Digital twinning-based power grid monitoring data visual interaction method and system

Abstract

The invention relates to the technical field of computers, and discloses a digital twinning-based power grid monitoring data visual interaction method and system, and the method comprises the steps: carrying out the real-time monitoring of the operation state of a target power grid, and collecting the operation data of the target power grid; mapping the operation data to a digital twin power grid model of the target power grid to carry out anomaly diagnosis, and obtaining a power grid diagnosis result of the target power grid; based on the power grid diagnosis result, through a fault tree model of a target power grid and a fault tree logic relationship, determining occurrence states of each intermediate event and each bottom event, and obtaining an abnormality diagnosis result; and displaying abnormal equipment and a fault area in a display interface of the digital twin power grid model with different colors, icons and flickering effects based on the abnormal diagnosis result, and viewing detailed information of the abnormal equipment and the fault area through click operation. According to the invention, the abnormal source can be rapidly and accurately positioned, and the power grid operation stability is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and system for visualizing and interacting with power grid monitoring data based on digital twins. Background Art

[0002] In the modern power grid operation system, ensuring that the abnormal diagnosis results of power grid monitoring data can be accurately and timely presented in a visual interactive form is a key link in ensuring the safe, stable and efficient operation of the power grid.

[0003] Current methods for visualizing and interactively diagnosing anomaly results from power grid monitoring data offer some advantages in assisting operations personnel in understanding power grid anomalies. However, existing methods suffer from vulnerabilities in the data collection and analysis process. They rely on manual integration and analysis of large amounts of dispersed data and lack systematic diagnostic logic, making it difficult to quickly and accurately locate the root cause of anomalies in complex power grid failure scenarios. Furthermore, existing methods experience delays in visually presenting diagnostic results to operations personnel, failing to meet the requirements for real-time power grid operations and rapid response. This significantly impacts the efficiency of operations personnel's decision-making, and consequently, the stability of power grid operations. Summary of the Invention

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

[0005] Therefore, the present invention provides a digital twin-based power grid monitoring data visualization and interaction method, which can quickly and accurately locate the root cause of anomalies and improve the stability of power grid operation.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a digital twin-based grid monitoring data visualization interaction method, comprising: real-time monitoring of the operating status of the target grid and collecting the operating data of the target grid; mapping the operating data to the digital twin grid model of the target grid for abnormal diagnosis to obtain a grid diagnosis result of the target grid; based on the grid diagnosis result, through the fault tree model of the target grid and the fault tree logical relationship, judging the occurrence status of each intermediate event and bottom event to obtain an abnormal diagnosis result; based on the abnormal diagnosis result, displaying abnormal equipment and fault areas in different colors, icons and flashing effects in the display interface of the digital twin grid model, and viewing detailed information of the abnormal equipment and the fault area by clicking.

[0007] As a preferred embodiment of the digital twin-based interactive method for visualizing power grid monitoring data described in the present invention, constructing the digital twin power grid model includes constructing a power grid topology model based on the collected power grid structure data of the target power grid; the power grid topology model describes the connection relationship between the various components in the target power grid; Based on the collected equipment parameter data of the target power grid, a mathematical model is established for the power equipment based on a physical modeling method; Building a meteorological environment model and a geographical environment model based on the collected operating environment data of the target power grid; the meteorological environment model describes the mapping relationship between meteorological parameters and operating characteristics of power grid equipment; the geographical environment model describes the risks of power grid operation under different terrain conditions; The power grid topology model, the mathematical model, the meteorological environment model and the geographical environment model are integrated to construct the digital twin power grid model.

[0008] As a preferred solution of the digital twin-based interactive method for visualizing power grid monitoring data described in the present invention, obtaining the power grid diagnosis result of the target power grid includes constructing a spatiotemporal feature matrix based on the operating data according to time series and spatial nodes; Based on the time-varying changes of the target power grid and the spatiotemporal characteristic matrix, a characteristic change rate matrix of adjacent time steps is constructed; Determine the spatial correlation strength between the nodes in the target power grid based on the spatiotemporal feature matrix, and construct a spatial correlation strength matrix based on the spatial correlation An abnormality sensitivity index is determined based on the characteristic change rate matrix and the spatial correlation strength matrix, and an abnormality diagnosis is performed on the target power grid according to the abnormality sensitivity index to obtain the power grid diagnosis result; the power grid diagnosis result indicates whether abnormal fluctuations occur in the target power grid.

[0009] As a preferred solution of the digital twin-based interactive method for visualizing power grid monitoring data described in the present invention, the fault tree model of the target power grid includes analyzing abnormal conditions of the power grid based on the historical fault data to obtain top events, intermediate events, and bottom events of the fault tree; Among them, the top events include system power outage and voltage anomaly; the intermediate events include equipment failure and line overload; the bottom events include component aging and external short circuit; Determining a correlation result between the intermediate event and the bottom event based on a conditional correlation degree between the intermediate event and the bottom event; Calculating a second probability of occurrence of the intermediate event based on the correlation result and the first probability of occurrence of the bottom event; A fault tree is constructed downward from the top event based on the conditional correlation and the second occurrence probability to obtain the fault tree model; each tree node in the fault tree model represents an event, and the node edges represent the causal relationship between events, and the event name, occurrence probability and conditional correlation with other events of each tree node are marked in the fault tree model.

[0010] As a preferred solution of the digital twin-based power grid monitoring data visualization interaction method described in the present invention, wherein: obtaining the fault tree model includes creating an initial matrix based on the first number of the intermediate events and the second number of the bottom events, and updating the matrix elements in the initial matrix based on the correlation relationship between the two events and their corresponding conditional correlation degrees to obtain a target matrix; In the target matrix, the matrix elements of two events with direct causal relationship are the corresponding conditional correlation degrees, and the matrix elements of two events without direct causal relationship are zero; Starting from the top event, obtain the first column index of all non-zero matrix elements in the row where the top event is located in the target matrix, and determine the intermediate event corresponding to the first column index as the first target intermediate event; For each of the first target intermediate events, constructing an upper fault tree structure based on the importance of the first association path between the first target intermediate event and the top event; For each of the first target intermediate events, obtain the second column index of the non-zero matrix element in the row of the first target intermediate event in the target matrix, and determine the event corresponding to the second column index as the first target bottom event or the second target intermediate event directly associated with the first target intermediate event; Constructing a target extended fault tree structure based on the importance of the second association path between the first target intermediate event and the first target bottom event, and the importance of the third association path between the first target intermediate event and the second target intermediate event; The upper fault tree structure and the target extended fault tree structure are merged to obtain an initial fault tree network, and the initial fault tree network is branch pruned based on the importance of the associated paths of all events on each branch path of the initial fault tree network to obtain the fault tree model.

