982 results about "Dataspaces" patented technology

The invention provides a cross-modal retrieval method for semantic and vector fusion in a data space, which belongs to the field of cross-modal information retrieval, and comprises the following steps: firstly, collecting and preprocessing multi-modal data; generating modal embedding and storing by utilizing the pre-training model; a shared semantic space is constructed, cross-modal vector alignment is optimized through comparative learning, and a modal mapping network is designed to enhance the embedding projection effect; storing the aligned embedding by using a Milvus database, and constructing an HNSW index; user text or image query is processed, text query analyzes limiting conditions to generate enhanced embedding, and image query extracts characters through OCR and fuses the characters with image features to generate embedding; in a database, through condition screening and semantic similarity calculation, a Top-K candidate item is retrieved; performing multi-modal correlation sorting on the candidate results and returning the results; according to the method, the shared semantic space is constructed, the alignment effect of different modal embedding is optimized, efficient storage and index management of multi-modal embedding are carried out, and real-time retrieval of large-scale cross-modal data is achieved.

The invention discloses a trusted data space cross-domain collaborative analysis method based on privacy computing, which comprises the following steps: step 1, trusted data space construction: edge computing nodes are deployed in a plurality of data owners participating in cross-domain collaborative analysis, and each edge computing node is interconnected through a block chain network to form a distributed trusted data space; a strategy engine is configured in the trusted data space, and the strategy engine is used for defining a data use permission, a privacy protection level and a calculation task life cycle management rule; and step 2, dynamically selecting privacy computing protocols, and selecting at least one privacy computing protocol from a pre-configured protocol library according to the data type, privacy protection requirement and computing complexity of the collaborative analysis task. When the scheme is implemented, the data participates in calculation in a ciphertext or desensitization form in the whole process, and the original data is not out of the domain, so that the privacy compliance requirement is met; and data use rules are automatically executed through block chain evidence storage and the intelligent contract, so that malicious node tampering is prevented.

The invention relates to the technical field of geographic information surveying and mapping, in particular to a city-level surveying and mapping geographic information standard system fusion construction method and system, and the method comprises the following steps: obtaining surveying and mapping data space coordinates, extracting terrain boundaries, ground feature distribution and road intersection features, calculating boundary point spacing, and calling a database to divide a topological structure. And detecting precision, spatial offset and overlapping conditions, and generating a topological correlation result. According to the method, by deeply analyzing the space coordinates, the time sequence, the geographic boundary and the classification information of the surveying and mapping data, the organizability and logicality of the data are improved, the tracking and updating capabilities of the data are enhanced through the refinement of the topological structure, the dynamic monitoring of the ground feature change is realized, the regional conflict is reduced through the optimization of the boundary adjustment mechanism, and the accuracy and the reliability of the surveying and mapping data are improved. The accuracy and adaptability of boundary data are improved, the consistency and application value of surveying and mapping data are enhanced through accurate matching of classification information and adaptive weight adjustment of data fusion, and accurate fusion of the data in a multi-source heterogeneous environment is ensured.

The invention discloses a data space multi-tenant isolation method and system based on a zero-trust architecture, particularly relates to the technical field of multi-tenant authority control, and is used for solving the problems of cross-tenant authority conflict and insufficient resource isolation caused by frequent strategy adjustment in the prior art. The method comprises the following steps: constructing a strategy dependence topological structure by acquiring a dynamic access strategy change request in real time so as to model a dependency relationship among a tenant permission node, a resource access path node and a time sequence connection edge; dynamically marking a resource sensitivity weight based on a historical access behavior, and analyzing and screening high-sensitivity conflict nodes in combination with a logic coupling degree; separating an independent resource access chain which directly depends on through reverse backtracking, and generating a hierarchical permission correction rule; finally, rules are injected, and blocking conditions are triggered to execute hierarchical verification and risk marking on the cross-tenant operation requests; the influence of strategy execution on a global system is reduced, and the permission boundary clearness and resource security of a multi-tenant data space are ensured.

