229 results about "Data availability" patented technology

The invention discloses a data text desensitization method and system for economic big data, and particularly relates to the field of text desensitization. In a data receiving stage, heterogeneous data sources are accessed through multiple channels, structured data are subjected to field-level standardization processing, and unstructured texts are subjected to OCR conversion and semantic segmentation; the method comprises the following steps: constructing a data consanguinity graph, identifying sensitive entities based on a pre-trained NER model, realizing three-layer hierarchical management in combination with dynamic weight calculation, and dividing the entities into a core layer, an association layer and a non-sensitive layer; an entity association network is constructed through a knowledge graph technology, a privacy association strength coefficient is quantified, and cross-entity desensitization consistency control is realized; a privacy risk assessment model is adopted to calculate a residual risk value, an adaptive enhancement mechanism is triggered when the residual risk value exceeds a threshold value, data availability is maintained through a utility assessment unit while privacy is protected, and dynamic balance between risk and utility is formed.

The invention relates to the field of data query, and particularly provides a medical big data query method and system based on dynamic desensitization, a terminal and a medium, and the method comprises the steps: collecting multi-source heterogeneous medical data, carrying out semantic normalization, constructing a unique identifier of a patient, and generating standardized intermediate plaintext data; constructing an adaptive isomorphic index structure based on the data, constructing a semantic hash B + tree index for structured data, constructing a feature vector index for unstructured data, and dynamically optimizing index hierarchy and parameters according to access frequency; and after a query request is received, the permission context is analyzed, intermediate plaintext data is positioned and read by using the index, and a differential privacy protection strategy is dynamically applied according to the access permission. According to the method, the problem of balance between privacy protection and data effectiveness as well as between retrieval efficiency and storage cost in medical data processing is effectively solved, and data availability and access performance are improved while safety compliance is guaranteed.

The invention discloses a dynamic privacy protection closed-loop control method and system for mobile edge crowd sensing. According to the method, information related to a communication channel and data content is obtained through risk perception, and a multi-dimensional comprehensive privacy risk score is calculated; then, dynamically determining a value of a differential privacy budget epsilon according to the risk score by utilizing a reinforcement learning algorithm in a strategy decision-making stage; then, performing noise addition disturbance on crowd sensing data according to the epsilon in a privacy protection execution stage so as to realize a preset privacy protection level; then, the privacy leakage risk and the data validity of the disturbed data are evaluated in the effect verification stage, and an evaluation result is obtained; and finally, privacy control strategy parameters are adjusted according to an evaluation result in a feedback optimization stage, and feedback is applied to a risk perception and decision process of a next cycle, so that closed-loop control is formed, and a privacy protection effect is continuously optimized. According to the method, the user data privacy security can be improved, and the data availability and the energy consumption efficiency are both considered.

The invention discloses a data privacy protection method for an industrial internet platform, and relates to the technical field of data security and privacy protection. According to the method, the sensitive information is accurately identified and deeply analyzed through the sensitive information feature library and the hierarchical matching algorithm, the problem of insufficient accuracy and flexibility during large-scale data processing is solved, the accuracy and reliability of data desensitization are remarkably improved, personal privacy is effectively protected, data availability is maximized, and the method is suitable for large-scale data processing. A hierarchical processing algorithm and a self-adaptive desensitization rule base are utilized, desensitization rules are dynamically adjusted according to dynamic access requirements and sensitivity levels of data, data security and availability are balanced, accurate protection under different scenes is ensured, system adaptability and flexibility are improved, and the data access process is monitored in real time, anomaly detection and rule verification are performed, so that the data access efficiency is improved. And the desensitization rule is dynamically adjusted, so that the security and reliability of the system are enhanced, the user credibility is improved, and the transparency and credibility of the data processing process are ensured.

The invention relates to the technical field of information security, in particular to a user information protection method and device based on a dynamic desensitization strategy, and the method comprises the steps: determining sensitive data and a sensitivity score thereof through a named entity recognition model and a sensitivity scoring algorithm, extracting scene parameters, and determining a scene risk score through a risk quantification algorithm; and then a dynamic desensitization strategy generation algorithm is called to generate a target desensitization strategy, and finally, the strategy is used for desensitization and a reversible desensitization data identifier is generated. Through the series of steps, the desensitization strategy is dynamically generated according to different use scenes, the problem of scene adaptability deficiency caused by static desensitization strategy stiffness is solved, meanwhile, the reversible desensitization reduces the security risk caused by reverse cracking of rules, excessive desensitization is avoided, and the data availability is improved.

