111 results about "Dataspaces" patented technology

This application discloses a method, device, and medium for collaborative training of intelligent agents based on a trusted data space. The method includes: classifying and grading heterogeneous financial data from participating parties to generate standardized data; assigning unique identifiers to generate a standardized data catalog and identifying heterogeneous data formats to convert them into a unified format dataset; storing the dataset in a trusted data space to record and document the full lifecycle operations of the unified format dataset, generating operation logs; and encrypting the dataset based on its sensitivity level and computational complexity to generate encrypted data; loading the encrypted data and the agent training code into a trusted execution environment and monitoring operational status indicators to generate an environment health report; coordinating multiple participating parties to train models using local encrypted data based on a federated learning framework and uploading model parameters to the trusted data space; and aggregating and processing model parameters to generate global model parameters and distributing them to multiple participating parties to update their local models.

The application relates to the technical field of memory detection, in particular to a memory usage tracking method and a storage medium. The method constructs a target memory block containing a header space and a data space through obtaining a memory allocation request, records memory management information in the header space, and hangs the target memory block to a ring-shaped bidirectional linked list; in a memory access process, the validity of the target memory block is checked and access time information is updated, and safe release is performed according to the memory management information when releasing; through periodic traversal of the ring-shaped bidirectional linked list, in combination with the time information and the reference state of the target memory block, a memory usage tracking result is generated, and continuous monitoring and abnormal occupation analysis of the memory life cycle are realized. The application realizes traceability, checkability and analysis of the memory usage state, thereby effectively improving the safety, reliability and problem positioning efficiency of memory management.

This application discloses a method, equipment, medium, and product for comprehensive evaluation of equalization of basic medical services, relating to the field of medical geography. The method includes: spatial processing of research data to construct a unified geospatial database; calculating multiple evaluation index values ​​representing the spatial allocation level of medical resources and the spatial convenience of residents' access to medical care through spatial analysis to form an original decision matrix; using the entropy weight method, calculating the objective weights of each evaluation index based on the degree of variation of the multiple evaluation index values ​​across different evaluation areas; processing the original decision matrix using the objective weights based on the TOPSIS model to calculate the comprehensive evaluation index of equalization of basic medical services for each evaluation area; and performing spatial hierarchical mapping based on the comprehensive evaluation index of equalization of basic medical services to generate a multidimensional diagnostic report, thereby avoiding evaluation distortion, accurately reflecting residents' comprehensive employment opportunities in accessing medical services, and enhancing public credibility.

This application provides a dynamic control method and system for data usage based on a data space, belonging to the field of network security technology. The method includes: acquiring access requests from data users and storing them in a preset data space, which includes control logic, access requests, and policies; acquiring environmental information of the access requests through a preset context-aware engine; evaluating the environmental information based on a preset blocking rule base and a preset security policy baseline to obtain a risk assessment coefficient; allowing data access when the risk assessment coefficient is less than a preset first threshold, and inputting the interaction pattern generated by the data access into a pre-trained access anomaly detection model to obtain a detection result; generating and outputting a denial information report if the detection result is abnormal behavior; and verifying the normal behavior against the access request and access policy if the verification fails, generating and outputting an alarm message. This application improves data security.

The application discloses an unmanned aerial vehicle inspection task execution method and device and electronic equipment. The method comprises the following steps: receiving and responding to a predetermined inspection task, acquiring multi-modal sensor data; performing cross-modal semantic alignment and feature fusion operation according to the multi-modal sensor data to obtain a fusion feature vector; determining component instance information according to the fusion feature vector; generating a first flight control instruction of the unmanned aerial vehicle according to the component instance information, and controlling the unmanned aerial vehicle to fly according to the first flight control instruction; and constructing a line space topology graph according to visual imaging data, spatial depth point cloud data, attitude inertia sequence data, position coordinate data and component instance information. The application solves the technical problems of low tracking flight accuracy of the unmanned aerial vehicle, lagging update of line topology information and missing connection relationship in the related art.

This application discloses a data processing method, apparatus, device, system, storage medium, and program product, relating to the field of data processing technology, to solve the problem of the large computational workload in existing methods for determining the median. The method includes: a first device sorting its security data through a data space connector to obtain first security data; the first device determining a target location increment index, the target location increment index being used to determine the median in the first security data and the second security data of a second device; and the first device obtaining the median of all security data. Embodiments of this application can reduce the computational workload of determining the median.

