A remote power supply maintenance virtual meeting collaboration method, system, and electronic equipment

Abstract

This invention discloses a remote power supply operation and maintenance virtual meeting collaboration method, system, and electronic device, specifically relating to the field of power operation and maintenance scheduling. The method includes the following steps: Ⅰ. Accessing various data from the power supply network, constructing a three-dimensional topology energy flow map in real time, and dynamically generating a security level matrix based on real-time equipment operating conditions; Ⅱ. Establishing a temporary operation permission tunnel and starting a virtual meeting space, while automatically matching the most suitable interaction method based on participant behavior and intent. This invention improves operational compliance and security, effectively prevents key leakage and instruction tampering, enhances the encryption strength of remote operations, prevents permission abuse, improves system resilience, and achieves secure closed-loop management from access to execution. It effectively improves the comprehensiveness and timeliness of power supply network status monitoring, enables more accurate identification of abnormal events, improves problem location efficiency, and ensures that users can prioritize obtaining the most reliable and representative abnormal propagation chain.

Description

Technical Field

[0001] This invention relates to the field of power operation and maintenance dispatching, and specifically to a remote power supply operation and maintenance virtual conference collaboration method, system, and electronic equipment. Background Technology

[0002] With the continuous expansion of power system scale and the constant improvement of its intelligence level, power supply operation and maintenance management is gradually moving towards a new stage of high complexity, multi-source integration, and dynamic collaboration. Traditional operation and maintenance models generally rely on manual on-site inspections, telephone command dispatch, and static rule base support. When facing sudden failures, complex operating states, and multi-role collaboration needs, response efficiency and processing accuracy are both subject to significant bottlenecks. Especially in power systems with wide geographical distribution and complex environments, remote collaboration across regions, levels, and positions faces many challenges such as information asymmetry, command execution deviations, and difficulties in controlling operational permissions. In recent years, the gradual maturation of key technologies such as 5G communication, artificial intelligence, augmented reality, and quantum encryption has provided a technological foundation for building a new remote collaborative operation and maintenance mechanism. At the same time, the real-time operating data, load status, and geographical location information generated by a large number of devices in the power supply network urgently need to be efficiently integrated and collaboratively utilized through digitalization, visualization, and intelligent means to support the closed-loop operation and maintenance process from "perception-decision-execution-verification". Current industry demands have moved beyond simple information display or remote communication capabilities. They now require a platform that enables seamless collaboration, rapid response, and intelligent closed-loop systems for multiple user roles, including experts, dispatchers, and field operators, all operating on the same platform. Therefore, developing a remote power supply operation and maintenance virtual meeting collaboration method, system, and electronic equipment has become a key breakthrough in promoting the modernization of power supply operation and maintenance systems.

[0003] Some remote power supply operation and maintenance virtual meeting collaboration methods, systems, and electronic devices have low operational compliance and security, posing risks of key leakage and instruction tampering, and reducing the encryption strength of remote operations. In addition, the existing remote power supply operation and maintenance virtual meeting collaboration methods, systems, and electronic devices have poor comprehensiveness and timeliness in monitoring the power supply network status, cannot accurately identify abnormal events, require a long time to locate problems, and cannot guarantee that users obtain the most reliable and representative abnormal propagation chain. To address these issues, we propose a remote power supply operation and maintenance virtual meeting collaboration method, system, and electronic devices. Summary of the Invention

[0004] The purpose of this invention is to address the deficiencies in the existing technology by proposing a remote power supply operation and maintenance virtual conference collaboration method, system, and electronic device.

[0005] In a first aspect of this invention, a remote power supply operation and maintenance virtual conference collaboration method is first proposed, the method comprising the following steps:

[0006] Ⅰ. Access various data from the power supply network, construct a three-dimensional topology energy flow map in real time, and dynamically generate a safety level matrix based on real-time equipment operating conditions;

[0007] II. Establish a temporary operation permission tunnel and start a virtual meeting space. At the same time, automatically match the most suitable interaction method based on the participants' behavior and intentions.

[0008] III. Tracing and simulating anomalies in the power supply network, with all participants conducting collaborative analysis based on the shared 3D topology map and the results of the source tracing simulation;

[0009] IV. Evaluate the impact of different solutions on power supply reliability and safety, and record in real time the participants' discussion content, operation path and final decision conclusions during the meeting;

[0010] V. Compare and analyze expert experience with on-site handling results, and automatically update the self-iterative operation and maintenance knowledge graph. After the meeting, integrate and train operation and maintenance data from various regions.

[0011] As a further aspect of the present invention, the specific steps for real-time construction of the three-dimensional topological energy flow graph in step I are as follows:

[0012] S1.1: Extract the geographic coordinates and spatial connection relationships of each power grid node, such as substation, switching station and distribution box, from the power supply network GIS system, and calculate the distance between each power grid device based on the extracted location information of each group of power grid devices, so as to establish the spatial topology of the corresponding power grid nodes.

[0013] S1.2: Based on the physical wiring relationships and spatial topology between power grid nodes, establish the corresponding power supply network diagram model. ,in Represents a set of nodes. The set of connection lines is represented by 1, and then based on the connection status between each power grid node, 1 is used to indicate that two sets of power grid equipment are directly connected, and 0 is used to indicate otherwise.

[0014] S1.3: Collect the operating status of transformers, switches and power equipment in the line, bind them to the corresponding power grid nodes, and then obtain the power load data of each power grid node from the field acquisition system and map it to the corresponding power grid node in the power supply network diagram model;

[0015] S1.4: Based on the power supply network diagram model, calculate the current energy flow intensity of each line according to the voltage, load and operating status of the connected equipment. Then, fuse the obtained node positions, connection relationships, energy flow intensity and equipment status, and map the fusion results into a three-dimensional space. Visualize the fusion results through color, width and arrow direction to generate the corresponding three-dimensional topology energy flow diagram.