The invention relates to the technical field of industrial internet-of-things data security, in particular to an edge calculation differential privacy industrial internet-of-things data desensitization verification system and method.According to the system and the method, edge nodes are divided in a three-dimensional mode according to resource capacity, function positioning and privacy requirements, differential differential privacy parameters are formulated in combination with data attributes and leakage influences, and the safety of the industrial internet-of-things data is improved. And a dynamic adaptive grid is matched to realize hierarchical alignment, scene binding and elastic reconstruction, so that a cross-hierarchical privacy risk is avoided, and verification logic is simplified. Noise intensity is dynamically corrected by integrating multi-dimensional factors based on grids, lightweight, medium and deep hierarchical desensitization is designed for three types of nodes, and privacy protection and data availability are balanced. A privacy security and data availability two-dimension, terminal-gateway-area three-level verification chain is constructed, the desensitization effect is comprehensively evaluated, closed-loop iteration is achieved through hierarchical judgment and accurate adjustment, and industrial scene requirements are efficiently met.

The invention discloses a differential privacy data set distillation method and system based on image generation data, and belongs to the technical field of data privacy protection and machine learning. The method comprises the following steps: firstly, synthesizing a synthetic data set meeting Gaussian differential privacy, training a feature extractor on the synthetic data set, and finely adjusting an expert model by using original data differential privacy; multiple rounds of iterative optimization are carried out on the distillation data set initialized according to the classes, wherein a feature extractor is randomly selected according to the classes in each round to align the features of the original data added with the noise and the distillation data, and an expert model is utilized to align the semantics of the distillation data hard labels and the semantics of the synthetic data soft labels; and finally outputting a distillation data set meeting the total privacy budget. According to the method, effective priori is provided by utilizing generated data, extra noise injection in the alignment process is reduced, convergence is accelerated, and better privacy protection and data availability balance compared with a previous method is realized under the same differential privacy budget.

The invention discloses a customer information storage management method and system based on a block chain, and relates to the technical field of distributed data evidence storage, and the method comprises the steps: collecting terminal customer information, generating an encrypted data packet, carrying out erasure coding, and generating an encrypted data fragment set; carrying out distributed storage based on the encrypted data fragment set, and calculating a data availability commitment at the same time; executing availability sampling according to the data availability commitment, and generating a fragment validity proof; based on the fragment validity proof, performing format and validity verification to obtain a to-be-processed proof set; and generating a recursive aggregation proof according to the to-be-processed proof set, and submitting the recursive aggregation proof to the block chain to verify and update a proof storage state. And cryptographic authentication is carried out on the persistent evidence storage state of the data assets, so that the dual beneficial effects of ensuring data privacy and credible evidence storage are achieved.

The invention discloses a multi-field data desensitization method and device, equipment and a medium, and relates to the technical field of data security, and the method comprises the steps: carrying out the semantic feature extraction of a field name and a field value of a target field through a target model, obtaining a field name semantic feature and a field value semantic feature, fusing the field name semantic feature and the field value semantic feature to obtain a target semantic vector; determining target association strength between the target semantic vectors corresponding to any two target fields, and constructing a cross-field semantic association matrix based on the target association strength; determining a target field combination meeting a preset high semantic correlation standard from a database based on the cross-field semantic association matrix, performing sensitivity scoring on the target field combination to obtain a sensitivity score, and determining a data type corresponding to the target field combination by using a preset classification model; and desensitizing the target field combination according to the sensitivity score and the data type. According to the method, the data availability is guaranteed while the privacy protection level is improved.

The invention discloses a user track privacy protection method and system fusing a generative model and three-stage personalized differential privacy, and relates to the technical field of data security, and the method comprises the steps: obtaining real user track data, and carrying out the preprocessing of the real user track data to form standardized track representation; training a depth generation model to generate synthetic trajectory data; performing density clustering on the synthetic trajectory to obtain a feature region and calculating a region centroid; point-level privacy budgets are distributed according to three stages of the area proportion, the centroid distance and the global density; constructing an adaptive elliptical disturbance domain based on the regional covariance, sampling candidate confusion points in the disturbance domain and applying road feasibility screening; and calculating a score based on the privacy budget and the utility function, and sampling replacement points by adopting an index mechanism to output a privacy protection trajectory. According to the technical scheme, the track structure continuity and the road semantic consistency are remarkably improved under the condition that the differential privacy constraint is met, and the privacy intensity and the data availability are both considered.

