Power transmission professional autonomous intelligent office system and method based on multi-mode perception and digital twinning

Abstract

The invention belongs to the technical field of power system informatization and intelligentization, and particularly relates to a power transmission professional autonomous intelligent office system and method based on multi-modal perception, digital twinning and artificial intelligence technologies, and the system comprises a cross-modal business model which serves as a core cognitive engine of the system; the multi-mode fusion sensing module is used for comprehensively judging the intention of a user and the state of an office scene through voiceprint, voice, gestures and environment sensor data; the power transmission line digital twinborn body is synchronized with the power transmission line of the physical world and the panoramic platform data in real time, and is used for state prediction, fault simulation and operation deduction; the workflow engine with intelligent driving autonomously plans, schedules and executes a complex office task sequence, and the system can realize the crossing from passive response to active prediction and from process solidification to intelligent adaptation.

Description

Technical Field

[0001] This invention belongs to the field of power system informatization and intelligentization technology, and specifically relates to an autonomous intelligent office system and method for power transmission based on multimodal perception, digital twin and artificial intelligence technologies. Background Technology

[0002] Currently, the daily office work and operation and maintenance management of power transmission professionals involve multiple dimensions of tasks, including operation monitoring, trip analysis, process reporting, document processing, and meeting minutes. Information sources are scattered, processes are cumbersome, and collaboration is highly demanding. As the backbone of the power grid, the safe and stable operation of transmission lines is crucial. Currently, the daily office work and production management of power transmission professionals heavily rely on various information technology tools such as power transmission panoramic platforms and production management systems. However, existing technical solutions have the following significant drawbacks: 1. Information silos and cumbersome operations: Information such as weather, line status, and work plans are scattered across different systems. Managers need to log into multiple platforms and consult a large number of reports to obtain overall information, which is inefficient and makes it difficult to make quick decisions during emergency command.

[0003] 2. Outdated interaction methods: The existing system mainly relies on keyboard and mouse operation, which is extremely unfriendly to on-site inspectors or those whose hands are occupied. The simple voice assistant lacks professional domain knowledge and cannot understand complex power transmission terminology and business processes.

[0004] 3. Low level of process rigidity and intelligence: Office processes such as work order filling and technical supervision form filling, although they have been digitized, still require a lot of manual operation. They cannot be filled in intelligently according to the context, and they do not have the ability to dynamically adjust the process in abnormal situations.

[0005] 4. Passive response and lack of foresight: Existing reminder functions are based on fixed rules and cannot predict the risk of task overdue or the probability of equipment failure, resulting in a passive management approach.

[0006] 5. Insufficient depth of data processing: There is a lack of effective intelligent analysis methods for unstructured data such as professional documents and meeting minutes, making it impossible to automatically extract core requirements, generate action guidelines, or track the implementation of resolutions.

[0007] In accordance with the requirements of intelligent transformation and lean management, power transmission professional offices need to achieve high efficiency, accuracy, and intelligence. However, due to the complexity of daily tasks, professional managers find it difficult to quickly focus on key information from massive amounts of data, easily leading to task omissions and response delays. Simultaneously, manual operation is prone to errors in process processing and technical document review, and process flows are time-consuming. Furthermore, when faced with professional management documents issued by higher-level units, personnel struggle to quickly grasp the core requirements and translate them into specific work items, resulting in policy implementation delays. These combined factors make the existing office model unable to meet the high demands of modern power grid management for efficiency, quality, and response speed.

[0008] Therefore, it is necessary to design a voiceprint-driven intelligent office terminal for power transmission professionals based on a power transmission panoramic platform. This terminal aims to achieve intelligent, automated, and efficient office work for power transmission professionals. Based on the full information from the power transmission panoramic platform, this assistant combines voice recognition, voiceprint authentication, natural language processing, and multi-source data fusion technologies to achieve six core functions: automatic reporting of operational status, voice interaction, professional process data entry, task reminders, document guidance, and meeting content extraction. The basic logic of this assistant is to comprehensively improve the efficiency of power transmission professional office work through an automated process of "automatic data collection - multi-source information fusion - voice interaction recognition - intelligent analysis and reminders - automatic data entry and guidance - content extraction and output," overcoming the aforementioned shortcomings and possessing extremely high application and promotion value. Summary of the Invention

