631 results about "Dataspaces" patented technology

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 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 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 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 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 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.

The invention discloses an implementation system, method and device for a multi-source heterogeneous data connector in a trusted data space and a storage medium, and relates to the technical field of data processing, the implementation system for the multi-source heterogeneous data connector in the trusted data space comprises a data source access layer, a data analysis layer, a data conversion and mapping layer and a data output layer; wherein the data source access layer is used for receiving a data source access request of a user and establishing connection with a target data source according to the data source access request; the data analysis layer is used for acquiring original data from the target data source and analyzing the original data according to a preset analysis strategy to obtain preliminarily analyzed data; the data conversion and mapping layer is used for processing the preliminary analysis data according to a semantic mapping rule to generate standardized data; and the data output layer is used for outputting the standardized data to a specified target application system through a standard interface.

The invention relates to the technical field of distributed resource scheduling, and discloses a power distribution network distributed resource cluster optimization regulation and control method, system and device considering power communication coupling and a medium, and the method comprises the steps: obtaining the related data of a power distribution network and a communication network, and building a comprehensive division index set based on a target communication network; performing cluster division according to the index set to obtain a plurality of power distribution network distributed resource clusters; an optimization regulation and control model is constructed based on the division result, a target function is oriented to the minimum cluster operation cost, and power flow, communication operation and resource operation characteristic constraints are comprehensively considered; solving the model by adopting a time and event dual-drive solving strategy; a rapid solving method based on multi-graph convolution is utilized to extract a data space dependency relationship, a mapping mechanism from an operation state to an optimal strategy is constructed, and solving efficiency is improved; and performing optimization regulation and control on the cluster according to the solved optimal strategy to realize efficient and stable operation of the power distribution network under power communication coupling.

The invention provides a secure sandbox system of a trusted data space, which comprises a data source module, a data secure sandbox platform module and an interaction layer module, the method comprises the steps that a data source module is used for encrypting data, encrypted data is transmitted to a data security sandbox platform module through an encryption transmission channel, and only a data source administrator has the authority to decrypt and calculate, and relates to the technical field of data security. Multiple security defense lines are constructed for the data, the data are always in an encrypted state in the circulation process, different operation environments are isolated from one another, and the risk of data leakage is greatly reduced.

The invention discloses a data space data directory transmission control method based on a digital contract, and the method comprises the steps: carrying out the abstract processing of a data directory in a data space into a standardized metadata model, and forming a tree directory structure; in response to the data directory transmission request, generating a digital contract instance based on an intelligent contract engine, and performing block chain anchoring binding on a digital contract and a target data directory node; analyzing a control strategy set in the digital contract through a contract interpreter; executing at least one of the following control situations in real time in the data transmission process: directory visibility control based on timeliness constraint, directory access chain control based on data blood relationship, directory content desensitization control based on sensitivity level and directory routing control based on transmission path credibility; and after directory transmission is completed, triggering verification logic in the digital contract, comparing the integrity hash value and the authority change record of the data directory before and after transmission, generating a verifiable auditing report, and storing the verifiable auditing report in the distributed account book.

The invention relates to the technical field of data processing, and discloses a distributed trusted data space construction method and system based on a block chain, and the method comprises the steps: enabling a participant to complete registration under a unique identifier of a data space through a digital identity certificate; dynamically creating a fragment block chain of the data space according to the unique identifier; the participant encrypts structured data and stores the encrypted structured data to the corresponding fragmented block chain to obtain a data block of the structured data; when a data interaction request of the cross-fragment block chain appears, generating a cross-chain verification certificate of the cross-fragment block chain based on zero-knowledge proof; data interaction of the fragmented block chain is completed through the cross-chain verification certificate, and an interaction audit record of the data interaction is generated; periodically aggregating the state hash of the fragmented block chain, and updating the global state snapshot under the unique identifier; according to the invention, the security of the trusted data space can be improved.

