450 results about "Electric power industry" patented technology

The invention discloses a power industry robot collaborative inspection and fault self-diagnosis system and method, and belongs to the technical field of power inspection, and the system comprises a management module which receives an inspection task instruction and obtains inspection task information according to the inspection task instruction; the environment identification module is used for acquiring inspection environment data and identifying obstacles; the path planning module is used for generating an inspection path set by adopting a multi-target particle swarm optimization algorithm according to the obstacle and inspection task information; the scheduling module is used for acquiring the state data of each robot and distributing the routing inspection paths in the routing inspection path set to each robot; the fault feature extraction module is used for acquiring multi-sensor data acquired by the robot and generating a fault feature vector; and the fault diagnosis module performs fault analysis on the fault feature vector to obtain a fault risk analysis report. The obstacle is recognized by acquiring the environment data, the inspection path set is generated by combining the inspection task information and adopting the multi-target particle swarm optimization algorithm, and the method can adapt to the complex inspection environment.

The invention discloses an electric power field knowledge question-answering system construction method based on a large language model, and relates to the field of electric power field knowledge question-answering, and the method comprises the steps: judging the data type of electric power field knowledge, and carrying out the processing of the electric power field knowledge according to the judgment result through matching with a processing technology, and generating an entity relation triple; constructing a power field knowledge graph; optimizing the power field knowledge graph based on the attention network, outputting an answer causal path of the fault problem by using the optimized power field knowledge graph, and marking a confidence score of the answer causal path; and inputting the solution causal path and the confidence score into a language model to obtain a fault question answering result, and optimizing the question answering result according to the consistency of the fault question answering result and the power field knowledge graph. According to the method, on the premise that the fault diagnosis logic is rigorous and the result is traceable, knowledge in large-scale unstructured literatures in the power industry is activated, so that accurate question and answer services can be provided for operation and maintenance personnel in real time.

The invention provides a power industry-oriented algorithm model operation monitoring method and system, and relates to the field of model monitoring. Comprising the steps of collecting physical quantity data of power equipment through a dynamic causal chain construction module, generating a causal chain after noise suppression and preprocessing, binding the causal chain with algorithm model parameters, and recording a sudden change event and an associated parameter update log; a four-dimensional space-time lock synchronization module is utilized to add geographic coordinates and timestamps, and data space-time consistency is ensured; the multi-target game strategy engine module constructs an optimization strategy based on historical misinformation events and resource consumption records in combination with SLA time constraints of real-time detection tasks; the DEED-Trigger anti-intuition triggering module triggers an emergency response according to the deviation between the real-time data and the optimization strategy; and the verification platform injects a composite fault through a digital twin technology to test the causal chain robustness, and dynamically adjusts the causal chain structure and strategy.

The invention relates to the technical field of electric power management and control, and discloses an electric power BIM type data GIS discount classification method and system, and the method comprises the following steps: 1, constructing an electric power industry exclusive classification system and semantic mapping, and building a three-stage classification frame based on a GIS and an electric power industry standard; 2, unifying a space coordinate reference and a topological relation, and verifying topological consistency between components and between the components and topography and roads through a GIS (Geographic Information System) tool; 3, multi-scale classification and dynamic LOD model generation are carried out, and LOD model details are automatically switched according to a GIS scale; 4, performing data format conversion and intelligent lightweight processing, performing lossless compression on key components, and performing simplified compression on secondary components; and step 5, performing classification verification and dynamic updating mechanism, and comparing whether BIM and GIS base map position classification is correct or not, thereby realizing data synchronization. According to a dynamic classification framework and an intelligent lightweight strategy in the power industry, the BIM model efficiently adapts to the spatial analysis requirement of the GIS, and the data fusion and application efficiency is improved.

The invention discloses a method and a system for constructing an intelligent simulation data set of an electric power scene. The method comprises the following steps: firstly, constructing a power operation virtual scene fusing power industry safety regulations and physical effects; secondly, decomposing complex homework, and designing a reward function containing expert operation skills and force feedback by adopting deep reinforcement learning; and finally, generating and automatically marking a multi-modal data set containing vision, state and key force or moment information on a large scale. According to the method, the problem of generating high-quality training data for contacting rich robot operation tasks is solved, the generated multi-modal data set can effectively meet the difference between simulation and reality, and a key data basis is provided for automation of the high-risk industry.