[0011] As a preferred solution of the digital twin-based interactive method for visualizing power grid monitoring data described in the present invention, obtaining the abnormal diagnosis result includes, when the power grid diagnosis result indicates that the target power grid has abnormal fluctuations, matching abnormal top events in the fault tree model based on the fluctuation characteristics of the abnormal fluctuations; For a third target intermediate event associated with the abnormal top event in the fault tree model, marking a first state variable of the third target intermediate event according to a contribution of the third target intermediate event to the occurrence of the abnormal top event; the first state variable includes a possible state and a non-occurrence state; For a first abnormal intermediate event whose first state variable in the third target intermediate event is a possible state, a second abnormal intermediate event in the first abnormal intermediate event is determined based on the probability of occurrence of a second target bottom event associated with the first abnormal intermediate event in the fault tree model, and the conditional relationship between the second target bottom event and the occurrence of the first abnormal intermediate event; the second state variable of the second abnormal intermediate event is a determined occurrence state; For a third target bottom event associated with the second abnormal intermediate event in the fault tree model, an abnormal bottom event is determined according to the occurrence probability of the third target bottom event; the third state variable of the abnormal bottom event is a determined occurrence state; The abnormal top event, the second abnormal middle event and the abnormal bottom event are determined as abnormal diagnosis results.

[0012] As a preferred solution of the digital twin-based power grid monitoring data visualization interaction method described in the present invention, wherein: the display of abnormal equipment and fault areas with different colors, icons and flashing effects includes performing an abnormality analysis based on the abnormal top event, the second abnormal intermediate event and the abnormal bottom event to determine the cause of the fault abnormality, the fault impact range, the fault abnormality type and the fault abnormality location that caused the abnormality of the target power grid; Locating the abnormal device based on the cause of the fault anomaly, generating a first color, a first icon, and a first flashing effect based on the fault anomaly type, and displaying the abnormal device on a display interface of the digital twin power grid model using the first color, the first icon, and the first flashing effect; The fault area is located based on the fault abnormality position, a second color, a second icon and a second flashing effect are generated based on the fault impact range, and the fault area is displayed on the display interface of the digital twin power grid model with the second color, the second icon and the second flashing effect.

[0013] As a preferred solution of the digital twin-based power grid monitoring data visualization interaction system described in the present invention, it includes: a visualization interaction middle platform, a monitoring and acquisition module, an abnormality diagnosis module, an abnormality positioning module and a visualization interaction module; The visualization interaction center is connected to the monitoring and acquisition module, the abnormality diagnosis module, the abnormality location module and the visualization interaction module respectively to manage each module; The monitoring and acquisition module is used to monitor the operating status of the target power grid in real time and collect operating data of the target power grid; The abnormality diagnosis module is used to map the operating data into the digital twin power grid model of the target power grid to perform abnormality diagnosis and obtain a power grid diagnosis result of the target power grid; The anomaly locating module is configured to traverse the target power grid's fault tree model from the top event downward layer by layer using a fault tree logic relationship based on the power grid diagnosis result, determine the occurrence status of each intermediate event and bottom event, gradually trace the root cause of the anomaly, and obtain an anomaly diagnosis result; The visualization interaction module is used to display abnormal equipment and fault areas in different colors, icons and flashing effects in the display interface of the digital twin power grid model based on the abnormal diagnosis results, so that operation and maintenance personnel can view detailed information of the abnormal equipment and the fault area by clicking.

[0014] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of a digital twin-based power grid monitoring data visualization interaction method.

[0015] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the computer program implements the steps of a digital twin-based power grid monitoring data visualization interaction method.

[0016] The beneficial effects of this invention include: tracing the root cause of anomalies through a fault tree model based on clear logical causal relationships, and conducting a layer-by-layer analysis from top events to intermediate events and then to bottom events, which can comprehensively and accurately locate the cause of the anomaly, avoiding misdiagnosis and missed diagnosis caused by data dispersion and manual analysis loopholes, and quickly and accurately locating the root cause of the anomaly. Furthermore, by leveraging the visualization advantages of the digital twin power grid model, after determining the anomaly diagnosis results, they are immediately displayed in an intuitive visual interface. Operation and maintenance personnel can gain in-depth understanding of the anomaly details through various interactive operations, and can also conduct comparative analysis of historical cases, thereby improving the decision-making efficiency of operation and maintenance personnel and enhancing the stability of power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A schematic flow chart of a method for visualizing and interacting with power grid monitoring data based on digital twins is provided as an embodiment of the present invention.

[0019] Figure 2 An embodiment of the present invention provides a schematic diagram of the working modules of a digital twin-based power grid monitoring data visualization and interaction system. DETAILED DESCRIPTION

[0020] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0023] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0024] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0025] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0026] Example 1, with reference to Figure 1, which is the first embodiment of the present invention, provides a digital twin-based power grid monitoring data visualization interaction method, including: S1: Monitor the operating status of the target power grid in real time and collect operating data of the target power grid.

[0027] Furthermore, the operating status of the target power grid is monitored in real time, and the operating data of the target power grid is collected.

[0028] Optionally, the visual interaction system of the embodiment of the present invention is connected to various sensors, such as current sensors, voltage sensors, power sensors, etc.

[0029] Therefore, the visual interactive system uses various sensors to monitor the operating status of any target power grid in the power grid system in real time, collects the operating data of the target power grid, and obtains the current data, voltage data, power data, etc. of the target power grid during operation.

[0030] S2: Mapping the operating data to the digital twin grid model of the target grid to perform abnormality diagnosis and obtain a grid diagnosis result of the target grid.

[0031] Furthermore, optionally, a digital twin power grid model is pre-built in the visual interactive system of an embodiment of the present invention, wherein the digital twin power grid model is a virtual power grid model that is built using digital twin technology and is completely consistent with the power grid structure, equipment parameters and operating environment of the target power grid. Therefore, the digital twin power grid model fully replicates the physical characteristics, electrical parameters and topological connection relationships of various power grid components such as transmission lines, transformers, switches in the target power grid. The specific construction process is as described in steps 201 to 204.

[0032] Step 201: construct a power grid topology model based on the collected power grid structure data of the target power grid.