[0016] As a further aspect of the present invention, the specific calculation formula for the distance between power grid nodes in S1.1 is as follows:

[0017] ;

[0018] In the formula, Representative node With nodes Spatial distance between them; Representative node The longitude, latitude, and elevation projection coordinates; Representative node The longitude, latitude, and elevation projection coordinates;

[0019] The specific calculation formula for the energy flow intensity mentioned in S1.4 is as follows:

[0020] ;

[0021] In the formula, Representative node With nodes The energy flow intensity; Representative node With nodes The continuity coefficient between the lines is within the range of [0,1]. Representative node The voltage value; Representative node The voltage value.

[0022] As a further aspect of the present invention, the power supply network data in step I includes GIS coordinates, equipment operating status, load data, voltage and current information, line parameter information, relay protection and automation configuration data, switch status and topology switching logic, power access point information, timestamp and synchronization information, environmental and meteorological information, load forecast and historical behavior data, operation and maintenance events and alarm information, as well as user type and power factor information.

[0023] As a further aspect of the present invention, the specific steps for dynamically generating the safety level matrix by combining real-time equipment operating conditions in step I are as follows:

[0024] S2.1: Read all users currently participating in the meeting or remote operation, including their corresponding unit, position, historical operation records and scope of responsibilities, and numerically evaluate the responsibilities of each participant according to the predefined responsibility weight model;

[0025] S2.2: Based on the established three-dimensional topological energy flow map, scan the operating status of all related power equipment, calculate the corresponding real-time risk factors according to the operating status of each power equipment, and use the real-time risk factors of each power equipment as the environmental basis for access control.

[0026] S2.3: Based on the responsibility assessment of all users and the real-time risk factors of each power device, construct a user-device matrix to represent the operational safety level score of each user for each power device in the current state, and mark user-device pairs with operational safety level scores higher than the preset permission threshold as allowed to operate.

[0027] As a further aspect of the present invention, the specific calculation formula for the responsibility weight model described in S2.1 is as follows:

[0028] ;

[0029] In the formula, On behalf of users The performance evaluation score; On behalf of users The job risk level; On behalf of users Audit credibility score of past operations; On behalf of users The jurisdictional level factor of the subordinate unit; as well as These represent the weight factors configured in the model, and satisfy the following conditions: ;

[0030] The specific calculation formula for the real-time risk factor mentioned in S2.2 is as follows:

[0031] ;

[0032] In the formula, Represents electrical equipment Operating condition risk factors; Represents electrical equipment The ontological risk coefficient; Represents electrical equipment The fluctuation range of the current state;

[0033] The specific calculation formula for the operational safety level score mentioned in S2.3 is as follows:

[0034] ;

[0035] In the formula, On behalf of users For power equipment Operational safety level rating; This represents a very small positive number, used to prevent numerical errors when dividing by zero; where, The higher the value, the higher the level of permission allowed. Operations will be disabled when the threshold is reached.

[0036] As a further aspect of the present invention, the specific steps for establishing the temporary operation permission tunnel in step II are as follows:

[0037] S3.1: Statistically count the user-device pairs that are allowed to operate, and select the polarization state encoding method to prepare to generate the original key sequence. The system transmitter uses a single photon source to transmit qubits to the user terminal and marks the polarization basis used. Then the user terminal randomly selects a measurement basis, measures the received qubits, and records the measurement results.

[0038] S3.2: Based on the measurement results, if the base used by the transmitter is consistent with the measurement base of the user, the corresponding measurement result is retained and used as the key bit; otherwise, the corresponding measurement result is filtered out. Based on the filtering results, an original key candidate sequence is generated, and then error correction and privacy amplification processing are performed on the original key candidate sequence to generate the final confidential operation key.

[0039] S3.3: Construct a temporary encrypted channel using the generated confidential operation key, encapsulate the operation request data, and set the maximum lifespan of the temporary encrypted channel. At the same time, detect in real time whether the user operation is completed. If the user operation time has not reached the maximum lifespan and the user operation has not been completed, the user's permission tunnel for the corresponding power equipment is valid; otherwise, the user's permission tunnel for the corresponding power equipment is invalid.

[0040] S3.4: When a user's access tunnel to the corresponding power equipment becomes invalid, the channel and the confidential operation key shall be destroyed immediately.

[0041] As a further aspect of the present invention, the specific form of the quantum bit described in S3.1 is as follows:

[0042] ;

[0043] In the formula, Representing the The state of each transmitted quantum bit; The state generation function of a quantum bit; Representing the Each quantum bit represents a logical bit, with a value of 0 or 1; Representing the The polarization basis used by each quantum bit;

[0044] The specific calculation formula for the confidential operation key described in S3.2 is as follows:

[0045] ;

[0046] In the formula, This represents the final generated confidential operation key; Represents a privacy amplification function; The length after filtering is The original key candidate sequence, where the specific selection formula for the original key candidate sequence is as follows:

[0047] ;

[0048] In the formula, The number of items retained after filtering One key bit; The first one, representing the measurement obtained by the user terminal One key bit; Represents the system transmitter. The polarization basis selected by each quantum bit; Representing the user terminal The polarization basis selected by each quantum bit; This means that the key bit was discarded due to inconsistency in polarization basis;

[0049] The temporary encrypted channel described in S3.3 takes the following specific form:

[0050] ;

[0051] In the formula, This represents the encrypted operation message content; Represented by key A symmetric encryption function with parameters; This represents the original operation instruction or request data packet; the constraints of the temporary encrypted channel are as follows:

[0052] ;

[0053] In the formula, Representative at At any moment, the user For power equipment Whether the temporary encrypted channel is valid; where 1 represents valid and 0 represents invalid. This represents the maximum lifespan of a temporary encrypted channel; represent At any moment, the user For power equipment Has the operation been completed?

[0054] In a second aspect of this invention, a remote power supply operation and maintenance virtual conference collaboration system is proposed. The system includes: a data acquisition module, a three-dimensional topology module, an authentication management module, a conference collaboration module, an interactive interface module, a detection and analysis module, a reasoning replay module, a simulation evaluation module, a record analysis module, a knowledge update module, a collaborative training module, and an archiving decision module.

[0055] The data acquisition module is used to collect status information, operating parameters, environmental data, and GIS spatial information of various power supply equipment;

[0056] The three-dimensional topology module is used to dynamically construct a three-dimensional topology map of the power supply network based on the various data collected in real time by the data acquisition module.