The invention discloses an electricity price prediction large model construction method and system based on federated learning, and relates to the related technical field of electricity price prediction, and the method comprises the steps: executing the data collection of a time window in a preset time window, and building a window data set; executing sample feature extraction; performing multi-dimensional feature clustering by using the multi-dimensional feature identifier and configuring N clusters; performing cluster division based on double identification channels, and establishing an independent cluster and a combined cluster; independently training the decision tree by using the independent cluster, and executing weak classifier mapping training of the combined cluster; and establishing an electricity price prediction large model. The technical problems that in the prior art, power industry data are difficult to share, multi-source heterogeneous data cannot be integrated through federal learning, the electricity price prediction precision is low, and the prediction model generalization ability is poor are solved, and the technical effects that multi-mechanism data collaborative modeling is achieved, and the electricity price prediction model generalization ability and the electricity price prediction precision are enhanced are achieved.

The invention relates to a thermal power plant electric energy prediction and dynamic optimization method based on a multi-modal large model and a hypergraph space-time network. The method comprises the steps that multi-modal data are collected and preprocessed; a DCMDM-MCL joint model is constructed, and a fusion feature vector is obtained; obtaining an electric energy prediction result; carrying out multi-objective optimization processing by adopting an NSGA-III algorithm to obtain a scheduling strategy preliminary scheme after multi-objective optimization; and carrying out fine tuning by utilizing TD3 reinforcement learning to obtain a dynamic optimization scheduling strategy of the electric energy of the thermal power plant. The instantaneous working condition change of the equipment is accurately captured, and the prediction reliability is enhanced; efficient and safe operation of the system is guaranteed; in the sudden environment change, the system completes adaptive adjustment within 2 hours, prediction errors are recovered to the baseline level, and the robustness of a complex scene is remarkably improved; structured and unstructured data are fused, and the deployment cost and period are greatly reduced; a closed loop of data fusion, prediction, scheduling and evolution is formed, and a high-precision, real-time and sustainable intelligent optimization solution is provided for the power industry.

The invention discloses a large model fine tuning method based on a causal graph and thinking chain enhancement and a related device, and relates to the technical field of large model fine tuning in the power industry, and the method comprises the steps: carrying out the causal mining of power equipment data, and constructing a power equipment causal graph containing causal weight information; disassembling the input of the large model into a thinking chain, correspondingly generating a chain type causal pair according to the thinking chain, and performing path retrieval matching and causal consistency check through the chain type causal pair and the constructed electrical equipment causal graph to realize alignment of the reasoning process; and exciting a reinforcement learning process through an alignment result of the reasoning process, optimizing a pre-established reinforcement learning reward model, constraining a thinking chain generation process, guiding the large model to generate a thinking chain under a causal constraint condition, and realizing fine tuning of the large model. According to the method, causal reasoning and causality are embedded into a reinforcement learning feedback process of large model fine tuning, so that the large model can learn a basic causal reasoning rule, and the logicality, the interpretability and the robustness of thinking chain reasoning can be improved.

The invention discloses a deep learning-based power utilization abnormal behavior detection method and system. The method comprises the following steps of: obtaining electrical quantity modal, environment modal and log modal data of a target system, and performing data cleaning, adaptive segmentation standardization, missing value filling and timestamp adding preprocessing; respectively inputting each piece of preprocessed modal data into a Transform model based on time decay attention and time perception position coding to carry out deep feature extraction, and obtaining features of corresponding modals; the electrical quantity modal features are enhanced and spliced with the environment modal features and the log modal features to obtain fusion features, the fusion features are utilized to train an anomaly detection model based on deep learning, and the anomaly detection model is updated in real time based on a dynamic threshold strategy to judge the anomaly standard; and using the trained anomaly detection model to carry out electricity consumption anomaly detection. According to the invention, the safety and stability of the power system can be significantly improved, and the intelligent development of the power industry is promoted.

The invention provides a power industry-oriented language understanding and dialogue generation method, which comprises the following steps of: retrieving a dialogue process template related to a current problem from a pre-constructed power industry multi-round dialogue knowledge base according to a field label of the problem, and determining a basic framework of a dialogue process and key information required to be acquired by each round of dialogue; after each round of dialogue is finished, the obtained information is synthesized, reasoning is carried out through a pre-constructed electric power knowledge graph, the incidence relation between different professional fields is analyzed, key points and possible solutions of questions are deduced, and candidate answers are generated; and establishing an answer sorting model by applying deep learning, scoring and sorting the candidate answers, and selecting the answer with the highest score as the reply of the current round in combination with the correlation, integrity and specialty of the answers.

