643 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 discloses an improved YOLOv11 power transmission line defect detection method, which introduces an attention mechanism to optimize the feature extraction capability, improves the performance of a model in a complex scene, remarkably enhances the understanding of the model for local and global information, and improves the target detection precision. Spatial offset is carried out on a feature map in a detection head by adopting feature extraction, feature association of different scales is enhanced, the detection precision of a small target is improved, redundant calculation is reduced, the calculation efficiency and performance of a model are further optimized, diversified requirements in a complex environment are met, and the method is suitable for popularization and application. The feature learning ability of a self-integrated attention head optimization target detection model is introduced, and the detection precision and robustness are improved under the scenes of small object detection and complex backgrounds. The method effectively improves the detection precision and training efficiency, can achieve excellent performance in a high-precision requirement and a real-time task, and meets the requirements of safety risk management and control in the power industry.

The invention belongs to the technical field of intelligent document management and auditing in the electric power industry, and provides an electric power document autonomous review method and system fusing causal reasoning and compliance review. According to the technical scheme, obtained electric power document data is subjected to semantic analysis, and semantic representation of a document is obtained; based on the semantic representation of the document, performing event extraction on each sentence recognized in the document, and encoding each event to obtain the semantic representation of the event; constructing an initial event causal chain based on the semantic representation of the event; verifying the constructed initial event causal chain by adopting a causal reasoning algorithm, comparing each event in the verified causal chain and the incidence relation between the events with a power industry compliance rule, calculating a compliance score, and calculating the incidence relation between the events of which the compliance score is lower than a set score value or the incidence relation between the events of which the compliance score is lower than the set score value; and generating a correction suggestion, and generating a review report. And the efficiency and the accuracy of document review are obviously improved.

The invention relates to the technical field of power supply chains, and discloses a power supply chain risk early warning method driven by a knowledge graph and a related device. The method comprises the following steps: acquiring multi-source data of a power supply chain; the multi-source data comprises structured data and unstructured data; inputting the multi-source data into a risk early warning model, and outputting a risk probability; wherein the risk early warning model takes a multi-modal graph neural network as a backbone network and combines a graph attention mechanism and comparative learning to realize fusion and risk prediction early warning of multi-source data. According to the invention, the advanced knowledge graph technology and the deep learning algorithm are utilized to realize intelligent risk early warning of the power supply chain; according to the power supply chain risk early warning method driven by the efficient knowledge graph, the risk management level of the power supply chain can be effectively improved, and a powerful guarantee is provided for stable operation of the power industry.

The invention relates to the technical field of intelligent data processing and artificial intelligence in the electric power industry, and provides an electric power semantic large model adaptive data query and dynamic response method and system.The method comprises the steps that electric power industry natural language query and related images are obtained, and after feature extraction is conducted through a large language model and a deep learning model, the images are obtained; the knowledge graph and the visual question and answer graph neural network are utilized, features of different modes are unified into the same representation in a bidirectional fusion mode, fused features are obtained, after intelligent error correction and natural language query optimization are conducted through the hypergraph neural network and multiple rounds of interaction, an extended query statement is obtained through hypergraph reasoning, and a query result is obtained. Converting the extended query statement into a structured query statement through an autoregression model to obtain a query result; and performing dynamic permission adaptation, sensitive data protection and compliance and security check on a query result to realize dynamic response. And the query accuracy and efficiency of the power industry are greatly improved.

The invention relates to the technical field of data sharing, and particularly discloses an electrical equipment supply chain data security access method and system, and the system can intelligently match table structure information in a power industry database based on natural language input of a user, and generates a corresponding SQL query. By combining a natural language processing technology with a large language model, a user can easily access and query required power industry data under the condition that a complex SQL statement or a database structure does not need to be mastered. According to the method, the efficient query task can be automatically generated, the data access difficulty is greatly reduced, meanwhile, a data field level encryption scheme and a user permission intelligent verification mechanism are designed, and the safety of the data access sharing process is ensured; and the method has good expansibility and can be compatible with power enterprise databases of different scales.

