Full-stack digital twinborn simulation method and system for power grid dispatching master station

Abstract

The invention relates to the technical field of digital twinning and simulation, and discloses a full-stack digital twinning simulation method and system for a power grid dispatching master station, and the method comprises the steps: obtaining a voltage power flow result data set and a temperature field distribution data set, building a unified virtual coordinate system, building an initial two-dimensional thermal distribution diagram according to an initial mapping matrix, and carrying out the simulation of the voltage power flow result data set and the temperature field distribution data set; carrying out Delaunay triangulation on the image to generate a mapping error distribution diagram; constructing a Markov chain state sequence, calculating a state transition probability matrix, and predicting and generating a corrected mapping matrix; the edge node loads the correction matrix to generate a lightweight state vector, the lightweight state vector is uploaded to the cloud through multi-level cache to form a set, and the cloud updates the matrix and issues a new version; according to the method, the voltage-temperature rise mapping relation is captured, predicted and corrected in real time along with working condition drifting, hidden coupling deviation caused by inconsistent data calibers is restrained, and it is guaranteed that the high-precision and high-consistency electrical-thermodynamic combined situation is always maintained in the operation process of full-stack digital twin simulation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital twinning and simulation, and more particularly, relates to a full-stack digital twinning simulation method and system for a power grid dispatching master station. BACKGROUND

[0002] With the deepening of the interaction of the new power system "source-grid-load-storage", the digital twin on the dispatching master station side must reflect both the millisecond electrical dynamic and the minute thermal cumulative effect. The upper layer power grid flow simulation and the lower layer equipment level thermodynamic simulation are independently iterated, and the data models, time granularity and spatial reference system are all inconsistent, leading to the continuous drift of the "voltage-temperature rise" coupling relationship in operation, which has become the main source of the degradation of the full-stack twinning accuracy.

[0003] A Chinese patent with the publication number CN115390894B discloses a power system simulation software data structure conversion method, which proposes to use an EXCEL report derived from PSASP as an intermediary to realize the rapid conversion of data of any voltage level to the PSMSD maintenance decision model through BusVoltage decoupling and field rearrangement. Its essence is a kind of "offline-centralized" data structure mapping method, which does not involve the dynamic error evolution of multi-source heterogeneous engines under the difference of time and space benchmarks. A Chinese patent with the publication number CN104967121B discloses a power flow calculation method for large-scale power system nodes, which splits the Jacobian matrix element calculation into key-value pairs and parallel accelerates them under the Map-Reduce framework, significantly improving the solution efficiency of large-scale power flow, but still being limited to numerical calculation optimization within a single electrical domain, and does not provide a correction mechanism for the implicit drift caused by the inconsistency of sampling frequency, coordinate system and physical dimension in the cross-domain coupling process of electricity-thermodynamics.

[0004] However, the above-mentioned prior art all assumes that the "electricity-thermodynamics" data has completed the caliber alignment on the input side, ignoring the "time-space-dimension" three-dimensional misalignment of the two sets of engines in the actual dispatching scenario due to step difference, topology coding conflict and sparse distribution of temperature measurement points. When the operating condition is switched, the misalignment degree dynamically changes with the voltage fluctuation rate and the difference in equipment thermal inertia, leading to systematic strong deviation between the predicted temperature and the actual temperature in the local triangular cells of the fixed mapping matrix. This deviation is spatially aggregated and temporally autocorrelated in the unified virtual coordinate system, and the traditional threshold calibration can only compensate afterwards, but cannot use the Markov evolution characteristics of the error state for feedforward correction, ultimately causing the "voltage-temperature rise" mapping implicit coupling drift in the full-stack twinning body at the critical periods such as heavy load and N-1 that the dispatcher most needs high confidence, causing the displacement of hot spot positioning and the lag of overload warning, forming a blind area of dispatching control. SUMMARY

[0005] The application is suitable for the scenario of full-stack digital twin simulation of the power grid dispatching master station on the cross-regional backbone power grid, the hub substation and the associated core equipment (such as transformers and high-voltage busbars), especially for the dispatching scenario in which the load fluctuates frequently and the working condition changes significantly due to seasonal changes, and real-time monitoring of the "voltage-temperature rise" correlation is required to avoid equipment thermal risk.

[0006] In order to overcome the above-mentioned defects of the prior art, the application provides a full-stack digital twin simulation method and system of a power grid dispatching master station, which realizes the spatio-temporal unification and mapping error quantization of power grid flow and device thermodynamic heterogeneous simulation data by constructing a unified virtual coordinate system and a mapping error distribution map, effectively solves the "voltage-temperature rise" mapping drift problem, and improves the accuracy of full-stack digital twin simulation. Relying on the evolution trend of Markov chain prediction error and generating a corrected mapping matrix, combined with the edge-cloud collaborative architecture to realize dynamic calibration, ensures the stability of the correlation of heterogeneous simulation data, and provides accurate equipment thermal risk prediction basis for the power grid dispatching master station.

[0007] To achieve the above object, the application provides the following technical scheme:

[0008] The full-stack digital twin simulation method of the power grid dispatching master station comprises:

[0009] Obtain the voltage flow result data set of the power grid flow simulation engine and the temperature field distribution data set of the device-level thermodynamic simulation engine, construct a unified virtual coordinate system based on the voltage flow result data set, and construct an initial two-dimensional thermal distribution map in the unified virtual coordinate system according to a preset initial mapping matrix; perform Delaunay triangulation on the initial two-dimensional thermal distribution map, and generate a mapping error distribution map in combination with the temperature field distribution data set;

[0010] According to the mapping error distribution map, a state sequence of a Markov chain is constructed, a state transition probability matrix is calculated through the state sequence of the Markov chain, and a corrected mapping matrix is generated by using the state transition probability matrix;

[0011] The edge node loads the corrected mapping matrix issued by the cloud to generate a lightweight state vector, and uploads the lightweight state vector to the cloud based on the difference judgment and multi-level cache strategy to form a lightweight state vector set, and the cloud updates the state transition probability matrix according to the lightweight state vector set and issues a new version of the corrected mapping matrix.

[0012] The voltage flow result data set includes the unique identifier of the power grid node, the simulation time, the voltage amplitude, the voltage phase angle and the power flow direction information of the associated line;

[0013] The construction method of the unified virtual coordinate system comprises:

[0014] The time reference alignment processing is performed on the voltage flow result data set to obtain a time reference aligned voltage flow result data set;

[0015] According to the unique identifier of the power grid node in the time reference aligned voltage flow result data set and the power flow direction information of the associated line, a power grid topology structure one-dimensional expansion code is generated; and according to the simulation time in the time reference aligned voltage flow result data set, a simulation discrete time sequence code is generated;

[0016] The unified virtual coordinate system takes the power grid topology structure one-dimensional expansion code as the horizontal axis and the simulation discrete time sequence code as the vertical axis.

[0017] The method for constructing the initial two-dimensional heat distribution map comprises:

[0018] In the unified virtual coordinate system, the time reference aligned voltage flow result data set is geometrically converted according to a preset initial mapping matrix to generate geometric mapping vertices;

[0019] The spatial distribution density of the geometric mapping vertices is judged, the sparse areas are identified, and the sparse areas are subjected to interpolation processing to obtain a density enhanced geometric mapping vertex set;

[0020] The density enhanced geometric mapping vertex set is subjected to rendering and rasterization processing to generate an initial two-dimensional heat distribution map.

[0021] The method for generating the mapping error distribution map comprises:

[0022] The Delaunay triangulation operation is performed on the initial two-dimensional heat distribution map to discretize the initial two-dimensional heat distribution map into a grid composed of a plurality of local triangular cells that do not overlap and cover the entire map;

[0023] For each local triangular cell, the mapping error of the local triangular cell is calculated in combination with the temperature field distribution data set.

[0024] The mapping errors of all local triangular cells are assigned to the corresponding positions of the initial two-dimensional heat distribution map to generate a mapping error distribution map.

[0025] The temperature field distribution data set at least comprises device level thermodynamic simulation temperature values;

[0026] The method for calculating the mapping error of the local triangular cell comprises:

[0027] The corresponding power grid node identifier and simulation time are calculated by using the coordinates of the three vertices of the local triangular cell in the unified virtual coordinate system.

[0028] Based on the grid node identifier and simulation time obtained by back calculation, the corresponding equipment-level thermodynamic simulation temperature values ​​are obtained by querying the temperature field distribution dataset, resulting in a total of three equipment-level thermodynamic simulation temperature values.

[0029] Based on three device-level thermodynamic simulation temperature values, the mapping error of the local triangular element is obtained.

[0030] The method for calculating the mapping error of local triangular elements further includes:

[0031] Perform planar interpolation on the three device-level thermodynamic simulation temperature values ​​to calculate the actual interpolated temperature at the centroid of the local triangular element.

[0032] Extract the pixel value of the corresponding position of the centroid of the local triangular unit in the initial two-dimensional thermal distribution map, and use the pixel value as the predicted mapping temperature;

[0033] The difference between the actual interpolated temperature and the predicted mapped temperature is calculated to obtain the mapping error of the local triangular element.

[0034] The method for constructing the state sequence of a Markov chain includes:

[0035] Based on a preset error threshold, abnormal triangular units are identified, and the number of abnormal triangular units is counted against the total number of triangular units.

[0036] Calculate the statistical mean, statistical variance, and maximum absolute value of all pixel values ​​from the mapping error distribution map to form a basic three-dimensional vector;

[0037] The proportion R of the number of anomalous triangular units to the total number of triangular units abn Determine whether to add the spatial distribution entropy dimension to the basic three-dimensional vector to form an extended four-dimensional state vector, and finally determine the final state vector at the corresponding simulation moment.

[0038] The final state vectors of multiple corresponding simulation moments are collected sequentially in chronological order and arranged to form a state sequence of a Markov chain.

[0039] The method for determining the final state vector includes:

[0040] Set the abnormality ratio threshold R th If R abn >R th Then the spatial distribution entropy S' is increased, forming a four-dimensional state vector, which is then used as the final state vector.

[0041] If R abn ≤R th Then the basic three-dimensional vector is used directly as the final state vector.

[0042] The calculation method of the state transition probability matrix comprises:

[0043] The state sequence of the Markov chain is traversed, the transition frequency between the final state vectors of adjacent simulation time points is counted, and the initial state transition probability is calculated;

[0044] The stability of the initial state transition probability is judged in the sliding time window, and the dynamic transition probability is calculated according to the stability judgment result;

[0045] All dynamic transition probabilities are combined to form a state transition probability matrix describing the evolution law of the system error state.

[0046] The full-stack digital twin simulation system of the power grid dispatching master station is used to implement the full-stack digital twin simulation method of the power grid dispatching master station, and the system comprises:

[0047] A two-dimensional thermal distribution map construction module is configured to obtain a voltage flow result data set of a power grid flow simulation engine and a temperature field distribution data set of a device-level thermodynamic simulation engine, construct a unified virtual coordinate system based on the voltage flow result data set, and construct an initial two-dimensional thermal distribution map in the unified virtual coordinate system according to a preset initial mapping matrix.

[0048] A mapping error calculation module is configured to perform Delaunay triangulation on the initial two-dimensional thermal distribution map and generate a mapping error distribution map in combination with the temperature field distribution data set.

[0049] A mapping matrix correction module is configured to construct a state sequence of a Markov chain according to the mapping error distribution map, calculate a state transition probability matrix through the state sequence of the Markov chain, and predict and generate a corrected mapping matrix using the state transition probability matrix.