[0009] The purpose of this invention is to provide an autonomous intelligent office system and method for power transmission professionals based on multimodal perception and digital twins, addressing the problems existing in the prior art. This system can achieve a leap from passive response to proactive prediction and from process rigidity to intelligent adaptation. To realize the intelligent, automated, and efficient office work of power transmission professionals, it is based on the full information of the power transmission panoramic platform and combines speech recognition, voiceprint authentication, natural language processing, and multi-source data fusion technologies to realize six core functions: automatic broadcast of operation status, voice interaction, professional process filling, task reminders, document guidance, and meeting content extraction.

[0010] The technical solution of this invention is: This invention provides an autonomous intelligent office system for power transmission based on multimodal perception and digital twins, comprising: a cross-modal business model, which serves as the core cognitive engine of the system. This model is pre-trained and fine-tuned using massive amounts of power transmission professional documents, procedures, historical work orders, and dialogue data, and possesses the ability to deeply understand power transmission professional semantics, logical reasoning, and business process planning.

[0011] A multimodal fusion perception module is used to comprehensively determine the user's identity, intent, and current office scene status through voiceprint, voice, gesture, and environmental sensor data, thereby achieving a natural and secure interaction entry point.

[0012] A digital twin of a power transmission line is synchronized in real time with data from the physical power transmission line and panoramic platform, and integrates physical mechanism models and data-driven models for status monitoring, trend prediction, fault simulation, and operational consequence deduction.

[0013] An embodied intelligence-driven workflow engine is capable of autonomously planning, scheduling, and executing complex, multi-step sequences of office tasks based on decision recommendations from the cross-modal business model and the inference results from the digital twin, and can dynamically adapt to abnormal situations.

[0014] Simultaneously, the system integrates and coordinates the following six functional modules: Intelligent prediction and broadcasting module for operational status: It integrates real-time data and predictive information to generate broadcasts that include risk warnings.

[0015] Multimodal interaction and immersive broadcasting module: provides high-security authentication, AR / VR visualization, and in-depth question-and-answer interaction.

[0016] Adaptive process generation and form filling module: Enables intelligent process generation, automatic form filling, and compliance self-check.

[0017] Predictive task alert module: Identifies high-risk tasks based on predictive models and provides proactive intervention suggestions.

[0018] The document intelligent compliance review and outline generation module: realizes document conflict detection and directly converts the outline into an executable work order.

[0019] Meeting Decision Tracking and Self-Execution Module: Enables automatic binding of resolutions and tasks and closed-loop tracking throughout their entire lifecycle.

[0020] Meanwhile, this invention provides an autonomous intelligent office method based on the above system, comprising the following steps: collecting the user's voiceprint, speech, and environmental context information through a multimodal fusion perception module, and then... Perform identity authentication and intent recognition; By using a cross-modal business model, the deep semantics of user requests are analyzed, and combined with real-time and historical data from the power transmission panorama platform, decision support information is generated. By using a digital twin of the transmission line, the current status of the line can be simulated synchronously, and future operational risks or consequences can be predicted. Through an embodied intelligence-driven workflow engine, task sequences are autonomously generated, scheduled, and executed based on the decision information of the large model and the inference results of the digital twin. It integrates intelligent prediction and broadcasting of execution status, multimodal interaction and immersive broadcasting, adaptive process generation and filling, predictive task reminders, intelligent compliance review and outline generation of documents, and a complete office workflow for tracking and self-execution of meeting decisions; To achieve fully autonomous and intelligent office operations for the power transmission industry, moving from passive response to proactive prediction and from rigid processes to intelligent adaptation.

[0021] It also includes model optimization, specifically including: A federated learning framework is adopted to train the large business model using local data on each terminal. The encrypted model parameter update values ​​are uploaded to the cloud for aggregation to generate a globally optimized model. The global optimization model is distributed to each terminal to continuously improve the system's intelligence level without sharing the original data.