[0033] Optionally, the interactive visualization system uses a geographic information system (GIS) to obtain grid structural data, including topological connectivity and geographic locations of substations, transmission lines, and distribution lines. For example, the system identifies the voltage level, number of outgoing lines, and the direction of connected transmission lines at each substation, as well as the specific substation or tower locations to which the transmission lines connect. Equipment parameter data, such as rated capacity, rated voltage, resistance, and reactance, can be obtained from manufacturer-provided technical documentation and equipment nameplates. Operating environment data, such as temperature, humidity, wind speed, rainfall, and lightning activity, can be obtained from meteorological monitoring stations.

[0034] Furthermore, the visualization interactive system uses the collected grid structure data and graph theory to construct a grid topology model. It regards substations, towers, etc. as nodes, and transmission lines and distribution lines as edges, and describes the connection relationship between various components in the grid. Therefore, the grid topology model intuitively displays the overall architecture of the grid and provides a basic framework for subsequent power flow calculations, fault analysis, etc.

[0035] Step 202 : Based on the collected device parameter data of the target power grid, mathematical models are established for different types of power equipment using a physical modeling method.

[0036] Furthermore, the interactive visualization system uses physical modeling methods based on device parameter data to build mathematical models for different types of power equipment. For example, for transformers, an equivalent circuit model is built based on their electromagnetic characteristics to simulate voltage conversion, power transmission, and losses under different operating conditions. For transmission lines, a distributed parameter model is built based on their electrical parameters to accurately calculate voltage and current distribution and power losses along the line.

[0037] Step 203: constructing a meteorological environment model and a geographical environment model based on the collected operating environment data of the target power grid.

[0038] Furthermore, the interactive visualization system combines operational environment data to construct meteorological and geographic environment models. The meteorological environment model simulates the impact of meteorological conditions on the power grid by mapping meteorological parameters to the operating characteristics of power grid equipment. For example, a temperature-line resistance model is used to analyze the impact of changes in line resistance on power grid currents as temperature changes. The geographic environment model utilizes topographic data to assess the risks of power grid operation under different terrain conditions, such as assessing the threat posed by debris flows and landslides to transmission lines in mountainous areas.

[0039] Step 204 : Integrate the power grid topology model, mathematical model, meteorological environment model, and geographical environment model to construct a digital twin power grid model.

[0040] Furthermore, the visual interactive system organically integrates the power grid topology model, mathematical model, meteorological environment model and geographical environment model to construct a unified digital twin power grid model.

[0041] The embodiment of the present invention constructs a digital twin power grid model, which enables the subsequent use of the visualization advantages of the digital twin power grid model. After the abnormal diagnosis results are determined, they are immediately displayed in an intuitive visual interface. Operation and maintenance personnel can gain an in-depth understanding of the abnormal details through various interactive operations, and can also conduct comparative analysis of historical cases, thereby improving the decision-making efficiency of operation and maintenance personnel and further improving the stability of power grid operation.

[0042] Furthermore, the visualization interactive system maps the operating data to the digital twin grid model of the target grid for abnormal diagnosis to obtain the grid diagnosis results of the target grid, wherein the grid diagnosis results represent whether abnormal fluctuations occur in the target grid, as specifically described in steps 205 to 208.

[0043] It should be noted that, in step 205 , a spatiotemporal feature matrix is ​​constructed based on the operating data in terms of time series and spatial nodes.

[0044] Optionally, the visual interactive system constructs a spatiotemporal feature matrix based on the target power grid's operating data in time series and spatial nodes. , for the node At the time point The eigenvalues ​​under It can be expressed as: , in, and Represents nodes respectively At the time point Different physical quantities under the condition of , such as voltage, current, power, etc.; represents a fusion function. In one embodiment, the fusion function It is used to fuse different physical quantities into one eigenvalue, where a and b are the input variables of the fusion function.

[0045] Step 206 : constructing a characteristic change rate matrix of adjacent time steps based on the time-varying target power grid and the spatiotemporal characteristic matrix.

[0046] Furthermore, in order to track the changes of characteristics over time, the visualization interactive system constructs the characteristic change rate matrix of adjacent time steps based on the time-varying changes of the target power grid and the spatiotemporal characteristic matrix. , according to the characteristic change rate matrix Matrix elements to determine whether there are abnormal rapid changes.

[0047] Among them, the characteristic change rate matrix The matrix elements in The calculation formula is: , in, Representation node From the time point At the time The characteristic rate of change, Representation node At the time point The eigenvalues ​​below.

[0048] Step 207 : determining the spatial correlation strength between the nodes in the target power grid based on the spatiotemporal feature matrix, and constructing a spatial correlation strength matrix based on the spatial correlation.

[0049] Furthermore, the visualization interactive system determines the spatial correlation strength between the nodes in the target power grid according to the spatiotemporal feature matrix. and nodes ,node and nodes The spatial correlation strength between The calculation formula is as follows: , in, Representation node In time The mean of the features within .

[0050] Furthermore, the visual interaction system is based on the spatial correlation strength Constructing spatial correlation intensity matrix as matrix elements .

[0051] Step 208 : determining an abnormality sensitivity index based on the characteristic change rate matrix and the spatial correlation strength matrix, and performing abnormality diagnosis on the target power grid according to the abnormality sensitivity index to obtain a power grid diagnosis result.

[0052] Furthermore, the visual interaction system evaluates the nodes based on the feature change rate matrix and the spatial correlation strength matrix. At the time point Abnormal sensitivity index under , where the node At the time point Abnormal sensitivity index under The calculation formula is: , in, Representation node The average of the spatial correlation strengths with all other nodes.

[0053] Furthermore, if the abnormal sensitivity index is determined , the visual interactive system determines the nodes of the target power grid At the time point The power grid diagnosis result under the condition is that abnormal fluctuations occurred, among which, is the preset sensitivity index.

[0054] Furthermore, if the abnormal sensitivity index is determined , the visual interactive system determines the nodes of the target power grid At the time point The power grid diagnosis result below is that no abnormal fluctuation occurs.

[0055] S3: Based on the power grid diagnosis result, the fault tree model of the target power grid and the fault tree logical relationship are used to determine the occurrence status of each intermediate event and bottom event to obtain an abnormality diagnosis result.

[0056] Furthermore, optionally, a fault tree model is pre-built in the visualization interactive system of an embodiment of the present invention, wherein the fault tree model is constructed based on the analysis of abnormal conditions of the power grid based on historical fault data, as specifically described in steps 301 to 304.

[0057] Step 301 : Analyze abnormal conditions of the power grid based on historical fault data to obtain top events, middle events, and bottom events of a fault tree.

