transmission lines.

[0012] In summary, this invention breaks through the limitations of traditional two-dimensional geographic or single-hazard monitoring, and for the first time proposes a multi-dimensional matrix construction framework with disaster coupling expression capability in the power transmission corridor scenario, providing fundamental methodological support for the next-generation intelligent power transmission channel disaster prevention system. Attached Figure Description

[0013] The present application will be further described below with reference to the accompanying drawings and embodiments. In the drawings: Figure 1 is a comparison diagram of wind field assimilation and reconstruction along the corridor in the embodiments of the present application; Figure 2 is a cross-sectional diagram of the four-quadrant sub-matrix in the embodiments of the present application; Figure 3 is a diagram of parallel extreme windows and tail dependence strength in the embodiments of the present application; Figure 4 is a spatiotemporal distribution diagram of the risk cube in the embodiments of the present application; Figure 5 is a diagram of risk time series and uncertainty band in the embodiments of the present application; Figure 6 is a diagram of risk dominant factors displayed by HEFC radar in the embodiments of the present application; Figure 7 is a flowchart of the steps in the embodiments of the present application; Figure 8 is a schematic diagram of the electronic equipment structure in the embodiments of the present application. Detailed Implementation

[0014] To provide a clearer understanding of the technical features, objectives, and effects of this application, the specific embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0015] The embodiments of this application provide a method for constructing a multi-dimensional matrix for disaster monitoring in power transmission corridors.

[0016] Please refer to Figure 7, which is a step diagram of a method for constructing a multi-dimensional matrix for disaster monitoring in a power transmission corridor according to an embodiment of this application. The method includes: Step S1: Establishing a three-dimensional principal coordinate system based on the centerline of the power transmission line, encompassing the chain pile, cross-section, and time dimensions; expanding this system to include disaster type, elements, assets, scale, and uncertainty dimensions; and constructing a corridor-based multi-dimensional coordinate system. Through spatial projection mapping, geographic observation points are mapped to corridor spatial coordinates to achieve spatial alignment of multi-source monitoring data. Step S2: Performing time resampling and covariance-constrained spatial interpolation on the multi-source monitoring data to achieve spatiotemporal alignment. The process involves: introducing an event-based coding strategy to map discrete disaster events into spatiotemporal pulse signals; constructing a fused disaster monitoring matrix and embedding an uncertainty dimension into the matrix; step S3: dividing each corridor local unit in the disaster monitoring matrix into a four-quadrant structure; modeling the coupling relationship between disaster types using the Copula function and embedding graph regularization constraints to obtain a multidimensional matrix; step S4: performing a slicing operation on the multidimensional matrix with embedded disaster coupling relationships to extract local risk views; calculating risk indicators through weighted fusion and propagating uncertainty to output a risk cube with confidence intervals.

[0017] This application, by adopting the aforementioned technical solution, establishes a multi-dimensional coordinate system encompassing corridor space, time, disaster type, element, asset, scale, and uncertainty; achieves standardized assimilation and resampling of multi-source heterogeneous monitoring data; intrinsically embeds disaster causal relationships and coupling relationships within the matrix structure; and outputs multi-dimensional risk slices with confidence information, thereby realizing the structuring, correlation, and computability of disaster monitoring data. This achieves an integrated technical closed loop from "multi-source monitoring data aggregation" to "disaster semantic organization" and then to "risk quantification expression."

[0018] Step S1 includes: As an example, to achieve the unification and computability of disaster monitoring data for power transmission corridors, it is first necessary to establish a corridor-based multidimensional coordinate system that combines spatial consistency, temporal comparability, and multi-element scalability. The core idea of ​​this step is to project the original geographical observation data from Cartesian coordinates or latitude and longitude coordinates to a three-dimensional principal coordinate system of "chain pile-cross section-time" with the centerline of the power transmission line as the reference, and on this basis, extend the dimensions of disaster type, element, asset, scale, and uncertainty to form a standardized embedding space for multi-source monitoring data fusion.

[0019] S11: Determine the centerline of the transmission line and define chain pile mileage parameters along the line direction to represent the position of each geographic observation point or observation unit on the main line. For each chain pile point, establish local cross-sectional coordinates with the tangent of the transmission line as the main axis to describe the vertical and horizontal distribution of tower locations, conductors, and topography. As an example, at the spatial level, first determine the centerline of the transmission line. Define chain pile mileage parameters along the line direction to represent the position of each geographic observation point or observation unit on the main line. For each chain pile point, establish local cross-sectional coordinates with the tangent of the line as the main axis to describe the vertical and horizontal distribution of tower locations, conductors, topography, and other elements. In this way, heterogeneous information originally in three-dimensional geographic space is embedded into an intrinsic coordinate system dominated by the line topology, thereby ensuring the physical comparability and continuity of data from different sources and with different sampling resolutions.

[0020] Spatial projection mapping function was established This represents the energy and disaster propagation characteristics of the corridor space of transmission lines, among which... The vector representing the starting position of the centerline of the transmission line; Indicates the integration path variable; This represents the distance parameter along the centerline of the transmission line, indicating the coordinates of the chain pile mileage. A vector function representing the position of the transmission line centerline in three-dimensional space; a spatial projection mapping function. The spatial representation of the centerline of a transmission line; as one embodiment, the spatial projection mapping function. The spatial representation of the centerline of the transmission line is given by means of the chain piles, where the positions of the chain piles are given by means of integrals to ensure the continuous guiding characteristics of the line under any curved or undulating terrain.

