323 results about "Master data" patented technology

The invention relates to a power distribution network abnormal state intelligent identification method based on unmanned aerial vehicle routing inspection, and the method comprises the following steps: S1, obtaining power distribution network routing inspection related data, including asset main data, historical operation and maintenance data and environment constraints; s2, constructing the digital twinning of the power distribution network based on the power distribution network inspection related data, and obtaining the mapping from the equipment ID to the spatial pose and parameters; s3, generating a risk heat map based on historical defects and environmental risk scoring; s4, constructing an equipment-geography-working condition knowledge graph, and generating inspection targets and priorities according to line sections in combination with mapping from equipment IDs to spatial poses and parameters and a risk heat map to obtain a task package; and S5, performing multi-target route inspection according to the task package, the risk heat map and the mapping from the equipment ID to the spatial pose and the parameter, and intelligently identifying the abnormal state of the power distribution network. The operation reliability of the power distribution network is effectively improved, and the power supply quality is improved.

A device to process data associated with a surgical event of a surgery may include a processor. The processor may be configured to receive multiple data streams during the surgical event. The processor may be configured to select a primary data stream based on a surgical data interface via which the primary data stream is received. The processor may be configured to select a secondary data stream based on a surgical data interface via which the second data stream is received. The processor may be configured to identify the surgical data interfaces. The processor may be configured to generate situational data associated with the primary data stream based on the secondary data stream. The situational data may indicate a medical decision-making factor of the surgical event. The primary data stream and the situational data may be sent during the surgical event.

The invention relates to a coal machine spare part similarity identification and coding unification system and method and a storage medium. The system comprises a data acquisition module for extracting coal machine spare part data from a plurality of systems; the cleaning and preprocessing module is used for carrying out unified formatting processing on the spare part data; the feature extraction and matching engine is used for performing multi-dimensional feature extraction on the preprocessed data, including text similarity features, semantic features, structural features and attribute features; the similarity calculation and clustering module is used for calculating the comprehensive similarity of spare part data through a deep fusion network according to the multi-dimensional features, and performing automatic clustering according to the similarity; and the master data generation and system docking module is used for generating unified spare part master data for each cluster and synchronizing the spare part master data to a plurality of systems. According to the method, a large model and deep learning are utilized, multi-dimensional features are extracted and fused for multi-source spare part data, spare part similarity matching is carried out, dependence on a spare part coding format is broken through, and the accuracy and robustness of cross-system matching are improved.

The invention discloses a vehicle information safety protection system based on combination of a national cryptographic algorithm and a PUF (Physical Unclonable Function), which belongs to the field of vehicle information safety and encrypted communication, and comprises a response generation module used for generating a PUF response in a safety chip of a vehicle; the key derivation module is used for generating an encrypted master key through a key derivation function based on the PUF response, the vehicle owner identity and the random number nonce; the encryption and signature module is used for generating a ciphertext and carrying out digital signature on the ciphertext; the state monitoring module is used for monitoring the running state of the vehicle hardware; the key management module is used for triggering a failure operation of the encrypted master key when the state monitoring module detects that the hardware state is abnormal; and the decryption verification module is used for regenerating the PUF response and verifying the consistency of the generated key so as to execute data decryption. According to the method, hardware-level encryption protection of the vehicle data is realized through combination of the PUF and the national cryptographic algorithm, so that the safety of the vehicle owner data is protected in the whole life cycle of the vehicle.

The invention relates to the technical field of industrial information processing of production logistics scheduling, in particular to an intelligent material scheduling decision-making method and system based on a large language model, and the method comprises the steps: obtaining historical scheduling data, a production plan text, real-time material state information, production rhythm information and production main data; constructing a time statistical model and analyzing the plan text to obtain a scheduling constraint set; generating a candidate transfer time window after consistency verification, and determining a target time window; and generating a timestamp sequence, aligning and correcting according to rhythms, and outputting a scheduling instruction sequence. Constraint conflicts and non-executable instructions can be reduced, and delivery and beat stability is improved.