[0050] A task sinking module is configured to load the corrected mapping matrix issued by the cloud on the edge node to generate a lightweight state vector, upload the lightweight state vector to the cloud to form a lightweight state vector set based on a difference judgment and a multi-level cache strategy, and update the state transition probability matrix according to the lightweight state vector set and issue a new version of the corrected mapping matrix.

[0051] Compared with the prior art, the present application has the following advantages:

[0052] The application provides a unified virtual coordinate system, which provides a unified correlation carrier for heterogeneous data of power grid flow simulation and device-level thermodynamic simulation, eliminates the mapping correlation obstacle of "voltage-temperature rise" caused by data caliber difference, accurately captures and quantifies the mapping drift state under different operating conditions based on the mapping error distribution diagram generated by Delaunay triangulation, provides a clear error basis for calibration, forecasts the drift trend based on the error evolution law and generates a corrected mapping matrix based on Markov chain state sequence and state transition probability matrix, avoids the hysteresis of traditional calibration, generates a light state vector through an edge node, uploads the cloud end by combining the difference judgment and multi-level cache strategy, reduces the full data transmission load and improves the data interaction efficiency, realizes the dynamic update and distribution of the global mapping matrix in the cloud end, ensures that the "voltage-temperature rise" mapping in the full-stack digital twin system is always adapted to the operating condition, and effectively maintains the stability and accuracy of the correlation of heterogeneous simulation data. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, a brief introduction will be given below to the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0054] Figure 1 The method flowchart of the full-stack digital twin simulation method of the power grid dispatching master station provided by the embodiment of the present application is shown in the figure.

[0055] Figure 2 The method flowchart of the method for calculating the mapping error of the local triangular element provided by the embodiment of the present application is shown in the figure.

[0056] Figure 3 The method flowchart of the method for constructing the state sequence of the Markov chain provided by the embodiment of the present application is shown in the figure.

[0057] Figure 4 The data interaction schematic diagram of the cloud end and the edge node provided by the embodiment of the present application is shown in the figure.

[0058] Figure 5 The functional module diagram of the full-stack digital twin simulation system of the power grid dispatching master station provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0059] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of the present application.

[0060] Embodiment 1

[0061] Please refer to Figure 1 As shown in the drawings, the embodiment provides a full-stack digital twin simulation method of a power grid dispatch master station, including:

[0062] In step S10, voltage flow result data set of a power grid flow simulation engine and temperature field distribution data set of a device-level thermodynamic simulation engine are acquired, a unified virtual coordinate system is constructed based on the voltage flow result data set, an initial two-dimensional thermal distribution map is constructed in the unified virtual coordinate system according to a preset initial mapping matrix, Delaunay triangulation is performed on the initial two-dimensional thermal distribution map, and a mapping error distribution map is generated in combination with the temperature field distribution data set.

[0063] Further, step S10 includes:

[0064] In step S11, voltage flow result data set of a power grid flow simulation engine and temperature field distribution data set of a device-level thermodynamic simulation engine are acquired, time reference alignment processing is performed on the voltage flow result data set, and time-aligned voltage flow result data set is obtained. The voltage flow result data set includes unique identification of a power grid node, simulation time, voltage amplitude, voltage phase angle and power flow direction information of an associated line.

[0065] Step S11 aims to obtain the data sets output by the power grid power flow simulation engine and the device-level thermodynamic simulation engine, and perform time benchmark alignment processing on the voltage power flow result data set to eliminate the time stamp misalignment caused by the simulation step difference of the heterogeneous engines, and provide a time dimension consistent data basis for subsequent construction of the "voltage-temperature" mapping relationship. First, the data set is obtained relying on the digital twin system interface of the power grid dispatching master station, and the voltage power flow result data set of multiple continuous simulation time points is extracted from the power grid power flow simulation engine. The voltage power flow result data set explicitly contains the unique identification of each power grid node, the simulation time, the voltage amplitude, the voltage phase angle and the power flow direction information of the associated line; the unique identification of the power grid node is the exclusive code used to distinguish different buses, transformers and other key nodes in the power grid, ensuring that each voltage data can be accurately associated with a specific physical node; the simulation time is the time stamp of the engine output data, used to mark the running time corresponding to the data; the voltage amplitude and the voltage phase angle are the core parameters representing the electrical state of the power grid node; the power flow direction information of the associated line reflects the energy transfer relationship between nodes, which together constitute a complete description of the macroscopic power grid state. At the same time, the temperature field distribution data set synchronized with the above simulation time is extracted from the device-level thermodynamic simulation engine, which explicitly contains the spatial three-dimensional coordinates of each temperature measurement point on the device physical surface, the simulation time and the corresponding device-level thermodynamic simulation temperature value; among them, the spatial three-dimensional coordinates of the temperature measurement point on the device physical surface are the fixed point coordinates preset based on the device CAD model for temperature acquisition, ensuring that the temperature data accurately corresponds to the physical position of the device; the simulation time needs to be matched one by one with the simulation time of the voltage power flow result data set to avoid time dimension deviation; the device-level thermodynamic simulation temperature value is the thermodynamic state parameter at the temperature measurement point of the simulation calculation, which constitutes the core description of the micro device temperature state.

[0066] Next, the time reference alignment process is performed, the first task is to determine the first frequency threshold, focusing on the comparison of the sampling frequency of the voltage flow result data set and the preset first frequency threshold, wherein the determination method of the first frequency threshold is: analyzing the sampling frequency characteristics of the temperature field distribution data set, combining the actual demand of the temperature field data on the time resolution in the power grid simulation, such as the response speed of the equipment temperature change is usually slower than the fluctuation speed of the voltage flow, the sampling frequency of the temperature field distribution data set is taken as the reference of the first frequency threshold, for example, if the sampling frequency of the temperature field distribution data set is 1 per minute, the first frequency threshold can be set to 1 per minute. When the sampling frequency of the voltage flow result data set is greater than the first frequency threshold, the time axis downsampling processing is adopted, the specific method is to extract the voltage data closest to the simulation time sequence of the temperature field distribution data set from the voltage flow result data set, or to use the sliding window average method to average adjacent multiple voltage data to match the time point of the temperature field, for example, if the voltage flow data is 1 per second and the temperature field data is 1 per minute, then take the average value every 60 voltage data or extract the voltage data of the 60th second as the alignment result; when the sampling frequency of the voltage flow result data set is less than or equal to the first frequency threshold, the original data is directly retained as the result of time alignment.

[0067] Step S11 solves the problem of timestamp misalignment caused by the difference in simulation step length of heterogeneous simulation engines, which will make the voltage data and temperature data unable to correspond in the time dimension, and further cause the "voltage-temperature rise" mapping to lose the time reference. By only processing the voltage flow result data set and taking the temperature field distribution data set as the reference, the interpolation of the temperature field data can be avoided, which is easy to introduce errors due to the complexity of physical characteristics, and at the same time reduces the redundancy of voltage data and reduces the subsequent calculation load. Step S11 establishes a unified time reference, which makes the voltage data and temperature data correspond at the same time point, and provides the accuracy of the time dimension for the construction of the subsequent "voltage-temperature" mapping relationship; by reducing the data quantity through downsampling, the system processing efficiency is improved on the premise of ensuring the integrity of the information; the error introduced by the interpolation of the temperature field data is avoided, and the reliability of the reference data is ensured. Step S11 provides a data basis for the subsequent steps, so that the vertical axis (discrete time sequence code) of the unified virtual coordinate system can be constructed based on the aligned time points, ensuring that the time dimension of the coordinate system accurately reflects the actual simulation time; if step S11 is missing, the initial two-dimensional thermal distribution map constructed in step S12 will cause the voltage and temperature data at the same time to be unmatched due to time misalignment, so that the thermal map cannot truly reflect the corresponding relationship of "voltage-temperature rise", and further cause the mapping error calculation in step S13 to lose accuracy, ultimately making the basic data of the entire calibration scheme unreliable, and unable to effectively correct the mapping drift.

[0068] Step S12: Establish a unified virtual coordinate system using the voltage power flow result dataset after time base alignment, and construct an initial two-dimensional thermal distribution map in the unified virtual coordinate system based on the preset initial mapping matrix.

[0069] Further, step S12 includes:

[0070] Step S121: Generate a one-dimensional expanded code for the power grid topology based on the unique identifier of the power grid node and the power flow direction information of the associated line in the time-base aligned voltage power flow result dataset; generate a simulation discrete time series code based on the simulation time in the time-base aligned voltage power flow result dataset; and unify the virtual coordinate system with the one-dimensional expanded code for the power grid topology as the horizontal axis and the simulation discrete time series code as the vertical axis.

[0071] Step S122: In the unified virtual coordinate system, the voltage power flow result dataset after time base alignment is geometrically transformed according to the preset initial mapping matrix to generate geometric mapping vertices.

[0072] Step S123: Determine the spatial distribution density of the geometric mapping vertices, identify sparse regions, and perform interpolation processing on the sparse regions to obtain a set of geometric mapping vertices with enhanced density.

[0073] Step S124: The density-enhanced geometric mapping vertex set is processed by rendering and rasterization to generate an initial two-dimensional thermal distribution map.

[0074] Step S12 achieves geometric unification of heterogeneous simulation data by constructing a unified virtual coordinate system, completes the spatial-temporal dimension transformation of voltage data based on the initial mapping matrix, and provides a structured graphical carrier for quantifying the "voltage-temperature rise" mapping error through sparse data enhancement and heatmap rendering. Its core objective is to address the technical deficiency in existing technologies where the mapping relationship between macroscopic voltage data of the power grid and microscopic temperature data of equipment lacks a unified representation carrier, resulting in an inability to visualize and quantify it. By using geometric methods, unstructured electrical data is transformed into graphical data suitable for subsequent error analysis.

[0075] Step S121 defines a unified virtual coordinate system based on the time-aligned voltage power flow result dataset as follows: First, the two-dimensional design logic of the coordinate system is clarified, with the horizontal axis focusing on the "spatial dimension" and the vertical axis focusing on the "time dimension," ensuring that the topological association and time series information of the power flow data can be integrated into the same framework simultaneously. The generation of the one-dimensional expansion code of the horizontal axis power grid topology strictly depends on the power grid topology information in the voltage power flow result dataset: the unique identifiers of all power grid nodes and the power flow direction information of the associated lines are extracted from the voltage power flow result dataset. A two-step method of "topology sorting-code mapping" is used to generate one-dimensional codes. The first step is to sort the power grid nodes according to the topological hierarchy using a breadth-first search (BFS) algorithm. For example, starting from the reference node specified by the power grid dispatching master station, such as the bus of a hub substation, adjacent nodes are traversed sequentially according to the power flow direction to form an ordered node sequence. The second step is to map this ordered sequence into a continuous horizontal axis code, such as the first node in the node sequence corresponding to X1, the second corresponding to X2, and so on, ensuring that adjacent codes correspond to adjacent physical nodes in the power grid topology, so that the horizontal axis code can reflect the spatial association between nodes. The generation of the vertical axis simulation discrete time series code directly relies on the simulation time information in the voltage power flow result dataset after time base alignment: all aligned simulation times are sorted in chronological order, and each time is assigned a unique vertical axis code, such as the earliest time corresponding to Y1, and subsequent times Y2, Y3, etc., ensuring that the order of the vertical axis codes is completely consistent with the actual simulation time order. The core advantage of the unified virtual coordinate system definition method is that it breaks through the limitation of the separation of spatial and temporal data in traditional methods, enabling each voltage data point to obtain a unique position in two-dimensional space through the coordinate combination of "topological code (X) - time code (Y)," providing a unified carrier for subsequent association with temperature field data. Without this unified virtual coordinate system, the voltage data is still in an unstructured form of "node-time-value," and it is impossible to establish a geometric correspondence with the "spatial point-time-value" data of the temperature field, and subsequent mapping error calculation will lose its quantification benchmark.