[0021] After obtaining the spatial representation of the transmission line centerline, any geographic observation point (such as a weather station, camera, micro-meteorological node, or remote sensing pixel) can be projected onto the local cross-sectional coordinates of this centerline. To this end, a projection function is defined from the original geographic space to the corridor space to express the spatial mapping relationship between each geographic observation point and the centerline. The projection function realizes the mapping from latitude, longitude, and altitude coordinates to corridorized coordinates, where the location of the chain post is obtained by the shortest distance projection, and the cross-sectional coordinates are determined by both the tangential and normal vectors, thus preserving the continuity of geographic direction and terrain features.

[0022] The spatial mapping relationship between each geographical observation point and the centerline of the transmission line is as follows: ,in The coordinates of the original geographic observation point are longitude, latitude, and elevation, respectively. The cross-sectional coordinates perpendicular to the centerline of the line are used to indicate the relative positions of towers, conductors, and ground features on the corridor cross section. The elevation dimension remains constant. As an example, the time dimension is constructed based on a multi-level resolution system to ensure that disaster characteristics at different time scales can be expressed. The hierarchical design of this time resolution set enables short-term high-frequency monitoring data (such as vibration, electric field, partial discharge) and low-frequency meteorological data (such as daily rainfall, temperature field) to be resampled and correlated under a unified time index.

[0023] Suppose the entire time domain is divided into several resolution levels, and the time step of each level forms a resolution set. Multi-scale time representation is achieved through nested levels:

[0024] in, This represents a set of time resolution levels, used to define the different time scales or sampling interval levels that are allowed to be used in a multi-source monitoring system. Indicates the first The time step or sampling interval corresponding to each time resolution level As one embodiment, in order to represent multi-hazard, multi-element, multi-asset hierarchy and uncertainty information in the data structure, this invention extends the corridor spatial and temporal dimensions into a high-dimensional pattern structure and defines the total space of the disaster monitoring matrix.

[0025] Extending the spatial and temporal dimensions of the corridor into a high-dimensional model structure, the total space of the disaster monitoring matrix is ​​defined as follows:

[0026] in For the time dimension, For disaster prevention, As an element dimension, For asset maintenance, For uncertainty dimension; as an example, each cell in the disaster monitoring matrix represents the monitoring status and quality measure at a specific location, time, disaster type, element, and asset.

[0027] Under the multidimensional model of disaster type, elements, assets, scale, and uncertainty, the monitoring data of any corridor segment in time and disaster type space can be abstracted into a tensor:

[0028] in, It represents a specific data unit in matrix M, representing a monitoring status or fusion feature value under a specific combination of spatial location, time scale, disaster type, monitoring elements, and power transmission asset categories; and Representing the sets of chain pile mileage respectively Cross-sectional coordinate set Time index set Set of disaster types Monitoring element set and power transmission asset category set The elements in the set; as an example, the contents of the set are shown in Table 1.

[0029] Table 1

[0030] Multi-source monitoring data refers to monitoring data in five dimensions: disaster type, element, asset, scale, and uncertainty.

[0031] As one embodiment, the matrix Each element represents the comprehensive state of a monitoring unit in multidimensional space. Through this structure, the originally scattered multi-source observations are unified into a computable multidimensional matrix, realizing the structuring, standardization, and algorithmic operability of disaster monitoring in power transmission corridors.

[0032] The corridor-based multidimensional coordinate system established through the above steps enables a one-to-one mapping relationship between various meteorological elements, geographic information, and monitoring data within a unified geometric and temporal framework, achieving physical consistency and computational compatibility among the data. This system lays the mathematical and structural foundation for subsequent steps such as multi-source monitoring data assimilation, disaster coupling modeling, and risk assessment.

[0033] Step S2 includes: As an example, after establishing the corridor-based multidimensional coordinate system, in order to assimilate and matrix-construct multi-source heterogeneous disaster monitoring data (multi-source monitoring data) within a unified coordinate system, and considering the extremely complex data sources along the power transmission corridor, including ground meteorological observations, online monitoring terminals, video recognition nodes, radar and satellite remote sensing, and historical disaster records, which differ significantly in temporal sampling intervals, spatial distribution density, and physical dimensions, this step achieves standardized fusion of different data types through spatiotemporal resampling, statistical interpolation, and covariance constraints, thereby forming a high-dimensional computable matrix within a unified corridor-based coordinate framework.

[0034] S21: Time resampling is performed on the time series data of multi-source monitoring data to align data from different sampling frequencies at the target time step. Specifically, this includes using a weighted interpolation-based time reconstruction method to form a standard time grid sequence by weighted averaging of adjacent samples within a time window. Its general form can be expressed as:

[0035] in This represents the confidence variance / estimation result of the estimated or reconstructed value of a certain monitoring quantity after assimilation / fusion. This represents the weighting coefficient of the i-th data source, sensor, or neighboring observation point in the fusion calculation; This represents the original variance or autocovariance of the monitored physical quantity corresponding to the i-th data source or the i-th observation point / sensor. As an example, at the spatial level, since the distribution of monitoring points is not a regular grid, direct interpolation may lead to local distortion or oversmoothing. This invention employs a covariance-constrained spatial assimilation model, treating multi-source monitoring data as sample points of a random field, and performing joint estimation within the local neighborhood of the corridor, thereby spatially restoring the continuous state distribution.