The invention discloses a sandbox type trusted data space construction method and equipment based on a trusted verification architecture, belongs to the technical field of data security circulation and trusted computing, and aims to overcome the defects that a traditional sandbox behavior is unknowable and untrusted and a strategy is rigid. The method comprises the following steps of: starting a sandbox in a hardware trusted execution environment and generating a task unique context identifier; intercepting system call by a monitoring agent; generating a chained operation certificate containing preorder certificate hash; compiling an advanced security policy into a deterministic finite automaton executable policy set; and the auditing unit is used for generating a decision instruction in an illegal scale, storing the chain anchor point to the non-tampering system, forcibly executing decision and generating a Merkel tree evidence. The equipment correspondingly comprises a sandbox management module, a behavior monitoring module, a strategy compiling module, a verification judgment module and an auditing execution module. The full-flow transparent tracing of the operation in the sandbox is realized, the strategy dynamic adaptation is supported, and the problem that a traditional sandbox monitoring log is easy to counterfeit and difficult to verify is solved.

The invention belongs to the technical field of artificial intelligence assessment, and provides an enterprise credit assessment method and system based on multi-source heterogeneous data fusion, and the method comprises the steps: obtaining data source data of different businesses of an enterprise; extracting data features of the obtained data of the different business data sources; the extracted data features are considered, entities of data sources of different services are determined, and multi-source heterogeneous data fusion of data of the data sources of the different services is carried out under the determined entities; performing verification adjustment on the fused multi-source heterogeneous data to obtain multi-source heterogeneous verification data; and according to the obtained multi-source heterogeneous verification data and the enterprise credit evaluation model, completing enterprise credit evaluation of multi-source heterogeneous data fusion in the trusted data space.

The invention discloses a large model corpus data interaction system and method based on a trusted execution environment, a terminal and a medium. The system comprises a data provider, a data demander and a trusted data space service platform. And the data provider is used for encrypting the corpus data, and performing preprocessing, directory sorting and packaging on the corpus data in the data provider connector to obtain the data capsule. And the data demander is used for submitting a calculation task in the data demander connector and obtaining corpus data of the data capsule for calculation after authorization. And the trusted data space service platform is used for storing the encrypted corpus data, controlling access of a data user, scheduling a calculation task, managing a key and generating an audit log. Through trusted execution environment hardware isolation and full-link encryption, it is ensured that corpus data only exists in a plaintext form in the trusted execution environment in the full life cycle from access to destruction, and unauthorized access and leakage are completely eradicated.

The invention discloses a scenarized hierarchical management and control method for an education application in a data space, and the method comprises the steps: obtaining a multi-modal input content in a corresponding preset period after the starting of the current time in response to a starting instruction of the education application in a target data space; calling a corresponding analysis strategy according to the content type of the multi-modal input content; extracting target features from the multi-modal input content based on an analysis strategy; according to an education scene corresponding to the target data space, obtaining a matching result of the target feature and a target database; determining a corresponding data space risk level according to a matching result; and calling and executing an application management and control strategy corresponding to the data space risk level. According to the method, education application hierarchical management and control adaptive to different scenes can be provided, the management and control precision is high, meanwhile, automatic analysis and management and control processes are achieved, and the method is suitable for scenes such as classroom teaching management, after-class and extracurricular training, remote examination and family interactive learning.

The invention provides a flow and form decoupling cooperative processing method, and relates to the technical field of business flow management, and the method comprises the steps: constructing a layered architecture, separating a flow control layer from a form service layer, defining a flow node jump rule by the flow control layer based on a BPMN standard, and dynamically defining a form structure by the form service layer through JSON Schema; a form identifier is declarated in the process definition file, a corresponding form template is matched through a dynamic mapping service according to context information, and the context information comprises a process instance state, a user role, a device type and a service type; establishing a data sandbox isolation mechanism, generating a unique data space identifier for each process instance, and managing data access authority through a multi-dimensional isolation strategy of a tenant level, a service level and a process instance level; deploying an event bus communication mechanism, triggering two-way communication between a process engine and a form engine through a predefined event type, and ensuring the consistency of cross-system operation by adopting a transaction compensation mechanism.