[0076] Geometric transformation requires a pre-defined initial mapping matrix as its core tool. The construction of this initial mapping matrix is ​​based on the historical operating data and theoretical physical model of the monitored object. First, multiple sets of voltage-temperature correlation data of the monitored object under stable operating conditions are collected. Through linear regression or least squares fitting, a mapping relationship is established between the voltage amplitude / phase angle of the power grid node and the "vertex weights in the virtual coordinate system." These vertex weights are used for subsequent thermal intensity rendering. This mapping relationship is stored in matrix form, i.e., the initial mapping matrix. The matrix rows correspond to voltage parameter types, such as amplitude and phase angle, and the columns correspond to the attributes of the geometric vertices, such as vertex weight coefficients. The specific geometric transformation process involves extracting the voltage data of a single power grid node at a specific simulation moment from the time-base aligned voltage power flow result dataset. This data includes the node identifier, simulation moment, voltage amplitude U, and voltage phase angle. ; Based on the node identifier, query the horizontal axis code X generated in step S121, and query the vertical axis code Y based on the simulation time to determine the coordinates (X,Y) of the voltage data in the unified virtual coordinate system; then, correlate the voltage amplitude U with the voltage phase angle. Substitute the initial mapping matrix and calculate the weight value of the "geometric mapping vertex" corresponding to the coordinate using matrix multiplication, such as W=[U, The process involves a geometric transformation, where M is the initial mapping matrix, ultimately forming geometrically mapped vertices containing coordinates (X, Y) and weights W. This geometric transformation converts abstract electrical parameters into concrete geometric attributes, i.e., vertex weights, upgrading the voltage-temperature rise mapping from a numerical correspondence to a geometric correspondence. Mapping errors can then be analyzed using graphical methods. Traditional numerical mapping methods cannot intuitively reflect the mapping differences at different topological locations and times. However, after geometric transformation, the spatial and temporal distribution characteristics of the mapping relationship can be intuitively presented through the distribution of vertices in the coordinate system.

[0077] The sparse region identification and interpolation process aims to address the problem of local data sparsity in voltage data caused by uneven topological distribution or time sampling intervals, thus avoiding bias in subsequent error calculations due to missing data. Sparse region identification first requires determining a density threshold. The density threshold is set based on the spatial resolution of the temperature field distribution dataset from the device-level thermodynamic simulation engine, such as the average spacing of temperature field measurement points, and the monitoring accuracy requirements of the power grid simulation. The required number of geometrically mapped vertices per unit area is calculated as the density threshold. The specific identification process for sparse regions is as follows: the horizontal and vertical axes of a unified virtual coordinate system are divided into grid cells of fixed size, and the number of geometrically mapped vertices in each grid cell is counted. If the number of vertices in a grid cell is less than the density threshold, the area containing that grid cell is determined to be a sparse region. The spatiotemporal kriging interpolation algorithm is used for sparse regions. The core reason for choosing this algorithm is that traditional pure spatial kriging interpolation only considers the spatial correlation of vertices and ignores the continuity of the time series. For example, the voltage of a node changes with a trend between adjacent times. Spatiotemporal kriging interpolation incorporates both "spatial distance weight" and "time interval weight". The spatial distance weight is calculated based on the difference in the horizontal axis encoding of vertices in the surrounding grid cells of the sparse region. The smaller the difference, the greater the weight. The time interval weight is calculated based on the difference in the vertical axis encoding between the corresponding time and the adjacent time in the sparse region. The smaller the difference, the greater the weight. By weighting the calculations, the coordinates and weights of the supplementary geometrically mapped vertices in the sparse region are generated. This ensures that the supplementary vertices conform to the spatial correlation of the power grid topology and continue the voltage change trend of the time series. This processing method avoids "empty cells" in subsequent triangulation caused by sparse data and ensures that the error calculation covers the entire coordinate system. Compared with pure spatial interpolation, spatiotemporal interpolation reduces the interpolation error caused by ignoring the time trend. For example, if the voltage of a power grid node continues to drop during a period of increased load, spatiotemporal interpolation can capture this trend, and the weights of the supplementary vertices generated are more consistent with the actual voltage change pattern.

[0078] Rendering and rasterization are crucial for transforming the density-enhanced geometrically mapped vertex set into an intuitive initial two-dimensional thermal distribution map. The rendering process requires establishing a "vertex weight - thermal intensity" mapping rule: based on the physical meaning of the initial mapping matrix (e.g., higher weight corresponds to higher voltage, and higher voltage may lead to higher device temperature rise), vertex weights are divided into multiple intervals, each corresponding to a unique thermal color (e.g., low weight corresponds to light blue, high weight to dark red), ensuring that the thermal color indirectly reflects the potential correlation strength between "voltage and temperature rise." Rasterization requires determining the raster resolution, which must match the dimensional accuracy of the unified virtual coordinate system. The number of raster cells along the horizontal axis matches the total number of one-dimensional expansion codes of the power grid topology, with each code corresponding to a raster column. The number of raster cells along the vertical axis matches the total number of codes for the simulated discrete-time series, with each code corresponding to a raster row, ensuring that each geometrically mapped vertex accurately falls into a unique raster cell. For cases where multiple vertices exist within a single raster cell, the arithmetic mean method is used to calculate the average weight of the raster, and the thermal color corresponding to the average weight is used as the raster color. The final generated initial two-dimensional heat map shows that the color of each grid cell corresponds to the voltage-mapped thermal state at a specific topological location and time, achieving a transformation from "voltage data" to "geometric graphics" to "thermal visualization." This process overcomes the limitation of traditional numerical tables in intuitively presenting the mapping distribution. Maintenance personnel can quickly identify "hot spots" in the voltage mapping through the heat map; for example, a high weight in a certain topological segment or at a certain time may correspond to a high risk of temperature rise, providing an intuitive graphical reference for subsequent error analysis.

[0079] In existing technologies, voltage data output from power grid power flow simulation exists in the form of "node-time-value," lacking a unified spatial carrier. Temperature data output from equipment-level thermodynamic simulation exists in the form of "three-dimensional point-time-value." Due to the lack of a unified spatial-temporal framework, a precise geometric relationship cannot be established, resulting in the "voltage-temperature rise" mapping relationship being judged only through numerical correspondence, making it difficult to quantify spatial or temporal mapping drift. Step S12 uses techniques such as a unified virtual coordinate system, geometric transformation, sparsity enhancement, and thermal map rendering to transform unstructured voltage data into structured graphical data, providing a unified carrier for subsequent error comparison with temperature field data. This invention, through a two-dimensional "space-time" coordinate system, enables the connection between power grid voltage data and equipment temperature data through a mapping of "coordinate system coordinates-physical location," overcoming the limitations of traditional numerical correspondence that cannot take into account both spatial and temporal dimensions. Using spatiotemporal kriging interpolation instead of traditional pure spatial interpolation considers both the spatial continuity of the power grid topology and the evolutionary trend of the time series. The generated supplementary vertices can accurately fill sparse regions, ensuring that subsequent Delaunay triangulation can cover the entire coordinate system without any omissions in error calculations caused by "empty cells". Without this processing, the triangulation cells in sparse regions may not be able to calculate mapping errors due to insufficient data, resulting in the neglect of mapping drift in some topologies or time periods. After the voltage data is converted into a heatmap, "topological weak segments" or "temporal weak points" can be intuitively identified. These weak areas are difficult to find in traditional numerical tables, but the visualization of the heatmap allows maintenance personnel to pay attention to high-risk areas in advance, enabling preventive monitoring of mapping drift.

[0080] Step S13: Perform Delaunay triangulation on the initial two-dimensional thermal distribution map to obtain local triangular elements. Calculate the mapping error of each local triangular element using the temperature field distribution dataset. Summarize the mapping errors of all local triangular elements to generate a mapping error distribution map. Based on the mapping error distribution map, identify abnormal triangular elements and count the number of abnormal triangular elements and the total number of triangular elements.

[0081] Further, step S13 includes:

[0082] Step S131: Perform Delaunay triangulation on the initial two-dimensional thermal distribution map to discretize the initial two-dimensional thermal distribution map into a mesh composed of multiple non-overlapping local triangular units that cover the entire map.

[0083] Step S132: For each local triangular unit, calculate the mapping error of the local triangular unit in conjunction with the temperature field distribution dataset;

[0084] Please see Figure 2 As shown, step S132 further includes:

[0085] Step S1321: Using the coordinates of the three vertices of the local triangular element in the unified virtual coordinate system, the corresponding power grid node identifier and simulation time are calculated in reverse.

[0086] Step S1322: Based on the grid node identifier and simulation time obtained by back calculation, query the corresponding equipment-level thermodynamic simulation temperature value from the temperature field distribution dataset, and obtain a total of three equipment-level thermodynamic simulation temperature values.

[0087] Step S1323: Perform planar interpolation on the three device-level thermodynamic simulation temperature values ​​obtained from the query to calculate the actual interpolated temperature at the centroid position of the local triangular element;

[0088] Step S1324: Extract the pixel value of the corresponding position of the centroid of the local triangular unit in the initial two-dimensional thermal distribution map, and use the pixel value as the predicted mapping temperature;

[0089] Step S1325: Perform a difference calculation between the actual interpolated temperature and the predicted mapped temperature to obtain the mapping error of the local triangular unit.

[0090] Step S133: Assign the mapping error of all local triangular units to the corresponding position of the initial two-dimensional thermal distribution map to generate a mapping error distribution map. Determine abnormal triangular units according to the preset error threshold and count the number of abnormal triangular units and the total number of triangular units.

[0091] Step S13 transforms the initial two-dimensional thermal distribution map into discrete local analysis units through Delaunay triangulation. Combining temperature field data, the mapping error of each unit is calculated, ultimately generating a global error distribution and anomaly statistics. This addresses the technical deficiency in existing technologies where "mapping errors in heterogeneous simulation data cannot be localized or quantified"—traditional methods can only determine whether the mapping has drifted through overall numerical comparison, but cannot locate the drift source at a specific topological location or time node. This step, through geometric subdivision and error calculation, achieves a breakthrough in "mapping drift from global perception to local location," providing accurate error input for subsequent dynamic calibration.

[0092] Step S131, using the initial two-dimensional thermal distribution map as input, involves the following specific process of Delaunay triangulation: First, the input data for triangulation is defined as the set of grid vertices of the initial two-dimensional thermal distribution map. This set of grid vertices contains all geometrically mapped vertices after density enhancement, and these vertices cover the entire spatial-temporal range of the unified virtual coordinate system. The core reason for choosing Delaunay triangulation over other triangulation methods, such as structured mesh triangulation, is its "maximizing minimum angle" characteristic: the generated local triangular cells have no elongated or flat shapes, and the cell shapes are uniform, ensuring the representativeness of subsequent error calculations within each cell and avoiding local error distortion caused by cell shape deformities. Simultaneously, this method automatically ensures that all triangular cells do not overlap and completely cover the boundary range of the initial two-dimensional thermal distribution map, with no missing areas, providing a full-area coverage basis for global mapping error calculation. During the meshing process, the grid vertex coordinates of the initial 2D heat map are first imported into the meshing algorithm module. The algorithm generates the mesh using a point-by-point insertion method: First, an initial triangle is constructed using three non-collinear vertices; second, the remaining vertices are sequentially inserted into the existing meshing structure. If a vertex falls inside a triangle, that triangle is split into three new triangles; if it falls on the edge of a triangle, the two triangles containing that edge are split into four new triangles; third, the meshing result is optimized using the Lawson algorithm, deleting triangle edges that do not conform to the Delaunay property, ensuring that all triangular elements ultimately satisfy the criterion that there are no other vertices within their circumcircle. The final meshing result is a set of local triangular elements, each containing the coordinates of three vertices and the corresponding weights. All elements together form a discrete mesh covering the entire heat map. Uniform cell shape ensures local accuracy in error calculation, avoiding the problem of elongated cells appearing in topologically complex regions, such as densely branched sections of the power grid, where error calculation is biased towards a particular vertex. Full-area coverage ensures that no mapping errors at any topological location or time node are missed, solving the "blind spot" problem caused by traditional local sampling analysis. The subdivided cells are directly associated with a unified virtual coordinate system, and the vertex coordinates of each cell can be traced back to the power grid topology and time series, providing spatial-temporal location data for subsequent error source tracing. Other subdivision methods, such as unstructured quadrilateral subdivision, may suffer from overlapping errors between adjacent cells due to irregular cell boundaries, or increased computational load due to an excessive number of cells. Delaunay triangulation achieves the optimal balance between accuracy and efficiency.