The invention provides an automatic pavement settlement monitoring method and system based on a shield undercrossing airport runway. The dynamic database is constructed by collecting operation parameters such as shield tunneling machine propelling pressure, cutterhead torque and synchronous grouting pressure in real time and combining three-dimensional deformation data obtained through laser scanning. Strain sensing units are arranged in the soil layers to form a layered monitoring network, strain changes of the soil layers of different depths are collected, and spatial correlation is established between the strain changes and the tunneling position. And analyzing a spatial attenuation rule through a dynamic association processing unit, and generating a multi-dimensional feature data set. And establishing a settlement prediction model, fusing the shield dynamic load sequence and the layered strain conduction characteristics, and outputting settlement distribution information. And the system automatically compares the settlement difference value of the adjacent areas with a preset safety threshold value, and generates a graded early warning signal after verification of the three-dimensional deformation data space. According to the technical scheme provided by the invention, real-time three-dimensional prediction and spatial verification type graded early warning of settlement of the airport runway are realized.

The invention relates to the technical field of traffic monitoring, in particular to a Beidou-based traffic flow real-time monitoring method and system, and the method comprises the following steps: receiving signal data and analyzing a signal source based on a Beidou short message communication terminal, extracting vehicle positioning information and road occupation information in vehicle operation state data, and transmitting the vehicle positioning information and the road occupation information to a Beidou short message communication terminal; and analyzing the vehicle speed and the traffic flow density, and carrying out matching operation on the analysis result and the road section number to obtain traffic flow state data. According to the method, vehicle positioning and road occupation information is extracted through signal analysis, efficient acquisition and integration of traffic state data are realized, road section numbers are matched to improve data space precision, traffic pressure is reflected, dynamic flow analysis is supported, adjacent road section data are integrated, abnormal areas are positioned, shunting paths and proportions are optimized, and emergency response efficiency is improved. Real-time regulation and control of traffic flow are realized through dynamic pushing, the flexibility and responsiveness of traffic operation are enhanced, and the monitoring precision and the road network operation efficiency are comprehensively improved.

The invention provides a business data sharing fusion system and method based on a trusted data space, and relates to the technical field of data sharing. According to the method, sharing and fusion of service data are realized by constructing a trusted data space infrastructure. The method comprises the following steps: firstly, establishing a node path planning model for priority evaluation and data matching; then forming a model collaborative deployment scheme to distribute tasks to edge nodes; then perception scoring and resource scheduling are carried out on the tasks, and a multi-task concurrent transmission scheme is generated; constructing a multi-domain network service level protocol evaluation model to perform service decomposition and business collaborative management; and finally carrying out heterogeneous processing and data fusion on the business data. According to the method, the problems of information islands, multi-source heterogeneous data fusion and cross-domain business collaboration among regional mechanisms are solved, and the system performance and the user experience are improved.

The invention discloses a federated learning driven trusted data space cross-domain cooperative computing method, and relates to the technical field of data processing, and the method comprises the following steps: carrying out access trust verification on a plurality of data providing domains according to a dynamic trust mechanism; after the verification is passed, accessing each data providing domain into a trusted data space for local training, and generating a local model; performing cross-domain cooperative calculation on the plurality of local models, and establishing an initial global model; and carrying out loss generalization evaluation on the initial global model, and triggering a differential feedback learning module to carry out iterative reinforcement to generate a global reinforcement model. According to the method, the technical problems of limited data privacy protection, trust missing among different mechanisms and insufficient model generalization ability of cross-domain data cooperative calculation in the prior art are solved, multi-party cooperation is realized on the premise of no data leakage through a dynamic trust mechanism and differential privacy protection, and the reliability of the cross-domain data cooperative calculation is improved. And the precision and generalization ability of the global model are improved.