The invention discloses a data fine-grained access control method, system and device and a medium, mainly relates to the technical field of access control, and is used for solving the problems of identity and attribute separation, low strategy updating efficiency and insufficient auditing capability in the existing scheme. Comprising the steps that a data user generates a distributed digital identity document containing at least one role attribute through a distributed digital identity system, and the distributed digital identity document is subjected to uplink storage after being audited by a registration mechanism; the data provider generates an ABE private key according to the main public key and an access control strategy, encrypts the ABE private key by using the DID public key of the data user, and sends the encrypted ABE private key to the data user; the data user obtains an ABE private key by using the DID private key; decrypting the ABE ciphertext on the block chain by using the ABE private key to obtain a symmetric key; and obtaining the original data in the trusted data space through symmetric key decryption.

The invention discloses a multi-type energy scheduling method based on a trusted data space, and the method comprises the steps: accessing a multi-type energy provider through a block chain node, and constructing a cross-domain trusted data space; obtaining energy data of each party; processing to obtain an energy data set; calling a federated learning model, training a load prediction neural network, and predicting to obtain a future load demand; constructing a multi-target optimization model by taking the maximum consumption efficiency and the minimum energy abandoning rate of each type of energy as targets, and solving the multi-target optimization model based on the predicted future load demand and the output curve of each type of energy to obtain an optimal scheduling strategy; and storing the optimal scheduling strategy on the chain, and obtaining the optimal scheduling strategy on the chain by each type of energy providers to perform energy scheduling. A decentralized trusted data space is constructed through a block chain technology, data source traceability and non-tampering operation are realized, a centralized platform trust risk is avoided, and data privacy zero leakage during cross-domain sharing is ensured by adopting privacy calculation.

The embodiment of the invention discloses a data space-oriented multi-source private domain data retrieval system, relates to the technical field of computers, and can realize cross-domain retrieval for multi-source private domain data on the premise of meeting data right and interest guarantee requirements. According to the system, a decentralized data index and discovery normal form is adopted, the local retrieval capability of a private domain data source is utilized, cross-domain retrieval is achieved on the premise that scattered private domain data cannot go out of the local private domain data source, the thought of retrieval and access decoupling is adopted, the retrieval process and the access process are designed to be completed through two independent modules respectively, and therefore the retrieval efficiency is greatly improved. In this way, the global retrieval agent cannot make contact with private domain data in the retrieval stage, and therefore the privacy and rights of a data owner are protected.

The invention relates to the technical field of digital display, and discloses a digital display method and system for tourism products, and the method comprises the steps: carrying out the scene semantic analysis of a real image of a target tourist attraction, and recognizing key landscape elements and a spatial relation, and obtaining scene structure data; fusing the digital display content of the tourism product with the scene structure data space, and determining a space anchor point; based on the real-time environment state data, performing adaptive rendering based on the real-time environment state data to obtain dynamic display content; packaging and displaying the action sequence and the trigger condition as interactive behavior logic and embedding to obtain intelligent interactive content; by taking the spatial anchor point as a reference, superposing the intelligent interaction content into the live-action image to obtain a reality display picture; encoding the real-time interaction intention of the user into a control instruction, and driving the intelligent interaction content in a real display picture to execute a corresponding display action; according to the invention, the efficiency of digital display of tourism products can be improved.

A data processing method using a scatter gather direct memory access (SG DMA), the method comprising: obtaining information for a rule table for specific subtasks of a SG DMA from a host, deriving the rule table for the specific subtasks based on the information, deriving source addresses, destination addresses and data sizes for the specific subtasks based on the rule table, and performing a SG DMA operation to transfer data of the data sizes located at the source addresses of a first memory to data spaces of the data sizes located at the destination addresses of a second memory.

The invention discloses a cross-industry data sharing method and system using a trusted data space, and relates to the technical field of data sharing, and the method comprises the steps: obtaining a cross-industry data dimension relation; obtaining a sharing request, and carrying out dimension migration analysis on the sharing request to obtain a dimension migration path set; according to the dimension migration path set, adaptive reorganization operation is executed on the to-be-shared data, and reorganized shared data is obtained; according to the cross-industry data dimension relationship and the dimension migration path set, performing cross-industry confidence evaluation on the recombined shared data to generate a shared decision result; and performing data sharing on the recombined shared data through a secure channel of the trusted data space. The technical problems that existing cross-industry data sharing is difficult, and data resources are wasted are solved.