[0093] Step S132 quantifies the mapping error for each local triangular unit through three steps: "temperature lookup - interpolation calculation - error comparison." Its core is establishing a local comparison between the predicted temperature from the initial thermal map and the actual temperature of the temperature field, solving the problem that traditional global comparison cannot locate the source of drift. The three vertices of each local triangular unit originate from a unified virtual coordinate system, with their coordinates corresponding to the "one-dimensional expansion encoding of the power grid topology" and the "simulation discrete time series encoding," respectively. The inverse calculation process relies on two pre-built mapping tables: one is the "topology encoding-power grid node identifier mapping table," which is established simultaneously when generating the horizontal axis encoding in step S121, recording a one-to-one correspondence between each horizontal axis encoding and the unique identifier of the power grid node; the other is the "time encoding-simulation time mapping table," which is established simultaneously when generating the vertical axis encoding in step S121, recording a one-to-one correspondence between each vertical axis encoding and the simulation time after time base alignment. The simulation time after time base alignment is the unified timestamp after eliminating step size differences in step S11, ensuring complete matching with the time of the temperature field data. The inverse calculation operation is as follows: For the three vertices (X1, Y1), (X2, Y2), and (X3, Y3) of the local triangular element, the above two mapping tables are used for lookup. For example, X1 of vertex 1 corresponds to the power grid node identifier ID1, and Y1 corresponds to the simulation time t1; X2 of vertex 2 corresponds to the power grid node identifier ID2, and Y2 corresponds to the simulation time t2; X3 of vertex 3 corresponds to the power grid node identifier ID3, and Y3 corresponds to the simulation time t3. Finally, three sets of "power grid node identifier-simulation time" pairs (ID1, t1), (ID2, t2), and (ID3, t3) are calculated for each local triangular element. It should be noted that the number of power grid node identifiers and simulation times obtained by inverse calculation are three. Since each vertex corresponds to an independent spatial-temporal coordinate, the three sets of data can completely reflect the coverage of the triangular element in the power grid topology and time series, providing three independent sampling points for subsequent acquisition of actual temperature data, avoiding temperature distribution fitting deviations due to insufficient sampling points. Step S1321 establishes a precise association between coordinates and physical information through two mapping tables, resolving the issue of "disconnection between geometric units and the actual power grid / time," and ensuring that the temperature data queried subsequently accurately corresponds to the spatial-temporal range of the triangular unit. Three sets of "node-time" pairs provide a sufficient sampling basis for subsequent temperature interpolation, avoiding the defect that a single sampling point cannot reflect the temperature distribution within the unit. The inverse calculation process relies on the pre-built mapping table, resulting in high computational efficiency and meeting the time requirements of real-time simulation. If the method of querying the original database in real time is adopted, the real-time performance of error calculation will be affected by the data interaction delay.

[0094] Based on the power grid physical topology and equipment CAD models, a mapping table of "3D coordinates of temperature measurement points on equipment physical surfaces - power grid node identifiers" is pre-established. For each power grid node (including a unique identifier), all preset temperature measurement points of its corresponding physical equipment are associated, and the spatial 3D coordinates of these temperature measurement points are recorded. This mapping table is static basic data, ensuring that "if the power grid node identifier is known, the 3D coordinates of all its corresponding temperature measurement points can be located, and vice versa." Through this correspondence, the spatial association between "macro-power grid nodes" and "micro-equipment temperature measurement points" is realized, avoiding the misalignment of "voltage-temperature" mapping caused by the data caliber differences of heterogeneous simulation engines. That is, the electrical data of each power grid node can be accurately associated with the temperature data of its physical equipment through this correspondence, providing a precise spatial dimension matching basis for subsequent calculation of "voltage-temperature rise" mapping errors. Based on the three sets of "power grid node identifiers - simulation time" pairs calculated in reverse, the corresponding equipment-level thermodynamic simulation temperature values ​​are queried from the temperature field distribution dataset, ultimately obtaining three equipment-level thermodynamic simulation temperature values.

[0095] Since the temperature of the equipment within a local triangular unit is continuously distributed, the overall temperature state of the unit cannot be directly obtained from the discrete temperature values ​​of the three vertices alone. Therefore, the centroid coordinate method is used to calculate the actual interpolated temperature at the centroid position of the unit. This method can accurately fit the temperature distribution at any point within the unit based on the temperature values ​​of the three vertices of the triangle, which conforms to the physical characteristic that the equipment temperature changes continuously within a small area. The specific process of planar interpolation is as follows: First, determine the centroid coordinates (Xc, Yc) of the local triangular unit. The centroid coordinates are the arithmetic mean of the coordinates of the three vertices. The centroid is chosen as the calculation point because it can represent the average position of the entire triangular unit, avoiding local deviations caused by selecting edge points or vertices, and ensuring that the calculation results reflect the overall temperature state of the unit. Then, decompose the centroid (Xc, Yc) into the weight coefficients α, β, and γ of the three vertices using the centroid coordinate method, satisfying α + β + γ = 1. The weight coefficients are calculated based on the inverse distribution of the distance from the centroid to each vertex. The closer to a vertex, the greater the contribution of the vertex's temperature to the centroid temperature. The actual interpolated temperature at the centroid position is obtained by weighted summation. The weighted average of the temperature based on spatial distance weights can accurately fit the continuous temperature distribution within the triangular unit. The centroid coordinate method was chosen over other interpolation methods, such as linear interpolation, because linear interpolation is only applicable to line segments and cannot fit the temperature distribution of a two-dimensional triangular region. The centroid coordinate method, on the other hand, can completely cover any point within the triangle, and its calculation process is simple, with no additional parameter dependencies, ensuring consistency in interpolation results across different triangular units. This method achieves the transformation from discrete temperature data to continuous temperature distribution, solving the problem that "vertex temperatures alone cannot represent the overall temperature of the unit."

[0096] In the initial two-dimensional thermal distribution map, the color of each pixel corresponds to a quantized pixel value. This pixel value is generated in step S124 through the "vertex weight-thermal intensity" mapping rule. Essentially, the initial mapping matrix transforms voltage data into a "voltage-temperature rise" prediction value. Therefore, extracting the pixel value at the centroid (Xc, Yc) position yields the predicted mapped temperature of that local triangular unit. The specific extraction process is as follows: First, the centroid coordinates (Xc, Yc) are converted to pixel coordinates (Px, Py) in the initial two-dimensional thermal distribution map. The pixel value at position (Px, Py) in the initial two-dimensional thermal distribution map is then read and directly used as the predicted mapped temperature. It should be noted that the direct correspondence between pixel values ​​and predicted mapped temperatures stems from the rendering rule in step S124. Vertex weights are directly mapped to pixel values; therefore, the pixel value is essentially a quantized expression of the predicted temperature rise, requiring no additional conversion and simplifying the calculation process. The mapping error is the absolute difference between the actual interpolated temperature and the predicted mapped temperature.

[0097] The mapping error of each local triangular element is assigned to its corresponding position in the initial 2D thermal distribution map. Using an element-filling method, the entire triangular element is filled with a color corresponding to the mapping error; for example, smaller mapping errors correspond to green, and larger mapping errors correspond to red, generating a mapping error distribution map. The mapping error of each local triangular element is compared with an error threshold. If the mapping error is greater than the error threshold, the element is determined to be an abnormal triangular element; otherwise, it is determined to be a normal element. The number N of abnormal triangular elements is counted. abn With the total number of triangular units N total Calculate the proportion R of abnormal triangular elements to the total number of triangular elements. abn =N abn / N total R abn This is used in subsequent step S21 to determine whether the spatial distribution entropy dimension needs to be added to the state vector. Mapping the error distribution map visualizes the spatial distribution of errors, allowing maintenance personnel to intuitively identify high-error areas, such as a certain section of the power grid topology or a specific moment, thus solving the problem of knowing only the global error but not the drift location.

[0098] Step S20: Construct the state sequence of the Markov chain based on the mapping error distribution map, calculate the state transition probability matrix using the state sequence of the Markov chain, and use the state transition probability matrix to predict and generate the corrected mapping matrix.

[0099] Further, step S20 includes:

[0100] Step S21: Based on the mapping error distribution map, the number of abnormal triangle units and the total number of triangle units, define a final state vector corresponding to each simulation time, and continuously collect the final state vectors of multiple simulation times to construct the state sequence of the Markov chain.

[0101] Please see Figure 3 As shown, step S21 further includes:

[0102] Step S211: Calculate the statistical mean, statistical variance, and maximum absolute value of all pixel values ​​from the mapping error distribution map to form a basic three-dimensional vector;

[0103] Step S212: Use the ratio of the number of abnormal triangular units to the total number of triangular units to determine whether to add the spatial distribution entropy dimension to the basic three-dimensional vector to form an extended four-dimensional state vector, and finally determine the final state vector at the corresponding simulation time.

[0104] Step S213: Collect the final state vectors of multiple corresponding simulation moments in chronological order and arrange them to form a state sequence of a Markov chain.

[0105] Specifically, step S21 abstracts the high-dimensional, continuously changing mapping error distribution data into a low-dimensional, discretized temporal state vector, providing suitable input data for the Markov chain model and addressing the technical deficiency in existing technologies where "high-dimensional data of heterogeneous simulation errors cannot be directly used for learning temporal evolution patterns." Its core logic is: extracting key statistical features from the error distribution, dynamically adjusting the dimension of the state vector to adapt to the spatial distribution characteristics of the error, and then constructing a state sequence in chronological order, achieving the transformation from "high-dimensional error → low-dimensional state → temporal sequence," thus laying the foundation for the subsequent step S22 to calculate the state transition probability matrix.

[0106] The specific process of generating the basic 3D vector includes: First, clarifying that the input data for the mapping error distribution map is the mapping error set composed of the mapping errors of all local triangular units. The mapping error set covers the entire space-time range of the unified virtual coordinate system, and each E is a non-negative value (in step S1325, the error is defined as the absolute difference). Then, select the statistical mean μ and the statistical variance σ. 2 Maximum absolute value E max The core basis for constructing the basic three-dimensional vector is that these three statistics can comprehensively characterize the global features of the error distribution from three dimensions: "global level, degree of dispersion, and extreme risk." The statistical mean reflects the average level of the overall mapping error; if μ increases, it indicates that the degree of global mapping drift intensifies; the statistical variance σ... 2 Reflecting the degree of dispersion of the error distribution, if σ 2An increase in μ without a change in the error distribution indicates a shift from concentrated to dispersed distribution, potentially a precursor to local hidden drift. By iterating through all mapping error values ​​in the set of mapping errors and selecting the maximum value, we arrive at the maximum absolute value E. max Maximum absolute value E max Reflecting the severity of extreme errors, if E max A value far exceeding μ indicates severe local drift, requiring close attention. The three statistics work together to cover the core features of the error distribution, avoiding the problem of "ignoring dispersion and extreme values" caused by traditional methods using only a single mean. Furthermore, the vector formed by the three statistics can be dynamically expanded according to the characteristics of the error space distribution, avoiding information loss or redundancy caused by fixed dimensions. Without step S211, the subsequent state vector will not be able to fully describe the error distribution, resulting in a one-sided understanding of the Markov chain's learning patterns and an inability to reflect the true mapping drift characteristics.