The invention discloses a multi-factor authentication trusted data space access control method, platform and medium, and relates to the technical field of data security, the method comprises the following steps: analyzing the authority, record, path and target of a request access user, and obtaining access parameters; generating a verification gradient based on the security levels of parameter matching permissions, records, paths and targets; in combination with the gradient and the user authentication record, creating an authentication demand factor with gradient constraint; and generating a space access permission according to the authentication result, and controlling the trusted data access permission. According to the scheme, the data security protection capability is enhanced through a dynamic multi-factor verification mechanism. According to the method and the device, the technical problem of insufficient data security protection capability caused by lack of a multi-factor dynamic authentication and fine-grained access control mechanism in a data space access process in the prior art is solved; the technical effects that the access authority is dynamically adjusted based on multi-factor authentication, and the data security and credibility in the data space access process are effectively improved are achieved.

The invention discloses a power transmission line state simulation and prediction method based on digital twinning, and the method comprises the following steps: accessing meteorological data, electrical data, space and asset data and historical state data, carrying out the preprocessing, and generating a multi-source spatial-temporal feature sequence set; constructing a double-domain cross-coupling time convolution twinborn model, and obtaining corresponding feature representation through a quick response path and a slow response path; physical consistency state representation is generated through a physical constraint cross gating layer; outputting a prediction result set through a simulation engine; online observation data are obtained, and the model is corrected based on virtual and real residual errors; and generating a current prediction result set by using the corrected model, and converting the current prediction result set into a scheduling and operation and maintenance strategy set. According to the method, the double-domain cross-coupling time convolution twin model and a virtual-real closed-loop correction mechanism are adopted, multi-scale state simulation prediction of the power transmission line is achieved, and the method has the advantages of being high in precision, robustness and performability.

The invention discloses an electric energy quality prediction and management platform based on a digital twin technology, which relates to the technical field of power system monitoring and comprises a physical space layer, a data space layer, a model space layer and an application space layer. The physical space layer is provided with an electric energy quality monitoring device and an electrical equipment state monitoring device, output multi-time-scale data are transmitted to the data space layer to be synchronously collected and preprocessed, the preprocessed data are input into the model space layer, a physical entity mapping relation is constructed through a digital twin basic model, and the physical entity mapping relation is obtained. The attribute expansion model constructs dynamic feature mapping, the multi-scale analysis model realizes dynamic analysis, and an analysis result is input into the application space layer to execute prediction, early warning and treatment. According to the method, a hierarchical digital twinborn system of'basic mapping-feature extension-dynamic analysis' is established, collaborative analysis of transient disturbance, short-term fluctuation and steady-state features is realized, and effective support is provided for optimal configuration of an electric energy quality management device.

The invention discloses a traffic accident risk prediction method and system based on space-time hypergraph comparative learning, and belongs to the field of traffic accident prediction, and the method comprises the steps: firstly obtaining historical traffic accident risk values and corresponding space-time features through data preprocessing; then, traffic accident area embedding based on local geographical perception and global semantic perception is generated through a multi-channel attention convolutional network and a hypergraph network, and dynamic time characteristic evolution is captured by adopting a GRU model and an Attention mechanism; in order to alleviate the problem of unbalanced spatial distribution of accident data, a comparative learning normal form is adopted to carry out cooperative supervision training on local and global region embedding so as to obtain robust accident characterization; and finally, integrating the ZINB model into traffic accident representation to better fit the probability distribution of sparse discrete data, thereby improving the accuracy of a model prediction result.