[0022] Furthermore, the intelligent prediction and broadcasting steps for operational status include: Based on machine learning models in digital twins, historical and real-time data are analyzed to predict the probability of line failure and load trends in a specific future period. Integrate predicted future risk warnings with current operational status information; Generate and broadcast dual-structured voice content that includes the current status and future warnings.

[0023] Furthermore, the multimodal interaction and immersive broadcasting steps include: Multimodal identity authentication is achieved by integrating voiceprint and facial information; When reporting complex faults, the system automatically calls up the 3D model of the digital twin and presents it in an immersive visual format through AR / VR devices. Based on a cross-modal business model, we engage in open-ended, multi-round question-and-answer interactions with users regarding the broadcast content.

[0024] Furthermore, the adaptive process generation and data entry steps include: By using process mining technology, historical process logs are analyzed to discover the optimal path, and the workflow engine dynamically generates or adjusts the handling process based on the current context. After automatically filling in the professional form, the built-in procedure knowledge base and cross-modal business big model are invoked to automatically verify the compliance of the filled content and provide risk warnings.

[0025] Furthermore, the predictive task reminder step includes: By linking the prediction results of the digital twin with the work plan, future tasks that may be delayed or are at high risk due to changes in the external environment or equipment status can be identified. Generate and push proactive intervention reminders that include specific recommendations (such as adjusting the execution time or inspection method).

[0026] Furthermore, the document intelligent compliance review and outline generation steps include: By leveraging the cross-modal business big model, newly imported files are cross-compared with existing standard files, and clauses with conflicts or ambiguities are automatically identified. The tasks, timelines, and responsible persons in the generated work outline are automatically converted into pre-set work orders that can be recognized and tracked by the embodied intelligent workflow engine.

[0027] Furthermore, the meeting decision tracking and self-execution steps include: When extracting meeting minutes, the system automatically and strongly links "meeting resolutions" with specific "to-do items" and "responsible persons"; The to-do items are automatically created as system tasks, and the workflow engine monitors and pushes progress throughout their entire lifecycle until a closed loop is formed.

[0028] Compared with the prior art, the system and method provided by the present invention bring the following significant benefits: 1. This invention represents a shift from a "tool-assisted" to an "autonomous intelligent agent," resulting in a qualitative leap in decision-making and execution capabilities. By introducing a cross-modal business large-scale model as a cognitive engine, a digital twin as a prediction and inference platform, and an embodied intelligent workflow engine as the execution mechanism, this invention constructs a closed loop capable of "thinking-simulation-action." The system provided by this invention is no longer a simple tool passively responding to instructions, but a domain-autonomous intelligent agent capable of proactively predicting risks (such as predicting power outages based on meteorological data), autonomously planning optimal handling processes, and dynamically adjusting execution paths. This solves the fundamental problem of existing technologies' low level of intelligence and heavy reliance on manual decision-making and operation.

[0029] 2. Overcame the bottlenecks in natural and safe interaction in complex industrial scenarios. Traditional voice assistants have limited comprehension capabilities in specialized scenarios. This invention, through the deep integration of voiceprint authentication and facial recognition in multimodal perception, enhances the convenience of interaction while ensuring the security and non-repudiation of core business operations. Furthermore, based on a business model fine-tuned specifically for the power transmission field, the system can accurately understand the deep semantics of technical terms such as "tripping details" and "technical supervision order," and conduct multi-round, open-ended deep question-and-answer sessions. This represents a leap from "voice control" to "semantic understanding," significantly reducing the cognitive load on operators.

[0030] 3. Shift the management model from "reactive response" to "pre-event early warning and in-event intervention". Existing systems can only display faults that have already occurred. This invention utilizes the simulation and prediction capabilities of digital twins to perform forward-looking calculations of line load, fault probability, and task overdue risk. This allows for proactive management actions; for example, the system can proactively warn of "high risk on a certain line in the next 3 hours" and suggest adjusting operating modes, or indicate "strong winds tomorrow, it is recommended to bring forward the inspection task to today," thus realizing a role transformation from a passive "firefighting team" to a proactive "early warning system."