[0107] Spatial distribution entropy is used to quantify the degree of spatial clustering of anomalous triangular units in a unified virtual coordinate system. Higher clustering results in lower entropy values, while higher dispersion results in higher entropy values. The calculation of spatial distribution entropy is based on the spatial coordinate distribution of the anomalous triangular units, and the specific formula is: Spatial Distribution Entropy , where p k To determine the proportion of anomalous triangular cells within the k-th grid cell to the total number of triangular cells, the horizontal and vertical axes of the unified virtual coordinate system are each divided into m grids, for a total of m... 2 There are _ _ grid cells, where ln is the natural logarithm. When p k When =0, define p k ×lnp k =0, avoiding meaningless calculations. The formula for calculating spatial distribution entropy is a probability-weighted logarithmic sum that describes the degree of disorder in the spatial distribution of anomalous units. A small entropy value indicates that anomalous units are concentrated in a few grids, while a large entropy value indicates that anomalous units are scattered across multiple grids.

[0108] The method for determining whether to add the spatial distribution entropy dimension to the basic three-dimensional vector is as follows: First, determine the anomaly proportion threshold R. th R th The basis for setting R is: historical stable operating data of the monitored object, collection of multiple anomaly proportions under stable operating conditions, and taking the 95th percentile as R. th For example, if the maximum anomaly rate under stable operating conditions is 25%, then R th =25%. This setting ensures that spatial clustering of errors is only determined when the proportion of anomalies exceeds the stable range. If R abn >R th This indicates that the anomalous triangular units exhibit spatial clustering, which cannot be described by the basic three-dimensional vector. Therefore, a spatial distribution entropy S' needs to be added to form a four-dimensional state vector [μ,σ]. 2 Emax ,S'], take the four-dimensional state vector as the final state vector; if R abn ≤R th This indicates that the abnormal triangular units are randomly distributed, and the basic three-dimensional vector can fully describe the error characteristics. Therefore, there is no need to add dimensions; the basic three-dimensional vector [μ,σ] can be used directly. 2 E max [ ] as the final state vector.

[0109] The time interval for continuous collection in step S213 is consistent with the time base alignment period in step S11, ensuring that the time resolution of the state sequence matches the time resolution of the simulation data; the process of arranging the state sequence to construct the Markov chain is as follows: The final state vector S corresponding to each simulation time t is... t Arranged in chronological order, forming a state sequence {S1, S2, ..., S...} Nseq}, where t=1,2,...,Nseq, Nseq is the number of continuously collected samples, that is, Nseq consecutively collected final state vectors corresponding to simulation time points, S t With S t+1 For each adjacent simulation time step, the final state vector corresponds to a state transition. The core purpose of this arrangement is to satisfy the core assumption of Markov chains that "state transitions are only related to the current state"—the temporal order of the sequence ensures that the transition relationship reflects the true temporal evolution and avoids distortion of the transition pattern due to temporal discrepancies. The temporal sorting provides a unique temporal reference for the subsequent step S22 to count the frequency of state transitions. If the sequence is unordered, the frequency statistics will lose their physical meaning. Step S213 works in conjunction with the temporal base alignment in step S11. Temporal base alignment ensures that the time node corresponding to each final state vector is accurate, while the sequence arrangement connects these accurate time nodes to form a continuous temporal data chain. The collaboration of both enables the state sequence to truly reflect the evolution of the error state over time. Without temporal base alignment, the state transitions in the sequence may include spurious transitions caused by time deviations. Without sequence arrangement, high-dimensional state vectors cannot be converted into input data for time series analysis.

[0110] High-dimensional mapping error distribution data, such as errors containing hundreds of local triangular units, cannot be directly used for learning temporal evolution patterns. Traditional methods can only perform local analysis and cannot capture the temporal changes of error states globally. Fixed-dimensional feature vectors cannot adapt to the dynamic characteristics of error spatial distribution, such as changing from random distribution to clustered distribution, resulting in incomplete feature descriptions and affecting the accuracy of subsequent model learning. Step S21 addresses these shortcomings one by one through the techniques of "extracting core statistics for dimensionality reduction, dynamically adjusting vector dimensions, and sorting by time to form a sequence," achieving the key transformation from "high-dimensional error data to low-dimensional temporal states." The three statistics of the basic three-dimensional vector extract the core features of the error distribution from a global perspective, compressing the error distribution that originally required hundreds of dimensions to be described into 3-4 dimensions without losing key information. For example, in a substation simulation, the error distribution containing 500 local triangular units is transformed into [μ=0.8℃, σ] through step S211. 2 =0.3℃ 2 E max The baseline 3D vector [=2.1℃] has a significantly reduced dimensionality, allowing it to be directly input into a Markov chain model and solving the "curse of dimensionality" problem of traditional methods. When R abn >R th The introduction of spatial distribution entropy enables the state vector to capture the clustering characteristics of errors. Learning the transfer patterns of clustered errors based on four-dimensional state vectors avoids the learning bias caused by fixed dimensions. The statistical variance σ in the basic three-dimensional vector... 2 This can detect hidden drifts in advance, where the mean remains constant but the error dispersion increases. For example, under a certain operating condition, μ remains stable at 0.7℃ for a long time, but σ... 2 From 0.2℃ 2 Gradually increase to 0.5℃ 2 This indicates that the error distribution has changed from concentrated to dispersed. Although it did not trigger the abnormal threshold, it suggests that the operating condition is beginning to show an unstable trend. Maintenance personnel can intervene in advance to adjust and avoid explicit drift caused by the subsequent increase in the mean. This effect goes beyond the basic function of "only providing input to the Markov chain" and realizes preventive monitoring of drift.

[0111] Step S22: Traverse the state sequence of the Markov chain, count the transition frequency between the final state vectors of adjacent simulation time points, and obtain the state transition probability matrix based on the transition frequency;

[0112] Further, step S22 includes:

[0113] Step S221: Traverse the state sequence of the Markov chain, count the transition frequency between the final state vectors of adjacent simulation time points, and calculate the initial state transition probability.

[0114] Step S222: Perform a stability assessment on the initial state transition probability within the sliding time window, and calculate the dynamic transition probability based on the stability assessment result;

[0115] Step S223: Combine all the dynamic transition probabilities to form a state transition probability matrix that describes the evolution of the system error state.

[0116] Specifically, step S22 calculates the initial transition probability by statistically analyzing the transition frequency in the state sequence, and dynamically adjusts the state transition probability using a sliding window. This ultimately forms a state transition probability matrix, providing a mathematical model for error state prediction in the subsequent step S23. Its core objective is to overcome the technical deficiency in existing technologies where "static probability matrices cannot adapt to dynamic changes in operating conditions, leading to low accuracy in error state prediction." By employing techniques such as "calculating initial state transition probabilities using statistical frequency, determining stability using a sliding window, and dynamically combining probability matrices," it achieves a breakthrough in "transforming the probability matrix from static to dynamic," ensuring that the matrix can reflect the error evolution pattern under the latest operating conditions in real time.

[0117] In a Markov chain, the state transition probability P(S) j |S i ) indicates that the current state is S i At that time, the next moment will shift to S. j The probability of transition depends solely on the current state S, and its core characteristic is "no aftereffect". i This characteristic, independent of historical states, forms the mathematical basis for subsequent predictions. The specific implementation of step S221 includes: since the final state vector is a continuous value, such as μ, σ... 2 Since the states are continuous, the continuous state vector needs to be discretized into a finite number of discrete states. The K-means clustering algorithm is then used to cluster all the final state vectors in the state sequence. The number of clusters K is set based on the dimension k' of the state vectors, K = 5 × k'. For example, when k' = 4, K = 20, ensuring that the discrete states cover all possible vector ranges without excessive subdivision leading to insufficient sample size. The discrete state sequence is then traversed. The superscript d denotes a discrete state, where, Let t represent the discrete state category number corresponding to the t-th simulation time in the discrete state sequence, where t = 1, 2, ..., Nseq. The value can be 1 to K, for example The discrete state category number representing the 5th simulation time belongs to the 3rd type of discrete state.

[0118] Traversing the discrete state sequence Focusing on the state transition relationships between adjacent time steps, let the discrete state category numbers of adjacent time steps be... Statistical analysis of each pair of discrete states transfer frequency That is, the sequence from discrete states Transition to discrete state The total number of times. Simultaneously, for each discrete state... Calculate its total frequency of occurrence. That is, all from The sum of the frequencies transferred out is given by the formula: Where i ≠ j, K is the total number of discrete states, and the values ​​of i and j range from 1 to K. For each discrete state i, the initial transition probability P0(j|i) to discrete state j is calculated according to the "transition frequency ratio", and the formula is: After calculation, it must be ensured that the sum of all initial transition probabilities corresponding to each discrete state i is 1, satisfying the probability normalization constraint and conforming to the mathematical assumptions of Markov chains. In existing technologies, the transition frequency of continuous state vectors cannot be directly statistically analyzed. Clustering discretization transforms continuous values ​​into discrete states, ensuring that the transition frequency can be statistically analyzed. K-means clustering can adapt to the distribution characteristics of state vectors, avoiding biases caused by manually dividing discrete intervals; probability normalization ensures conformity to the mathematical assumptions of Markov chains, providing a foundation for subsequent matrix operations.

[0119] The core of step S222 is to address the problem that the initial probability, based on full-sequence statistics, cannot adapt to changes in operating conditions. The sliding time window contains state transition samples corresponding to the most recent M' simulation moments, i.e., M'+1 discrete states, corresponding to M' adjacent transitions. The value of M' is determined by the simulation period and the rate of change of operating conditions. When operating conditions change rapidly, M' takes a smaller value (e.g., M=20 periods). When operating conditions are stable, M' takes a larger value (e.g., M'=50 periods), ensuring that the window reflects the transition characteristics of the latest operating conditions. The specific implementation process of step S222 is as follows: The discrete state sequence is divided into multiple consecutive sliding windows in chronological order. The window sliding step size is one transition pair; that is, for each new state transition, the window slides backward by one step, ensuring that each transition pair is covered by multiple windows to avoid missing edge samples. For each sliding window q, where q is the window number, the transition frequency statistics and probability calculation in step S221 are repeated to obtain the state transition probability of window q.

[0120]

[0121] in:

[0122]

[0123] This represents the frequency of transitions from discrete state i to j within window q. Represents the discrete state within window q Total frequency of occurrence Indicates the discrete state within the window The state transition probability to state j.