[0058] Optionally, the interactive visualization system collects historical fault data for the target power grid, covering multiple years. This data can be from grid operation records in different regions, seasons, and load conditions. This data includes the time of fault occurrence, fault duration, fault impact range, real-time voltage and current monitoring values ​​at each node, and equipment operating status parameters.

[0059] Furthermore, the visual interactive system analyzes abnormal conditions of the power grid based on historical fault data.

[0060] For example, if the proportion of power-off load to total load exceeds a set threshold and the outage duration exceeds a time threshold, the system is considered to have experienced a power outage. If the voltage at a node is detected to be outside the preset normal voltage fluctuation range, the system is considered to have experienced a voltage anomaly, and the system power outage and voltage anomaly are identified as the top events of the fault tree.

[0061] When the probability of a device failure exceeds a set threshold, the device is considered to have failed. When the line load rate exceeds a set overload threshold, the line is considered to be overloaded, and the device failure and line overload are identified as intermediate events in the fault tree.

[0062] When the probability of component failure exceeds a set threshold over the course of its lifetime, the component is considered aged. When the current value increases dramatically within a short period of time and exceeds the short-circuit current threshold, and the fault location indicates an external factor, it is considered an external short circuit. Component aging and external short circuits are identified as bottom events in the fault tree.

[0063] Step 302: Determine the correlation result between the intermediate event and the bottom event based on the conditional correlation between the intermediate event and the bottom event.

[0064] Furthermore, the visual interaction system analyzes the association between the intermediate event and the bottom event, that is, calculates the conditional association between the intermediate event and the bottom event. Hedi Incident Conditional correlation , , in, Indicates bottom event Occurrence and intermediate events The number of times it happened, Indicates bottom event The number of occurrences, Is the bottom event and intermediate events The distance factor between the bottom events and intermediate events The interval of occurrence time is calculated, , in, Represents intermediate events The time of occurrence, Indicates bottom event The time of occurrence, Indicates the time when the intermediate event occurs, Indicates the time when the bottom event occurs.

[0065] Furthermore, if the conditional correlation is determined Greater than or equal to the set threshold (like ), then determine the intermediate event Hedi Incident The result of the association is that there is an association. If the conditional association is determined Less than the set threshold When the intermediate event is determined Hedi Incident The result of the association is that there is no association.

[0066] Step 303 : Calculate the second occurrence probability of the intermediate event based on the correlation result and the first occurrence probability of the bottom event.

[0067] Furthermore, if the correlation result between the intermediate event and the bottom event is determined to be a correlation, the visual interaction system counts the number of occurrences of the bottom event and calculates the first occurrence probability of the bottom event based on the number of occurrences of the bottom event. , its first probability of occurrence The calculation formula is: , in, Indicates bottom event The number of occurrences, Indicates the total number of records of historical fault data. It is a correction factor set according to the importance of the equipment and the operating environment factors, and its value range is between.

[0068] Furthermore, the visual interaction system calculates the second probability of occurrence of the intermediate event based on the first probability of occurrence of the bottom event. There are associated intermediate events , its second probability of occurrence The calculation formula is: , in, is with the intermediate event The number of associated bottom events.

[0069] Step 304 : construct a fault tree from the top event downwards based on the conditional association degree and the second occurrence probability to obtain a fault tree model.

[0070] Furthermore, the visual interactive system constructs a fault tree from the top event downwards according to the conditional correlation between the intermediate event and the bottom event, and the second occurrence probability of the intermediate event, to obtain a fault tree model, wherein each tree node in the fault tree model represents an event, and the node edge represents the causal relationship between the events, and the event name, occurrence probability (for the bottom event, the first occurrence probability) of each tree node are marked in the fault tree model. , for the intermediate event, the second probability of occurrence is And the conditional correlation with other events, as described in steps 3041 to 3046.

[0071] In one embodiment, the visual interactive system verifies the constructed fault tree model using historical fault data that is not involved in the modeling. The deviation between the probability of occurrence of the top event predicted by the fault tree model and the actual probability of occurrence is calculated. , where the deviation The specific calculation formula is as follows: , in, Indicates the The probability of occurrence of the top event predicted, Indicates the The actual probability of occurrence, Indicates the number of historical fault data that are not involved in modeling. If it is greater than the set acceptable range (such as 0.1), the correction coefficient of the bottom event will be readjusted. and correlation threshold , repeat steps 301 to 304 until the deviation is within the set acceptable range.

[0072] It should be noted that the descriptions of steps 3041 to 3046 are as follows: Step 3041: Create an initial matrix based on the first number of intermediate events and the second number of bottom events, and update the matrix elements in the initial matrix based on the correlation relationship between the two events and their corresponding conditional correlation degrees to obtain a target matrix.

[0073] Optionally, the visual interaction system obtains a first number of intermediate events and a second number of bottom events, and creates an initial matrix according to the first number of intermediate events and the second number of bottom events. , where the initial matrix The size of , represents the first number of intermediate events, Represents the second number of bottom events, the initial matrix The extra row and column are used to represent the top events.

[0074] Optionally, in the embodiment of the present invention, the initial matrix The initial matrix elements of are all 0, and the matrix elements need to be updated according to the correlation between the events. Therefore, the visual interaction system determines the correlation between the two events, where the correlation includes the existence of a direct causal relationship and the absence of a direct causal relationship.

[0075] Furthermore, for the initial matrix The matrix elements in If the event and events ( It can be a top event, an intermediate event or a bottom event) has a direct causal relationship, and the visual interactive system converts the matrix elements The value of the event is updated and events Corresponding conditional association If the event and events If there is no direct causal relationship, the visual interaction system will not update the matrix elements. The value of the matrix element , get the target matrix In one embodiment, the intermediate event Top Events There is a direct causal relationship, intermediate events Top Events The conditional correlation is ,but .

[0076] Step 3042 , starting from the top event, obtain the first column indexes of all non-zero matrix elements in the row where the top event is located in the target matrix, and determine the intermediate event corresponding to the first column index as the first target intermediate event.

[0077] Furthermore, starting from the top event, the visual interaction system obtains the target matrix The first column index of all non-zero elements in the row where the top event is located (the top event is in row 0), and the intermediate event corresponding to the first column index is determined as the first target intermediate event directly associated with the top event.

[0078] Step 3043: For each first target intermediate event, construct an upper-level fault tree structure based on the importance of the first association path between the first target intermediate event and the top event.

[0079] Furthermore, for each first target intermediate event , the visual interaction system calculates the first target intermediate event Importance of the first association path with the top event , the specific calculation formula is: , in, Indicates the first target intermediate event probability of occurrence.