[0031] 4. It has connected the final link from "decision information" to "implementation," forming a closed-loop management system. Addressing the pain points of difficulties in document transmission and meeting decision implementation, this invention does not simply extract information. Through an intelligent document review module, the system can automatically identify policy conflicts. More importantly, through automatic conversion technology of work outlines and work orders, as well as automatic binding and tracking technology of meeting decisions and system tasks, it can instantly transform textual requirements and resolutions into pre-set work orders that can be identified, tracked, and managed by an embodied intelligent workflow engine. This ensures that upper-level decisions can be transmitted downwards seamlessly and efficiently and rigidly executed, completely eliminating information dissipation and delays during transmission.

[0032] 5. Under the premise of strictly protecting data privacy, it has achieved collaborative evolution of global intelligence level. The power industry is characterized by sensitive data, making centralized model training difficult. This invention employs a federated learning framework, where each terminal trains its model locally using private data, uploading only encrypted model parameter updates. This design allows the system to continuously optimize global model performance through cloud aggregation without touching or aggregating the sensitive raw data from various subsidiaries. This perfectly aligns with the power industry's data security standards and solves the critical problem of data silos hindering AI effectiveness. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the intelligent office system provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart illustrating the intelligent office method provided in Embodiment 2 of the present invention; Figure 3 This is a flowchart of the intelligent prediction and broadcasting method for operational status; Figure 4 This is a flowchart of multimodal interaction and immersive broadcasting methods; Figure 5 This is a flowchart of the adaptive process generation and data entry method; Figure 6 This is a flowchart of a model optimization method based on federated learning. Detailed Implementation

[0034] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0035] refer to Figure 1 The system described in this embodiment adopts a "cloud-edge-device" collaborative architecture. The cloud deploys a cross-modal business large model, a digital twin of transmission lines, and an aggregation server for federated learning. Users access the system through local smart terminals (such as dedicated devices or mobile terminals integrating microphones, cameras, and AR glasses).

[0036] Example 1: This embodiment provides an autonomous intelligent office system for power transmission based on multimodal sensing and digital twins, such as... Figure 1 As shown, it specifically includes: The cross-modal business model, through pre-training and fine-tuning, is used to understand power transmission professional terminology, procedures, workflows and contexts and make decisions. As the core cognitive engine of the system, it is pre-trained and fine-tuned through massive power transmission professional documents, procedures, historical work orders and dialogue data, and has the ability to deeply understand power transmission professional semantics, logical reasoning and business process planning.

[0037] The multimodal fusion perception module is used to comprehensively determine the user's identity, intent, and current office scene status through voiceprint, voice, gesture, and environmental sensor data, thereby achieving a natural and secure interaction entry point.

[0038] The digital twin of the transmission line is synchronized in real time with the data of the physical transmission line and the panoramic platform. It is used for condition prediction, fault simulation and operation simulation. It also integrates physical mechanism model and data-driven model for condition monitoring, trend prediction, fault simulation and operation consequence simulation.

[0039] The embodied intelligence-driven workflow engine can autonomously plan, schedule, and execute complex, multi-step office task sequences based on the decision suggestions of the cross-modal business big model and the inference results of the digital twin, and can dynamically adapt to abnormal situations.

[0040] The system integrates and coordinates the following six functional modules: 1. Intelligent Prediction and Broadcasting Module for Operational Status: Integrates real-time data and predictive information to generate broadcasts with risk warnings; 2. Multimodal Interaction and Immersive Broadcasting Module: Provides high-security authentication, AR / VR visualization, and in-depth Q&A interaction; 3. Adaptive Process Generation and Form Filling Module: Enables intelligent process generation, automatic form filling, and compliance self-checking; 4. Predictive Task Reminder Module: Identifies high-risk tasks based on predictive models and provides proactive intervention suggestions; 5. Intelligent Document Compliance Review and Outline Generation Module: Enables document conflict detection and directly converts outlines into executable work orders; 6. Meeting Decision Tracking and Self-Execution Module: Enables automatic binding of resolutions and tasks and closed-loop tracking throughout the entire lifecycle.