The invention relates to the technical field of geological disaster monitoring, and discloses a point-like landslide and debris flow potential degree adaptive monitoring and early warning method. According to the method, a dot matrix monitoring equipment array is arranged, and displacement data and environmental parameters of multiple monitoring points are collected; analyzing the displacement data timestamp through a timeline analysis module, generating a dynamic timeline, and marking geological event nodes; a geographic information system is combined to carry out spatial mapping on displacement data, and environmental parameters are fused to obtain a preliminary potential degree evaluation result. The pattern recognition engine classifies displacement speed and deformation characteristics in the preliminary result to generate a deformation characteristic spectrum with a space-time label; calculating a dynamic potential degree evaluation map according to the multi-factor weighted evaluation model; and adjusting an early warning threshold according to a user instruction, generating a personalized early warning rule set, and scanning the atlas to output a multi-stage early warning response scheme. On the basis of an early warning feedback iteration updating model and an engine, monitoring data are newly added to trigger and re-analyze in real time, a related scheme is updated in a linkage mode, and different scene requirements are met.

The invention relates to the technical field of risk management, in particular to a multi-scale urban rainstorm flood risk assessment method. The method comprises the following steps: acquiring a regional basic data set; performing data preprocessing on the regional basic data set to generate a regional basic preprocessed data set; constructing a rainstorm flood risk assessment index system based on the regional basic preprocessing data set to obtain an urban scale flood assessment index, and performing urban scale rainstorm flood risk assessment to obtain an urban scale rainstorm flood risk assessment result; according to the method, the index information is obtained from the open source data, and the defects that in previous researches, part of index data is poor in spatial precision and long in time are overcome; the multi-scale urban rainstorm flood risk assessment method comprehensively considering the urban scale and the block scale is provided, and the accuracy, scientificity and comprehensiveness of urban rainstorm flood risk assessment can be improved.

The invention discloses a remote proving method for a trusted data space, which relates to the technical field of computer and data security, and comprises the following steps: initializing a trusted environment and generating a dynamic identity key for a heterogeneous terminal containing a CPU (Central Processing Unit), a GPU (Graphic Processing Unit) and an FPGA (Field Programmable Gate Array) based on a hardware security module and a physical unclonable function, and generating a challenge value by a verifier by using a quantum random number; the method comprises the following steps of: firstly, transmitting to a proving party through quantum encryption and a neuromorphic photonic network, constructing proving by the proving party by adopting a tensor decomposition zero-knowledge proving protocol, finishing multi-stage verification on a verification party in combination with a space-time cause and effect graph and quantum signature aggregation, and finally realizing dynamic trust evaluation and adaptive strategy adjustment through a quantum Bayesian network, reinforcement learning and biological feedback. According to the method, the remote attestation performance of the trusted data space is improved, dynamic trust evaluation is realized by means of the quantum Bayesian network and biological feedback, attacks are effectively resisted, real-time requirements are met, trust is accurately evaluated, and the development of the trusted data space is promoted.

The invention belongs to the technical field of logistics information, and discloses a multimodal transport trusted data space construction method based on privacy calculation and a block chain. The method comprises the following steps: deploying a modular intelligent data gateway at a core node of a multimodal transport line, unifying the format of multi-source heterogeneous original data, and chaining a hash value; after the multi-source heterogeneous data is subjected to customization processing, key metadata attributes of all the data are registered and linked in real time; constructing a core ontology model, and establishing a cross-domain semantic interoperation capability; establishing a privacy calculation and block chain collaborative dual-channel architecture, and cooperatively training an ETA prediction model in a local data isolation environment by adopting transverse federal learning to ensure that original trajectory data is not out of a domain; constructing a dispute arbitration and credible verification system, and realizing an arbitration process of judicial verification when a transportation dispute occurs; data minimization disclosure and compliance sharing are achieved through dynamic access control. According to the method, multimodal transport data can be available and invisible, and the whole operation process can be audited.

The present disclosure describes methods, systems, apparatus, and media for object identification and classification, utilizing multi-feature and multi-modal data. This includes shape, material, brand, price, odor, taste, tactility, and sound. The system integrates a server space for data processing, a querying device for iterative searches, and a data interface module for refining results. It features AI-driven image optimization, feature extraction, and pattern recognition, employing novel techniques for fusing multi-feature and multi-modal embeddings utilizing multi-head attention. Additionally, a linker module powered by two active learning with feedback loops AI models consolidates scattered data into a unified object information database. The system also employs novel AI algorithms for isolating the object of interest through a saliency map and semantic analysis, as well as for enhancing raw images with a GAN-autoencoder.