The invention discloses a child activity data desensitization processing method and system based on differential privacy, and relates to the technical field of privacy data protection, and the method comprises the steps: carrying out the time synchronization and unit normalization of multi-source data from a wearing device and an environment sensor, and building a unified multi-dimensional time sequence matrix; then, introducing a change point detection method to dynamically segment the time series data, so that the data is divided into event segments with clear behavior semantics; a sensitivity scoring mechanism is designed, factors such as activity types, time, places and behavior frequencies are comprehensively considered, privacy risk quantification is performed on each event segment, adaptive privacy budget allocation is implemented based on a scoring result, and key protection of sensitive data segments and efficient utilization of conventional data segments are realized; after differential privacy noise is injected, time sequence consistency correction and cross-feature correlation correction are combined. Therefore, differentiated and refined protection of the child activity data is realized, and privacy protection and data availability are effectively balanced.

The invention discloses a data protection method and system based on federated learning and homomorphic encryption, belongs to the technical field of data security, and aims to solve the technical problem of how to effectively protect user privacy while ensuring data availability. Comprising the following steps: a medical data node performs model training on a local AI model deployed locally based on a local data set, and uploads an encryption model gradient to a federal coordination server; the global parameters issued by the federal coordination server are decrypted, and gradient updating is performed on the local AI model based on the decryption result of the global parameters; calling the trained local AI model to perform model reasoning on the basis of the reasoning request for the third-party user who passes the identity authentication and has the access permission, and returning a reasoning result to the third-party user; and based on the auditing log, analyzing the running state and resource consumption of the medical data node according to a predefined auditing rule, and generating alarm information based on the abnormal operation.

The invention discloses a route determination method, system and device and a storage medium. The method comprises the steps of obtaining route notification data and a candidate route set; constructing a knowledge graph based on the route notification data, and calculating a reputation value of each autonomous domain node based on the knowledge graph; and based on the reputation value of each autonomous domain node, screening out a credible route set from the candidate route set, performing reputation weighted scoring on paths in the credible route set, and selecting the path with the highest score as the optimal route. Semantic organization is carried out on the autonomous domain, the routing prefix and the strategy relation through the knowledge graph, the data availability and analysis efficiency are improved, dynamic evaluation of the autonomous domain reputation is achieved in combination with neighbor recommendation, and the accuracy of credibility judgment is improved; a reputation evaluation result is directly applied to path selection, untrusted nodes and paths are effectively filtered, potential attack risks are reduced, and the overall security and stability of a routing system are improved.

The invention discloses a blockchain zero-knowledge proof collaborative credit risk control data privacy protection query method, and belongs to the technical field of financial science and technology and information security. According to the method, a four-layer architecture of a feature importance analysis layer, a zero-knowledge proof verification layer, a block chain intelligent contract layer and an audit log layer is constructed, so that the contradiction between privacy protection and data availability in a traditional credit risk control system is solved. According to the system, an SHAP value is adopted to calculate feature importance, a zk-SNARKs verification circuit is deployed for high-sensitivity features, zk-STARKs lightweight verification is adopted for low-sensitivity features, and differential privacy protection is achieved. And dynamically adjusting the complexity of a verification algorithm according to the credit risk score of the user, and realizing dynamic authority control based on a verification result through an intelligent contract. The verification metadata is recorded to the block chain to form a tamper-resistant auditing log, and it is ensured that the query process is transparent and traceable. According to the method, the real-time risk control requirement is met while the privacy of the user is protected, and a compliant and efficient data query solution is provided for financial institutions.

The invention relates to the technical field of intelligent operation and maintenance of photovoltaic power station equipment, and discloses a photovoltaic power station equipment operation data analysis method and system based on the Internet of Things. The operation data analysis method is applied to data analysis equipment and specifically comprises the following steps that S101, a data analysis request is received, and the data analysis request collects parameter data of current, voltage, temperature, irradiance and environment humidity in real time through Internet of Things sensor nodes deployed on a photovoltaic module, a combiner box, an inverter and a meteorological station; according to the method, the problem of space-time misalignment of multi-source heterogeneous data is solved, the outlier recognition accuracy is improved, the data availability rate is greatly optimized, the limitation of traditional single-dimensional analysis is broken through by innovative four-dimensional feature engineering, the ripple spectrum entropy in the electrical features and the response delay time in the environment coupling features are effectively improved, and the reliability of the multi-source heterogeneous data is improved. And by combining a thermal-electric propagation model constructed by GCN, the detection rate of early faults such as microscopic subfissure is increased from 60% to 95%.