[0057] The authentication management module is used to confirm the identity of users participating in the meeting, dynamically allocate user operation permissions, and establish a one-time temporary operation tunnel.

[0058] The meeting collaboration module is used to build an immersive remote meeting environment, supporting multiple people to access the virtual space simultaneously for real-time collaboration.

[0059] The interactive interface module is used to integrate multiple different interactive methods, and users can freely switch between the various interactive methods according to their usage preferences or actual situation.

[0060] The detection and analysis module identifies anomalies in the power supply network by comparing and analyzing the device data stream with historical operating conditions.

[0061] The reasoning and reenactment module constructs the fault formation path based on the identified anomaly information and dynamically reenacts it in virtual space, generating a source tracing report;

[0062] The simulation evaluation module is used to formulate multiple processing schemes, simulate the execution process of each processing scheme, and evaluate the impact of each scheme on power supply stability, recovery time, and potential risks.

[0063] The recording and analysis module is used to record voice commands, interactive behaviors, expert discussions and operation logs throughout the meeting, and to label important nodes.

[0064] The knowledge update module is used to analyze the differences between expert decisions and on-site implementation effects after the meeting, and to update the system's built-in operation and maintenance knowledge graph.

[0065] The collaborative training module is used to collect operation and maintenance data from various regions and perform integrated training.

[0066] The archiving decision module is used to archive the final meeting conclusions, simulation evaluation results, and expert consensus solutions to the power supply operation and maintenance platform, forming standardized meeting documents.

[0067] As a further aspect of the present invention, the specific steps for the detection and analysis module to identify anomalies in the power supply network are as follows:

[0068] S4.1: Collect and synchronize the monitoring data of each node of the power supply network generated by the SCADA, DMS, PMU and protection devices of the power supply network at the same time, and perform alignment and aggregation processing on the monitoring data of each node on the same time axis to generate multi-source status data of the corresponding node.

[0069] S4.2: Extract the state deviation features of the multi-source state data of each node through time sliding window analysis, calculate the standardized anomaly index of the state deviation features of each node, perform similarity matching between the current multi-source state data of each node and the known fault and anomaly pattern template library, and make a joint judgment based on the anomaly index and matching value of all nodes.

[0070] S4.3: If the anomaly index of a node is higher than or equal to the preset anomaly intensity and the matching value is higher than or equal to the preset anomaly form, then the corresponding node is determined to be an event node that meets the anomaly triggering criteria and is marked as 1. Otherwise, it is marked as 0. The event nodes determined to be the triggering source are bound to their corresponding GIS coordinates and timestamps, and each event node is counted to establish an initial set of anomaly nodes.

[0071] As a further aspect of the present invention, the specific calculation formula for the standardized anomaly index in S4.2 is as follows:

[0072] ;

[0073] In the formula, Representing the Each node at time... Standardized anomaly index; Representing the Each node at time... Real-time measurement values; Representing the The average value of each node under normal operating conditions; Representing the The standard deviation of each node under normal operating conditions;

[0074] The specific calculation formula for similarity matching described in S4.2 is as follows:

[0075] ;

[0076] In the formula, Representing the Each node at time... The state is the maximum similarity to all templates; Representing the A node in the past The state sequence of the time window; Representing the A preset exception mode template; This represents the sequence similarity function.

[0077] As a further aspect of the present invention, the specific steps of the reasoning replay module in constructing the fault formation path and dynamically replaying it in virtual space are as follows:

[0078] S5.1: The operation and maintenance units of substations, circuit breakers, busbars, cables, capacitors and protection devices in the power supply network are coded and each coded operation and maintenance unit is treated as an entity node. At the same time, attribute information is added to each operation and maintenance unit according to its physical attributes. The Granger causal method is used to mine potential causal structures through historical fault data and state sequences and use them as candidate causal edges. Then, based on the experience of the control personnel and the standard procedures, the operation and response chains between each operation and maintenance unit are manually defined.

[0079] S5.2: Based on the candidate causal edges and the manually defined results, connect the entity nodes with causal relationships, and add trigger conditions, propagation delay and confidence strength attributes to the causal edges connecting the entity nodes to construct the full causal graph of the corresponding power supply network, and iterate and update the corresponding full causal graph in real time based on the real-time data of the power supply network.

[0080] S5.3: Take each initial abnormal node in the initial abnormal node set as the starting node, and then start from each initial abnormal node. Within the preset topological depth and time constraints, backtrack along the causal edges of the full causal graph and record the searched node sets. At the same time, iterate through the confidence strength of the causal edges between each node and filter out causal edges with confidence strength lower than the preset threshold.

[0081] S5.4: After the screening is completed, the edge set that satisfies the topological depth and time constraints and whose causal weight is greater than the set threshold, together with its nodes, constitutes a causal subgraph. At the same time, a corresponding real historical evolution chain is established based on the causal subgraph, and each node in the causal subgraph is used as an intervention variable set. Counterfactual conditions are set for each node in the intervention variable set to construct counterfactual scenario samples.

[0082] S5.5: Based on counterfactual scenario samples, the state chain unaffected by intervention conditions is reconstructed through temporal evolution and causal path propagation mechanisms, new influence paths are identified, and corresponding counterfactual chains are formed according to the time points of node state changes during the deduction process. By comparing the counterfactual chains with the real historical evolution chains, the propagation path segments eliminated by intervention, root cause nodes, and secondary influence nodes are identified.

[0083] S5.6: Sort the confidence values ​​of all counterfactual causal paths from largest to smallest, select one or more paths with the highest confidence values ​​as the anomaly tracing results, label the confidence values, and automatically generate reenactment scenarios in chronological order based on the selected anomaly tracing results for user review or digital twin simulation verification.

[0084] In a third aspect of the present invention, an electronic device for remote power supply and maintenance virtual conference collaboration is proposed, comprising a CPU, a GPU, a quantum key chip, a multimodal input unit, a haptic feedback control unit, an AR augmented display unit, a multi-channel communication unit, a maintenance data acquisition card, an electronic storage unit, a security encryption chip, and a power management unit.