The invention discloses a transformer product carbon footprint modeling method based on full life cycle evaluation. The transformer product carbon footprint modeling method comprises the steps of S1, system boundary confirmation; s2, integrating multi-source data; s3, constructing an accounting model; according to the method, through system boundary confirmation, multi-source data integration and accounting model construction, the full-life-cycle carbon emission of a transformer product is accurately quantified, and the method comprises the steps of raw material acquisition, raw material transportation, production and manufacturing, product transportation, product use and carbon footprints of all stages of retirement and recovery. All the processes are calculated according to the model, an effective tool is provided for environmental protection evaluation and green design, carbon emission of all links is assisted to be optimized, and theoretical support and practical guidance are provided for low-carbon transformation of the power industry.

The invention relates to the technical field of small target detection and identification in images in the power industry, and provides an energy storage inverter optimization deployment defense method based on a double-layer Stackelberg game and related equipment. The method comprises the following steps: constructing a LinDistFlow model based on load power disturbance and reactive power disturbance of each node attacked by LAA; maximizing the voltage deviation of all attacked nodes in the whole attack time period as a first objective function, and combining a first constraint condition to construct a dynamic model of the LAA attack; taking minimization of the operation cost of the energy storage inverter and the voltage deviation of the attacked node as a second objective function, and combining with a second constraint condition to construct a dynamic model of LAA defense; introducing confidence parameters based on load power disturbance and reactive power disturbance of each node, and constructing an optimization model based on opportunity constraint; and if the defender knows / knows the injection power of the attacker, solving the model, and making a defense strategy in advance by using the data of the attacker.

The invention discloses an electric power real-time data dynamic encryption transmission method and system based on a national secret algorithm. According to the method, the real-time updating of the session key is realized by fusing the SM3 Hash algorithm and the multi-dimensional dynamic factor, and the anti-attack capability of data transmission of the power system is improved. A timestamp and data counter double-trigger mechanism is introduced in the key generation process, so that the key is automatically alternated under a fixed time interval or a data volume threshold value, a key exposure window is shortened, and the security risk caused by using the same key for a long time is reduced. Key synchronization is carried out in combination with an SM2 bidirectional authentication channel, the confidentiality of key distribution is ensured, access of unauthorized equipment is avoided through an identity authentication mechanism, full-closed-loop security protection from key generation to transmission is formed, the high-security requirement of the power industry for key data transmission is met, and the security of key data transmission is improved. Through deep combination of a cryptographic algorithm system and a dynamic adaptation mechanism, the high efficiency and compliance of power real-time data transmission are considered while the security is ensured.

The invention discloses an electric power multi-modal corpus construction query method and system based on a sliding window, which is applied to the field of electric power data query, and comprises the following steps: obtaining a structured document according to electric power multi-modal data, segmenting the structured document to obtain a plurality of segmented text blocks, and storing the segmented text blocks into a database; inputting each segmented text block into a large language model to generate a to-be-stored text vector and construct an electric power multi-mode corpus, when a user query request is received, generating a plurality of query variants according to query data, performing nearest neighbor search on each query variant in the electric power multi-mode corpus to obtain a corresponding nearest neighbor search result, and storing the nearest neighbor search result in the electric power multi-mode corpus. And fusing each nearest neighbor search result to generate an electric power related document set comprising a multi-modal association mark. According to the method, the semantic units can be accurately captured, semantic breakage is avoided, the retrieval continuity and coverage rate are improved, the comprehensiveness and context adaptability of retrieval results are improved, and the data retrieval requirement under the complex scene of the power industry is met.

The invention provides a multi-stage desensitization electricity charge sensitive data protection system, belongs to the technical field of information security in the power industry, and relates to a full-life-cycle protection scheme of sensitive data such as an identity label, account information and contact information in an electricity charge service system. The problems that in the prior art, only simple masking or full-table encryption is carried out on a database layer, the desensitization dimension is single, multiple links are prone to leakage, no role fine-grained desensitization strategy exists, and exported files are difficult to trace are solved. The system comprises a data acquisition source and acquisition layer desensitization, a transmission layer encryption, a storage layer transparent encryption, an API gateway, a desensitization strategy center, a front-end dynamic mask, a watermark export, a log desensitization plug-in and an auditing log module, covers the whole process from data acquisition to log auditing, dynamically regulates and controls data display according to roles, and exports a file embedded identity watermark. And desensitizing the log and reserving operation marks. Full-link leakage prevention, fine-grained permission control and leakage traceability can be realized, and the transformation cost is low.