[0080] Furthermore, the visual interaction system is based on the importance of the first associated path Sort the first target intermediate events from largest to smallest, and place the (such as ) The first target intermediate event is used as the initial branch, connecting the top event and the previous event The first target intermediate event is used to construct the upper fault tree structure.

[0081] Step 3044: For each first target intermediate event, obtain the second column index of the non-zero matrix element in the row where the first target intermediate event is located in the target matrix, and determine the event corresponding to the second column index as the bottom event or the second target intermediate event directly associated with the first target intermediate event.

[0082] Furthermore, for each first target intermediate event , the visual interaction system obtains the target matrix First target intermediate event The second column index of the non-zero matrix element in the row, and the event corresponding to the second column index is determined as the intermediate event of the first target Directly related first target bottom event or second target intermediate event .

[0083] Step 3045: construct a target extended fault tree structure based on the importance of the second correlation path between the first target intermediate event and the first target bottom event, and the importance of the third correlation path between the first target intermediate event and the second target intermediate event.

[0084] Furthermore, for the intermediate event with the first target The first target bottom event associated , the visual interaction system calculates the first target intermediate event The first target bottom event The importance of the second association path , where the specific formula is: , in, The first target event The probability of occurrence, is the corresponding intermediate event in the target matrix Hedi Incident The matrix element values ​​of .

[0085] Furthermore, for the intermediate event with the first target The associated second target intermediate event , the visual interaction system calculates the first target intermediate event Intermediate event with the second target The importance of the third association path , where the specific calculation formula is: , Is the second target intermediate event The probability of occurrence, is the first target intermediate event in the target matrix The associated second target intermediate event The matrix element values ​​of .

[0086] Furthermore, the visual interaction system is based on the importance of the second association path and the importance of the third association path In descending order, select the first r first target bottom events and the first s second target middle events, and put the first target middle events They are connected with the first r first target bottom events and the first s second target intermediate events respectively to construct the initial extended fault tree structure.

[0087] Furthermore, the visual interactive system continuously repeats step 3045, continuing to expand the initial extended fault tree structure from the newly connected intermediate event downward until all intermediate events have been expanded to the bottom events associated therewith, thereby constructing the target extended fault tree structure.

[0088] In step 3046, the upper fault tree structure and the target extended fault tree structure are merged to obtain an initial fault tree network, and branches of the initial fault tree network are pruned based on the importance of the associated paths of all events on each branch path of the initial fault tree network to obtain a fault tree model.

[0089] Furthermore, the visual interactive system merges the upper fault tree structure and the target extended fault tree structure to obtain the initial fault tree network. For each branch path in the initial fault tree network, the visual interactive system multiplies the associated path importance of all events on each branch path to obtain the branch path importance of each branch path. .

[0090] Furthermore, if the importance of the branch path is determined ,in, is the set pruning factor, according to the actual setting, such as , it is determined that the branch path has a small impact on the top event. Therefore, the visual interactive system prunes the branch path from the initial fault tree network to obtain the final fault tree network, and determines the final fault tree network as the fault tree model.

[0091] Furthermore, after determining that the power grid diagnosis result is that the target power grid has an abnormal fluctuation, the visualization interactive system uses the fault tree model of the target power grid to traverse downward from the top event layer by layer using the fault tree logic relationship, determines the occurrence status of each intermediate event and bottom event, and gradually traces the root cause of the abnormality to obtain the abnormal diagnosis result, as specifically described in steps 305 to 309.

[0092] It should be noted that the descriptions of steps 305 to 309 are as follows: Step 305: If the power grid diagnosis result indicates that the target power grid has abnormal fluctuations, an abnormal top event is matched in the fault tree model based on the fluctuation characteristics of the abnormal fluctuations.

[0093] Optionally, if the power grid diagnosis result is determined to be an abnormal fluctuation in the target power grid, the visualization interactive system obtains the fluctuation characteristics of the abnormal fluctuation, and matches the fluctuation characteristics with the event characteristics of each top event in the fault tree model to determine the degree of correlation between each top event in the fault tree model and the abnormal fluctuation, wherein the degree of correlation can be calculated according to the similarity formula, which will not be repeated here.

[0094] Furthermore, the visual interactive system traverses each correlation degree and determines the top event corresponding to the maximum correlation degree as the abnormal top event causing abnormal fluctuation in the fault tree model.

[0095] Step 306 : For the third target intermediate event associated with the abnormal top event in the fault tree model, mark the first state variable of the third target intermediate event according to the contribution of the third target intermediate event to the occurrence of the abnormal top event.

[0096] Furthermore, when an abnormal top event occurs, starting from the abnormal top event, the intermediate events directly associated with the abnormal top event in the fault tree model are analyzed. Therefore, for the third target intermediate event associated with the abnormal top event in the fault tree model, , the visual interaction system calculates each third target intermediate event Contribution to the occurrence of abnormal top events , where the specific calculation formula is as follows: , in, Indicates the third target intermediate event The number of times an abnormal top event occurs and also occurs, Indicates the number of times the abnormal top event occurs. Indicates the third target intermediate event The distance factor between the occurrence time of the abnormal top event and the occurrence time of the abnormal top event, Indicates the time when the abnormal top event occurs, Indicates the third target intermediate event The time of occurrence, Indicates the time when the intermediate event occurs.

[0097] Furthermore, for the contribution For a third target intermediate event that is greater than a preset contribution threshold, the visual interaction system marks the first state variable of the third target intermediate event as a possible state. For a third target intermediate event whose contribution is less than or equal to a preset contribution threshold, the visual interaction system marks the first state variable of the third target intermediate event as a non-occurrence state.

[0098] Step 307: For the first abnormal intermediate event in which the first state variable in the third target intermediate event is a possible state, determine the second abnormal intermediate event in the first abnormal intermediate event based on the probability of occurrence of the second target bottom event associated with the first abnormal intermediate event in the fault tree model, and the conditional relationship between the second target bottom event and the occurrence of the first abnormal intermediate event.

[0099] Furthermore, for each first abnormal intermediate event in which the first state variable in the third target intermediate event is a possible state, its association with the bottom event is analyzed, that is, according to the fault tree logical relationship, if the first abnormal intermediate event is caused by multiple bottom events through an "and" relationship, then the condition for the occurrence of the first abnormal intermediate event is that all associated bottom events occur; if it is an "or" relationship, then as long as one associated bottom event occurs, it will be sufficient.

[0100] Therefore, the visual interactive system determines the probability of occurrence of each first abnormal intermediate event based on the probability of occurrence of the second target bottom event associated with the first abnormal intermediate event in the fault tree model, and the conditional relationship between the second target bottom event and the occurrence of the first abnormal intermediate event.