[0085] The CPU is used to execute instructions, coordinate the operation of various components, and support data parsing, graph processing, and inference calculation tasks.

[0086] The GPU is used to accelerate 3D topology rendering, multimodal data fusion calculation, and deep neural network model inference;

[0087] The quantum key chip is used to provide one-time operation permission encryption capability;

[0088] The multimodal input unit is used to acquire various user operation intentions.

[0089] The tactile feedback control unit generates physical feedback of vibration and resistance based on changes in topology data.

[0090] The AR augmented display unit is used to provide a visual overlay of the three-dimensional topology of the power supply network, displaying fault points, operating parameters and meeting instructions;

[0091] The multi-channel communication unit is used for remote audio and video conferencing, data transmission, key distribution, and remote device access.

[0092] The operation and maintenance data acquisition card is used to collect operating data, load status, and environmental parameters from on-site analog or digital devices;

[0093] The electronic storage unit is used to save the running graph, model parameters, meeting content, and playback records;

[0094] The security encryption chip is used to provide local identity authentication, key management, and conference access control.

[0095] The power management unit is used to provide regulated power supply.

[0096] The beneficial effects of this invention are:

[0097] 1. This invention assesses the operational security level of each user for each device by reading the identity, responsibilities, and historical records of participating users, combined with a three-dimensional energy flow topology map and the real-time operating status of power equipment. Based on permission thresholds, it filters out user-device pairs with operational permissions. Subsequently, the system uses polarization-state encoding and quantum key distribution technology to generate a one-time confidential key between the user and the system, establishing a temporary encrypted channel to transmit operation instructions. A survival constraint is set for this encrypted channel; if the operation is not completed within the valid time, the system will immediately destroy the key and the channel. This improves operational compliance and security, effectively prevents key leakage and instruction tampering, enhances the encryption strength of remote operations, prevents permission abuse, improves system resilience, and achieves secure closed-loop management from access to execution.

[0098] 2. This invention performs time alignment and aggregation on power network monitoring data collected from various systems including SCADA, DMS, PMU, and protection devices. It extracts multi-source state deviation features from each node and calculates a standardized anomaly index. Then, it combines a fault mode template library for similarity matching to identify abnormal event nodes. Subsequently, it encodes and models the operation and maintenance units, constructs a full causal graph using Granger causality analysis and expert knowledge, and backtracks to search for high-confidence causal chains based on the set of abnormal nodes under topological and temporal constraints, forming a causal subgraph. Combined with intervention settings, it constructs counterfactual scenario samples, reconstructs state evolution paths, identifies propagation segments and root cause nodes affected by intervention, and finally sorts and filters the most likely anomaly tracing paths by confidence level. It generates a visualized reenactment scenario for user verification and simulation, effectively improving the comprehensiveness and timeliness of power network state monitoring. This enables more accurate anomaly event identification, improves problem location efficiency, and ensures users can prioritize obtaining the most reliable and representative anomaly propagation chains. Attached Figure Description

[0099] The present invention will now be further described with reference to the accompanying drawings.

[0100] Figure 1 A flowchart illustrating a remote power supply operation and maintenance virtual meeting collaboration method provided in an embodiment of the present invention;

[0101] Figure 2 This is a system block diagram of a remote power supply operation and maintenance virtual conference collaboration system provided in an embodiment of the present invention. Detailed Implementation

[0102] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0103] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0104] This invention provides a remote power supply maintenance virtual meeting collaboration method. See also... Figure 1 , Figure 1 This is a flowchart illustrating a remote power supply maintenance virtual meeting collaboration method provided in an embodiment of the present invention. The method includes the following steps:

[0105] By accessing various data from the power supply network, a three-dimensional topology energy flow map is constructed in real time, and a safety level matrix is ​​dynamically generated based on real-time equipment operating conditions.

[0106] Specifically, the geographic coordinates and spatial connections of substations, switching stations, and distribution boxes are extracted from the power supply network GIS system. Based on the extracted location information of each group of power grid equipment, the distances between each power grid equipment are calculated to establish the spatial topology of the corresponding power grid nodes. Based on the physical wiring relationships and spatial topology between the power grid nodes, a corresponding power supply network graph model is established. ,in Represents a set of nodes. The system represents the connection set. Based on the connection status between each power grid node, 1 indicates that two sets of power grid equipment are directly connected, and 0 indicates otherwise. The system collects the operating status of transformers, switches, and power equipment on each line and binds them to the corresponding power grid nodes. Then, it obtains the power load data of each power grid node from the field acquisition system and maps it to the corresponding power grid node in the power supply network diagram model. Based on the power supply network diagram model, the system calculates the current energy flow intensity of each line according to the voltage, load, and operating status of the connected equipment. Then, it fuses the obtained node positions, connection relationships, energy flow intensity, and equipment status, and maps the fusion result to a three-dimensional space. The fusion result is visualized by using color, width, and arrow direction to generate a corresponding three-dimensional topological energy flow map.

[0107] Specifically, the system reads all users currently participating in the meeting or remote operation, including their corresponding unit, position, historical operation records, and scope of responsibility. Based on a predefined responsibility weight model, it numerically evaluates the responsibilities of each participant. Based on the established three-dimensional topological energy flow map, it scans the operating status of all related power equipment and calculates the corresponding real-time risk factor according to the operating status of each power equipment. The real-time risk factor of each power equipment is used as the environmental basis for access control. Based on the responsibility evaluation of all users and the real-time risk factor of each power equipment, a user-device matrix is ​​constructed to represent the operational safety level score of each user for each power equipment in the current state. User-device pairs with operational safety level scores higher than the preset permission threshold are marked as allowed to operate.