A data processing architecture controls data processing arbitration in a high performance computing system that includes one or more premises. Individual premises can include one or more server computers executing an instance of a local file system and including one or more temporary data storage devices. Individual instances of the local file system can access files stored in objects of a primary data store. Individual objects of the primary data store can be accessed using a common identifier indicating a storage location of the individual objects in the primary data store.

The invention provides a deep processing method and system for a digital delivery result, and relates to the technical field of data processing. A delivery result file set is disassembled and recombined into a unified object main data set and a three-dimensional entity set; each object attribute record and each object relation record have a unified object identifier, a source identifier and evidence positioning information at the same time, and multiple sets of coding calibers of the same object can be gathered to the same unified object identifier; refined model replacement is executed by taking a unified object identifier as an anchor point in three-dimensional treatment, continuity of a composition relationship and a connection relationship is kept, and after project handover and commissioning, online and field inconsistency found by operation and maintenance personnel is converted into a revision chain capable of being rechecked, judged and audited, so that the operation and maintenance efficiency is improved. Therefore, the problems that the unstructured data is difficult to reuse, the coding calibers are different, the object relationship is easy to lose and the data in the operation and maintenance period is not credible are solved.

The invention belongs to the field of commodity purchasing, and discloses a blind-eye selective purchasing control method and system based on an intelligent matching technology, and the method comprises the steps: only displaying commodity standard library information through initialization setting to hide a supplier identity, and achieving blind-eye selective purchasing; supplier main data, commodity standard library data and real-time purchase demand data are acquired in real time; calculating a supplier performance score by using a multi-dimensional weighted scoring model to measure a comprehensive performance ability, calculating a cost performance index based on a commodity standard library and a real-time demand to evaluate a commodity comprehensive value, and dynamically generating a contract group quota allocation value in combination with a quota occupancy rate and the performance score; and finally, a matching score is calculated by integrating the performance score, the cost performance index and the quota distribution value, and suppliers are intelligently screened. By adopting the method, the contract resource allocation efficiency is improved, and the limit is prevented from exceeding the limit or being idle; the data security is enhanced, and order tampering and historical migration conflict risks are reduced; and the fairness, the reliability and the automation level of the purchasing system are integrally optimized.

The invention discloses a mass spectrum data high-throughput alignment and parallel qualitative method based on deep characterization learning, and relates to the field of chromatography-mass spectrometry data processing and calculation mass spectrography. The method comprises the following steps: performing streaming, micro-batch and single traversal analysis on original mass spectrum data files of a plurality of samples to be detected; extracting metadata and main data by taking the fragmented spectrogram as a unit, inputting the metadata and the main data into a pre-trained depth representation learning model to generate a high-dimensional embedded vector, and incrementally writing the metadata and the main data into a column type storage database; constructing a sparse approximate nearest neighbor graph; executing a clustering algorithm on the graph to generate a consensus spectrogram set, and generating a consensus feature vector for each clustering cluster and a single example cluster; determining the nature of the consensus feature vector; and constructing a sample * feature matrix based on the cluster affiliation relationship and the qualitative result. According to the method, the technical problems of I / O bottleneck, memory overflow, combinatorial explosion, computing power waste and the like in large-scale mass spectrum data analysis are effectively solved.

The application discloses a kind of nnUNet zebra fish juvenile whole brain vascular system segmentation methods based on autonomous data set training, it is related to high-resolution imaging technology, image processing and medical image segmentation field, the method makes full use of zebra fish live transparency and fluorescent label advantage, obtains high-resolution whole brain three-dimensional vascular image data, and constructs high-quality segmentation truth value database by semi-automatic segmentation and artificial correction, training is carried out using nnU-Net deep learning model, realize the three-dimensional automatic segmentation of zebra fish brain vascular system signal.The application method significantly improves the degree of automation and precision of image segmentation, effectively solves the problems of low efficiency, high artificial dependence and poor repeatability of traditional brain vascular segmentation.The method is suitable for large-scale high-throughput data processing, can provide efficient, standardized image processing scheme for zebra fish brain vascular development mechanism and brain vascular disease model research, and has wide application prospect.