The invention relates to a data integrity joint verification method and system for a trusted data space, and relates to the technical field of industrial data verification, and the method comprises the steps: taking the data importance, the Hash calculation frequency and the uplink evidence storage frequency in a preset time zone as an adaptive data integrity verification strategy; processing a real-time operation state data stream of the industrial equipment, generating a data integrity verification certificate, and storing the certificate to a credible data space distributed account book; when a maintenance analysis request is initiated, performing integrity verification on the obtained original operation state data stream based on a data integrity verification voucher on the distributed account book; and if the verification is passed, performing equipment health state diagnosis and targeted maintenance according to the original operation state data stream. The method solves the problems that a conventional data integrity verification method mostly adopts a fixed verification strategy, is not adaptive to data importance and network states, and cannot meet the requirements of predictive maintenance on efficiency and real-time performance while guaranteeing data credibility.

The embodiment of the invention discloses a data-space-oriented private domain data federal retrieval method and system, relates to the technical field of computers, and can realize effective and accurate query of dispersed private domain data in a data space on the premise of meeting the requirement of a data owner for locally controlling and managing the private domain data of the data owner. According to the method, the local retrieval capability of a federated private domain data source is called, so that effective retrieval of dispersed private domain data in a data space is realized on the premise that a centralized index does not need to be established; on the basis of the probability locality sensitive hash table, the target data source with the stored private domain data conforming to the query intention of the user can be selected in a fuzzy mode on the premise that the original data in the private domain data source does not need to be known, and the retrieval request is sent to the target data source, so that the leakage risk of the original data in the private domain data source can be reduced; and accurate query of dispersed private domain data in the data space is realized.

The embodiment of the invention discloses a reachable probability calculation model training method, a control method, equipment and a storage medium. The training method comprises the following steps that according to design parameters of the mechanical arm, to-be-screened tail end pose data are determined; selecting reachable end pose data from the end pose data to be screened, and generating a training set; a multivariate Gaussian mixture model used for fitting reachable pose data space distribution is obtained based on training of the training set, a reachable probability calculation model is generated based on the multivariate Gaussian mixture model, and the reachable probability calculation model is used for outputting the reachable probability corresponding to any tail end pose. Therefore, the calculation model can be constructed and trained by adopting the architecture of the multivariate Gaussian mixture model. When control is carried out, control is carried out through model calculation of the reachable probability, the failure caused by the fact that the mechanical arm can be grabbed but cannot be reachable is avoided, and the operation success rate of the mechanical arm in the complex environment is increased.

The invention discloses a credible industrial data space construction method, and particularly relates to the technical field of industrial data credible management and sharing. Collecting identity feature data and behavior data of an industrial subject, and establishing a behavior data pool; generating a behavior view map based on the behavior ontology model; constructing a behavior consensus index model, and evaluating historical behavior credibility; performing semantic alignment on the behavior credibility index and the current access intention, constructing a credibility mapping function in combination with a multi-source decision factor, and generating a credibility rating value of the access behavior; a one-time pass token is generated or access is denied according to a rating result, behavior feedback is used for model optimization and graph evolution, and finally an extensible trusted industrial data space is constructed; according to the method, dynamic trust judgment, behavior semantic understanding and cross-domain cooperative control of industrial data access are realized, and the credibility, the adaptive capability and the structure treatment capability of a data space are improved.

The invention provides a fusion method of ground station monitoring and satellite remote sensing inversion PM2.5 data, and relates to the technical field of PM2.5 pollution prevention and control. Ground station data and satellite remote sensing data are subjected to time matching and space matching and then are fused by using a universal Kriging method to obtain a high-precision and large-area coverage PM2.5 product. The PM2.5 data spatial correlation is considered, so that the fusion result is closer to reality, the calculation complexity is reduced, and meanwhile, the reliability of the fusion result can be judged by giving an error after data fusion. According to the method, the limitations of insufficient spatial consideration and high calculation complexity of a traditional method are made up, and the PM2.5 product can be used for pollution process analysis, climate change and other researches.