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 discloses an electric power knowledge question-answering system construction method and system combined with engineering ethical analysis, and the construction method comprises the steps: constructing all modules, and carrying out the fine adjustment of a large language model based on a P-Tuning method, so as to construct an electric power system question-answering knowledge base; the question understanding and classification step is responsible for receiving input questions of a user and performing semantic analysis, technical classification and ethical risk identification on the input questions; searching background knowledge; carrying out engineering ethical analysis; generating questions and answers; and a feedback and improvement step: entering a model tuning stage, and carrying out adaptive tuning on the model by adopting a reinforcement learning framework. According to the method, comprehensive engineering ethical analysis can be carried out on technical schemes and operation suggestions, finally, answers generated by the system are accurate and feasible in technology and have ethical compliance at the same time, it is ensured that a user can balance technical requirements and ethical responsibility in practical application, the intelligent level of an electric power knowledge question-answering system is remarkably improved, and the user experience is improved. And important support is provided for safe operation and sustainable development of the power industry.

The invention discloses an electric power industry data analysis method, device and equipment based on a large model and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining historical multi-modal data of the electric power industry, and determining a target feature corresponding to the historical multi-modal data based on a preset multi-modal fusion strategy; according to the historical multi-modal data and the target features corresponding to the historical multi-modal data, performing fine tuning processing on a pre-trained initial large model to obtain a target large model; and constructing a corresponding knowledge graph based on a preset graph database technology and the historical multi-modal data, and carrying out data analysis on target multi-modal data, collected in real time, of the power industry by using the target large model and the knowledge graph. In this way, data of different modes can be integrated and analyzed through a multi-mode data fusion strategy. In addition, a multi-dimensional data analysis result can be determined through a large model technology and a knowledge graph technology.

The invention provides a power transmission line icing condition monitoring and early warning method and system, and relates to the technical field of power transmission line icing early warning. According to the scheme, line image data are acquired based on a power transmission panoramic system, and grid meteorological data are acquired from a meteorological center; based on a pre-trained icing identification model and in combination with a multi-head attention mechanism, carrying out icing identification on the line image data to obtain an image identification result, and based on a support vector machine algorithm, predicting an icing condition according to grid meteorological data to obtain a meteorological prediction result; and matching the grid meteorological data with the monitoring and photographing device point location to obtain positioning information, fusing a meteorological prediction result with an image recognition result for correction, and performing icing degree division according to a preset threshold value to obtain an icing degree report. According to the method, the traditional machine learning technology, the deep learning technology and the data processing and analysis technology are comprehensively utilized, the precision, the real-time performance and the reliability of icing monitoring are improved, meanwhile, electric power accidents caused by icing are reduced, and support is provided for development of the electric power career.

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 provides an intelligent operation and maintenance method for an electric power automatic monitoring system based on multi-modal data, and relates to the field of artificial intelligence, and the method comprises the following steps: carrying out the screening and classification processing of structured data and unstructured data on the operation and maintenance data of the electric power automatic monitoring system obtained in advance; performing preliminary anomaly detection on the structured data, and if no anomaly is detected, performing secondary anomaly verification to obtain an anomaly verification result; performing feature vector extraction on the unstructured data, and performing exception classification processing on the extracted feature vectors; a knowledge graph is constructed based on historical cases of the electric power automatic monitoring system, and fault causes and treatment measures are obtained through comparison and analysis in combination with an anomaly verification result and an anomaly classification result. The reliability, the stability and the intelligent level of the power system can be improved, and green transformation of the power industry is promoted.

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 virtual power plant adjustable capacity boundary construction method considering conditional value-at-risk, and the method comprises the steps: firstly constructing a virtual power plant electric energy dispatching model based on time-of-use electricity price guidance by taking the minimum electricity purchasing cost of a virtual power plant spot market as an objective function; constructing a virtual power plant regulation capacity boundary depicting model by taking maximum output and minimum output of the virtual power plant in the regulation time period as objective functions; the maximum output and the minimum output of the virtual power plant in the adjustment period respectively correspond to the upper limit and the lower limit of the adjustment capacity boundary of the virtual power plant; solving the virtual power plant electric energy scheduling model to obtain a scheduling strategy of the virtual power plant in the electric energy market; and finally, according to a scheduling strategy of the virtual power plant in the electric energy market, performing global optimization on the virtual power plant adjustment space capacity description model to obtain an adjustable capacity boundary of the virtual power plant in an adjustment time period. The invention is beneficial to improving the flexibility of the power system, promoting the green and low-carbon development of the power industry and optimizing the energy structure.