The invention provides an intelligent prediction model construction method based on multi-modal data fusion, and the method comprises the following steps: collecting multi-modal data in different electric power scenes, and carrying out the preprocessing of the multi-modal data, the electric power scenes including electric power inspection, electric power transaction and electric power equipment operation and maintenance; performing feature extraction, semantic association and space-time alignment on the preprocessed multi-modal data through a cross-modal attention mechanism and association analysis; constructing an intelligent prediction model, and performing knowledge injection on the intelligent prediction model through a retrieval enhancement generation technology and a lightweight adapter; a multi-task learning framework is introduced into the intelligent prediction model, exclusive output heads are set for different power scenes, and training is carried out through multi-modal data; after training is completed, lightweight compression is carried out on the intelligent prediction model through a knowledge distillation technology, a multi-modal data fusion technology system suitable for the power industry is formed, and key technology support is provided for deepening application of the intelligent prediction model of the power system.

The invention provides an electric power industry-oriented A I intelligent question answering and professional report generation method, which comprises the steps of extracting key information from a constructed knowledge graph to form a preliminary electric power knowledge base structure, and the extraction process comprises the steps of identifying high-frequency nodes, analyzing connectivity among the nodes and evaluating importance weights of the nodes; based on the preliminary knowledge base structure, integrating structured and unstructured data in the power field to form a complete power knowledge base, and keeping synchronous updating with the knowledge graph; based on the electric power knowledge base, training a natural language processing model in the electric power field, which comprises named entity recognition and relation extraction by adopting a deep learning model in sequence; and performing semantic annotation on nodes and edges in the knowledge graph by utilizing a trained natural language processing model, and integrating new semantic information into the knowledge graph through entity alignment and relation mapping.

The invention provides a scanning method and system for hidden asset identification in an electric power industrial control network, and aims to solve the problem that silent, non-registered or illegal access equipment is difficult to discover by the existing asset surveying and mapping means. According to the method, multiple protocol induced detection messages are injected into a target network, equipment response data are collected, feature vectors are extracted, and abnormal equipment behaviors are identified by using an unsupervised clustering and anomaly detection algorithm; and meanwhile, in combination with a graph neural network and a hidden Markov model, modeling is performed on a network communication chain structure and a historical behavior sequence, and potential hidden asset nodes are deduced. According to the method, active discovery and risk reasoning of hidden assets can be realized on the premise of not influencing industrial control services, and the method is suitable for industrial control scenes such as transformer substations and dispatching centers in the power industry and has high safety, intelligence and feasibility.

The invention discloses a power industry dynamic knowledge base construction method and system based on a large language model, and relates to the field of electric digital data processing, and the method comprises the steps: receiving a power field document imported by a user, and converting the power field document into an electronic document; calling a large language model to analyze the electronic document, extracting a terminology definition, chart position information and a document logic level in the electronic document, and generating a knowledge unit containing a plurality of contexts; connecting the knowledge units into directed knowledge links, calculating semantic similarity between adjacent knowledge units in the directed knowledge links, and segmenting the directed knowledge links to obtain a plurality of knowledge topics; receiving a knowledge retrieval instruction, and determining a target knowledge theme corresponding to the knowledge retrieval instruction; and extracting a corresponding target knowledge unit in the target knowledge theme, and generating a knowledge retrieval result based on a target context of the target knowledge unit. By implementing the method, knowledge base construction can be optimized, and the integrity of knowledge base data is ensured.

The invention discloses an electric power field operation management system, which relates to the technical field of electric power and comprises the following process modules: S1, planning; s1-1, an operation plan and task management module; s1-2, a power industry core combined scale block; s2, preparing; s2-1, a personnel and qualification management module; s2-2, a tool and material management module; s2-3, a risk assessment and scheme management module; s3, executing; s3-1, a field execution and monitoring module; s3-2, an emergency processing module; s4, checking and accepting; s4-1, an acceptance check and filing module; s4-2, a performance and data analysis module; s4-3, a system management module; and S5, summarizing. According to the electric power field operation management system, through systematicness and integrity of emergency plan management, a process covers full life cycle management of an emergency plan from formulation, auditing, issuing to updating, and meanwhile, links such as emergency response starting, disposal, resource management, information communication and recovery summarization are included; and a complete closed loop of prevention, preparation, response and recovery is formed.