[0108] In addition, it should be noted that the specific formula for calculating the distance between power grid nodes is as follows: ;

[0109] In the formula, Representative node With nodes Spatial distance between them; Representative node The longitude, latitude, and elevation projection coordinates; Representative node The longitude, latitude, and elevation projection coordinates;

[0110] The specific formula for calculating energy flux intensity is as follows:

[0111] ;

[0112] In the formula, Representative node With nodes The energy flow intensity; Representative node With nodes The continuity coefficient between the lines is within the range of [0,1]. Representative node The voltage value; Representative node The voltage value;

[0113] The specific calculation formula for the responsibility weight model is as follows:

[0114] ;

[0115] In the formula, On behalf of users The performance evaluation score; On behalf of users The job risk level; On behalf of users Audit credibility score of past operations; On behalf of users The jurisdictional level factor of the subordinate unit; as well as These represent the weight factors configured in the model, and satisfy the following conditions: ;

[0116] The specific formula for calculating the real-time risk factor is as follows:

[0117] ;

[0118] In the formula, Represents electrical equipment Operating condition risk factors; Represents electrical equipment The ontological risk coefficient; Represents electrical equipment The fluctuation range of the current state;

[0119] The specific formula for calculating the operational safety level score is as follows:

[0120] ;

[0121] In the formula, On behalf of users For power equipment Operational safety level rating; This represents a very small positive number, used to prevent numerical errors when dividing by zero; where, The higher the value, the higher the level of permission allowed. Operations will be disabled when the threshold is reached.

[0122] It should be further explained that the power supply network data includes GIS coordinates, equipment operating status, load data, voltage and current information, line parameter information, relay protection and automation configuration data, switch status and topology switching logic, power access point information, timestamps and synchronization information, environmental and meteorological information, load forecast and historical behavior data, operation and maintenance events and alarm information, as well as user type and power factor information.

[0123] Establish a temporary operation permission tunnel and start a virtual meeting space. At the same time, automatically match the most suitable interaction method based on the participants' behavior and intentions.

[0124] Specifically, the system statistically analyzes allowed user-device pairs and selects a polarization state encoding method to prepare for generating the original key sequence. The system transmitter uses a single-photon source to transmit qubits to the user terminal and marks the polarization basis used. Then, the user terminal randomly selects a measurement basis, measures the received qubits, and records the measurement results. Based on each measurement result, if the basis used by the transmitter is consistent with the measurement basis of the user terminal, the corresponding measurement result is retained and used as the key bit; otherwise, the corresponding measurement result is rejected. Based on the rejection results, an original key candidate sequence is generated. Error correction and privacy amplification processing are then performed on the original key candidate sequence to generate the final confidential operation key. The generated confidential operation key is used to construct a temporary encrypted channel, encapsulate the operation request data, and set the maximum lifetime of the temporary encrypted channel. At the same time, the system monitors in real time whether the user operation is completed. If the user operation time has not reached the maximum lifetime and the user operation has not been completed, the user's permission tunnel for the corresponding power device is valid; otherwise, the user's permission tunnel for the corresponding power device is invalid. When the user's permission tunnel for the corresponding power device is invalid, the channel and confidential operation key are immediately destroyed.

[0125] It should be further explained that the specific form of a quantum bit is as follows:

[0126] ;

[0127] In the formula, Representing the The state of each transmitted quantum bit; The state generation function of a quantum bit; Representing the Each quantum bit represents a logical bit, with a value of 0 or 1; Representing the The polarization basis used by each quantum bit;

[0128] The specific formula for calculating the confidential operation key is as follows:

[0129] ;

[0130] In the formula, This represents the final generated confidential operation key; Represents a privacy amplification function; The length after filtering is The original key candidate sequence, where the specific selection formula for the original key candidate sequence is as follows:

[0131] ;

[0132] In the formula, The number of items retained after filtering One key bit; The first one, representing the measurement obtained by the user terminal One key bit; Represents the system transmitter. The polarization basis selected by each quantum bit; Representing the user terminal The polarization basis selected by each quantum bit; This means that the key bit was discarded due to inconsistency in polarization basis;

[0133] The specific form of a temporary encrypted channel is as follows:

[0134] ;

[0135] In the formula, This represents the encrypted operation message content; Represented by key A symmetric encryption function with parameters; This represents the original operation instruction or request data packet; the constraints of the temporary encrypted channel are as follows:

[0136] ;

[0137] In the formula, Representative at At any moment, the user For power equipment Whether the temporary encrypted channel is valid; where 1 represents valid and 0 represents invalid. This represents the maximum lifespan of a temporary encrypted channel; represent At any moment, the user For power equipment Has the operation been completed?

[0138] Anomalies in the power supply network are traced and simulated, and all participants conduct collaborative analysis based on the shared 3D topology map and the results of the source tracing simulation.

[0139] The impact of different solutions on power supply reliability and security is assessed, and the participants' discussions, operational procedures, and final decision conclusions are recorded in real time.

[0140] By comparing and analyzing expert experience with on-site handling results, and automatically updating the self-iterative operation and maintenance knowledge graph, the operation and maintenance data of various regions are integrated and trained after the meeting.

[0141] A remote power supply operation and maintenance virtual meeting collaboration method provided by an embodiment of the present invention is as follows:

[0142] This invention also provides a remote power supply operation and maintenance virtual conference collaboration system, such as... Figure 2As shown, the system includes the following modules: data acquisition module, 3D topology module, authentication management module, conference collaboration module, interactive interface module, detection and analysis module, reasoning replay module, simulation evaluation module, record analysis module, knowledge update module, collaborative training module, and archiving decision module.

[0143] The data acquisition module is used to collect status information, operating parameters, environmental data, and GIS spatial information of various power supply equipment; the 3D topology module is used to dynamically construct a 3D topology map of the power supply network based on the data collected in real time by the data acquisition module.

[0144] The authentication management module is used to verify the identity of users participating in the meeting and dynamically assign user operation permissions, while establishing a one-time temporary operation tunnel; the meeting collaboration module is used to build an immersive remote meeting environment, supporting multiple people to access the virtual space for real-time collaboration; the interaction interface module is used to integrate various interaction methods, and users can freely switch between different interaction methods according to their usage preferences or actual situation.

[0145] The detection and analysis module identifies anomalies in the power supply network by comparing and analyzing the equipment data stream with historical operating conditions.