[0036] S22: At the spatial level, a covariance-constrained spatial assimilation model is adopted, treating multi-source monitoring data as sample points of a random field. Joint estimation is performed within the local neighborhood of the corridor, thereby spatially recovering the continuous state distribution. This spatial reconstruction process is represented by a linear assimilation expression under the minimum mean square error criterion:

[0037] in This represents the optimal estimation result of the multidimensional monitoring state vector or tensor after assimilation and fusion. Represents the background state error covariance matrix; This represents the transpose of the observation mapping matrix; Represents the observation error covariance matrix; This indicates the input observation vector or the result of splicing monitoring data from multiple sources; This represents the background state of the corridor; as an example, this formula achieves spatially optimal fusion in the sense of error statistics, enabling observation information from different sources and densities to form a continuous field on a unified corridor coordinate system.

[0038] S23: An event-based coding strategy is introduced to map discrete events into computable spatiotemporal pulse signals. Discrete events include vibration anomalies, lightning strikes, icing detection, and video recognition events. As an example, for event-type data (such as vibration anomalies, lightning strikes, icing detection, and video recognition events), since they exhibit discrete burst processes in time and space, this invention introduces an event-based coding strategy to map these discrete events into computable spatiotemporal pulse signals. The event-based process uses a Gaussian kernel for spatiotemporal expansion to form a smooth probability distribution surface in the matrix.

[0039] in, This represents the mileage along the route in a corridor-based coordinate system. With time Spatiotemporal event energy density / response intensity at the location; This represents the total number of discrete disaster events detected within the current analysis time domain of the corridor segment; Mileage coordinates representing the direction of the corridor centerline; This represents the mileage position of the p-th discrete disaster event on the main axis of the corridor; The method represents the square of the standard deviation of the spatial kernel expansion; it enables discrete event information to be incorporated into the matrix structure along with continuous environmental elements, thereby improving the semantic integrity and computability of the matrix.

[0040] In this application, the spatiotemporal pulse signal is a unified mathematical expression for various discrete disaster events along the corridor (such as sudden conductor vibration, lightning strike point, wildfire pixel identification, icing inflection point warning, and abnormal wind galloping triggering). Essentially, it transforms events that are originally sudden and irregular in time and space into computable signals with local energy representation and probabilistic extended semantics in the corridor's intrinsic coordinate system (s,x,y,z,t). During the data acquisition phase, multi-source monitoring devices can only provide the time, location, and type of the event, but cannot directly describe its impact range in the corridor's neighborhood or the degree of synergistic correlation with other disaster types. Therefore, this application constructs a spatiotemporal pulse and introduces Gaussian kernel expansion, making it a structured input that can participate in subsequent assimilation, coupled reasoning, and risk aggregation.

[0041] At the application stage, this pulse signal (spatiotemporal pulse signal) is first used for event assimilation gain calculation and state correction. In step S2, within the covariance-constrained assimilation framework, the observed innovation term D... HEb represents the continuous measurement residual, while discrete events (such as lightning strike point, icing trigger point, wildfire identification pixel cluster, and abnormal contact moment of disconnecting switch) are written as pulse innovation inputs into the observation vector D of the assimilation system. This allows the background state Eb to obtain the optimal update correction in the corridor space under the least mean square meaning, outputting the fused reliable state. This step solves the problem of event data "only recording, not correcting," enabling it to truly participate in the physical reconstruction and error statistical propagation of the corridor state.

[0042] Secondly, spatiotemporal pulses are used for tail correlation and coupling dependency modeling of multiple hazards. In the Copula structure embedding in step S3, the intensity and density of the pulse signal are used to calculate the tail dependency strength of the joint distribution of hazards (such as empirical Kendall and conditional probability collaborative window annotation) to explicitly characterize the temporal window and spatial hotspots of "simultaneous hazard exceeding the threshold or triggering". This allows concurrent hazard events to be structurally identified in matrix H and nonlinear dependency constraints to be established without the need for ex-post rule splicing in the risk calculation stage, thereby improving the sensitivity and stability of concurrent hazard identification.

[0043] S24: During the assimilation process in step S22, variance propagation is simultaneously calculated, jointly mapping the background state error and the observation vector error to the output variance field. The quality confidence of each corridor unit is recorded in the uncertainty dimension of the disaster monitoring matrix. The propagation relationship is as follows:

[0044] in, This represents the state error covariance matrix after assimilation and fusion. This represents the assimilation and fusion gain matrix; Represents the observation mapping matrix; This represents the background error covariance matrix. As an example, to quantify the uncertainty of data fusion, this invention simultaneously calculates variance propagation during the assimilation process, jointly mapping background error and observation error to the output variance field. This allows the quality confidence level of each element to be recorded in the "uncertainty dimension" of the matrix. By writing this uncertainty information into the uncertainty dimension of the matrix, a dual output of "value-confidence level" can be achieved in subsequent disaster analysis and risk assessment, thereby significantly improving the interpretability of the results.

[0045] As one embodiment, at the matrix storage level, this invention employs a block structure for data organization. The entire corridor is divided into several spatial-temporal subdomains, each corresponding to a local matrix unit block. By recording the block index and dimension mapping table during construction, on-demand reading and incremental updates are achieved, thereby ensuring the scalability and computational efficiency of the matrix under large-scale data.