The invention discloses an AI-based automatic connection and handshake protocol for a trusted data space connector. The AI-based automatic connection and handshake protocol comprises a data supply end connector, a data demand end connector and a platform operation end, the data supply end connector is used for collecting and preprocessing data; the demand end connector is used for receiving and processing data; the platform operation end is used for connection management, authority distribution and security auditing; the method has the beneficial effects that through automatic connection and an intelligent handshake protocol, manual intervention is reduced, and the data circulation cost is reduced; multi-factor authentication, dynamic encryption and privacy protection technologies are adopted to ensure the safety of the whole life cycle of the data; aI and machine learning technologies are introduced, so that the system can intelligently select an optimal strategy according to real-time network conditions and data features; interoperation and data sharing among different data spaces are realized, and circulation and value mining of data elements are promoted.

The invention discloses a control strategy optimization method for a laser cutting machine, and relates to the technical field of laser cutting, and the method comprises the following steps: obtaining cutting design information of a target laser cutting machine and a workpiece by constructing an operation data space of the laser cutting machine; and performing matching retrieval on the operation data, and establishing an operation data memory bank. Performing equivalent region segmentation on the workpiece based on the cutting design information to obtain an equivalent cutting region set; and performing control strategy analysis, and determining a control strategy parameter set. And the control strategy parameter set is used for conducting cutting feedback detection on the laser cutting machine, and a sensing feedback data stream is obtained. And optimization analysis is conducted on the control strategy parameter set, an optimized control strategy parameter set is determined, and strategy optimization control is conducted on the laser cutting machine. The technical problem that an existing laser cutting machine is unstable in cutting quality under different workpieces and cutting conditions is solved, and the technical effects that the control strategy optimization accuracy is improved, and the cutting quality is kept stable are achieved.

The invention discloses a real-time interaction and intelligent management method for a multi-dimensional data space, and relates to the technical field of data interaction management, and the method comprises the following steps: I, collecting multi-source data, including structured data, unstructured data and spatio-temporal data, in real time through a diversified collection terminal, and dynamically adjusting the sampling frequency based on a data entropy value, carrying out cross-device space-time synchronization calibration and edge end lightweight processing on the acquired data; iI, preprocessing the data flow in real time, fusing the multi-modal data, and the like. According to the method, the sampling frequency is dynamically adjusted based on the data entropy value, and in combination with edge end lightweight processing, invalid data transmission is reduced, and the key data acquisition efficiency is improved; hot data is stored in a time sequence database and a memory database, real-time monitoring of an emergency scene and second-level response of emergency command are supported, dynamic capacity expansion and shrinkage of computing resources are matched, edge-cloud end collaboratively distributes tasks, network delay is reduced, and system stability and response speed in a high-concurrency scene are guaranteed.

The invention provides a data security method and system fusing an MIN and a unified AI theory, and the method comprises the steps: S1, completing the multi-dimensional identification registration and synchronization of a data space in a multi-identification network MIN based on a data space identity identification DSID and an ontology semantic mapping frame; s2, acquiring a data acquisition record from a multi-identifier network MIN, and constructing an access control atlas in combination with a semantic vector of a data subject identifier DSID-Item; s3, analyzing the access request and verifying the authority, identifying the sensitivity level of the access request, and triggering a privacy enhancement mechanism when the sensitivity level is marked as medium-high-level privacy data; and S4, judging whether the data object reaches a value end point in the life cycle of the data object or not by taking a data space identity (DSID) registered in the multi-identifier network (MIN) as a retrieval anchor point, and if so, executing a verifiable multi-stage destruction operation. According to the invention, a full-life-cycle trusted data security technical scheme can be realized, and intelligent, explainable and efficient security management of data streams in a network space is realized.