[0146] Specifically, monitoring data from various systems of the power supply network, including SCADA, DMS, PMU, and protection devices, generated at the same time are collected and synchronized. The monitoring data from each node on the same time axis are aligned and aggregated to generate multi-source status data for the corresponding nodes. State deviation characteristics of the multi-source status data of each node are extracted through time sliding window analysis, and a standardized anomaly index of the state deviation characteristics of each node is calculated. The current multi-source status data of each node is matched with a known fault and anomaly pattern template library for similarity. Based on the anomaly index and matching value of all nodes, a joint judgment is made. If the anomaly index of a node is higher than or equal to a preset anomaly intensity, and the matching value is higher than or equal to a preset anomaly form, the corresponding node is judged as an event node that meets the anomaly triggering criteria and marked as 1; otherwise, it is marked as 0. The event nodes judged as trigger sources are bound to their corresponding GIS coordinates and timestamps, and statistics are collected for each event node to establish an initial set of anomaly nodes.

[0147] It should be further explained that the specific formula for calculating the standardized anomaly index is as follows:

[0148] ;

[0149] In the formula, Representing the Each node at time... Standardized anomaly index; Representing the Each node at time... Real-time measurement values; Representing the The average value of each node under normal operating conditions; Representing the The standard deviation of each node under normal operating conditions;

[0150] The specific calculation formula for similarity matching described in S4.2 is as follows:

[0151] ;

[0152] In the formula, Representing the Each node at time... The state is the maximum similarity to all templates; Representing the A node in the past The state sequence of the time window; Representing the A preset exception mode template; This represents the sequence similarity function.

[0153] The reasoning and reenactment module constructs the fault formation path based on the identified anomaly information and dynamically reenacts it in the virtual space, generating a source tracing report.

[0154] Specifically, the operation and maintenance units of substations, circuit breakers, busbars, cables, capacitors, and protection devices in the power supply network are coded and treated as entity nodes. Attribute information is added to each unit based on its physical properties. The Granger causality method is used to mine potential causal structures from historical fault data and state sequences, and these are used as candidate causal edges. Operation and response chains between operation and maintenance units are manually defined based on the experience and standards of dispatching personnel. Based on the candidate causal edges and the manually defined results, entity nodes with causal relationships are connected. Trigger conditions, propagation delay, and confidence strength are added to the causal edges connecting entity nodes to construct a full causal graph of the corresponding power supply network. The full causal graph is iteratively updated in real-time based on real-time data from the power supply network. Each initial abnormal node in the initial abnormal node set is used as a starting node. Then, starting from each initial abnormal node, a backtracking search is performed along the causal edges of the full causal graph within a preset topology depth and time constraint, and the searched node sets are recorded. The confidence strength of causal edges between nodes is assessed, and edges with confidence strengths below a preset threshold are removed. After removal, the set of edges satisfying topological depth and time constraints, and whose causal weights are greater than the set threshold, together with their nodes, constitute a causal subgraph. A corresponding real-world evolutionary chain is then established based on this subgraph, and each node in the subgraph is used as a set of intervention variables. Counterfactual conditions are set for each node in the intervention variable set to construct counterfactual scenario samples. Based on these counterfactual scenario samples, the evolutionary chain unaffected by the intervention conditions is reconstructed through temporal evolution and causal path propagation mechanisms. The state chain identifies new impact paths and forms corresponding counterfactual chains based on the time points of node state changes during the simulation. By comparing the counterfactual chains with the real historical evolution chains, it identifies the propagation path segments, root cause nodes, and secondary impact nodes eliminated by intervention. It sorts the confidence values ​​of all causal paths evolved from counterfactual to lowest, selects one or more paths with the highest confidence values ​​as anomaly tracing results, and labels their confidence levels. Based on the selected anomaly tracing results, it automatically generates reenactment scenarios in chronological order for user review or digital twin simulation verification.

[0155] The simulation evaluation module is used to develop multiple processing schemes, simulate the execution process of each processing scheme, and evaluate the impact of each scheme on power supply stability, recovery time, and potential risks. The recording and analysis module is used to record voice commands, interactive behaviors, expert discussions, and operation logs throughout the meeting, and to label important nodes.

[0156] The knowledge update module is used to analyze the differences between expert decisions and on-site implementation effects after the meeting, and update the system's built-in operation and maintenance knowledge graph; the collaborative training module is used to collect operation and maintenance data from various regions and conduct integrated training; the archiving decision module is used to archive the final meeting conclusions, simulation evaluation results, and expert consensus solutions to the power supply operation and maintenance platform to form standardized meeting documents.

[0157] In another embodiment of the present invention, an electronic device for remote power supply and maintenance virtual conference collaboration is also provided, including a CPU, a GPU, a quantum key chip, a multimodal input unit, a haptic feedback control unit, an AR augmented display unit, a multi-channel communication unit, a maintenance data acquisition card, an electronic storage unit, a security encryption chip, and a power management unit.

[0158] The CPU is used to execute instructions, coordinate the operation of various components, and support data parsing, graph processing, and inference calculation tasks; the GPU is used to accelerate 3D topology graph rendering, multimodal data fusion calculation, and deep neural network model inference.

[0159] The quantum key chip is used to provide one-time operation permission encryption capability; the multimodal input unit is used to acquire multiple operation intentions input by the user; the haptic feedback control unit generates physical feedback of vibration and resistance based on changes in topology data.

[0160] The AR augmented reality display unit is used to provide a visual overlay of the three-dimensional topology of the power supply network, displaying fault points, operating parameters, and conference instructions; the multi-channel communication unit is used for remote audio and video conferencing, data transmission, key distribution, and remote access to devices.

[0161] The operation and maintenance data acquisition card is used to collect operation data, load status, and environmental parameters from on-site analog or digital devices; the electronic storage unit is used to save operation diagrams, model parameters, meeting content, and playback records; the security encryption chip is used to provide local identity authentication, key management, and meeting access permission control; and the power supply management unit is used to provide regulated power supply.