[0124] Calculate the relative rate of change of state transition probabilities between adjacent windows:

[0125]

[0126] in, This represents the state transition probability from discrete state i to j within window q-1. For the discrete state pair between window q and window q━1 The relative rate of change of state transition probabilities; if the relative rate of change of state transition probabilities for k2 consecutive adjacent windows is less than or equal to the preset stability threshold ΔP th This indicates that the transition pattern is stable, and the current window's state transition probability is used as the dynamic transition probability. For example, k2=5; if Greater than ΔP th If this indicates a significant change in operating conditions, the window M' should be immediately adjusted to half its original size, such as reducing it from 50 cycles to 25 cycles. The probability within the window should then be recalculated to quickly adapt to the new operating conditions. ΔP th The determination is based on the probability fluctuations under historical stable operating conditions. The relative change rate of the state transition probability of 10 adjacent windows under stable operating conditions is collected, and the 95th percentile is taken as ΔP. th Exemplary ΔP th =5%. For each new state transition pair, the sliding window is updated synchronously, repeating the stability assessment and probability calculation to ensure that the dynamic transition probability always reflects the transition patterns of the latest M' cycles. The dynamic adjustment of the sliding window allows the probability to track changes in operating conditions in real time. For example, when a power grid switches from daytime peak load to nighttime off-peak load, the relative change rate of the state transition probability jumps from 3% to 8%, exceeding ΔP. th =5%, window M' decreases from 50 to 25. The probability of the new window quickly reflects the transfer pattern under low load, avoiding the error of traditional static matrices using peak data to predict low load conditions; stability judgment avoids frequent probability fluctuations, and the judgment condition of k2 consecutive windows can filter out random fluctuations, ensuring the reliability of dynamic probability; if =0 can directly trigger the "significant change in operating conditions" judgment without calculation. Adjust the sliding window size M' to half of its original size and recalculate the transition probability of the current window q.

[0127] The state transition probability matrix P is K×K, where K is the total number of discrete states determined in step S221, the row index of the matrix is ​​discrete state i, and the column index is discrete state j (1~K). The matrix element P(i,j) of the state transition probability matrix is ​​directly equal to the dynamic transition probability. All dynamic transition probabilities obtained in step S222 are filled into the K×K matrix according to the correspondence between row i and column j to obtain the state transition probability matrix. The static state transition probability matrix cannot adapt to the dynamic changes in power grid operating conditions (such as load fluctuations and seasonal changes), resulting in a sharp decrease in the accuracy of error state prediction as the operating conditions change; the dispersed state transition probabilities lack systematic integration and cannot form a mathematical model that can be directly used for prediction, affecting the efficiency of subsequent calibration. Through the technical means of "statistical frequency calculation of initial probability, dynamic adjustment of sliding window, and matrix structured combination", the transformation from "static probability to dynamic matrix" is realized, ensuring that the matrix can reflect the error evolution law under the latest operating conditions in real time and has the characteristics of being systematic and computable.

[0128] Step S23: Perform matrix operations on the final state vector of the latest simulation time in the state sequence of the Markov chain and the state transition probability matrix to obtain the predicted state vector of the next simulation time. Calculate the mapping matrix correction amount based on the predicted state vector of the next simulation time and generate the corrected mapping matrix.

[0129] Further, step S23 includes:

[0130] Step S231: Extract the final state vector of the latest simulation moment from the state sequence of the Markov chain, and obtain the predicted state vector of the next simulation moment based on the state transition probability matrix.

[0131] Step S232: Calculate the mapping matrix correction amount for the initial mapping matrix based on the predicted state vector at the next simulation time.

[0132] Step S233: Determine the norm of the mapping matrix correction quantity, and generate the corrected mapping matrix based on the determination result.

[0133] Specifically, step S23, as the core execution step of "rule learning → dynamic calibration," transforms the error state predicted by the Markov chain into a directional correction amount for the initial mapping matrix, achieving feedforward dynamic calibration of the "voltage-temperature rise" mapping relationship. This addresses the technical deficiency in existing technologies where "mapping drift can only be passively responded to and cannot be corrected in advance based on the error evolution trend." Its core logic is: using the latest error state as input, the error trend at the next moment is predicted through the state transition probability matrix, the predicted error is transformed into a quantitative correction amount for the mapping matrix, and then the magnitude of the correction is used to determine whether to apply it in stages. This ensures that the mapping relationship adjustment accurately adapts to changes in operating conditions while avoiding system oscillations, ultimately outputting a corrected mapping matrix that adapts to the latest operating conditions.

[0134] The state sequence of a Markov chain {S1,S2,...,S} Nseq The final state vector at the latest simulation time in} is S Nseq S is clustered using the K-means clustering algorithm. Nseq Transform it into a 1×K row vector S curr This describes the probability of the system being in each discrete state at the current moment. One row represents the current moment, and K columns correspond to K discrete states. The column indices 1 to K correspond one-to-one with the discrete state numbers 1 to K. The element in the i-th column represents the probability of the system being in the i-th discrete state at the current moment, and the sum of all elements is equal to 1, which conforms to the probability axiom.

[0135] For example, assuming the final state vector is three-dimensional, then K = 5 × 3 = 15, i.e., 15 discrete states; the discretized 1 × K row vector S of the final state vector at a certain latest simulation moment. curr For: S curr =[0.0,0.0,0.6,0.2,0.1,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]; where the element corresponding to column index 3 is 0.6, indicating that the system has a 60% probability of being in the 3rd discrete state at the current time; the element corresponding to column index 4 is 0.2, indicating that there is a 20% probability of being in the 4th discrete state; the element is 0.0, indicating that there is no probability of being in these discrete states; the sum of all elements = 0.6 + 0.2 + 0.1 + 0.1 = 1.0: which meets the basic constraints of probability distribution.

[0136] Predicted state vector The calculation formula is The calculation result is still... The row vector, where the first row is the row vector. element The physical meaning of this formula is: by weighting the probabilities of each discrete state at the current time step with the transition probabilities from the current state to the next state, we obtain the predicted probabilities of each discrete state at the next time step. For example... That is, the current The probability is in discrete state 1. That is, the probability of transitioning from state 1 to state 2 is Then the path is for The contribution is All discrete state pairs The sum of contributions is Select "Row Vector" The order of operations for "square matrix" is because The probability distribution representing the current state. The transition patterns between representative states, when multiplied together, naturally reveal the "probability distribution". Transfer pattern The mathematical logic of "predicting the distribution" aligns with the core assumption of Markov chains that they have "no aftereffects." Quantitative prediction of error states is achieved through matrix multiplication, avoiding the ambiguity of traditional methods that rely on "qualitative judgment of error trends."

[0137] Step S232, based on the predicted state vector generated in step S231 and combined with the preset error control target, calculates the mapping matrix correction ∆M for the initial mapping matrix M in step S12. The core of this step is to transform the "predicted error state" into a "quantitative adjustment value of the mapping matrix," thus addressing the deficiency of traditional methods where "the mapping matrix adjustment lacks clear quantitative basis." The initial mapping matrix M is a 2×1 matrix, with each row corresponding to the voltage amplitude U and voltage phase angle. The column corresponds to the vertex weight W, i.e. This is used to convert electrical parameters into geometric vertex weights, where the element M(1,1) is the influence coefficient of voltage amplitude on the weight, denoted as m. U , The influence coefficient of voltage phase angle on weight is denoted as . .

[0138] Predicted state vector It needs to be inverted into the prediction error statistic first, because To determine the probability distribution of discrete states, a discrete state-error statistic mapping table is needed. This table is established simultaneously during clustering in step S221, recording the mean error value corresponding to each discrete state. ,variance Calculate the mean of the prediction error With prediction error variance Mean prediction error The probability-weighted average of the mean errors of each discrete state is given by the following formula: The prediction error variance is calculated using the law of total probability, including the contributions of the variance of each discrete state and the mean deviation. The formula is as follows: Mapping matrix correction The calculation formula is ,in, The target error mean is denoted as , and the error mean of the monitored object under stable operating conditions is denoted as . The method for determining is: collect N consecutive data points from the monitored object. stable The average error over several stable simulation cycles is taken as μ. target For example, N stable =30 cycles to ensure it reflects the "ideal error level when there is no drift". The historical maximum mean error is the maximum value of all mean errors in the state sequence of step S213, used for normalization. To avoid the correction amount getting out of control due to differences in the magnitude of the error; if If it is 0, then Replace it with a very small positive number ε, where ε is a very small positive number, such as 1 × 10. -6 . The correction coefficient is set based on the "convergence rate of the corrected error," and is determined through verification using historical data: if the corrected μ pred To μ target If the convergence speed is too slow, such as a decrease of less than 10% over 3 cycles, then increase λ, but not exceeding 1.0; if the convergence is too fast and causes oscillations, then decrease λ, but not less than 0.1. An example initial λ = 0.3.

[0139] The physical logic of this formula is: when μ pred >μ target At that time, μ target -μ pred If negative, ∆M is opposite to M, and m decreases. U and This reduces the impact of voltage on vertex weights, thereby decreasing the deviation between the predicted mapped temperature and the actual temperature; when μ pred <μ target When ∆M and M are in the same direction, increasing the influence coefficient m U and Balance the mapping relationship. When μ pred =μ target When, the influence coefficient m U and No change. The core advantage of choosing this formula is that the correction amount is proportional to the initial mapping matrix M, ensuring that the adjustment direction is consistent with the original mapping logic and avoiding abrupt changes in the mapping relationship after correction; at the same time, through μ max Normalization ensures that the magnitude of the correction is positively correlated with the degree of error deviation, avoiding both under-correction and over-correction. The correction amount is directly linked to the error statistics, ensuring that the correction direction precisely targets the prediction drift trend; for example, it corrects in the opposite direction when the prediction error increases, avoiding indiscriminate adjustments. It can serve as the basis for dynamic adjustment of the correction coefficient λ, if Increasing the value of λ, i.e., increasing the error dispersion, reduces the stability of the mapping. λ can be automatically reduced, for example, from 0.3 to 0.2, to avoid excessive correction that could exacerbate stability problems and to expand the adaptive capability of the correction. Without step S232, the mapping matrix adjustment would lack a quantification standard, potentially leading to insufficient correction that fails to suppress drift or excessive correction that introduces new deviations, thus negating the core value of step S23 for dynamic calibration.

[0140] Step S233 determines the correction magnitude by calculating the norm of the correction amount ∆M of the mapping matrix, and generates the corrected mapping matrix M for each scenario. newThe core objective is to avoid abrupt changes in the mapping relationship caused by large corrections, ensuring a smooth calibration process. First, the calculation method for the correction norm is clarified: the L2 norm (Euclidean norm) is used, and the formula is... This norm reflects the overall magnitude of the correction; the larger the norm, the more significant the change in the mapping relationship. A threshold TH for the correction magnitude is set. adjust TH adjust The setting is based on the maximum allowable fluctuation range of the mapping relationship, and the determination method is: collect N data under stable operating conditions of the monitored object. test The mapping matrix M for each simulation cycle test Calculate adjacent period M test norm difference ,Pick quantile as TH adjust This ensures that the mapping relationship reflects the maximum fluctuations that can be tolerated under stable operating conditions.

[0141] Please refer to Table 1. The scenario-specific processing logic is as follows: When When this occurs, it indicates that the correction magnitude exceeds the allowable range for stability. Direct application will cause a sudden change in the voltage-weight mapping relationship, thereby distorting the initial two-dimensional thermodynamic distribution map generated in step S12. For example, a sudden increase in the weight of a certain region may cause the color to change from blue to red, which does not match the actual temperature. At this time, Decomposed into three equal sub-corrections The following three consecutive simulation cycles will be applied gradually: the first cycle The second cycle The third cycle The core reason for decomposing it into three cycles is that the temperature change of power grid equipment has inertia, and the temperature response speed is slower than the voltage. Phased correction allows the mapping relationship to synchronize with the temperature change trend, avoiding matching deviations caused by the mapping adjustment being faster than the temperature change. When the value is within the allowable range of stability, the correction can be applied directly, and the formula is: This ensures that the mapping relationship quickly adapts to the prediction error trend, avoiding correction lag. It balances calibration speed and system stability by handling different scenarios, applying large corrections in stages to avoid oscillations, and applying small corrections directly to ensure timeliness, thus overcoming the shortcomings of traditional "one-size-fits-all" corrections. These are core parameters distributed from the cloud to the edge nodes, and their stability directly affects the accuracy of the local heatmap at the edge nodes in step S31. Mutations can cause false high-error regions to appear in the local mapping error distribution map generated by edge nodes, leading to distortion in the uploading of lightweight state vectors. Phased correction can ensure... Smooth updates provide reliable input for edge-cloud collaborative calibration.