The application discloses an intelligent monitoring system for Eriocheir sinensis breeding environment, and particularly relates to the technical field of intelligent monitoring of aquaculture, which collects data through multiple climbing cage monitoring modules, calculates average weight according to the quantity and weight of image recognition and corrects the climbing cage frequency, carries out spatial analysis based on the corrected frequency to obtain a spatial aggregation global index and a local aggregation index, fuses to generate a spatial aggregation degree characteristic value, compares and calculates a deviation degree index by a deviation calculation module, and selectively starts the regulation and control equipment of different spatial regions according to the deviation degree index size by a regulation and control execution module; the climbing cage frequency is corrected through image and weight data, the single semantic problem of frequency data is solved, the spatial aggregation degree characteristic value is compared with the historical benchmark, the dynamic correlation between the environment and the behavior is realized, the deviation degree index is classified and partitioned for regulation and control, the execution lag problem is solved, and the precise intervention and energy consumption optimization are realized.

The invention discloses a carbon electrode process control method and system, and the method comprises the steps: carrying out the secondary index division of multi-source data indexes of a carbon raw material, and constructing a comparable data space of the indexes of the raw material; establishing a comprehensive index system based on the comparable data space, and constructing a two-stage linear weight coupling structure based on the comprehensive index system to form an index fusion weight vector; based on the index fusion weight vector, constructing a multi-path evaluation decision model, and performing weighted calculation on each index in the comparable data space according to the index fusion weight to generate a corresponding sorting result; based on the sorting result, establishing a standard reaching probability prediction model, and correcting the sorting result according to the standard reaching probability; based on the corrected sorting result, unification processing of the process window is achieved, and a process parameter map is constructed; and carrying out process execution optimization and process monitoring on the carbon electrode production process based on the process parameter atlas. According to the method, the stability and reusability of the carbon electrode process decision are improved.

The application discloses a data processing method for a trusted data space, comprising: analyzing a data collaboration request by a trusted scheduling gateway, determining a temporary access token and local task data of an edge device, a data provider for collaborative processing of a multi-party collaborative task, a dynamic access route of the data provider, and a target data dimension required to be provided by the data provider; checking the access legality of the edge device based on the temporary access token and the local task data of the edge device; if the checking is passed, requesting the data provider to feed back target task data corresponding to the target data dimension based on the dynamic access route; desensitizing the target task data based on a data desensitization rule corresponding to the data provider to obtain desensitized task data; and sending the desensitized task data and the local task data of the edge device to a task execution party, so that the task execution party processes the multi-party collaborative task. The application guarantees data privacy and access security, and realizes efficient and trusted collaboration of multi-party data.

This invention relates to the field of simulation technology and discloses an automatic setup device, deployment system, and method for hardware-in-the-loop simulation test engineering. The method includes: a standardized path aggregation module for storing the hardware-in-the-loop simulation test engineering in a first preset path; a DBC parsing and classification module for extracting preset information from DBC files, performing signal verification, attribute extraction, and automatic import and export classification, and storing the data in a data space; an automatic model update and compilation module for automatically updating and compiling the simulation model; a configuration file generation module for importing dependency libraries and device APIs, generating device configuration files according to their structure, and verifying the validity of the configuration files. This invention improves the efficiency of setting up simulation test engineering.

This application relates to the field of data security protection technology, specifically to a method and system for sensitive data security protection based on a trusted data space. The method includes: real-time acquisition of the energy output value and core temperature value of each target device; constructing an efficiency deviation degree based on the conversion efficiency of each target device at each moment and its deviation from the conversion efficiency of devices of the same model and in the same environment; correcting the static temperature difference based on the rate of change of the core temperature value of each target device at each moment, and constructing an output-to-heat dissipation ratio based on the energy output value; constructing a heat dissipation deviation degree based on its deviation from the output-to-heat dissipation ratio of devices of the same model and in the same environment; and, in conjunction with the efficiency deviation degree, determining the status of the data of each target device at each moment and performing corresponding defense operations. This application, by utilizing the relationship between output and loss in the law of conservation of energy, can accurately distinguish between two types of anomalies: false data injection and genuine equipment failure.

A method of registration of a certification entity of a data space for at least one target criterion associated with a service, the method being implemented by a data space management entity capable of managing the certification entity. The method includes: receiving self-description data from a candidate for certification entity status for the data space for the at least one target criterion, the candidate being capable of certifying at least one criterion of a service; and registering the candidate in a registry specific to the data space, as a certification entity of the data space for the at least one target criterion in the event that, based on the self-description data, the candidate is determined to be capable of verifying the conformity of the at least one target criterion with the service.