The invention provides a data traceability verification method in a trusted data space, and relates to the technical field of zero-knowledge certification, the method comprises the following steps: collecting a plurality of operation records of target data in the trusted data space, and arranging the operation records according to a timestamp sequence to generate an operation sequence; converting into a state transition constraint condition; inputting the operation sequence into a pre-trained key node identification model, and outputting position identifiers of one or more key state transition nodes in the operation sequence; constructing a zero-knowledge proof circuit according to the operation sequence, the state transition constraint condition and the key state transition node, and operating the zero-knowledge proof circuit to generate a composite zero-knowledge proof; and providing the composite zero-knowledge proof as a data traceability voucher of the target data to a verification party to complete traceability verification of the whole processing process of the target data. The technical problems of privacy disclosure risk and low verification efficiency of data traceability verification in the prior art are solved.

The invention relates to the field of data traceability, and particularly discloses a natural resource data dynamic monitoring traceability method and system, and the method comprises the steps: building a multi-tense database, automatically writing real-time data into a corresponding partition according to a timestamp, and carrying out the incremental updating of a workflow; data confidence coefficient weights are introduced into SQL screening statements according to attribute tracing logic, and data legality is judged through weighted logic; constructing a land class evolution state transition matrix, determining a legal change path and an illegal path, and carrying out path deduction on attribute data missing to judge whether the attribute data missing is illegal change or not; and detecting land type mutation according to NDVI index change of a target remote sensing image, forming a three-dimensional tracing database of attribute data, space evidence and business files according to the attribute data and the spatial relationship, and dynamically monitoring and tracing natural resource data. The problems that the legal source of the construction land cannot be accurately traced due to land judgment errors and real-time data analysis and traceability are difficult in the prior art are solved.

The invention relates to a regional distributed photovoltaic power prediction method and system based on multivariate data cross-modal fusion, and belongs to the technical field of photovoltaic power prediction. Firstly, adaptive extraction of meteorological data spatial features and power data spatial heterogeneity is realized through a dynamic perception convolution kernel of AG-CNN, and a high-quality spatial basis is provided for cross-modal fusion; secondly, utilizing a two-dimensional dynamic attention mechanism of a DR-Transform to deeply mine cross-modal association of'historical power time sequence-future weather driving ', adapting to fusion requirements in different scenes through dynamic weight distribution, and capturing association evolution in a long period; thirdly, designing a cross-modal feature bridging module; and finally, verifying the robustness of the model in a typical scene, and ensuring that the model meets the precision requirement of multi-scale scheduling of the power grid.

The invention relates to the technical field of data processing, in particular to a circuit breaker operation state monitoring method and system based on big data, and the method comprises the steps: obtaining an operation state vector; obtaining a gain influence at each acquisition moment by using a filtering algorithm, and determining a fluctuation characteristic value based on a curve fitting result in a data space constructed by the gain influence; determining the fluctuation degree of the monitoring data at the acquisition moment according to the influence degree of the data fluctuation at the data abnormal moment on the monitoring data at the acquisition moment and the fluctuation characteristic value, and completing the updating of the Kalman gain matrix at the acquisition moment; and obtaining a prediction result of the operation state vector based on the updated Kalman gain matrix by using an extended Kalman filtering algorithm, and obtaining a monitoring result of the operation state of the circuit breaker based on the prediction result. According to the invention, through more accurately predicting the operation state monitoring data of the circuit breaker, the accuracy of monitoring the operation state change of the circuit breaker is improved.

The invention discloses a trusted data space privacy protection method and system based on zero knowledge proof, and belongs to the technical field of data processing.The method comprises the steps that real-time operation data of multiple data sources are collected through the Internet of Things, and a real-time operation data set and a data source information set are obtained; on the basis of the data source information set, zero-knowledge certification is conducted on the real-time operation data set according to real-time certification complexity, a certification data set is generated, and the real-time certification complexity is comprehensively analyzed and determined on the basis of safety perception information, the data privacy degree, the data anomaly degree and the data fluctuation degree; and carrying out encrypted storage on the proof data set by utilizing a block chain technology, and carrying out data sharing and data verification according to the encrypted storage data set. The technical problems that in the prior art, a dynamic privacy protection mechanism for multi-source data in the sharing process is lacked, so that sensitive data is prone to leakage, and credible verification is difficult to achieve are solved.