[0162] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for remote power supply operation and maintenance virtual conference collaboration, characterized in that, Includes the following steps: Ⅰ. Access various data from the power supply network, construct a three-dimensional topology energy flow map in real time, and dynamically generate a safety level matrix based on real-time equipment operating conditions; II. Establish a temporary operation permission tunnel and start a virtual meeting space. At the same time, automatically match the most suitable interaction method based on the participants' behavior and intentions. III. Tracing and re-simulating anomalies in the power supply network, with all participants conducting collaborative analysis based on the shared three-dimensional topological energy flow diagram and the source tracing simulation results; IV. Evaluate the impact of different solutions on power supply reliability and safety, and record in real time the participants' discussion content, operation path and final decision conclusions during the meeting; V. Compare and analyze expert experience with on-site handling results, and automatically update the self-iterative operation and maintenance knowledge graph. After the meeting, integrate and train operation and maintenance data from various regions. The specific steps for constructing the three-dimensional topological energy flow graph in real time as described in step I are as follows: S1.1: Extract the geographic coordinates and spatial connection relationships of each power grid node, such as substation, switching station and distribution box, from the power supply network GIS system, and calculate the distance between each power grid device based on the extracted location information of each group of power grid devices, so as to establish the spatial topology of the corresponding power grid nodes. S1.2: Establishing the corresponding power supply network graph model based on the physical wiring relationship and spatial topology between the grid nodes wherein represents a set of nodes, represents a set of connecting lines, and according to the connection between each grid node, 1 represents that two groups of grid devices are directly connected, and otherwise, 0 represents. S1.3: Collect the operating status of transformers, switches and power equipment in the line, bind them to the corresponding power grid nodes, and then obtain the power load data of each power grid node from the field acquisition system and map it to the corresponding power grid node in the power supply network diagram model; S1.4: Based on the power supply network diagram model, calculate the current energy flow intensity of each line according to the voltage, load and operating status of the connected equipment. Then, fuse the obtained node positions, connection relationships, energy flow intensity and equipment status, and map the fusion results into a three-dimensional space. Visualize the fusion results through color, width and arrow direction to generate the corresponding three-dimensional topology energy flow diagram. The specific steps for dynamically generating the safety level matrix by combining real-time equipment operating conditions, as described in Step I, are as follows: S2.1: Read all users currently participating in the meeting or remote operation, including their corresponding unit, position, historical operation records and scope of responsibilities, and numerically evaluate the responsibilities of each participant according to the predefined responsibility weight model; S2.2: Based on the established three-dimensional topological energy flow map, scan the operating status of all related power equipment, calculate the corresponding real-time risk factors according to the operating status of each power equipment, and use the real-time risk factors of each power equipment as the environmental basis for access control. S2.3: Based on the responsibility assessment of all users and the real-time risk factors of each power device, construct a user-device matrix to represent the operational safety level score of each user for each power device in the current state, and mark user-device pairs with operational safety level scores higher than the preset permission threshold as allowed to operate; The specific steps for establishing a temporary operation permission tunnel as described in step II are as follows: S3.1: Statistically count the user-device pairs that are allowed to operate, and select the polarization state encoding method to prepare to generate the original key sequence. The system transmitter uses a single photon source to transmit qubits to the user terminal and marks the polarization basis used. Then the user terminal randomly selects a measurement basis, measures the received qubits, and records the measurement results. S3.2: Based on the measurement results, if the base used by the transmitter is consistent with the measurement base of the user, the corresponding measurement result is retained and used as the key bit; otherwise, the corresponding measurement result is filtered out. Based on the filtering results, an original key candidate sequence is generated, and then error correction and privacy amplification processing are performed on the original key candidate sequence to generate the final confidential operation key. S3.3: Construct a temporary encrypted channel using the generated confidential operation key, encapsulate the operation request data, and set the maximum lifespan of the temporary encrypted channel. At the same time, detect in real time whether the user operation is completed. If the user operation time has not reached the maximum lifespan and the user operation has not been completed, the user's permission tunnel for the corresponding power equipment is valid; otherwise, the user's permission tunnel for the corresponding power equipment is invalid. S3.4: When a user's access tunnel to the corresponding power equipment becomes invalid, the channel and the confidential operation key shall be destroyed immediately. 2.The remote power supply operation and maintenance virtual conference collaboration method of claim 1, wherein, The specific form of the qubit described in S3.1 is as follows: ; where, represents the state of the th transmitted qubit; represents a qubit state generation function; represents the state of the th qubit, which can be either 0 or 1; represents the state of the th qubit using a polarization basis; The specific calculation formula for the confidential operation key described in S3.2 is as follows: ; wherein, represents the final generated secret operation key; represents a privacy amplification function; represents a screened original key candidate sequence of a length, wherein the original key candidate sequence is screened according to the following formula: ; wherein represents the i-th key bit reserved after screening; represents the i-th key bit measured by the user terminal; represents the i-th quantum bit selected by the transmitting end of the system; represents the i-th quantum bit selected by the user terminal; represents that the key bit is discarded due to the inconsistency of the polarization bases.​​​​ The temporary encrypted channel described in S3.3 takes the following specific form: ； In the formula, This represents the encrypted operation message content; Represented by key A symmetric encryption function with parameters; This represents the original operation instruction or request data packet; the constraints of the temporary encrypted channel are as follows: ； In the formula, Representative at At any moment, the user For power equipment Whether the temporary encrypted channel is valid; where 1 represents valid and 0 represents invalid. This represents the maximum lifespan of a temporary encrypted channel; represent At any moment, the user For power equipment Has the operation been completed? 3. A remote power supply operation and maintenance virtual conference collaboration system, used to implement the steps of the remote power supply operation and maintenance virtual conference collaboration method according to any one of claims 1-2, characterized in that, include: The system includes a data acquisition module, a 3D topology module, an authentication management module, a conference collaboration module, an interactive interface module, a detection and analysis module, a reasoning replay module, a simulation evaluation module, a record analysis module, a knowledge update module, a collaborative training module, and an archiving decision module. The data acquisition module is used to collect status information, operating parameters, environmental data, and GIS spatial information of various power supply equipment; The three-dimensional topology module is used to dynamically construct a three-dimensional topology map of the power supply network based on the various data collected in real time by the data acquisition module. The authentication management module is used to confirm the identity of users participating in the meeting, dynamically allocate user operation permissions, and establish a one-time temporary operation tunnel. The meeting collaboration module is used to build an immersive remote meeting environment, supporting multiple people to access the virtual space simultaneously for real-time collaboration. The interactive interface module is used to integrate multiple different interactive methods, and users can freely switch between the various interactive methods according to their usage preferences or actual situation. The detection and analysis module identifies anomalies in the power supply network by comparing and analyzing the device data stream with historical operating conditions. The reasoning and reenactment module constructs the fault formation path based on the identified anomaly information and dynamically reenacts it in virtual space, generating a source tracing report; The simulation evaluation module is used to formulate multiple processing schemes, simulate the execution process of each processing scheme, and evaluate the impact of each scheme on power supply stability, recovery time, and potential risks. The recording and analysis module is used to record voice commands, interactive behaviors, expert discussions and operation logs throughout the meeting, and to label important nodes. The knowledge update module is used to analyze the differences between expert decisions and on-site implementation effects after the meeting, and to update the system's built-in operation and maintenance knowledge graph. The collaborative training module is used to collect operation and maintenance data from various regions and perform integrated training. The archiving decision module is used to archive the final meeting conclusions, simulation evaluation results, and expert consensus solutions to the power supply operation and maintenance platform, forming standardized meeting documents.