The invention provides a laser radar saturation waveform reconstruction method based on a hybrid neural network, and the method comprises the steps: carrying out the normalization processing of laser radar full waveform data, carrying out the traversal through employing a sliding window with a fixed length, and extracting a sub-waveform segment with a large sum of amplitudes in the sliding window; respectively extracting local mutation features and global morphological features of each sub-waveform segment by using a double-branch convolutional network, fusing the extracted features by using a multi-head attention fusion network, and detecting abnormal sub-waveform segments and corresponding saturated sampling points; jointly establishing a reconstruction network based on an encoder structure and a residual convolution structure, and reconstructing the amplitude value of the saturated sampling point to obtain a reconstructed sub-waveform segment; and inserting the reconstructed sub-waveform segments into corresponding positions of the normalized laser radar full-waveform data, and carrying out reverse normalization operation to obtain reconstructed full-waveform data. The method is high in recognition accuracy and small in reconstruction error, and can remarkably improve the ranging precision of the laser radar and the usability of waveform data.

The invention relates to the technical field of oil exploration and development, and discloses a drilling speed real-time prediction method only using historical data and controllable data. In order to solve the problem that traditional prediction depends on uncontrollable feedback data and unknown data under the undrilled depth, well drilling parameters are classified in detail, data constraints in real-time well drilling are analyzed, a prediction framework is provided, and missing real-time input is compensated through information in historical data. By integrating previously drilled areas, the model can improve prediction accuracy even when excluding uncontrollable parameters of the undrilled depth. The method emphasizes the importance of combination of a drilling speed prediction method and operation constraints in the real world. The discovery contributes to improving the applicability in drilling operation, provides a feasible solution for predicting the mechanical drilling speed under the actual conditions of limited data availability and complex geological environment, and contributes to intelligent development and management of an oil field.

The invention relates to the technical field of power grid data privacy protection, in particular to a smart power grid data privacy protection system and method based on a homomorphic encryption technology. The system protects data privacy of an intelligent power grid based on a homomorphic encryption technology, and comprises eight modules: a data acquisition module is connected with an intelligent electric meter in a multi-protocol manner in a 15-minute period to adopt electric data; the data filtering and preprocessing module optimizes data quality; the homomorphic encryption module generates a ciphertext capable of being added and multiplied by using an FHE algorithm; the multi-level key management module manages multiple types of keys in the whole life cycle; the secure transmission module transmits the ciphertext by means of a hybrid protocol; the data analysis module decrypts and analyzes the ciphertext in the data center; the decryption module decrypts after authorization; and the auditing and monitoring module records operation and monitors abnormity. According to the invention, it is ensured that the power use data is kept in an encrypted state in the whole life cycle of collection, transmission and analysis, the data is available and invisible, and the data privacy protection level and quantum attack resistance of the smart grid are improved.

The invention discloses a privacy protection type data desensitization method for a big data analysis platform, and relates to the technical field of data security and privacy protection. According to the method, the sensitive information is accurately identified and deeply analyzed through the sensitive information feature library and the hierarchical matching algorithm, the problem of insufficient accuracy and flexibility during large-scale data processing is solved, the accuracy and reliability of data desensitization are remarkably improved, personal privacy is effectively protected, data availability is maximized, and the method is suitable for large-scale data processing. A hierarchical processing algorithm and a self-adaptive desensitization rule base are utilized, desensitization rules are dynamically adjusted according to dynamic access requirements and sensitivity levels of data, data security and availability are balanced, accurate protection under different scenes is ensured, system adaptability and flexibility are improved, and the data access process is monitored in real time, anomaly detection and rule verification are performed, so that the data access efficiency is improved. And the desensitization rule is dynamically adjusted, so that the security and reliability of the system are enhanced, the user credibility is improved, and the transparency and credibility of the data processing process are ensured.