The present disclosure provides an obstacle detection method, device, vehicle, storage medium and computer program product, wherein the method comprises: fusing image data and point cloud data to generate spatio-temporal feature data and spatial position encoding; determining information of a first obstacle according to the spatio-temporal feature data and the spatial position encoding, the first obstacle having a corresponding obstacle category; determining information of a second obstacle according to the spatio-temporal feature data, the spatial position encoding, map information and driving path information, the second obstacle including a third obstacle without a corresponding obstacle category and a road ground facility; and fusing the information of the first obstacle and the information of the second obstacle to obtain an obstacle detection result. The present disclosure can reduce the consumption of computing power, reduce the computing cost, respond quickly to obstacle detection, improve real-time performance, improve detection accuracy, and improve the safety of vehicle driving.

This application discloses a method for parallel integration of geospatial data based on spatial grid-aware routing. The method includes: acquiring source information of geospatial data files and identifying their format and metadata; logically segmenting the data files into record blocks; calculating the bounding rectangle of the geometric objects of geospatial elements in each record block and determining the covering grid set according to spatial grid partitioning rules, then determining a unique master spatial grid identifier according to master attribution rules; constructing grid clusters based on the master spatial grid identifier, merging adjacent grids into scheduling partitions and routing them to corresponding parallel processing tasks; and having each task stream-read and process elements before batch writing them to the target storage. This invention overcomes the bottleneck of single-file parallel processing, achieves spatial locality-aware routing and dynamic load balancing, improves the spatial aggregation and query performance of target data, reduces the risk of memory overflow, and possesses high-throughput writing and precise breakpoint resumption capabilities.

The application relates to a photovoltaic pile driver terrain identification method and system, a terminal and a storage medium, and relates to the technical field of intelligent equipment. The application comprises the following steps: analyzing camera pixel data, laser radar data, a camera internal parameter matrix, a camera radar external parameter matrix and a radar satellite external parameter matrix to determine pixel camera data, radar world data and camera world data; fusing the radar world data and the camera world data to determine simultaneous space fusion data and coordinate point gray values; analyzing the coordinate point gray values and the simultaneous space fusion data to determine spatial terrain features and pile driving terrain classification; and analyzing the radar world data, the camera world data, the spatial terrain features and the pile driving terrain classification to determine real-time terrain modeling. The application has the effects of improving the photovoltaic pile driving terrain identification accuracy and terrain updating efficiency.

This invention relates to a method and system for analyzing the meteorological suitability of tobacco cultivation, specifically a method for evaluating and spatially predicting the climate suitability of tobacco cultivation based on multi-source data fusion. It addresses the problems of insufficient spatial resolution of meteorological data, static evaluation models, inadequate multi-source data fusion, separation of disaster early warning and suitability evaluation, and lack of intelligent service capabilities in existing technologies. This invention collects and fuses multi-source data, filters key meteorological elements, constructs fuzzy membership functions and spatial distribution models, calculates grid meteorological element values, and calculates a comprehensive suitability index. Based on this, it classifies the data into levels and generates a climate suitability zoning map. This invention is applicable to fields such as climate suitability evaluation for tobacco cultivation, crop planting zoning, and agricultural meteorological disaster risk assessment.

This invention discloses a hybrid distributed dense-state fine-tuning method and system for models based on a trusted data space. The method includes: acquiring plaintext data to be trained; splitting a large model into a first model and a second model, and deploying them respectively on a first computing node group and a second computing node group; the first computing node group performing forward computation of the first model and dense-state processing of the results based on the plaintext data to be trained, and transmitting the dense-state processed data to the second computing node group; decrypting the dense-state processed data, and the second computing node group performing forward computation and backward propagation of the second model based on the decrypted data to obtain feature map gradients, performing dense-state processing, and sending the gradients to a trusted execution environment; decrypting the dense-state gradient data and performing backward propagation of the first model based on it, and obtaining the updated parameters of the first model based on the propagation results. This provides an efficient computational solution for dense-state fine-tuning of large models with hundreds of billions of parameters in dense-state scenarios.