[0101] For the first abnormal intermediate event with a conditional relationship of "and" , the probability of its occurrence The calculation formula is: , in, Indicates the intermediate event with the first exception The associated second target bottom event, is the number of second target bottom events, Represents an index.

[0102] For the first abnormal intermediate event with a conditional relationship of "or" , the probability of its occurrence The calculation formula is: , Furthermore, for a first abnormal intermediate event whose probability of occurrence is less than or equal to a preset probability threshold, the visual interaction system marks the second state variable of the first abnormal intermediate event as a non-occurrence state. For a first abnormal intermediate event whose probability of occurrence is greater than the preset probability threshold, the visual interaction system marks the second state variable of the first abnormal intermediate event as a certain occurrence state. A first abnormal intermediate event whose second state variable is a certain occurrence state is a second abnormal intermediate event.

[0103] Step 308 : For the third target bottom event associated with the second abnormal intermediate event in the fault tree model, determine the abnormal bottom event according to the occurrence probability of the third target bottom event.

[0104] Furthermore, for each third target bottom event associated with the confirmed second abnormal intermediate event in the fault tree model, , calculate each third target bottom event The probability of occurrence , the specific calculation formula is: , in, Indicates the third target bottom event The number of occurrences, Indicates the total number of monitoring times. This represents a correction factor determined based on device aging and environmental factors, with a value range of 0.7-1.3. For a third target bottom event with a probability of occurrence greater than or equal to a preset threshold, the visual interaction system identifies the third target bottom event as an abnormal bottom event. This means that the third state variable of the abnormal bottom event is in the confirmed occurrence state.

[0105] Step 309 : Determine the abnormal top event, the second abnormal middle event, and the abnormal bottom event as abnormal diagnosis results.

[0106] Furthermore, the visual interaction system determines the abnormal top event, the second abnormal middle event, and the abnormal bottom event as abnormal diagnosis results.

[0107] S4: Based on the abnormal diagnosis results, abnormal devices and fault areas are displayed in the display interface of the digital twin power grid model with different colors, icons and flashing effects, and detailed information of the abnormal devices and the fault areas can be viewed by clicking.

[0108] Furthermore, the visualization interactive system performs an abnormal analysis on the abnormal diagnosis results, determines and displays abnormal devices and fault areas, and displays the abnormal devices and fault areas in different colors, icons, and flashing effects in the display interface of the digital twin power grid model, as described in steps 401 to 403. The description of steps 401 to 403 is as follows: Step 401 : performing an abnormality analysis based on the abnormal top event, the second abnormal intermediate event, and the abnormal bottom event to determine the fault abnormality cause, fault impact range, fault abnormality type, and fault abnormality location that cause the target power grid abnormality.

[0109] Optionally, in the embodiment of the present invention, the abnormal top event obtained by the fault tree model analysis in step 30 is T out ={t1,t2,...,t x}, the second abnormal intermediate event is M out ={m1,m2,...,m y}, the abnormal bottom event is E={e1,e2,...,ez}, t1, t2, ..., t x is the identifier of the top event, m1,m2,...,m y is the identifier of the intermediate event, e1, e2, ..., e z is the identifier of the bottom event, and x, y, and z represent the event dimensions of the abnormal top event, the second abnormal middle event, and the abnormal bottom event, respectively.

[0110] Furthermore, the visual interactive system constructs a causal relationship matrix based on the abnormal top event, the second abnormal intermediate event and the abnormal bottom event. , where the causal relationship matrix The dimension is , the causal relationship matrix The matrix elements in Representing an event (event It can be an abnormal top event or second abnormal intermediate event ) and abnormal bottom events The degree of causal relationship is in the range of .

[0111] For each abnormal top event , Visual interactive system calculates abnormal top events and abnormal bottom events The causal contribution of , where causal contribution The specific calculation formula is as follows: , in, Indicates the second abnormal intermediate event and abnormal bottom events The degree of correlation, Indicates abnormal top events and the second abnormal intermediate event The degree of correlation, Is the loop variable.

[0112] Furthermore, the visual interactive system will assign the largest causal contribution The cause corresponding to the abnormal bottom event is determined as the fault abnormal cause.

[0113] Furthermore, the visual interactive system constructs an impact propagation matrix based on abnormal bottom events and different areas or equipment groups in the target power grid. , where the influence propagation matrix The dimension is , is the number of different areas or device groups in the target grid, affecting the propagation matrix The matrix elements of Indicates abnormal bottom events For a zone or device group The degree of influence is in the range of .

[0114] For a certain abnormal cause of a fault (i.e. an abnormal bottom event The quantitative value of the scope of its fault impact For a zone or device group for: Quantitative value at that time , region or device group That is, within the scope of the fault, where Indicates the preset threshold.

[0115] Furthermore, in the embodiment of the present invention, different fault anomaly type attributes of bottom events are defined. "Short circuit fault", "Overload fault", etc. When the abnormal bottom event corresponding to the abnormal cause of the fault is determined After that, the fault exception type is .

[0116] Furthermore, the visual interactive system constructs a location correlation matrix based on abnormal bottom events and different geographical locations in the target power grid. , where the position correlation matrix The dimension is , is the number of different geographical locations in the target grid, and the location correlation matrix The matrix elements of Indicates abnormal bottom events and geographical location The degree of association ranges from For a certain abnormal cause of a fault (i.e. an abnormal event Cause), abnormal fault location Corresponding geographical location Determined by the following formula: , Step 402: locate the abnormal device based on the cause of the fault, generate a first color, a first icon, and a first flashing effect based on the fault type, and display the abnormal device on the display interface of the digital twin power grid model with the first color, the first icon, and the first flashing effect.

[0117] Optionally, the device set in the embodiment of the present invention is , the visual interactive system builds a device-bottom event association matrix based on abnormal bottom events and device sets , the dimension is , the incidence matrix The matrix elements in Representation device and abnormal bottom events The degree of association ranges from For a certain abnormal cause of a fault (i.e. an abnormal event Cause), abnormal device Determined by the following formula: , in, is the set threshold, such as .

[0118] Optionally, in the embodiment of the present invention, a mapping relationship between fault anomaly type and color, icon, and flashing effect is pre-built, and the fault anomaly type set is Therefore, according to the color mapping function ,in, Is a color representation (e.g. RGB value). According to the icon mapping function ,in, It is an icon identifier. According to the flashing effect mapping function ,in, It is a description of the flickering effect (such as flickering frequency, etc.).