[0046] The fused disaster monitoring matrix is ​​defined as follows:

[0047] in, This is the integrated disaster monitoring matrix; This represents the input data of the nth independent monitoring data source / observation channel; This represents a multi-source data fusion function.

[0048] As one embodiment, this invention achieves the transformation from raw, multi-source, heterogeneous data to a structured, highly consistent, multi-dimensional matrix. This matrix not only possesses physical continuity in space, resolution consistency in time, and unified identification at the disaster type and element levels, but also carries confidence information in each matrix unit, thereby providing a solid data foundation for subsequent disaster coupling analysis and risk slicing.

[0049] Step S3 includes: As an example, after completing multi-source assimilation and spatiotemporal matrix construction, the assimilated multidimensional data is organized according to the logic of disaster mechanisms, so that the matrix not only has data continuity, but also the ability to express disaster causality. To this end, the present invention introduces a "four-quadrant sub-matrix splicing" mechanism, which partitions and calculates each corridor local unit (i.e., the intersection area of ​​mileage segment and time window) according to the structured semantics of "disaster-causing factor - exposure - vulnerability - importance", thereby forming a semantic monitoring cell for disaster monitoring. This design enables the matrix to have a complete disaster analysis closed loop locally and can directly support disaster coupling reasoning and risk aggregation.

[0050] In this structure, each monitoring unit (corridor local unit) is divided into four quadrants, each recording characteristic indicators for its corresponding category. To ensure that the data can be combined and processed across quadrants, this invention transforms the indicator scale for each quadrant into a dimensionless standardized representation, ensuring that its value range is consistent and can be compared relatively.

[0051] S31: The four quadrants include: hazard hazard, exposure, vulnerability, and importance; four-quadrant structure. It is expressed as follows:

[0052] in: This is the disaster-causing factor matrix; This is the exposure matrix; Vulnerability matrix; The importance matrix is ​​used. Within each quadrant of the four-quadrant structure, feature aggregation is achieved through index normalization and principal component transformation. In one embodiment, feature aggregation is achieved within each quadrant through index normalization and principal component transformation. For any type of feature, extreme value normalization and weighting coefficients are used to synthesize a comprehensive index. This process ensures that features with different dimensions and measurement methods can be aggregated under the same index system, facilitating subsequent calculations of disaster intensity and coupling.

[0053] S32: Modeling the coupling relationships between disaster types using Copula functions, as follows:

[0054] in Disaster type and Joint distribution function in a certain spatiotemporal unit of the corridor; Indicates disaster Marginal distribution function; Indicates disaster Marginal distribution function; Represents the dependency structure parameters (coupling parameters) of a Copula; Indicates index 1 for disaster; Indicates disaster index 2; This represents the combination of values ​​for two different disaster indicators within the same spatiotemporal unit. As an example, based on a four-quadrant structure, this invention proposes an endogenous coupling structure embedding method to reveal the intrinsic correlation between disasters. By establishing joint probabilistic relationships and spatial adjacency constraints among disaster types, the matrix can automatically identify multi-disaster synergistic effects. This model allows for nonlinear dependencies between different disaster types, and is particularly suitable for typical disaster coupling scenarios such as "low temperature + high humidity → icing" and "strong wind + dryness → wildfire." By embedding this structure during the matrix construction stage, probabilistic constraints on disaster synergy can be implemented at the data layer without the need for post-event modeling.

[0055] As one embodiment, in order to ensure that the matrix achieves a balance between spatial continuity and abrupt changes in disaster boundaries, the present invention further introduces a graph-based regularization constraint, which connects adjacent matrix blocks in space through adjacency weights to form a graph Laplace smoothing term.

[0056] S33: Embedded graph regularization constraints specifically include: introducing graph-based regularization constraints, connecting adjacent matrix blocks spatially through adjacency weights to form a graph Laplace smoothing term. This regularization process is represented in the following form:

[0057] in, The graph structure regularization loss function is represented. Represents the nodes in the graph node The adjacency weight; Represents a node The fusion of feature vectors or state estimation; The fusion feature vector or state estimate of node j is represented; the matrix block represents a local high-dimensional monitoring sub-matrix unit divided along the transmission line corridor according to the chain pile mileage and a unified time window; adjacent matrix blocks refer to two matrix sub-blocks that have topological continuity or boundary sharing relationship on the main axis of the corridor or on the time window, which are used to establish spatial or temporal correlation constraints in fusion and risk calculation; as an example, the graph Laplace smoothing term can simultaneously suppress local jumps caused by noise and retain structural differences at terrain abrupt changes during the model optimization process, so that the matrix has both spatial smoothness and maintains the clarity of disaster boundaries.

[0058] S34: In the time dimension, collaborative annotation is achieved by identifying parallel extreme windows for multiple hazards, and a time-related hazard collaborative function is defined:

[0059] in, This represents the parallel extreme collaborative triggering function, i.e., the multi-hazard collaborative indicator function; This indicates the total number of disaster types participating in the collaborative assessment; Indicates an indicator function; Indicates the type of disaster In time Fusion index / state feature value at the location; Disaster type The trigger threshold; when the disaster coordination function is 1, it means that multiple disasters exceed their respective thresholds at the same time window, forming a parallel extreme coordination period; the disaster coordination function is used in the matrix to generate time tags, thereby explicitly identifying disaster coordination segments at the data layer.