The invention discloses a traceability verification method and device for a trusted data space, and relates to the technical field of data security, and the method comprises the steps: embedding a dynamic watermark identifier in a data operation flow, and carrying out the real-time multi-dimensional traceability of data; performing cross-domain feature fusion, constructing a data evolution topological graph, and generating a credibility evaluation matrix; according to the data evolution topological graph and a credibility evaluation matrix, evaluating a traceability chain fracture risk level; inputting the feature vector of the data tampering event and the risk level into a test mapping array to obtain a verification instruction set; and according to the verification instruction set, generating a credibility calibration report and reconstructing a credible data space. The technical problems that in the prior art, a data operation process is difficult to monitor in the whole process, data is tampered easily and difficult to find in time, and tracing is difficult due to incomplete tracing information are solved, and the technical effects that the whole data operation process is monitored and managed, and data safety, integrity and traceability are improved are achieved.

The invention discloses a multi-model fusion efficient intrusion detection method and system. The method comprises the steps of removing abnormal values in sample data, screening time features by using TSODE, screening fixed format features by using a user-defined regular specification, and screening statistic features by using Z-score. Using a sliding window method to confirm minority classes conforming to decision boundary properties, using BorderLine-SMOTE to generate samples, and using ENN verification to generate samples; extracting spatial features of the sample data by using TCN, and extracting traffic features of the sample data by using BiLSTM; fusing and normalizing the time and space features; counting sample mean values by using time windows with different time step lengths to generate an initial weight matrix, and calculating a QKV matrix; based on an attention mechanism result, outputting a classification result in a full connection layer; through deep extraction and fusion of time and space features by combining BiLSTM and TCN networks, a multi-head attention mechanism is proposed, and the problems of unbalanced sample distribution and unbalanced feature importance in traffic data are effectively solved.

The invention provides a credible data space construction method and system based on data sharing, and relates to the technical field of data processing.The method comprises the steps that S1, in response to a request of a user for sharing target data, credible preprocessing is conducted on the target data to obtain credible data; s2, establishing a decision-making capability model for a user to autonomously perform credible authorization on the credible data; s3, analyzing a multi-dimensional sharing risk of the trusted data; s4, planning an optimal authorization auxiliary unit sequence based on the multi-dimensional sharing risk and the decision ability model; s5, in the process that the user autonomously performs credible authorization on the credible data, sequentially executing the authorization auxiliary units in the optimal authorization auxiliary unit sequence; and S6, aggregating the credible data subjected to credible authorization to construct a credible data space. According to the method, the credibility and the security in the data sharing process are remarkably improved, and the quality of the shared data and the protection of user privacy are ensured.

The invention relates to the technical field of data cross-domain access control, in particular to a block chain-based trusted data space cross-domain access control method, system and device and a medium, comprising: receiving a data access request, and analyzing a target data identifier, a target heterogeneous system identifier and access scene information; obtaining the data attribute of the target data according to the target data identifier, generating a dynamic access strategy in combination with the access scene information, and deploying the dynamic access strategy into the block chain; mapping the dynamic access strategy into an equivalent access control rule, and converting the data access request into target compatible request data; verifying zero-knowledge proof provided by the user based on an equivalent access control rule and a user public key; after the verification is passed, routing the target compatible request data to the target heterogeneous system; and the target heterogeneous system generates original response data, converts the original response data into user-side compatible response data and returns the user-side compatible response data to the user. According to the invention, cross-system efficient cooperation is realized, and the real-time adaptability and privacy enhancement security of access control can be improved.