[0041] Example 2 This embodiment provides a method for autonomous and intelligent office operations in the power transmission field based on multimodal sensing and digital twins, such as... Figure 2 As shown, the method includes the following steps: The multimodal fusion perception module collects the user's voiceprint, speech, and environmental context information for identity authentication and intent recognition. By using a cross-modal business big model, the deep semantics of user requests are analyzed, and combined with real-time and historical data from the power transmission panorama platform, decision support information is generated. By using a digital twin of the transmission line, the current status of the line can be simulated synchronously, and future operational risks or consequences can be predicted. Through an embodied intelligence-driven workflow engine, task sequences are autonomously generated, scheduled, and executed based on the decision information of the large model and the inference results of the digital twin. It integrates intelligent prediction and broadcasting of execution status, multimodal interaction and immersive broadcasting, adaptive process generation and filling, predictive task reminders, intelligent compliance review and outline generation of documents, and tracking and self-execution of meeting decisions into a complete office process; thereby realizing autonomous and intelligent office work throughout the entire power transmission process, from passive response to proactive prediction and from process solidification to intelligent adaptation.

[0042] The intelligent prediction and broadcasting steps for operational status described in this embodiment include: Based on machine learning models in digital twins, historical and real-time data are analyzed to predict the probability of line failure and load trends in specific future periods; the predicted future risk warning information is fused with the current operating status information; and dual-structured voice content containing the current status and future warnings is generated and broadcast.

[0043] The multimodal interaction and immersive broadcasting steps described in this embodiment include: fusing voiceprint and facial information for multimodal identity authentication; automatically calling the three-dimensional model of the digital twin when broadcasting complex faults, and presenting it in an immersive visualization through AR / VR devices; and conducting open-ended, multi-round question-and-answer interaction with users based on the cross-modal business big model for the broadcast content.

[0044] The adaptive process generation and filling steps described in this embodiment include: analyzing historical process logs through process mining technology to discover the optimal path, and dynamically generating or adjusting the handling process based on the current context by the workflow engine; after automatically filling in the professional form, calling the built-in procedure knowledge base and cross-modal business big model to automatically verify compliance and provide risk warnings for the filled content.

[0045] The predictive task reminder steps described in this embodiment include: associating the prediction results of the digital twin with the work plan, identifying future tasks that may be overdue or high-risk due to changes in the external environment or equipment status; and generating and pushing proactive intervention reminders that include specific suggested measures (such as adjusting the execution time or inspection method).

[0046] The document intelligent compliance review and outline generation steps described in this embodiment include: using a cross-modal business big model, cross-comparing the newly imported document with the existing standard document, and automatically identifying conflicting or ambiguous clauses; automatically converting the task items, time nodes, and responsible persons in the generated work outline into pre-set work orders that can be identified and tracked by the embodied intelligent workflow engine.

[0047] The meeting decision tracking and self-execution steps described in this embodiment include: when extracting meeting minutes, automatically binding "meeting resolutions" with specific "to-do items" and "responsible persons"; automatically creating the to-do items as system tasks, and having the workflow engine monitor and push progress throughout the entire lifecycle until a closed loop is formed.

[0048] The method described in this embodiment also includes a model optimization step: using a federated learning framework, the business model is trained on each terminal using local data; the encrypted model parameter update values ​​are uploaded to the cloud for aggregation to generate a globally optimized model; the globally optimized model is distributed to each terminal to continuously improve the system's intelligence level without sharing the original data.