[0060] As one embodiment, the four-quadrant submatrix structure of this invention simultaneously possesses four types of information—hazard intensity, environmental exposure, structural vulnerability, and asset importance—within each local area of ​​the corridor. Through a triple mechanism of Copula dependency structure, graph regularization constraints, and collaborative time window annotation, the coupling relationships between hazards are intrinsically embedded during the matrix construction stage. This design makes the final multidimensional matrix not only a data container but also a relational network with hazard semantics and physical interpretation, providing a traceable and quantifiable input foundation for subsequent uncertainty-aware risk calculations.

[0061] Step S4 includes: As an example, after completing the multi-source assimilation of disaster elements and the splicing of four-quadrant sub-matrices, the multi-dimensional disaster characteristics contained in the matrix are transformed into risk outputs with practical decision-making significance. During this process, uncertainty is simultaneously quantified, ensuring that the output not only has the ability to determine the value range but also the ability to express a confidence interval. This step constructs a risk cube that can be queried at the time, space, disaster type, and asset levels through multi-dimensional slicing, weighted fusion, and confidence propagation, achieving a closed loop from "data structure" to "decision quantification."

[0062] Slicing a multidimensional matrix yields multidimensional submatrices, which are used to extract local views of disaster intensity, exposure, vulnerability, and importance under specific dimensional combinations. The slicing operation of a multidimensional matrix takes the following form:

[0063] in, Indicates satisfaction Submatrices of M or slices of the monitoring subspace under given conditions; A complete multidimensional matrix or monitoring tensor for disaster monitoring of power transmission corridors; The constraint domain is the selected spatiotemporal or semantic subdomain. As an example, this slicing operation allows the extraction of local data from the complete matrix, such as "the wind-induced disaster exposure and vulnerability submatrix of a certain tower location during a specific time period" or "the comprehensive risk profile of a certain area within a parallel extreme window", enabling flexible data access and computation.

[0064] As one embodiment, in order to transform multidimensional slices into indicators with practical significance, this invention calculates disaster-related intensity, exposure, vulnerability and importance indicators in each submatrix, and integrates them into a risk score through a hierarchical weighting mechanism.

[0065] In a multidimensional submatrix, disaster-related intensity, exposure, vulnerability, and importance indicators are calculated, and these are integrated into a risk score through a hierarchical weighting mechanism. The general form of the weighted fusion calculation of the risk score can be expressed as:

[0066] in: These are the weights for each quadrant; Assign a risk score; This formula represents the intensity, exposure, vulnerability, and importance indicators related to disasters. As one example, it achieves a weighted fusion of different disaster drivers, ensuring that the final risk value reflects both the impact of disaster intensity and the differences in environmental and equipment conditions. Since both monitoring data and model calculations contain errors, this invention introduces uncertainty propagation into the risk calculation process to simultaneously output confidence intervals.

[0067] Uncertainty propagation is introduced into the calculation of risk score, and confidence intervals are output simultaneously. The risk variance propagation formula is defined as follows:

[0068] in, This represents the variance of the risk score; Indicates the first Information sources / indicators in the risk of fusion Weighting coefficients in the calculation; Indicates the first The inherent uncertainty of the input corresponding to the information source / indicator; Indicates the first and the The joint covariance of each data source / channel; This represents the feature representation corresponding to the two fused input indicators involved in the covariance calculation; as an example, the risk result is output in the form of mean ± confidence interval, thus reflecting the impact of the uncertainty of the input data and the model on the final risk assessment. This is to describe the spatial and temporal evolution of the risk distribution.

[0069] A general representation of the risk cube is constructed to describe the spatial and temporal evolution and distribution of risk. The general representation is as follows:

[0070] in, A statistical description of the error and noise in the input data of the sensor or identification terminal itself; Indicates the distance in the corridor ,time Disaster types Asset Types Fusion risk / status score under combination; The risk-uncertainty encapsulation mapping function is represented; as an example, in a multi-hazard scenario, in order to identify the superimposed impact of parallel extreme events on risk, the present invention further defines a disaster synergistic enhancement function.

[0071] Define the disaster collaborative enhancement function as follows:

[0072] in, This represents the collaborative risk score after incorporating the enhancement correction of multi-hazard coupling; The adjustment coefficient representing the enhanced synergy among multiple disasters; Disaster type With disaster The correlation coefficient, i.e. the coupling dependency strength.

[0073] As one example, the disaster co-enhancement function enables risk results to reflect the overall risk enhancement effect brought about by disaster superposition, and is particularly suitable for quantitative assessment of multiple disaster scenarios such as severe convection, icing, and landslides.

[0074] In summary, this step achieves a complete transformation from a structured matrix to a risk cube, enabling the risk output to not only be multidimensionally queryable but also include traceable uncertainty measurements and disaster coordination information. This mechanism allows disaster monitoring results from transmission corridors to be directly used for operation control, maintenance scheduling, and risk early warning, realizing a closed-loop process across the entire "data-model-decision" chain.

[0075] The method also includes: outputting a risk cube that supports multi-dimensional queries by mileage, time, and disaster type; and mapping continuous risk values ​​to early warning levels through fuzzy membership functions.

[0076] As one implementation example, the risk cube, as the final output structure, can be directly used for visualization, querying, and alerting, supporting multi-dimensional risk retrieval by mileage segment, disaster type, time window, and asset type. Each cell carries both risk value and confidence information, providing quantitative basis for scheduling and disaster prevention decisions.