[0142] Table 1. Scenario-specific processing strategies for mapping matrix corrections

[0143] Judgment condition Processing mode ‖ΔM‖2>TH adjust ]]> Apply the correction amount step by step in 3 simulation cycles ‖AM‖2≤TH adjust ]]> Directly apply the correction amount

[0144] The calibration of voltage-temperature rise mapping drift lacks feedforward capability. Traditional methods can only passively adjust based on existing errors, failing to proactively correct according to error evolution trends, resulting in calibration lagging behind changes in operating conditions. Furthermore, the mapping matrix correction lacks amplitude control; large corrections can easily trigger abrupt changes in the mapping relationship, while small corrections cannot suppress drift in time, making it difficult to balance calibration accuracy and system stability. Step S23 addresses these shortcomings by employing a technique of "predicting the state vector → quantifying the correction amount → applying it according to different scenarios," achieving a unified approach of "feedforward prediction + precise correction + stable calibration." Step S231 predicts the error state at the next moment using the state transition probability matrix. Step S232 calculates the correction amount based on the predicted error, ensuring that the mapping matrix adjustment precedes the actual error occurrence, avoiding the lag of correction after the traditional error occurs. The correction formula in Step S232 is calculated based on the error statistics and the initial matrix ratio. Step S233 uses the norm to determine the control amplitude, ensuring that the correction amount is sufficient to suppress drift without triggering abrupt changes.

[0145] Step S30: Load the corrected mapping matrix issued by the cloud at the edge node to generate a lightweight state vector. Based on the difference judgment and multi-level caching strategy, upload the lightweight state vector to the cloud to form a lightweight state vector set. The cloud updates the state transition probability matrix according to the lightweight state vector set and issues a new version of the corrected mapping matrix.

[0146] Step S30 utilizes an edge-cloud collaborative architecture to offload lightweight tasks such as error calculation and state generation to edge nodes, uploading only high-value, lightweight state vectors to the cloud. The cloud then integrates global data to iteratively optimize the model and distributes correction strategies, addressing the technical shortcomings of existing technologies that suffer from bandwidth overload and computational latency caused by centralized cloud processing of full data. Its core logic is: leveraging the local computing power of edge nodes to complete data preprocessing and preliminary error analysis; through a collaborative mode of "on-demand upload + batch optimization," reducing cloud load while ensuring the real-time performance and accuracy of global mapping calibration; ultimately achieving a self-healing error loop of "distributed error monitoring - centralized model iteration - differentiated strategy distribution."

[0147] Please see Figure 4 As shown, step S30 further includes:

[0148] Step S31: The edge node receives and loads the corrected mapping matrix sent from the cloud, collects local simulation data and reuses the mapping error calculation method of the local triangular element to generate a local mapping error distribution map, and extracts statistical features from the local mapping error distribution map to generate a lightweight state vector.

[0149] Step S31 achieves "localized error calculation - lightweight feature extraction" by deploying an error monitoring module on edge nodes. The core of this approach is to offload some cloud-based computational tasks, reducing the amount of raw data uploaded. The specific implementation process is as follows: An error monitoring module is deployed on the edge computing node of the power grid dispatch master station, located close to the data source, such as at a substation or a power grid zone monitoring station. The error monitoring module receives real-time output data from the local power grid power flow simulation engine and the equipment-level thermodynamic simulation engine. The data content is consistent with that in step S11, including voltage data with node identifiers, simulation time, and amplitude / phase angle; and temperature data with spatial three-dimensional coordinates, time, and temperature value. However, only data from sensor units within the local jurisdiction is collected, such as the 10 power grid nodes and corresponding equipment temperature measurement points covered by a substation, rather than the full dataset, thus reducing the scale of data processing from the source. The calculation of the local mapping error distribution map reuses the method of step S13. Specifically, it calls the "inverse calculation node - query temperature - interpolation calculation - error comparison" logic of step S132, and combines it with the loaded corrected mapping matrix to generate a local mapping error distribution map within the local jurisdiction, i.e., the local mapping error distribution map. The generation of the lightweight state vector reuses the method of step S21. It extracts statistical features based on the local mapping error distribution map to form a lightweight state vector (3D or 4D) with the same dimension as step S21. The lightweight state vector only contains the statistical features of the error, and the data volume is much smaller than the original mapping error distribution map, which greatly reduces the amount of data uploaded subsequently. Offloading the computation task can reduce the bandwidth pressure on the cloud and solve the bandwidth congestion caused by uploading all the original data in the existing technology. If step S31 is missing, all error calculations still need to be centralized in the cloud, which will lead to continuously high bandwidth consumption, and the data transmission delay of edge nodes in remote areas will exceed the simulation cycle, causing the calibration to lose real-time performance.

[0150] Step S32: Perform a difference judgment on the lightweight state vectors generated by the edge nodes, and upload the lightweight state vectors to the cloud according to the difference judgment result and the preset caching strategy. Collect all the lightweight state vectors uploaded to the cloud to form a lightweight state vector set.

[0151] Further, step S32 includes:

[0152] Step S321: Calculate the Euclidean distance between the lightweight state vector currently generated at the edge node and the lightweight state vector that was successfully uploaded last time, and make a difference judgment based on the Euclidean distance;

[0153] Step S322: When the Euclidean distance is greater than the preset change threshold, immediately upload the currently generated lightweight state vector; otherwise, temporarily store the currently generated lightweight state vector in the local cache of the edge node.

[0154] Step S323: Monitor the number of lightweight state vectors and the caching time in the local cache, and perform batch upload or forced upload according to the preset packaging threshold or the maximum waiting time;

[0155] In step S324, the cloud center receives the lightweight state vectors uploaded by all edge nodes and aggregates them to form a lightweight state vector set covering the global distributed sampling.

[0156] Step S32 achieves on-demand uploading of lightweight state vectors through "difference judgment + multi-level caching strategy," with the core being to avoid "periodic blind uploading" and further optimize communication efficiency. The specific implementation process is as follows: Euclidean distance D is used to quantify the degree of difference between the current lightweight state vector and the previously successfully uploaded vector. Before calculating the Euclidean distance, the features of each dimension of the vector are standardized. Change threshold Th change The settings are based on historical stable operating condition data, collecting data from edge nodes under stable operating conditions for N consecutive times. change The lightweight state vector for each simulation cycle is used to calculate the Euclidean distance between vectors of adjacent cycles, and the 95th percentile is taken as Th. change For example, if the maximum difference under steady-state conditions is 0.15, then Th change =0.15, ensuring that uploading is only triggered when the vector difference exceeds the normal fluctuation range, avoiding invalid communication caused by normal fluctuations. Please refer to Table 2, when D>Th change When D ≤ Th, it indicates a significant change in the local error state, such as increased mapping drift, requiring immediate uploading of the current vector to the cloud to ensure timely acquisition of anomaly information by the cloud; change When the current vector is temporarily stored in the local cache of the edge node, the cache adopts the first-in-first-out (FIFO) mechanism to avoid old data occupying storage space.

[0157] Table 2 Lightweight State Vector Upload Strategy Table

[0158] Trigger condition Upload mode Euclidean distance D > Th change ]] Immediately upload D < Th change ]]> Temporary local cache (FIFO mechanism)

[0159] A multi-level caching strategy ensures a balance between communication efficiency and data integrity: Firstly, it controls packaged uploads, setting a packaged threshold N. pack N pack The settings are based on the buffer capacity and communication frequency of the edge nodes, such as N pack =5, when the number of lightweight vectors in the local cache reaches N. pack First, these vectors are packaged, compressed, and uploaded in batches to reduce the number of communication handshakes per upload and save communication overhead; second, mandatory upload control is implemented by setting a maximum waiting time T. wait T wait The settings are based on the monitoring accuracy requirements, such as T wait =30 seconds, if the number of cached vectors does not reach N packHowever, it has been more than 1000 terabytes since the last upload. wait Forced uploading of cached vectors avoids the loss of historical data due to prolonged periods without significant changes. On-demand uploading significantly reduces communication frequency, resolving the bandwidth waste caused by periodic uploading in existing technologies. The multi-level caching strategy balances efficiency and integrity, with packaged uploading reducing communication overhead and forced uploading preventing data loss. Steps S32 and S31 work together to form a combination of "lightweight computation + precise uploading." Step S31 generates small-volume vectors, while step S32 selects high-value vectors for uploading. Together, they minimize communication volume while ensuring that the data obtained from the cloud is all critical information. Without step S32, edge nodes would upload all vectors indiscriminately, consuming bandwidth even with normal fluctuations, leading to wasted communication resources, and a single upload of multiple vectors would increase the processing pressure on the cloud.

[0160] In step S33, the cloud reconstructs the lightweight state vector set into a new Markov chain state sequence, updates the state transition probability matrix using the new Markov chain state sequence to obtain the updated state transition probability matrix, generates a global correction strategy and a personalized correction strategy based on the updated state transition probability matrix, and sends the new version of the corrected mapping matrix to the edge nodes.

[0161] The cloud receives lightweight state vectors uploaded by all edge nodes and aggregates them into a lightweight state vector set covering global distributed sampling. This set contains the error characteristics of edge nodes under different regions and operating conditions, comprehensively reflecting the global error state of the full-stack digital twin system and avoiding global judgment bias caused by data from a single node. When reconstructing a new Markov chain state sequence in the cloud, the global lightweight state vector set is arranged in chronological order, with the arrangement logic consistent with step S213, ensuring that the temporal resolution of the state sequence matches the simulation cycle. The update of the state transition probability matrix reuses the method in step S22, that is, first discretizing the continuous state vector into K discrete states through K-means clustering, then counting the state transition frequency within the sliding time window, calculating the dynamic transition probability, and combining them into a matrix. This reuse design ensures that the matrix update logic is consistent with step S22, avoiding fluctuations in prediction accuracy caused by changes in model logic. The global correction strategy is generated based on error characteristics common to all edge nodes, such as mapping drift caused by seasonal changes across the entire system; this strategy applies to all edge nodes. The personalized correction strategy is generated based on the unique error characteristics of a single edge node; for example, if a certain edge node has a larger local error due to equipment aging, this strategy applies only to that node. Combining these two strategies ensures the accuracy of the correction and avoids insufficient or excessive correction for some nodes due to a "one-size-fits-all" approach. The new version of the corrected mapping matrix is ​​generated according to the correction strategy, with the generation logic consistent with step S23: calculating the mapping matrix correction amount based on the predicted error state, and determining whether to apply it in stages or directly based on the correction amount norm. After generation, it is distributed to the corresponding edge nodes through a secure data channel. After loading the new version matrix, the edge nodes can update the local error calculation and state generation benchmarks, ensuring that subsequent calibrations are synchronized with the global strategy. Steps S33 and S20 work together to form a closed loop of "model iteration - policy deployment": S20 constructs the initial state transition probability matrix, and S33 updates the matrix based on global edge node data, so that the matrix can reflect the error evolution law of the whole system in real time. At the same time, the correction matrix deployed by S33 provides an updated calculation benchmark for the edge nodes in S31, so that the local calibration of the edge nodes is consistent with the global optimization in the cloud.