[0049] Example 3 This embodiment uses the intelligent prediction and broadcasting of actual operating conditions as an example for illustration. Figure 3 As shown, the details are as follows: The user issues a voice command: "Report the operational status of all power lines in the province." The system first authenticates the user's identity through voiceprint verification. Then, the digital twin invokes a machine learning model to analyze weather forecasts, historical load curves, and equipment health data to predict the probability of a lightning strike causing a power outage in a certain area within the next 6 hours. Next, the intelligent prediction and broadcasting module integrates real-time operational data (such as "how many 500kV lines are currently operating normally") with predicted information (such as "however, a thunderstorm is expected in XX area at 3 PM, and the risk of line A tripping is as high as 80%)" to generate and broadcast structured content: "The weather across the province is sunny turning to thunderstorms. Currently, 198 lines are operating normally. One line is under high-risk warning. It is recommended to pay close attention to line A and deploy patrols in advance." Example 4 This embodiment uses multimodal interaction and immersive broadcasting as examples to provide detailed operation instructions, such as... Figure 4 The details are as follows: The user followed up on the broadcast in Example 3 by asking, "What was the tripping situation on Line A last month?" The multimodal interaction module transmitted the voice command to the cross-modal service big model, which understood this to be a request for historical data query and comparative analysis. It retrieved the tripping records, fault waveforms, and repair reports for Line A from the panoramic platform for the past month. Simultaneously, the system generated a 3D model of Line A using a digital twin and highlighted the historical fault points on the user's AR glasses. The content depth analysis unit organized the analysis results into a voice report: "Line A tripped twice last month, both caused by lightning strikes. The first repair took 3 hours, while the second repair was shortened to 1.5 hours due to timely availability of spare parts. The lightning protection facilities for this line have been strengthened, and the relevant fault waveforms have been pushed to your screen." Example 5 This embodiment uses adaptive process generation and data entry as an example to provide a detailed operation description, such as... Figure 5 As shown The user says, "Fill out the inspection work order for the lightning strike fault on Line A." The system unlocks permissions via voiceprint authentication, and the adaptive workflow generation and filling module is activated. Its workflow mining unit discovers that for inspections under high-risk warnings, the historically optimal workflow is the "emergency inspection workflow." The workflow engine automatically adopts this workflow and instructs the form intelligent filling unit to retrieve data from the digital twin and the user's work library, automatically filling in fields such as "Line Name: Line A," "Fault Type: Suspected Lightning Strike," and "Inspection Level: Emergency." After completion, the compliance self-check unit calls the large model for inspection, discovering that the inspection personnel qualification requirements must include a "High-Altitude Work Permit," and automatically prompts the responsible person for confirmation. Finally, based on the emergency workflow path, the form automatically skips the regular approval process and is sent directly to the inspection team.

[0050] Example 6 This embodiment uses federated learning-based model optimization as an example for detailed explanation, such as... Figure 6 As shown, the specific operation is as follows: To improve the accuracy of fault prediction models in digital twins, this invention employs federated learning. Local terminals in each provincial company train their local model copies using local, domain-independent historical fault data. After training, only the encrypted update values ​​of the model weight parameters are uploaded to a cloud aggregation server. The cloud securely aggregates the update values ​​from multiple terminals to generate a more accurate global model, which is then distributed back to each terminal. This cycle repeats, achieving global knowledge sharing and improved model performance while strictly protecting the original data privacy of each company.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. A power transmission professional autonomous intelligent office system based on multimodal perception and digital twin, characterized in that: include: Cross-modal business models, through pre-training and fine-tuning, are used to understand power transmission terminology, procedures, workflows, and contexts and make decisions. The multimodal fusion perception module is used to comprehensively determine the user's intent and the status of the office scene by using voiceprint, voice, gesture and environmental sensor data; A digital twin of a power transmission line is synchronized in real time with data from the physical power transmission line and panoramic platform, and is used for condition prediction, fault simulation and operation simulation. The embodied intelligence-driven workflow engine autonomously plans, schedules, and executes complex sequences of office tasks based on the decisions of the cross-modal business model and the inference results of the digital twin. The system integrates and coordinates the following modules for intelligent prediction and broadcasting of operational status, multimodal interaction and immersive broadcasting, adaptive process generation and filling, predictive task reminder, intelligent document compliance review and outline generation, and meeting decision tracking and self-execution, achieving a leap from passive response to proactive prediction and from process rigidity to intelligent adaptation.

2. The power transmission professional autonomous intelligent office system according to claim 1, characterized in that, The intelligent prediction and reporting module for operational status is used to: based on historical and real-time data, and through a machine learning model embedded in the digital twin, Predict the probability of line failure, load trends, and external risks within a specific future period; The prediction results are fused with real-time operational status data to generate dual-structured broadcast content that includes "current status" and "future risk warning".