[0077] As one embodiment, to support the integration of monitoring and decision-making, this invention provides a risk grading and time-based early warning mechanism in the output stage. Risk grading achieves the conversion from continuous values ​​to levels through fuzzy membership functions. By representing risk levels with continuous membership degrees, risk levels can smoothly transition within the matrix, avoiding the problem of unstable early warnings caused by threshold jumps.

[0078] In one embodiment, all data in this embodiment are semi-synthetic (constructed) data, not measured data. The purpose is to verify the correctness and usability of the method, rather than to reproduce the absolute values ​​of the real-world scenario. The construction strategy follows three points: First, a master field is generated using deterministic "physical trends" (e.g., sea breeze advancing along the corridor, terrain magnification, cold and humid windows) to ensure the algorithm can capture interpretable patterns. Second, small-amplitude noise and spatiotemporally uneven sampling are superimposed on the master field to simulate measurement errors and sparse station distribution in real monitoring, verifying the robustness of the algorithm. Third, key coupling windows are set (e.g., 22–34 hours of "sea breeze intrusion + cold and humidity") to ensure verifiable responses from BI-CETF / assimilation, four-quadrant HEFC, Copula coupling, parallel collaboration, risk cube, and uncertainty propagation. Therefore, this data set is a controllable, repeatable, and interpretable "quasi-physical" scenario used to evaluate the advantages, disadvantages, and parameter sensitivity of the method.

[0079] In another embodiment, Figure 1 shows a comparison of wind fields along the corridor: True value vs. Assimilation reconstruction. The left figure shows the distribution of "true wind speed" with distance and time, while the right figure shows the wind speed reconstructed through "sparse observations at stations + covariance-constrained spatial assimilation".

[0080] Key takeaway: In the 22–34 hour and 20–60 km range, the wind speed ridge line was significantly raised, and the assimilation results successfully reproduced this structure, indicating that spatiotemporal assimilation can still recover key spatiotemporal features under sparse site conditions.

[0081] Purpose: To verify the effectiveness of "Step 2: Multi-source assimilation and spatiotemporal resampling".

[0082] In another embodiment, Figure 2 four-quadrant sub-matrix profile (t=28 h): disaster-causing / exposed / vulnerable / important (HEFC) meaning: at a fixed time t=28 h, four standardized indicators are plotted along the mileage: disaster-causing (wind + rain + icing potential), exposed (topographic amplification / channel effect), vulnerable (icing history / vibration activity), and important (critical crossing / main trunk power supply).

[0083] Key points: In areas with amplified topography and cold and humid conditions, the risk of disaster and vulnerability increase simultaneously; exposure is higher in areas with steep slopes; and importance peaks near key crossings.

[0084] Purpose: To verify the semantic interpretability and spatial consistency of "Step 3: Four-quadrant submatrix splicing".

[0085] In another embodiment, Figure 3 shows the meaning of the parallel extreme co-operation window and tail correlation (empirical Kendall): the left figure shows the "parallel extreme co-operation window" (multiple disasters exceeding the threshold at the same time) with time as the axis; the right figure uses a bar chart to give the tail dependence strength of wind-icing and rain-terrain (slip agent).

[0086] Key takeaways: The parallel window is concentrated in 22–34 hours; the tail dependency is significant, indicating that disaster coupling is stronger in above-threshold extreme events.

[0087] Purpose: To verify the rationality of "Step 3: Endogenous Coupling Structure" and provide quantitative basis for "Step 4: Synergistic Enhancement Correction".

[0088] In another embodiment, Figure 4 shows a risk cube slice: the spatial-temporal distribution of Risk*(s, t).

[0089] Meaning: Shows the risk after collaborative correction on the two-dimensional plane of mileage and time (including HEFC fusion and collaborative enhancement).

[0090] Key takeaway: High-risk zones overlap with parallel windows and corridor hotspots, reflecting the combined effect of strong drivers, high exposure / vulnerability, and high importance.

[0091] Purpose: To verify the distribution rationality and positioning capability of "Step 4: Risk Cube Output".

[0092] In another embodiment, the meaning of the risk time series and uncertainty band (s=40 km) in Figure 5 is as follows: Select the risk time series with a mileage of s=40 km and plot the confidence band of "mean ± standard deviation".

[0093] Key takeaway: Within the parallel window, the risk mean increases and the confidence band widens, indicating that the input uncertainty is correctly transmitted to the output, making the warning more interpretable.

[0094] Purpose: To verify the engineering significance of "uncertainty propagation".

[0095] In another embodiment, the meaning of the HEFC radar chart in Figure 6 (s=40 km, t=28 h) is: a radar chart showing the relative trade-offs of four dimensions with a fixed distance and time.

[0096] Key focus: Easily identify risk-driving factors (such as higher hazard and vulnerability) to help develop differentiated prevention and control strategies.

[0097] Purpose: To support visual diagnostics and inspection prioritization for assets.

[0098] This application also discloses an electronic device. Referring to FIG8, FIG8 is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0099] The communication bus 502 is used to enable communication between these components.

[0100] The user interface 503 may include a display screen, and optionally, the user interface 503 may also include a standard wired interface or a wireless interface.

[0101] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0102] This application also discloses a computer-readable storage medium storing multiple instructions adapted for loading by a processor to execute the above-described method for constructing a multi-dimensional matrix for disaster monitoring in power transmission corridors.