The invention provides an electric power document keyword extraction method based on Prompt and a knowledge graph, relates to the technical field of electric power document processing, and constructs a lightweight multi-level index knowledge graph in the electric power field by combining entity type and relation type division based on an electric power industry standard document and an electric power field corpus. The method comprises the following steps: performing vector modeling on a power document, constructing a multi-level index from an entity to a vector, realizing standardized semantic modeling and efficient hybrid retrieval of a power document field background, and obtaining a topic vector and a core paragraph of the power document in combination with power key information; according to the method, entity types are indexed in a knowledge graph by using subject vectors, similar entities are obtained to form knowledge sub-graphs, so that multilayer Prompt is obtained to guide a large language model to extract keywords, then knowledge graph similarity constraints are introduced to decode the output of the large language model, the keyword recognition capability in the power field is improved, and the keyword recognition efficiency is improved. And the accuracy of keyword type identification and the normalization of term naming are both considered.

The invention provides an electric power overhaul key safe release control method based on a safety measure conflict algorithm, and the method comprises the steps: collecting safety measure contents, carrying out the structural analysis, and constructing a safety measure key pair incidence matrix; detecting the relevance between the safety measure associated with the current application key and the executed or to-be-executed work ticket or operation ticket in the system, and identifying a potential conflict risk according to a preset rule base; performing conflict analysis on the identified conflict risk through a dynamic calculation model to obtain a conflict degree score; and performing key releasing or locking operation according to the conflict degree score. Through automatic conflict detection, the risk of misoperation caused by safety measure conflicts is reduced by more than 90%, and fatal hidden dangers such as overlapping isolation of safety measure operations are eliminated; the maintenance efficiency is improved, a manual checking process is replaced, and rapid key allocation during multi-task parallel maintenance is supported; full-digital tracing of safety measure execution, key use and conflict processing is achieved, and the five-prevention management requirement of the power industry is met.

The invention relates to the field of electric power industry, and discloses a digital twinborn GIS partial discharge mode identification method, which comprises the following steps of S1, establishing a high-fidelity digital twinborn simulation model according to a typical structure, operating parameters and potential abnormal working conditions of GIS equipment; s2, in the simulation model, performing accurate modeling on a physical structure which may cause partial discharge, and simulating to generate a simulation data set containing common and rare partial discharge fault scenes; s3, extracting spatio-temporal features and constructing a fault diagnosis model based on spatio-temporal data collaborative learning; s4, combining laboratory GIS equipment and a digital twinborn model under laboratory conditions, refining spatial-temporal characteristics, and enriching a data set by continuously collecting data, so that the model learns more fault types in an incremental learning mode; and S5, integrating the model which is verified to be effective into an actual engineering scene monitoring system, and performing intelligent identification based on continuous fusion of simulation and actual data.

The invention discloses a power industry electroencephalogram cap-oriented key channel screening method and system, and the method comprises the following steps: firstly, collecting electroencephalogram signals of a power worker in typical states of waking, fatigue and the like by adopting a 32 or 64 channel electroencephalogram cap, and carrying out the preprocessing operations of band-pass filtering, ICA (Independent Component Analysis), signal-to-noise ratio evaluation and the like; artifacts and inferior channels are removed, and then multi-dimensional features such as the frequency domain, the time domain, the entropy value and statistics of each channel are extracted to comprehensively score the importance of the channels. Constructing a plurality of channel subsets, verifying the recognition performance of the channel subsets through a classification model, and screening out a group of key channels with the highest information expression capability in cognitive state discrimination; according to the method, the number of electroencephalogram channels is reduced, the complexity and calculation overhead of acquisition equipment are reduced, the feasibility of real-time deployment of the model in an electric power field is improved, and the method has relatively high industry adaptability and engineering application value and is suitable for various key scenes with relatively high requirements on personnel state perception.

The invention discloses a power industry language model training data dynamic selection method, system and device, and the method comprises the steps: obtaining original scene corpus data based on a power industry power grid dispatching and fault handling application scene aimed at language model training, and constructing a candidate data pool and a scene verification data set; performing parallel or pipelined multi-dimensional quality evaluation on the candidate data in the candidate data pool, dynamically adjusting the weight corresponding to each dimension in the multi-dimensional quality evaluation based on an adjustment strategy, and calculating to obtain a comprehensive quality score; a failure case library is constructed, in the language model training process, the failure case library is updated regularly, semantic relevance between candidate data and the failure case library is quantified, and batch construction of training data is carried out based on the semantic relevance; and based on the batch construction result, performing language model training feedback circulation. According to the invention, the overall efficiency and utilization rate of training data are effectively improved.