[0162] The edge-cloud collaborative architecture in step S30 addresses the shortcomings of centralized processing of the entire dataset. Lightweight tasks are offloaded to edge nodes to reduce cloud computing load, and the on-demand upload mechanism reduces bandwidth consumption. The combination of global and personalized correction strategies improves calibration accuracy. Global strategies address common drift problems, while personalized strategies adapt to node characteristics, avoiding the limitations of a single strategy. Error self-healing closed loop enables preventative monitoring, and abnormal states of edge nodes are uploaded in a timely manner. The cloud predicts drift trends in advance based on global data and generates feedforward correction strategies to avoid simulation distortion caused by drift amplification. Step S30 resolves the contradiction in existing technologies of "high latency in centralized processing and lack of global optimization in distributed processing." Through edge-cloud collaboration, it achieves full-process calibration of "precise local monitoring - intelligent global optimization - personalized strategy implementation," providing an efficient and real-time solution for the mapping drift problem of heterogeneous simulation engines in full-stack digital twins.

[0163] Example 2

[0164] This embodiment, based on Embodiment 1, provides a full-stack digital twin simulation system for the power grid dispatch master station, such as... Figure 5 As shown, it includes:

[0165] Two-dimensional thermal distribution map construction module: used to acquire the voltage power flow result dataset of the power grid power flow simulation engine and the temperature field distribution dataset of the equipment-level thermodynamic simulation engine, construct a unified virtual coordinate system based on the voltage power flow result dataset, and construct an initial two-dimensional thermal distribution map in the unified virtual coordinate system according to the preset initial mapping matrix;

[0166] Mapping error calculation module: used to perform Delaunay triangulation on the initial two-dimensional thermal distribution map and generate a mapping error distribution map by combining the temperature field distribution dataset;

[0167] Mapping matrix correction module: Constructs the state sequence of the Markov chain based on the mapping error distribution map, calculates the state transition probability matrix through the state sequence of the Markov chain, and uses the state transition probability matrix to predict and generate the corrected mapping matrix;

[0168] Task sinking module: The edge node loads the corrected mapping matrix issued by the cloud to generate a lightweight state vector. Based on the difference judgment and multi-level caching strategy, the lightweight state vector is uploaded to the cloud to form a lightweight state vector set. The cloud updates the state transition probability matrix according to the lightweight state vector set and issues a new version of the corrected mapping matrix.

[0169] Furthermore, in the two-dimensional heat map construction module, the method for constructing the initial two-dimensional heat map in the unified virtual coordinate system includes:

[0170] Step S121: Generate a one-dimensional expanded code for the power grid topology based on the unique identifier of the power grid node and the power flow direction information of the associated line in the time-base aligned voltage power flow result dataset; generate a simulation discrete time series code based on the simulation time in the time-base aligned voltage power flow result dataset; and unify the virtual coordinate system with the one-dimensional expanded code for the power grid topology as the horizontal axis and the simulation discrete time series code as the vertical axis.

[0171] Step S122: In the unified virtual coordinate system, the voltage power flow result dataset after time base alignment is geometrically transformed according to the preset initial mapping matrix to generate geometric mapping vertices.

[0172] Step S123: Determine the spatial distribution density of the geometric mapping vertices, identify sparse regions, and perform interpolation processing on the sparse regions to obtain a set of geometric mapping vertices with enhanced density.

[0173] Step S124: The density-enhanced geometric mapping vertex set is processed by rendering and rasterization to generate an initial two-dimensional thermal distribution map.

[0174] Furthermore, in the mapping error calculation module, the method for generating the mapping error distribution map includes:

[0175] Step S131: Perform Delaunay triangulation on the initial two-dimensional thermal distribution map to discretize the initial two-dimensional thermal distribution map into a mesh composed of multiple non-overlapping local triangular units that cover the entire map.

[0176] Step S132: For each local triangular unit, calculate the mapping error of the local triangular unit in conjunction with the temperature field distribution dataset;

[0177] Step S133: Assign the mapping error of all local triangular units to the corresponding position of the initial two-dimensional thermal distribution map to generate a mapping error distribution map. Determine abnormal triangular units according to the preset error threshold and count the number of abnormal triangular units and the total number of triangular units.

[0178] The methods and systems of this application may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the method is for illustrative purposes only, and the steps of the method of this application are not limited to the order specifically described above, unless otherwise specifically stated.

[0179] In addition, the parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of the corresponding technical solutions in the prior art have not been described in detail, so as to avoid excessive elaboration.

[0180] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A full-stack digital twin simulation method for power grid dispatching master stations, characterized in that, The method includes: Obtain the voltage power flow result dataset from the power grid power flow simulation engine and the temperature field distribution dataset from the equipment-level thermodynamic simulation engine. Construct a unified virtual coordinate system based on the voltage power flow result dataset. Based on the preset initial mapping matrix, construct an initial two-dimensional thermal distribution map in the unified virtual coordinate system. Perform Delaunay triangulation on the initial two-dimensional thermal distribution map and generate a mapping error distribution map by combining it with the temperature field distribution dataset. Construct the state sequence of the Markov chain based on the mapping error distribution map, calculate the state transition probability matrix using the state sequence of the Markov chain, and use the state transition probability matrix to predict and generate the corrected mapping matrix; At the edge node, the modified mapping matrix issued by the cloud is loaded to generate a lightweight state vector. Based on the difference judgment and multi-level caching strategy, the lightweight state vector is uploaded to the cloud to form a lightweight state vector set. The cloud updates the state transition probability matrix according to the lightweight state vector set and issues a new version of the modified mapping matrix.

2. The full-stack digital twin simulation method for power grid dispatching master station according to claim 1, characterized in that, The voltage power flow result dataset includes the unique identifier of the grid node, simulation time, voltage amplitude, voltage phase angle, and power flow direction information of the associated lines; The method for constructing the unified virtual coordinate system includes: Time base alignment is performed on the voltage power flow result dataset to obtain a time base aligned voltage power flow result dataset; Based on the unique identifiers of the grid nodes and the power flow direction information of the associated lines in the time-aligned voltage power flow result dataset, a one-dimensional expansion code of the grid topology is generated; based on the simulation time in the time-aligned voltage power flow result dataset, a simulation discrete time series code is generated. The unified virtual coordinate system uses the one-dimensional expansion encoding of the power grid topology as the horizontal axis and the simulated discrete time series encoding as the vertical axis.

3. The full-stack digital twin simulation method for power grid dispatching master station according to claim 2, characterized in that, The method for constructing the initial two-dimensional thermal distribution map includes: In a unified virtual coordinate system, the voltage power flow result dataset after time base alignment is geometrically transformed according to a preset initial mapping matrix to generate geometrically mapped vertices; The spatial distribution density of the geometrically mapped vertices is determined, sparse regions are identified, and interpolation is performed on the sparse regions to obtain a set of geometrically mapped vertices with enhanced density. The density-enhanced geometric mapping vertex set is rendered and rasterized to generate an initial two-dimensional heat map.

4. The full-stack digital twin simulation method for power grid dispatching master station according to claim 3, characterized in that, The method for generating the mapping error distribution map includes: Perform a Delaunay triangulation operation on the initial two-dimensional thermal distribution map to discretize the initial two-dimensional thermal distribution map into a mesh composed of multiple non-overlapping local triangular units that cover the entire map; For each local triangular element, the mapping error of the local triangular element is calculated by combining the temperature field distribution dataset; The mapping error of all local triangular units is assigned to the corresponding positions in the initial two-dimensional thermal distribution map to generate a mapping error distribution map.

5. The full-stack digital twin simulation method for power grid dispatching master station according to claim 4, characterized in that, The temperature field distribution dataset includes at least device-level thermodynamic simulation temperature values; The method for calculating the mapping error of local triangular elements includes: By using the coordinates of the three vertices of the local triangular element in a unified virtual coordinate system, the corresponding power grid node identifier and simulation time are calculated inversely. Based on the grid node identifier and simulation time obtained by back calculation, the corresponding equipment-level thermodynamic simulation temperature values ​​are obtained by querying the temperature field distribution dataset, resulting in a total of three equipment-level thermodynamic simulation temperature values. Based on three device-level thermodynamic simulation temperature values, the mapping error of the local triangular element is obtained.

6. The full-stack digital twin simulation method for power grid dispatching master station according to claim 5, characterized in that, The method for calculating the mapping error of local triangular elements further includes: Perform planar interpolation on the three device-level thermodynamic simulation temperature values ​​to calculate the actual interpolated temperature at the centroid of the local triangular element. Extract the pixel value of the corresponding position of the centroid of the local triangular unit in the initial two-dimensional thermal distribution map, and use the pixel value as the predicted mapping temperature; The difference between the actual interpolated temperature and the predicted mapped temperature is calculated to obtain the mapping error of the local triangular element.

7. The full-stack digital twin simulation method for power grid dispatching master station according to claim 6, characterized in that, The method for constructing the state sequence of a Markov chain includes: Based on a preset error threshold, abnormal triangular units are identified, and the number of abnormal triangular units is counted against the total number of triangular units. Calculate the statistical mean, statistical variance, and maximum absolute value of all pixel values ​​from the mapping error distribution map to form a basic three-dimensional vector; The proportion R of the number of anomalous triangular units to the total number of triangular units abn Determine whether to add the spatial distribution entropy dimension to the basic three-dimensional vector to form an extended four-dimensional state vector, and finally determine the final state vector at the corresponding simulation moment. The final state vectors of multiple corresponding simulation moments are collected sequentially in chronological order and arranged to form a state sequence of a Markov chain.

8. The full-stack digital twin simulation method for power grid dispatching master station according to claim 7, characterized in that, The method for determining the final state vector includes: Set the abnormality ratio threshold R th If R abn >R th Then the spatial distribution entropy S' is increased, forming a four-dimensional state vector, which is then used as the final state vector. If R abn ≤R th Then the basic three-dimensional vector is used directly as the final state vector.

9. The full-stack digital twin simulation method for power grid dispatching master station according to claim 8, characterized in that, The method for calculating the state transition probability matrix includes: Traverse the state sequence of the Markov chain, count the transition frequency between the final state vectors of adjacent simulation time points, and calculate the initial state transition probability; Within the sliding time window, the stability of the initial state transition probability is assessed, and the dynamic transition probability is calculated based on the stability assessment result. All dynamic transition probabilities are combined to form a state transition probability matrix that describes the state evolution law of system error.

10. A full-stack digital twin simulation system for a power grid dispatching master station, used to implement the full-stack digital twin simulation method for a power grid dispatching master station as described in any one of claims 1-9, characterized in that, The system includes: Two-dimensional thermal distribution map construction module: used to acquire the voltage power flow result dataset of the power grid power flow simulation engine and the temperature field distribution dataset of the equipment-level thermodynamic simulation engine, construct a unified virtual coordinate system based on the voltage power flow result dataset, and construct an initial two-dimensional thermal distribution map in the unified virtual coordinate system according to the preset initial mapping matrix; Mapping error calculation module: used to perform Delaunay triangulation on the initial two-dimensional thermal distribution map and generate a mapping error distribution map by combining the temperature field distribution dataset; Mapping matrix correction module: Constructs the state sequence of the Markov chain based on the mapping error distribution map, calculates the state transition probability matrix through the state sequence of the Markov chain, and uses the state transition probability matrix to predict and generate the corrected mapping matrix; Task sinking module: The edge node loads the corrected mapping matrix issued by the cloud to generate a lightweight state vector. Based on the difference judgment and multi-level caching strategy, the lightweight state vector is uploaded to the cloud to form a lightweight state vector set. The cloud updates the state transition probability matrix according to the lightweight state vector set and issues a new version of the corrected mapping matrix.

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