[0103] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure.

[0104] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for constructing a multi-dimensional matrix for disaster monitoring in power transmission corridors, characterized in that, The method includes the following steps: Step S1: Establish a three-dimensional principal coordinate system based on the centerline of the transmission line, encompassing the chain pile, cross-section, and time. Extend this system to include dimensions of disaster type, element, asset, scale, and uncertainty, constructing a corridor-based multi-dimensional coordinate system. Through spatial projection mapping, map geographical observation points to corridor spatial coordinates to achieve spatial alignment of multi-source monitoring data. S2: Perform temporal resampling and covariance-constrained spatial interpolation on the multi-source monitoring data to achieve spatiotemporal alignment. Introduce an event-based coding strategy to map discrete disaster events into spatiotemporal pulse signals. Construct a fused disaster monitoring matrix and embed the uncertainty dimension within it. S3: Divide each corridor local unit in the disaster monitoring matrix into a four-quadrant structure. Model the coupling relationships between disaster types using the Copula function, embedding graph regularization constraints to obtain a multi-dimensional matrix. S4: Perform slicing operations on the multi-dimensional matrix embedded with disaster coupling relationships to extract local risk views. Risk indicators are calculated by weighted fusion and uncertainty is propagated to output a risk cube with confidence intervals.

2. The method for constructing a multi-dimensional matrix for disaster monitoring in power transmission corridors as described in claim 1, characterized in that, Step S1 includes: S11: Determine the centerline of the transmission line, define the chain pile mileage parameters along the line direction to represent the position of each geographical observation point or observation unit on the main line; for each chain pile point, establish local cross-sectional coordinates with the tangent of the transmission line as the main axis to describe the distribution relationship of tower locations, conductors, and terrain in the vertical and horizontal directions; establish a spatial projection mapping function. This represents the energy and disaster propagation characteristics of the corridor space of transmission lines, among which... This vector represents the starting position of the centerline of the transmission line. Indicates the integration path variable; This represents the distance parameter along the centerline of the transmission line, indicating the coordinates of the chain pile mileage. A vector function representing the position of the transmission line centerline in three-dimensional space; a spatial projection mapping function. The spatial representation of the transmission line centerline; the spatial mapping relationship between each geographical observation point and the transmission line centerline is as follows: ,in The coordinates of the original geographic observation point are longitude, latitude, and elevation, respectively. The cross-sectional coordinates perpendicular to the centerline of the line are used to indicate the relative positions of towers, conductors, and ground features on the corridor cross-section. The elevation dimension remains constant. The entire time domain is divided into several resolution levels, with the time step of each level forming a resolution set. Multi-scale time representation is achieved through nested levels. in, This represents a set of time resolution levels, used to define the different time scales or sampling interval levels that are allowed to be used in a multi-source monitoring system. Indicates the first The time step or sampling interval corresponding to each time resolution level Extending the spatial and temporal dimensions of the corridor into a high-dimensional model structure, the total space of the disaster monitoring matrix is ​​defined as follows: in For the time dimension, For disaster prevention, As an element dimension, For asset maintenance, For uncertainty dimension; under the multidimensional model of disaster type, element, asset, scale and uncertainty, the monitoring data of any corridor segment in time and disaster type space can be abstracted as a tensor: in, It represents a specific data unit in matrix M, representing a monitoring status or fusion feature value under a specific combination of spatial location, time scale, disaster type, monitoring elements, and power transmission asset categories; and Representing the sets of chain pile mileage respectively Cross-sectional coordinate set Time index set Set of disaster types Monitoring element set and power transmission asset category set The elements in the data; multi-source monitoring data refers to monitoring data in five dimensions: disaster type, element, asset, scale, and uncertainty.

3. The method for constructing a multi-dimensional matrix for disaster monitoring in power transmission corridors as described in claim 1, characterized in that, Step S2 includes: S21: Time resampling of the time series data of multi-source monitoring data to align data with different sampling frequencies at the target time step. Specifically, this includes: using a time reconstruction method based on weighted interpolation to form a standard time grid sequence by weighted averaging of adjacent samples within the time window. Its general form can be expressed as: in This represents the confidence variance / estimation result of the estimated or reconstructed value of a certain monitoring quantity after assimilation / fusion. This represents the weighting coefficient of the i-th data source, sensor, or neighboring observation point in the fusion calculation; S22: At the spatial level, a covariance-constrained spatial assimilation model is adopted, treating multi-source monitoring data as sample points of a random field, and performing joint estimation within the local neighborhood of the corridor zone, thereby recovering the continuous state distribution in space. This spatial reconstruction process is represented by a linear assimilation expression under the minimum mean square error criterion. in This represents the optimal estimation result of the multidimensional monitoring state vector or tensor after assimilation and fusion. Represents the background state error covariance matrix; This represents the transpose of the observation mapping matrix; Represents the observation error covariance matrix; This indicates the input observation vector or the result of splicing monitoring data from multiple sources; S23: Introducing an event-based coding strategy to map discrete events into computable spatiotemporal pulse signals; discrete events include: vibration anomalies, lightning strikes, icing detection, and video recognition events; S24: Simultaneously calculating variance propagation during the assimilation process in step S22, jointly mapping the background state error and the observation vector error to the output variance field, recording the quality confidence of each corridor unit in the uncertainty dimension of the disaster monitoring matrix, with the propagation relationship as follows: in, This represents the state error covariance matrix after assimilation and fusion. This represents the assimilation and fusion gain matrix; Represents the observation mapping matrix; The background error covariance matrix is ​​represented; the fused disaster monitoring matrix is ​​defined as follows: in, This is the integrated disaster monitoring matrix; This represents the input data of the nth independent monitoring data source / observation channel; This represents a multi-source data fusion function.