3. The power transmission professional autonomous intelligent office system according to claim 1, characterized in that, The multimodal interaction and immersive broadcasting module includes: The multimodal identity authentication unit integrates voiceprint recognition and facial recognition for dual authentication, ensuring a high level of security for operation permissions; The immersive data presentation unit automatically invokes a digital twin when a complex fault is reported, generating a 3D visualization model of the faulty line on AR / VR devices or screens, and highlighting the fault point, the scope of impact, and comparisons with similar historical cases. The interactive question-and-answer unit, based on the aforementioned cross-modal business big model, can understand and answer users' in-depth and open-ended questions about the broadcast content, and provide comprehensive analysis.

4. The power transmission professional autonomous intelligent office system according to claim 1, characterized in that, The adaptive process generation and data entry module includes: The process mining and adaptive generation unit can automatically discover the optimal process path based on historical process data through process mining technology, and dynamically generate new and compliant handling processes by the embodied intelligent workflow engine when encountering anomalies or special situations. The intelligent form filling and compliance self-checking unit automatically fills in the form and performs compliance checks on the filled content based on the built-in procedure knowledge base and the large model, and prompts for risks or errors.

5. The power transmission professional autonomous intelligent office system according to claim 1, characterized in that, The predictive task reminder module includes: The risk task prediction unit, by associating the prediction results and work plans of the digital twin, automatically identifies tasks that may be delayed or have high risks due to factors such as weather and equipment status. The proactive intervention reminder unit not only reminds you of tasks to be done, but also provides prediction-based suggestions.

6. The power transmission professional autonomous intelligent office system according to claim 1, characterized in that, The document intelligent compliance review and outline generation module includes: The compliance intelligent review unit utilizes the cross-modal business model to cross-compare and analyze the consistency of newly imported management documents with existing superior documents and technical standards, and automatically identifies clauses that may have conflicts or ambiguities. The executable workflow generation unit not only generates a work outline, but also directly converts the task items, time nodes, and responsible persons in the outline into pre-set work orders that can be recognized and tracked by the embodied intelligent workflow engine, achieving a seamless connection from "document reading" to "task generation".

7. The power transmission professional autonomous intelligent office system according to claim 1, characterized in that, The meeting decision tracking and self-execution module includes: The automatic decision-task binding unit automatically establishes a strong association between "meeting resolutions" and "to-do items" when extracting meeting minutes, and assigns responsibility to specific individuals. The closed-loop tracking unit automatically enters the generated to-do items into the task system, and the embodied intelligent workflow engine monitors the entire lifecycle of the task, regularly pushing task progress to the meeting host or relevant responsible persons until all resolutions are closed.

8. The power transmission professional autonomous intelligent office system according to claim 1, characterized in that, The system employs a federated learning framework for model optimization. This involves training the local business models on local data on each terminal, encrypting the updated values ​​of the model parameters, uploading them to the cloud for aggregation, forming a globally optimized model, and then distributing it to each terminal. This allows for continuous improvement of the overall intelligence level of the system without aggregating sensitive original business data.

9. A method for autonomous and intelligent office work in power transmission based on multimodal perception and digital twins, characterized in that: The method includes the following steps: The multimodal fusion perception module collects the user's voiceprint, speech, and environmental context information for identity authentication and intent recognition. By using a cross-modal business model, the deep semantics of user requests are analyzed, and combined with real-time and historical data from the power transmission panorama platform, decision support information is generated. By using a digital twin of the transmission line, the current status of the line can be simulated synchronously, and future operational risks or consequences can be predicted. Through an embodied intelligence-driven workflow engine, task sequences are autonomously generated, scheduled, and executed based on the decision information of the large model and the inference results of the digital twin. It integrates intelligent prediction and broadcasting of execution status, multimodal interaction and immersive broadcasting, adaptive process generation and filling, predictive task reminders, intelligent compliance review and outline generation of documents, and a complete office workflow for tracking and self-execution of meeting decisions; To achieve fully autonomous and intelligent office operations for the power transmission industry, moving from passive response to proactive prediction and from rigid processes to intelligent adaptation.

10. The method according to claim 9, characterized in that, The method also includes model optimization, specifically comprising the following steps: A federated learning framework is adopted to train the large business model using local data on each terminal. The encrypted model parameter update values ​​are uploaded to the cloud for aggregation to generate a globally optimized model. The global optimization model is distributed to each terminal to continuously improve the system's intelligence level without sharing the original data.

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