The invention discloses an intelligent conference voice transcription and summary generation method and system for voiceprint recognition and hot word optimization in the power industry. The method comprises the following steps: acquiring voice samples in advance to construct an encrypted voiceprint database; mining terminologies from multi-source data in the power industry and dynamically allocating weights to establish a hot word library; performing unified decoding and adaptive preprocessing on the input audio; after speech features are extracted, speaker separation and recognition are achieved through deep embedding clustering; integrating the hot word bank, and generating a transliteration text with a speaker tag by adopting a transform-based field adaptive model; and carrying out multi-granularity semantic understanding and hierarchical abstract generation by utilizing the pre-training model in the power field, and automatically outputting a structured conference summary. According to the method, the problems that the accuracy of professional term recognition in the power industry is low, separation of multiple speakers is difficult, and summary generation depends on manpower are effectively solved, and the efficiency and the intelligent level of conference recording are remarkably improved.

The invention provides an intelligent electric meter communication network building method based on Bluetooth Mesh. The technical scheme is characterized by comprising the following steps: deploying hardware facilities of an electric energy meter and a communication network; designing a network architecture, and constructing a layered network structure; configuring network security, and establishing a key management system; a communication protocol is adapted, and the Bluetooth Mesh is in butt joint with a standard protocol in the power industry; detecting the link quality of each intelligent electric meter, and selecting a routing path for uploading data; performing verification test on the operation of the intelligent electric meter communication network, and establishing an energy consumption management scheme; key indexes during operation of the communication network are monitored, and the communication network is maintained and adjusted through feedback information; the communication stability of the intelligent electric meter can be improved, the network coverage range is expanded, the energy consumption is reduced, and the deployment, management and maintenance of a communication network are simplified.

The invention discloses a large model training method based on electric power intelligent engineering. The method comprises the following steps: acquiring sample data; the sample data comprises first text data and picture data; performing format conversion on the picture data to obtain second text data converted from the picture data; constructing an initial electric power knowledge graph based on a preset model framework; performing association mapping on the first text data and the second text data with entities and relationships in the initial electric power knowledge graph to obtain a fused electric power knowledge graph; and performing fine tuning training on the pre-trained large model based on the fused electric power knowledge graph to obtain a trained target large model. According to the method, the large model can be optimized towards a specific task and an application scene in the field of electric power intelligent engineering on the basis of the ability of general language understanding and the like, so that the trained target large model better meets the actual demand of the electric power industry, and the output result is more targeted and professional; and the application effect in electric power related services is effectively improved.

The invention relates to the technical field of risk assessment based on big data, and provides a post-disaster regional operation risk assessment method based on a Beidou technology, and the method comprises the steps: building an impact analysis model based on disaster types through multi-dimensional information, and combining with the condition of a target work site, so as to assess the risk of post-disaster field operation. In order to achieve the purpose, the post-disaster regional operation risk assessment method based on the Beidou technology is provided and comprises the steps that daily multi-source data collection and standardization processing are carried out on a target region, and indoor and outdoor scene dynamic judgment models are established respectively for risk index calculation; and after the disaster occurs, detecting the target area again, comparing the detection result with the comprehensive risk index, and accurately evaluating the risk again. According to the method, after-calamity risk assessment is converted from fuzzy judgment to quantitative accuracy, operation preparation is converted from experience driving to data driving, and direction guidance is provided for after-calamity safe and efficient operation in the power industry through risk assessment.

The invention discloses a thermal power generating unit main reheating steam temperature wide-load self-adaptive adjusting method and system, relates to the technical field of electric power industry automation and control, and aims at solving the problems that an existing adjusting method is large in steam temperature fluctuation, high in thermal stress and poor in main reheating collaboration under the wide-load working condition. The method comprises the steps of calculating main steam phase saturation and mapping a target value; calculating the maximum change rate based on the thermal physical parameters of the material, and generating a reference track with the limited change rate; the steam phase saturation of the main steam is used as a phase reference, and the reheated steam is controlled to respond according to a preset phase difference; a combustion system, a flue gas temperature adjusting baffle and a temperature reducing water system are cooperatively controlled; the system comprises a vapor phase saturation processing module, a dynamic boundary generation module, a phase coordination control module, an execution mechanism coordination module, a steady state detection module and a mode switching module. Accurate steam temperature adjustment under the wide load is achieved, material thermal stress is reduced, unit operation safety and economical efficiency are improved, and the variable-load operation requirement of the unit is met.