[0119] When the fault abnormality type is determined to be When the first color , the first icon , the first flash effect .

[0120] Furthermore, the visual interaction system uses the first color , first icon and the first flash effect Display abnormal equipment on the display interface of the digital twin power grid model .

[0121] Step 403: locate the fault area based on the fault abnormality position, generate a second color, a second icon and a second flashing effect based on the fault impact range, and display the fault area on the display interface of the digital twin power grid model with the second color, the second icon and the second flashing effect.

[0122] Optionally, the region set in the embodiment of the present invention is , the visual interactive system constructs a region-location correlation matrix based on abnormal bottom events and region sets , the dimension is , the incidence matrix The matrix elements in Indicates area and abnormal bottom events The degree of association ranges from For abnormal fault location (Geographic location ), fault area Determined by the following formula: , in, is the set threshold, such as .

[0123] Optionally, in the embodiment of the present invention, based on the fault impact range The color generating function is .For example, , where the fault impact range The larger the value, the more red components and the less blue components. The icon generation function is , different icons are generated according to the scope of influence, for example, when hour, For large icons, when hour, It is a small icon. Based on the scope of the fault Flashing effect generation function ,For example, frequency , the scope of the fault The larger the value, the higher the flashing frequency.

[0124] For the fault area Each area in the Generate a second color , the second icon , second flashing effect .

[0125] Furthermore, the visual interactive system uses a second color , the second icon , second flashing effect Display the fault area on the display interface of the digital twin power grid model Each area in .

[0126] Furthermore, when detailed information about an abnormality is needed, the operation and maintenance personnel can click the buttons corresponding to the abnormal equipment and fault area on the display interface of the digital twin power grid model through mouse clicks, zoom clicks, and rotation clicks to obtain detailed information about the abnormal equipment and fault area.

[0127] Embodiment 2, the second embodiment of the present invention, is different from the previous embodiment in that: If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0128] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0129] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0130] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0131] Example 3, reference Figure 2 , is an embodiment of the present invention, which provides a digital twin-based power grid monitoring data visualization interaction system, including a visualization interaction middle platform 1, a monitoring and acquisition module 2, an abnormality diagnosis module 3, an abnormality positioning module 4 and a visualization interaction module 5.

[0132] In the embodiment, the visualization interaction center 1 is connected to the monitoring and acquisition module, the abnormality diagnosis module, the abnormality location module and the visualization interaction module respectively to manage each module.

[0133] Optionally, the monitoring and acquisition module 2 can be understood as various types of sensors, such as current sensors, voltage sensors, power sensors, etc. Therefore, the monitoring and acquisition module monitors the operating status of any target power grid in the power grid system in real time and collects the operating data of the target power grid, thereby obtaining the current data, voltage data, power data, etc. of the target power grid during operation.

[0134] Optionally, the anomaly diagnosis module 3 of this embodiment of the present invention pre-builds a digital twin power grid model. This digital twin power grid model utilizes digital twin technology to create a virtual power grid model that is completely identical to the target power grid's grid structure, equipment parameters, and operating environment. Therefore, the anomaly diagnosis module maps operational data to the digital twin power grid model of the target power grid to perform anomaly diagnosis, obtaining a power grid diagnosis result for the target power grid. The power grid diagnosis result indicates whether abnormal fluctuations have occurred in the target power grid.

[0135] Optionally, an anomaly location module 4 of the present embodiment pre-builds a fault tree model, wherein the fault tree model is constructed based on historical fault data and an analysis of power grid anomalies. Therefore, after determining that the power grid diagnosis result indicates an abnormal fluctuation in the target power grid, the anomaly location module uses the target power grid's fault tree model to traverse downwards layer by layer, starting from the top event and using the fault tree's logical relationships, to determine the occurrence status of each intermediate event and bottom event, gradually tracing the root cause of the anomaly to obtain an anomaly diagnosis result.

[0136] Optionally, the visualization interaction module 5 performs an anomaly analysis on the anomaly diagnosis results, identifies and displays abnormal devices and fault areas, and displays these abnormal devices and fault areas in the display interface of the digital twin power grid model using different colors, icons, and flashing effects. Thus, operation and maintenance personnel can view detailed information about abnormal devices and fault areas by clicking, zooming, and rotating the mouse.

[0137] The present invention uses a fault tree model to trace the root cause of anomalies based on clear logical causal relationships. This layer-by-layer analysis, from top events to intermediate events and then to bottom events, comprehensively and accurately locates the cause of the anomaly, avoiding misdiagnosis and missed diagnosis due to data dispersion and manual analysis vulnerabilities, and quickly and accurately pinpointing the root cause of the anomaly. Leveraging the visualization advantages of the digital twin power grid model, after determining the anomaly diagnosis results, they are immediately displayed in an intuitive visual interface. Operations and maintenance personnel can use a variety of interactive operations to gain in-depth understanding of the anomaly details and conduct comparative analysis of historical cases, improving their decision-making efficiency and enhancing the stability of power grid operations.

[0138] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A digital twin-based interactive method for visualizing power grid monitoring data, characterized by: include, Performing real-time monitoring of the operating status of the target power grid and collecting operating data of the target power grid; Mapping the operating data to a digital twin power grid model of the target power grid for abnormality diagnosis to obtain a power grid diagnosis result of the target power grid; Based on the power grid diagnosis result, the fault tree model of the target power grid and the fault tree logical relationship are used to determine the occurrence status of each intermediate event and the bottom event to obtain an abnormality diagnosis result; Based on the abnormal diagnosis results, abnormal devices and fault areas are displayed in the display interface of the digital twin power grid model with different colors, icons and flashing effects, and detailed information of the abnormal devices and the fault areas can be viewed by clicking.

2. The method for visualizing and interacting with power grid monitoring data based on digital twins according to claim 1, characterized in that: Constructing the digital twin power grid model includes constructing a power grid topology model based on the collected power grid structure data of the target power grid; the power grid topology model describes the connection relationship between the various components in the target power grid; Based on the collected device parameter data of the target power grid, a mathematical model is established based on a physical modeling method; Building a meteorological environment model and a geographical environment model based on the collected operating environment data of the target power grid; the meteorological environment model describes the mapping relationship between meteorological parameters and operating characteristics of power grid equipment; the geographical environment model describes the risks of power grid operation under different terrain conditions; The power grid topology model, the mathematical model, the meteorological environment model and the geographical environment model are integrated to construct the digital twin power grid model.