Certain example embodiments leverage counterfactuals to repair a dataset used in a downstream machine learning (ML) model. Features from the dataset stored are extracted, portion-by-portion. For each portion, an error detection algorithm identifies erroneous data instances in the dataset using the extracted features associated with the respective portion. For each identified erroneous data instance in each portion: a set of feature-space repair candidates is determined, with each having at least one change with respect to the extracted features associated with the respective erroneous data instance; for each feature-space repair candidate, a data-space repair candidate is generated by (a) finding a matching non-erroneous data instance within the respective portion, and (b) using one or more values from the match; a repaired instance is generated based on one or more of the data-space repair candidates; and the portion is updated to include the repaired instance rather than the respective erroneous data instance.

The embodiment of the specification provides a credit investigation data processing method and device, wherein the credit investigation data processing method comprises the following steps: in the credit investigation data processing process of the credit investigation node of the credit investigation trusted data space, first, the signing process and contract authority synchronization of the credit investigation digital contract of the credit investigation client are performed; in the case that the encrypted credit investigation data uploaded by the credit investigation client is obtained, the encrypted credit investigation data is transmitted into the trusted execution environment to obtain the credit investigation data by performing the structural conversion on the encrypted credit investigation data, the credit investigation data is transmitted to the virtual isolation space in the form of the secret state to perform the user interaction confirmation, and the target credit investigation data obtained by the confirmation is encrypted and stored as evidence, so that the safe processing and storage of the credit investigation data are realized within the credit investigation signing range.

The application relates to the technical field of smart homes, and discloses a method for generating a smart housekeeper, which comprises the following steps: constructing a model of a virtual housekeeper; generating a virtual data space; the virtual data space is used for feeding back the state of an entity home appliance in a preset area; the model of the virtual housekeeper is integrated into the virtual digital space to obtain a smart housekeeper. The smart housekeeper is a digital virtual person. The model of the virtual housekeeper is constructed, then a virtual data space for feeding back the state of an entity home appliance in a preset area is generated, and the model of the virtual housekeeper is integrated into the virtual digital space to obtain a smart housekeeper. In this way, a user can know the state of each entity home appliance in the preset area through the smart housekeeper, and does not need to go to the front of the home appliance to be known to check, so that the use experience of the user is improved. The application further discloses an apparatus for generating a smart housekeeper, an electronic device and a storage medium.

The application discloses a privacy protection joint diagnosis method based on confusion and belongs to the technical field of information security, and comprises the following steps: three computing servers acquire feature secret shares and weight secret shares in the form of replicated secret sharing respectively; each computing server performs privacy protection reasoning of a diagnosis model according to the feature secret shares and the weight secret shares held by the computing server, wherein multiplication based on replicated secret sharing is used to realize linear function reasoning in the diagnosis model, intermediate feature secret shares of input nonlinear functions are disordered by a random permutation protocol, then the intermediate feature secret shares are reconstructed into plaintext, plaintext nonlinear operations are performed, and inverse permutation protocol is used to restore the nonlinear function reasoning results in the form of replicated secret sharing, that is, nonlinear function reasoning in the diagnosis model is realized. The data space structure is disordered, so that the privacy is ensured before the data is opened, and complex nonlinear operations are removed from the MPC closed loop, so that the interaction cost is greatly reduced.

This invention discloses an intelligent early warning method and system for trusted data spaces based on multi-source behavioral analysis, belonging to the field of data processing technology. The method includes: acquiring multi-source behavioral data generated by a target account entity within a trusted data space and extracting multi-dimensional behavioral features; configuring a series of stepped analysis windows with stepped time lengths, using the current time node as the analysis starting point, and combining entropy weight analysis to determine the dynamic feature weights under different stepped analysis windows, forming a stepped feature weight matrix; based on the sliding window method and a preset sliding step size, iteratively performing entropy weight analysis on the multi-dimensional behavioral features with time backtracking as the sliding direction, forming a backtracking stepped feature weight matrix series; combining the stepped feature weight matrix and the backtracking stepped feature weight matrix series to verify the behavioral consistency of the target account entity, and issuing an account warning based on the verification results. This invention achieves early and accurate early warning for trusted data spaces.

The application relates to a method and system for improving the speed of writing shared main memory critical resources in parallel by a new generation Shenwei many-core processor, which comprises the following steps: a slave core applies for a data space on its private local data memory; critical resource data in the main memory is copied to the respective private local data memory; each slave core performs read-write operation; each slave core initiates a reduction operation through a remote memory access (RMA) channel, wherein the reduction operation refers to performing certain aggregation function operation on the critical resource data in the private local data memory of the plurality of slave cores to obtain a final result; and the critical resource data in the private local data memory after the reduction operation is written back to the main memory through a direct memory access (DMA) channel. The method can effectively improve the speed of reading and writing the shared main memory critical resources by the slave core of the Shenwei many-core processor, and improve the performance and efficiency of the supercomputer.