The invention relates to the technical field of data processing, and discloses a multi-modal data encryption fusion method and system, the multi-modal data encryption fusion method comprises the following specific steps: receiving multi-modal data from different sources, the multi-modal data at least comprising image / video data, audio data and text data, according to the multi-modal data encryption fusion method and system, customized encryption strategies are adopted for different data types, such as chaotic mapping visual password protection images / videos, improved AES-CTR optimization audio encryption and homomorphic encryption retention text semantics, the encryption efficiency is improved, and meanwhile, the security of each modal data is ensured; according to the multi-modal data encryption fusion method and system, the features are directly extracted from the encrypted data through the deep learning model, semantic association fusion is realized in combination with an attention mechanism, privacy leakage caused by decryption is avoided, and meanwhile, data availability is kept.

The invention discloses an interpretable differential privacy parameter adaptive method for a generative medical record, and relates to the technical field of medical data privacy protection and artificial intelligence. The method comprises the following steps: acquiring an original medical record data set, and identifying a field containing sensitive information; constructing a multi-dimensional evaluation model, and respectively calculating the sensitivity score of each field and the utility score of the downstream task; dynamically allocating differential privacy budget parameters for each field by using a constraint optimization algorithm based on a game relationship between sensitivity and utility; in the training process of the generative model, corresponding noise is injected into a gradient or an input layer according to the distributed parameters; and finally, generating a synthetic medical record and outputting an interpretability report of privacy parameter distribution. By automatically adjusting the DP parameters, the data features of the low-sensitivity and high-value fields are reserved while the high-sensitivity fields are ensured to obtain strong privacy protection, the problem that privacy protection and data availability are difficult to consider in medical data sharing is effectively solved, and a transparent auditing basis is provided.

The invention belongs to the technical field of smart power grid information security, and provides a privacy protection system and method based on smart power grid distributed data aggregation. The method comprises the following steps: constructing a privacy core, a power grid state and a privacy grading data stream of an electric vehicle charging pile network; generating a one-time session blind factor by using the power grid interference characteristics to perform data encryption; channel interference is evaluated based on the power grid state, dynamic verification is carried out on the ciphertext, and interference confidence is generated; executing anti-interference aggregation in a ciphertext state according to the privacy grade and the credibility score; and finally, carrying out differential privacy enhancement on an aggregation result, and outputting security data capable of directly driving power grid services. According to the method, dynamic binding of the secret key and power grid disturbance, elastic fault-tolerant aggregation based on interference perception and differential privacy enhancement fusing data classification and credibility evaluation are realized, so that the problem of balancing privacy protection and data availability in a plug-and-play scene of the electric vehicle is solved.

The invention relates to a dynamic data migration method and device, equipment and a medium, and the method comprises the steps: obtaining a database analysis log, extracting a first access record of each data table in a preset period, merging the first access record corresponding to each data table with a historical access record, and obtaining an updated data access record; scoring the data access records based on scoring rules, generating corresponding table scoring data, and constructing a priority scoring table based on the table scoring data and screening conditions; calling a data migration service to regularly obtain a priority score table, comparing table score data in the priority score table with an evaluation threshold value, and determining a data table list needing data migration; and calling the metadata interface to update the storage configuration information corresponding to the data table list, and executing data migration operation on the data table based on the data migration service. According to the method, different score data tables stored in the same city are dynamically migrated, and the data availability is improved while the method is compatible with an existing technical architecture.

The invention relates to the technical field of computers, discloses a full-life-cycle big data management method and system for a meteorological engineering project, and aims to solve the problems of data islands, different standards, processing splitting, decision lag and the like in the prior art. The method comprises the following steps: collecting multi-source heterogeneous original data; performing standardization processing to form a data set with unified semantics and space-time reference; classifying and storing according to the full life cycle stage of the project, and constructing a project entity association index; calling a stage adaptation model based on the data set to execute prediction, diagnosis or optimization; generating visual early warning and operation and maintenance suggestions; and pushing a decision instruction to realize closed-loop management. Through adoption of the technical scheme, unified convergence, efficient retrieval, intelligent analysis and closed-loop decision of meteorological engineering project data can be realized, and data availability, early warning timeliness and management automation level are remarkably improved.