4. The remote power supply operation and maintenance virtual conference collaboration system according to claim 3, characterized in that, The specific steps taken by the detection and analysis module to identify anomalies in the power supply network are as follows: S4.1: Collect and synchronize the monitoring data of each node of the power supply network generated by the SCADA, DMS, PMU and protection devices of the power supply network at the same time, and perform alignment and aggregation processing on the monitoring data of each node on the same time axis to generate multi-source status data of the corresponding node. S4.2: Extract the state deviation features of the multi-source state data of each node through time sliding window analysis, calculate the standardized anomaly index of the state deviation features of each node, perform similarity matching between the current multi-source state data of each node and the known fault and anomaly pattern template library, and make a joint judgment based on the anomaly index and matching value of all nodes. S4.3: If the anomaly index of a node is higher than or equal to the preset anomaly intensity and the matching value is higher than or equal to the preset anomaly form, then the corresponding node is determined to be an event node that meets the anomaly triggering criteria and is marked as 1. Otherwise, it is marked as 0. The event nodes determined to be the triggering source are bound to their corresponding GIS coordinates and timestamps, and each event node is counted to establish an initial set of anomaly nodes.

5. A remote power supply operation and maintenance virtual conference collaboration system according to claim 4, characterized in that, The specific steps of the reasoning reenactment module in constructing the fault formation path and dynamically reenacting it in the virtual space are as follows: S5.1: The operation and maintenance units of substations, circuit breakers, busbars, cables, capacitors and protection devices in the power supply network are coded and each coded operation and maintenance unit is treated as an entity node. At the same time, attribute information is added to each operation and maintenance unit according to its physical attributes. The Granger causal method is used to mine potential causal structures through historical fault data and state sequences and use them as candidate causal edges. Then, based on the experience of the control personnel and the standard procedures, the operation and response chains between each operation and maintenance unit are manually defined. S5.2: Based on the candidate causal edges and the manually defined results, connect the entity nodes with causal relationships, and add trigger conditions, propagation delay and confidence strength attributes to the causal edges connecting the entity nodes to construct the full causal graph of the corresponding power supply network, and iterate and update the corresponding full causal graph in real time based on the real-time data of the power supply network. S5.3: Take each initial abnormal node in the initial abnormal node set as the starting node, and then start from each initial abnormal node. Within the preset topological depth and time constraints, backtrack along the causal edges of the full causal graph and record the searched node sets. At the same time, iterate through the confidence strength of the causal edges between each node and filter out causal edges with confidence strength lower than the preset threshold. S5.4: After the screening is completed, the edge set that satisfies the topological depth and time constraints and whose causal weight is greater than the set threshold, together with its nodes, constitutes a causal subgraph. At the same time, a corresponding real historical evolution chain is established based on the causal subgraph, and each node in the causal subgraph is used as an intervention variable set. Counterfactual conditions are set for each node in the intervention variable set to construct counterfactual scenario samples. S5.5: Based on counterfactual scenario samples, the state chain unaffected by intervention conditions is reconstructed through temporal evolution and causal path propagation mechanisms, new influence paths are identified, and corresponding counterfactual chains are formed according to the time points of node state changes during the deduction process. By comparing the counterfactual chains with the real historical evolution chains, the propagation path segments eliminated by intervention, root cause nodes, and secondary influence nodes are identified. S5.6: Sort the confidence values ​​of all counterfactual causal paths from largest to smallest, select one or more paths with the highest confidence values ​​as the anomaly tracing results, label the confidence values, and automatically generate reenactment scenarios in chronological order based on the selected anomaly tracing results for user review or digital twin simulation verification.

6. An electronic device for remote power supply operation and maintenance virtual conference collaboration, used to implement the steps of the remote power supply operation and maintenance virtual conference collaboration method according to any one of claims 1-2, characterized in that, It includes a CPU, GPU, quantum key chip, multimodal input unit, haptic feedback control unit, AR augmented display unit, multi-channel communication unit, maintenance data acquisition card, electronic storage unit, security encryption chip, and power management unit; The CPU is used to execute instructions, coordinate the operation of various components, and support data parsing, graph processing, and inference calculation tasks. The GPU is used to accelerate 3D topology rendering, multimodal data fusion calculation, and deep neural network model inference; The quantum key chip is used to provide one-time operation permission encryption capability; The multimodal input unit is used to acquire various user operation intentions. The tactile feedback control unit generates physical feedback of vibration and resistance based on changes in topology data. The AR augmented display unit is used to provide a visual overlay of the three-dimensional topology of the power supply network, displaying fault points, operating parameters and meeting instructions; The multi-channel communication unit is used for remote audio and video conferencing, data transmission, key distribution, and remote device access. The operation and maintenance data acquisition card is used to collect operating data, load status, and environmental parameters from on-site analog or digital devices; The electronic storage unit is used to save the running graph, model parameters, meeting content, and playback records; The security encryption chip is used to provide local identity authentication, key management, and conference access control. The power management unit is used to provide regulated power supply.

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