3. The method for visualizing and interacting with power grid monitoring data based on digital twins according to claim 2, wherein: Obtaining the power grid diagnosis result of the target power grid includes constructing a spatiotemporal feature matrix according to time series and spatial nodes based on the operation data; Based on the time-varying changes of the target power grid and the spatiotemporal characteristic matrix, a characteristic change rate matrix of adjacent time steps is constructed; Determine the spatial correlation strength between the nodes in the target power grid based on the spatiotemporal feature matrix, and construct a spatial correlation strength matrix based on the spatial correlation An abnormality sensitivity index is determined based on the characteristic change rate matrix and the spatial correlation strength matrix, and an abnormality diagnosis is performed on the target power grid according to the abnormality sensitivity index to obtain the power grid diagnosis result; the power grid diagnosis result indicates whether abnormal fluctuations occur in the target power grid.

4. The method for visualizing and interacting with power grid monitoring data based on digital twins according to claim 3, wherein: The fault tree model of the target power grid includes analyzing the abnormal conditions of the power grid based on the historical fault data to obtain the top event, intermediate events and bottom events of the fault tree; Among them, the top events include system power outage and voltage anomaly; the intermediate events include equipment failure and line overload; the bottom events include component aging and external short circuit; Determining a correlation result between the intermediate event and the bottom event based on a conditional correlation degree between the intermediate event and the bottom event; Calculating a second probability of occurrence of the intermediate event based on the correlation result and the first probability of occurrence of the bottom event; A fault tree is constructed downward from the top event based on the conditional correlation and the second occurrence probability to obtain the fault tree model; each tree node in the fault tree model represents an event, and the node edges represent the causal relationship between events, and the event name, occurrence probability and conditional correlation with other events of each tree node are marked in the fault tree model.

5. The method for visualizing and interacting with power grid monitoring data based on digital twins according to claim 4, characterized in that: Obtaining the fault tree model includes creating an initial matrix based on the first number of the intermediate events and the second number of the bottom events, and updating matrix elements in the initial matrix based on the correlation relationship between the two events and their corresponding conditional correlation degrees to obtain a target matrix; In the target matrix, the matrix elements of two events with direct causal relationship are the corresponding conditional correlation degrees, and the matrix elements of two events without direct causal relationship are zero; Starting from the top event, obtain the first column index of all non-zero matrix elements in the row where the top event is located in the target matrix, and determine the intermediate event corresponding to the first column index as the first target intermediate event; For each of the first target intermediate events, constructing an upper fault tree structure based on the importance of the first association path between the first target intermediate event and the top event; For each of the first target intermediate events, obtain the second column index of the non-zero matrix element in the row of the first target intermediate event in the target matrix, and determine the event corresponding to the second column index as the first target bottom event or the second target intermediate event directly associated with the first target intermediate event; Constructing a target extended fault tree structure based on the importance of the second association path between the first target intermediate event and the first target bottom event, and the importance of the third association path between the first target intermediate event and the second target intermediate event; The upper fault tree structure and the target extended fault tree structure are merged to obtain an initial fault tree network, and the initial fault tree network is branch pruned based on the importance of the associated paths of all events on each branch path of the initial fault tree network to obtain the fault tree model.

6. The method for visualizing and interacting with power grid monitoring data based on digital twins according to claim 5, characterized in that: Obtaining the abnormal diagnosis result includes, when the power grid diagnosis result indicates that the target power grid has abnormal fluctuations, matching an abnormal top event in the fault tree model based on the fluctuation characteristics of the abnormal fluctuations; For a third target intermediate event associated with the abnormal top event in the fault tree model, marking a first state variable of the third target intermediate event according to a contribution of the third target intermediate event to the occurrence of the abnormal top event; the first state variable includes a possible state and a non-occurrence state; For a first abnormal intermediate event in which the first state variable in the third target intermediate event is a possible state, determining a second abnormal intermediate event in the first abnormal intermediate event based on the occurrence probability of a second target bottom event associated with the first abnormal intermediate event in the fault tree model and the conditional relationship between the second target bottom event and the occurrence of the first abnormal intermediate event; The second state variable of the second abnormal intermediate event is a confirmed occurrence state; For a third target bottom event associated with the second abnormal intermediate event in the fault tree model, an abnormal bottom event is determined according to the occurrence probability of the third target bottom event; the third state variable of the abnormal bottom event is a determined occurrence state; The abnormal top event, the second abnormal middle event and the abnormal bottom event are determined as abnormal diagnosis results.

7. The method for visualizing and interacting with power grid monitoring data based on digital twins according to claim 6, characterized in that: The displaying of abnormal devices and fault areas with different colors, icons and flashing effects includes performing an abnormality analysis based on the abnormal top event, the second abnormal intermediate event and the abnormal bottom event to determine the cause of the abnormality, the scope of the fault, the type of the fault and the location of the fault that caused the abnormality of the target power grid; Locating the abnormal device based on the fault abnormality cause, generating a first color, a first icon, and a first flashing effect based on the fault abnormality type, and displaying the abnormal device on the display interface of the digital twin power grid model with the first color, the first icon, and the first flashing effect; The fault area is located based on the fault abnormality position, a second color, a second icon and a second flashing effect are generated based on the fault impact range, and the fault area is displayed on the display interface of the digital twin power grid model with the second color, the second icon and the second flashing effect.

8. A system using the digital twin-based power grid monitoring data visualization interaction method according to any one of claims 1 to 7, characterized in that: It includes a visual interaction platform, a monitoring and acquisition module, an anomaly diagnosis module, an anomaly location module, and a visual interaction module; The visual interaction center is connected to the monitoring and acquisition module, the abnormality diagnosis module, the abnormality location module and the visual interaction module respectively to manage each module; The monitoring and acquisition module is used to monitor the operating status of the target power grid in real time and collect operating data of the target power grid; The abnormality diagnosis module is used to map the operating data into the digital twin power grid model of the target power grid to perform abnormality diagnosis and obtain a power grid diagnosis result of the target power grid; The anomaly locating module is configured to traverse the target power grid's fault tree model from the top event downward layer by layer using a fault tree logic relationship based on the power grid diagnosis result, determine the occurrence status of each intermediate event and bottom event, gradually trace the root cause of the anomaly, and obtain an anomaly diagnosis result; The visualization interaction module is used to display abnormal equipment and fault areas in different colors, icons and flashing effects in the display interface of the digital twin power grid model based on the abnormal diagnosis results, so that operation and maintenance personnel can view detailed information of the abnormal equipment and the fault area by clicking.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the processor implements the steps of a digital twin-based power grid monitoring data visualization interaction method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a digital twin-based power grid monitoring data visualization interaction method according to any one of claims 1 to 7 are implemented.

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