The invention provides an improved public key searchable encryption method, system and device, and belongs to the technical field of information security and cryptography, the method comprises the following steps: firstly generating a public and private key pair, encrypting a keyword by using a public key to generate a searchable ciphertext, and associating a storage document; receiving a search request and a trap door derived by a private key, and returning an index after matching; the client downloads the ciphertext and decrypts the ciphertext through a private key; an anti-guess attack mechanism is integrated, and user interests are prevented from being speculated through access mode hiding; a dynamic index structure is constructed, and rapid addition, deletion and modification of documents, Boolean query and multi-user permission control are supported. According to the invention, the encryption security is guaranteed through the public and private key separation and trap door mechanism; the dynamic index supports efficient data operation; user privacy protection is enhanced through anti-guess attack and access mode hiding; and the method is compatible with multi-query and authority control, and is suitable for various scenes with dual requirements on privacy protection and data availability, such as cloud storage, e-mail systems, medical health data management and the like.

The invention discloses a while-drilling electromagnetic forward modeling simulation system and method for a multi-layer non-uniform lossy stratum, and belongs to the technical field of while-drilling electromagnetic forward modeling simulation systems. Comprising a stratum parameter modeling module, an excitation source configuration module, an electromagnetic field component calculation module, a data processing optimization module, a simulation result output module, a model verification calibration module and a multi-scene adaptation module. The three-dimensional model of the multi-layer non-uniform lossy stratum is constructed through the stratum parameter modeling module, the physical characteristics of the complex stratum are accurately adapted, and the limitation that a traditional model can only simulate a uniform or simple stratum is solved. The system completely calculates the components of the three directions phi, r and z of the electric field and the components of the three directions r, z and phi of the magnetic field, and the defect that component calculation of a traditional model is not comprehensive is overcome. Through a combination mode of absolute value conversion, natural logarithm conversion and noise reduction processing, the presentation effect of the electromagnetic field data is optimized, interference factors are eliminated, and the data availability is improved.

The invention provides an offshore wind power plant heterogeneous data lake snapshot integration method supporting dynamic backtracking, and aims to solve the problems of high operation and maintenance cost, inconsistent mass real-time data access and low historical backtracking efficiency caused by the fact that an existing Lambda architecture needs to maintain double computing engines. And constructing an incremental snapshot chain to support efficient data version backtracking. Specifically, the method comprises the following steps: designing a dynamic snapshot generation module based on time window division, and converting real-time streaming data into traceable continuous snapshot segments; a multi-version concurrency control engine is developed, and ACID transaction overhead is reduced through column type differential storage; meanwhile, a dynamic metadata sensing layer is established, a backward compatible mapping rule is automatically generated when a data mode is changed, and continuous and stable operation of a calculation engine is guaranteed; according to the method, the operation and maintenance complexity of the offshore wind power plant data lake is remarkably reduced, efficient integration of heterogeneous data and accurate backtracking of historical states are realized, and the data availability of fan fault diagnosis is effectively improved.

A system for managing data availability for database transactions is disclosed. The system is programmed to receive source data and extract a plurality of events from the source data. The system is programmed to create a halt record corresponding to each event of the plurality of events. The system is programmed to resolve conflicts among one or more halt records. The system is programmed to receive a request for a specific database transaction related to a specific database item from a client device. The system is programmed to determine whether any halt record is in effect for the specific database item. The system is programmed to perform the specific database transaction in response to determining no halt record is in effect. The system is programmed to transmit a result of the specific database transaction in response to the request to the client device.

The invention relates to the technical field of calculation models, and particularly discloses a petroleum knowledge question-answering method and system based on a large language model.The method comprises the steps of data availability preprocessing, question-answering pair application quality judgment and model output effect judgment, and a petroleum knowledge question-answering platform firstly collects petroleum field original data of all data sources; an available data set is obtained after availability preprocessing and verification of the data processing component; constructing a question and answer pair set according to the data set in a predefined mode, and screening out a question and answer pair data set through calibration and comparison; the platform sends a fine tuning instruction, the petroleum question and answer component finely tunes the model according to a predefined mode, evaluates question and answer output effect indexes of the petroleum question and answer component, verifies the question and answer output effect indexes with predefined indexes, judges whether fine tuning is repeated or not, and finally realizes the petroleum knowledge question and answer based on the large language model by repeating the steps.

The invention discloses a safety production performance evidence storage verification method based on ZKP and multi-dimensional verification. The method comprises the steps of daily data aggregation and commitment generation, zero knowledge proof generation, optimistic summarization and batch uplink, data availability guarantee, time trigger automatic presumption and multi-dimensional verification execution. According to the method, zero-knowledge proof, optimistic summary, data availability sampling, Vickers tree and other frontier cryptography and block chain capacity expansion technologies are fused, and the method has the advantages of low cost, high efficiency and verifiability.