The invention discloses a power system network topology automatic identification method and system. The method comprises the following steps: acquiring power information of each node in a power system; analyzing relevance among the nodes according to the power information of the nodes to obtain associated nodes and independent nodes in the power system; determining a connection relationship between the nodes based on the associated nodes and the independent nodes, and constructing a network topology structure according to the connection relationship between the nodes; and obtaining a real-time operation state of each node, and adjusting the network topology structure according to the real-time operation state of each node to obtain an updated topology structure. Manual intervention can be reduced, the operation and maintenance cost can be reduced, the intelligent management level of an electric power system can be improved, efficient operation of an electric power network and optimal configuration of resources can be achieved, and powerful technical support is provided for development of the electric power industry.

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 belongs to the field of intelligent operation and maintenance of an electric power system, and provides an electric power intelligent question and answer multi-modal retrieval method and system. Converting the multi-modal data of the power industry into structured representation of nodes and edges of the knowledge graph; performing optimization and de-duplication processing on the edges between the data nodes to obtain optimized edges between the nodes, and constructing an initial knowledge graph; incremental change information is determined based on the newly added multi-modal data, and after neighborhood optimization and consistency check are adjusted according to the incremental change information and the edge weight, an optimized knowledge graph is generated; extracting multi-modal features, and aligning the multi-modal features in the same feature space to obtain aligned multi-modal features; and matching the query features with the knowledge graph, retrieving to obtain a node most related to the query, dynamically analyzing the content and associated information of the selected node, and generating corresponding multi-modal response data. And the complex query requirement of the user is met.

The invention discloses a noise monitoring analysis and fault early warning method and system for a large transformer, and relates to the technical field of noise monitoring, and the method comprises the following steps: collecting and preprocessing a coverage broadband noise signal through a sensor; a CNN-LSTM model is established for the collected noise signals through a deep learning module; and setting an early warning threshold value, performing judgment through the CNN-LSTM model, and triggering an early warning signal. According to the invention, key features can be automatically extracted from transformer noise, real-time analysis is carried out through the deep learning model, multiple fault types can be rapidly identified without manual intervention, early warning can be timely given out, power failure accidents caused by faults are reduced, the stability of power supply is improved, intelligent upgrading of a power equipment monitoring technology is promoted, and the power supply efficiency is improved. And a key technical support is provided for digital and intelligent transformation of the power industry.

The invention discloses a power industry carbon emission prediction method and system based on an XGBoost algorithm, and belongs to the technical field of carbon emission prediction. The method comprises the following steps: acquiring current data related to carbon emission in the power industry; inputting the current data related to the carbon emission in the power industry into a prediction model, and solving the prediction model to predict the carbon emission of the power industry in a future time period; training samples of the prediction model are a training set and a test set which are generated on the basis of historical data related to carbon emission in the power industry. The XGBoost algorithm is adopted to construct the model for prediction, and the predicted carbon emission is more accurate.

The invention provides a power industry knowledge question-answering method and system based on a large language model, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining power industry data; constructing a knowledge graph with node weights; acquiring a training set taking the power industry question as a feature and taking the power industry answer as a label; constructing a generative question and answer model with knowledge graph embedded information; establishing a generation loss function; taking the generation loss function value smaller than a preset generation loss function value as a target, training the generative question-answer model to determine an optimal hyper-parameter of the generative question-answer model; obtaining a to-be-processed power industry problem; and inputting a to-be-processed power industry question into the trained generative question and answer model, and outputting an expected power industry answer corresponding to the to-be-processed power industry question. And in combination with the knowledge graph with the node weight and the generative question and answer model, the interference of training set noise on model output answers is reduced, and the consistency and accuracy of the generated answers are improved.

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.