4. The method for constructing a multi-dimensional matrix for disaster monitoring in power transmission corridors as described in claim 1, characterized in that, Step S3 Includes: S31: The four quadrants include: hazard causative agent, exposure level, vulnerability, and importance; the four-quadrant structure. It is expressed as follows: in: This is the disaster-causing factor matrix; This is the exposure matrix; Vulnerability matrix; The importance matrix is ​​used; within each quadrant of the four-quadrant structure, feature aggregation is achieved through index normalization and principal component transformation; S32: The coupling relationship between disaster types is modeled using the Copula function, as follows: in Disaster type and Joint distribution function in a certain spatiotemporal unit of the corridor; Indicates disaster Marginal distribution function; Indicates disaster Marginal distribution function; Represents the dependency structure parameters (coupling parameters) of a Copula; Indicates index 1 for disaster; Indicates disaster index 2; This represents the combination of values ​​for two different disaster indicators within the same spatiotemporal unit; S33: Embedded graph regularization constraints specifically include: introducing graph-based regularization constraints, connecting adjacent matrix blocks spatially through adjacency weights to form a graph Laplace smoothing term. This regularization process is represented in the following form: in, The graph structure regularization loss function is represented. Represents the nodes in the graph node The adjacency weight; Represents a node The fusion of feature vectors or state estimation; This represents the fused feature vector or state estimate of node j; a matrix block represents a local high-dimensional monitoring sub-matrix unit divided along the transmission line corridor according to the chain pile mileage and a unified time window; adjacent matrix blocks refer to two matrix sub-blocks with topological continuity or boundary sharing relationship on the main axis of the corridor or on the time window, used to establish spatial or temporal correlation constraints in fusion and risk calculation; S34: In the time dimension, collaborative annotation is achieved by identifying parallel extreme windows of multiple hazards, and a time-related hazard collaborative function is defined: in, This represents the parallel extreme collaborative triggering function, i.e., the multi-hazard collaborative indicator function; This indicates the total number of disaster types participating in the collaborative assessment; Indicates an indicator function; Indicates the type of disaster In time Fusion index / state feature value at the location; Disaster type The trigger threshold; when the disaster coordination function is 1, it means that multiple disasters exceed their respective thresholds at the same time window, forming a parallel extreme coordination period; the disaster coordination function is used in the matrix to generate time tags, thereby explicitly identifying disaster coordination segments at the data layer.

5. The method for constructing a multi-dimensional matrix for disaster monitoring in power transmission corridors as described in claim 1, characterized in that, Step S4 includes: slicing the multidimensional matrix to obtain multidimensional submatrices, which are used to extract local views of disaster intensity, exposure, vulnerability, and importance under specific dimensional combinations. The slicing operation of the multidimensional matrix takes the following form: in, Indicates satisfaction Submatrices of M or monitoring subspace slices under certain conditions; A complete multidimensional matrix or monitoring tensor for disaster monitoring of power transmission corridors; The constraint domain is represented as the selected spatiotemporal or semantic subdomain; disaster-related intensity, exposure, vulnerability, and importance indices are calculated in the multidimensional submatrix, and these are integrated into a risk score through a hierarchical weighting mechanism; the general form of the weighted fusion calculation of the risk score can be expressed as: in: These are the weights for each quadrant; Assign a risk score; It represents the intensity, exposure, vulnerability, and importance indicators related to disasters; it incorporates uncertainty propagation into the calculation of risk scores and outputs confidence intervals. The risk variance propagation formula is defined as follows: in, This represents the variance of the risk score; Indicates the first Information sources / indicators in fusion risk Weighting coefficients in the calculation; Indicates the first The inherent uncertainty of the input corresponding to the information source / indicator; Indicates the first and the The joint covariance of each data source / channel; The feature representations corresponding to the two fused input indicators involved in the covariance calculation are shown below; the overall representation of the risk cube is constructed to describe the spatial and temporal evolution and distribution of risk, as detailed below: in, A statistical description of the error and noise in the input data of the sensor or identification terminal itself; Indicates the distance in the corridor ,time Disaster types Asset Types Fusion risk / status score under combination; The risk-uncertainty encapsulation mapping function is represented; the disaster co-enhancement function is defined as follows: in, This represents the collaborative risk score after incorporating the enhancement correction for multi-hazard coupling; The adjustment coefficient representing the enhanced synergy among multiple disasters; Disaster type With disaster The correlation coefficient, i.e. the coupling dependency strength.

6. The method for constructing a multi-dimensional matrix for disaster monitoring in power transmission corridors as described in claim 1, characterized in that, The method also includes: outputting a risk cube that supports multi-dimensional queries by mileage, time, and disaster type; and mapping continuous risk values ​​to early warning levels through fuzzy membership functions.

7. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to perform the multi-dimensional matrix construction method for power transmission corridor disaster monitoring as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a computer, perform the method for constructing a multi-dimensional matrix for monitoring disasters in power transmission corridors as described in any one of claims 1-6.

Images