The invention relates to a method and equipment for tracing and repairing water conservancy data quality abnormity, and a medium, and belongs to the technical field of intelligent water conservancy. Collecting multi-source water conservancy data, extracting data features and generating a standardized feature vector set; constructing a three-layer Bayesian network topological structure of circulation link-business influence factor-anomaly type, training to obtain a dynamic weight Bayesian network model adaptive to the water conservancy scene, inputting the standardized feature vector into the dynamic weight Bayesian network model obtained by training, outputting quality anomaly root cause probability distribution, and obtaining the quality anomaly root cause probability distribution. The posterior probability of each abnormal root cause is calculated through a Bayesian formula, and a root cause output traceability result is judged; performing graded repair and verification according to different traceability results; and the repaired qualified data is synchronized to a data asset module of the water conservancy credible data space for service output and generation of a visual data quality report. According to the method, the traceability precision is remarkably improved, and the service adaptability is higher.

The application discloses an industrial process fault detection method and system based on double subspace division, which firstly performs normality evaluation on process variables, divides a data space into Gaussian subspace and non-Gaussian subspace; a fault detection model based on principal component analysis is established in the Gaussian subspace, and a fault detection model based on support vector data description is established in the non-Gaussian subspace; current test data variables are divided into the Gaussian subspace and the non-Gaussian subspace, and are respectively input into the trained models, so that fault detection statistics corresponding to each subspace are calculated; each subspace statistic is converted into a fault probability by using Bayesian inference and is weightedly fused to construct a comprehensive monitoring statistic, and fault detection is realized. The application effectively solves the problem that a traditional method has poor adaptability to Gaussian and non-Gaussian mixed distribution data, improves the fault detection rate while significantly reducing the false alarm rate, and is suitable for real-time monitoring requirements of complex industrial processes.

The present application relates to the technical field of electric digital data processing, and discloses a data processing method of multi-source error fusion, comprising: acquiring geometric static error, non-uniform temperature field distribution and strain load mixed data containing thermal stress characteristics of a large controlled system; thermal elastic compensation modeling is used to calculate thermal deformation compensation and map thermal induced pseudo-tension component; the component is used to decouple and separate strain load mixed data, and extract net mechanical load component; according to the dynamic distribution weight of each data compensation component observation variance, a multi-dimensional matrix fusion is used to generate a comprehensive error state vector; a physical logic threshold is compared, and a space deviation control instruction is outputted; the present application decouples thermal induced interference at the level of electric digital signal, cuts off the coupling parasitic link between temperature characteristics and strain characteristics, avoids data space collinearity interference, suppresses control instruction oscillation, and improves the deviation hedging stability and real-time processing efficiency of a large controlled system.

The application discloses a data parallel distributed fine-tuning method and system based on a trusted data space. In the method, a large model is divided into a first model part deployed in a trusted execution environment and a second model part deployed in a GPU cluster, and low-rank adaptation is used for fine-tuning; each data provider transmits encrypted data to the trusted execution environment, and after decryption and integration, the data is divided into multiple batches by a master node in one training round and distributed to each slave node for parallel forward calculation to obtain a feature map; the feature map is transmitted to the GPU cluster after being processed in a secret state to complete forward and backward propagation, and the gradient is returned in a secret state; the low-rank parameters of the first model part are updated after the trusted execution environment node decrypts the gradient; and after each training round ends, cross-node synchronization of the low-rank parameters is performed between nodes. The application realizes efficient distributed training while guaranteeing data privacy, and has high engineering practicability.

The application discloses a multi-party authentication method and system based on a distributed digital identity and an electronic device, and belongs to the technical field of computers. The application independently deploys an identity authentication function as a decoupled first authentication node, and separates the first authentication node from a control plane and a data plane, so that the identity authentication is no longer dependent on a centralized certificate issuing agency, effectively eliminates single-point failure risks, and improves the overall availability and robustness of a data space authentication system. A nested token structure including an outer-layer identity token and an inner-layer scope token is generated, the node identity information is borne by the outer-layer token, and the inner-layer token limits the credential acquisition range, so that token-level fine-grained permission constraints are realized, the credential query process is strictly matched with the authorized range, and the unauthorized acquisition of the credential is prevented. Public key analysis and signature verification are performed based on a distributed digital identity identifier document, so that each participating node can independently publish and manage its own identity information.