The invention relates to an electric power asset full life cycle management method and system based on lean management, and the method comprises the following steps: S1, building a provincial storage interface bus, achieving the standardized intercommunication of business data, and enabling the system to automatically obtain the inventory state and distribution progress data; s2, based on the inventory state and the delivery progress data, applying an inventory and delivery prediction optimization model, and intelligently scheduling and allocating goods to obtain asset arrival data; s3, performing priority judgment according to asset arrival data and business control rules, and automatically allocating verification tasks and detection work orders; s4, according to a verification and detection result, directly flowing into asset main data, and automatically binding the verification and detection result with a machine account to realize seamless connection with a provincial data supervision node; and S5, checking the asset main data quality according to a preset rule, giving an alarm in real time if there is an abnormality, and automatically backtracking a data attribution source. According to the invention, lean transformation of power asset management is realized.

Systems and methods call a portion of an API to access a portion of a current state data server of a blockchain computing platform (BCP) configured to perform virtual machine processing, the BCP includes interconnected data processing nodes including: a master ledger configured to store trust data asset records for master data and reference data received from decentralized application transactions performed via the processing nodes, and peer nodes configured to provide cryptographic algorithmic trust control for node-based core operations and trust data asset management. An API call to the current state data server obtains current state results of certified transaction(s) in the master ledger that have been certified at least in part via an endorsement protocol. Current state results of the certified transaction(s) are received from the current state server and displayed via a user interface, thereby rendering a world state representation of trust data asset results.

The invention discloses an equipment data unified integration and supply method based on dynamic metadata and intelligent routing and an Internet of Things home platform, and the method comprises the steps: obtaining to-be-updated equipment data, classifying the equipment data according to the data structure, data increment and change frequency of the equipment data, and respectively marking the equipment data as main data or transaction data; based on task characteristics of the main data and the transaction data, intelligent scheduling is carried out by utilizing dynamic node pressure sensing, and different types of data are distributed to optimally matched processing nodes; and performing corresponding update processing on the main data and the transaction data, uniformly storing update processing results in a dimension table mode and a fact table mode, and enabling the update processing results to be called by a smart home language model through a uniform data service interface.

The gateway, which connects a low-trust domain (12) and a high-trust domain (13) of an avionics computing infrastructure, provides a plurality of security functions, each function being performed by a data processing node. The gateway comprises, connected in series along a filtering chain of a data flow received from the low-trust domain: a firewall data processing node (4); a protocol break data processing node (5); a master data processing node (1) and an inverse protocol break data processing node (6), the gateway further including a security data processing node (2) connected to each of the data processing nodes of the filtering chain, the different data processing nodes being physically segregated.

An enhanced blockchain data computing platform for node-based core operations and trust data asset management provides a package-built solution using a blockchain platform for core banking and data asset management. Identity Management and Asset Management are constructed as coordinated extensions of inherent blockchain capabilities. Queries against a Master Data Asset World State provide interactive views into the “lives” of Trust Data Assets (TDAs) and a full-provenance historical view of Trust Data Asset transactions recorded in a Master Ledger. This ledger is then automatically shared as Distributed Ledger copies across the blockchain network to other Network Node applications thereby providing access to the same TDA objects and improving source data quality and TDA analytical results over time as the Network Nodes synchronize on a single trusted TDA Source of Record.

A system and method of autonomous data hub processing that uses semantic metadata, machine learning models, and a permissioned blockchain to autonomously standardize, identify and correct errors in supply chain data is disclosed. Embodiments input supply chain data stored in a supply chain database, train with the machine learning model trainer, one or more machine learning models to identify one or more data errors in the supply chain data, clean the one or more identified data errors from the supply chain data, and store cleaned supply chain data. Embodiments also update one or more machine learning models to identify one or more data errors in cleaned supply chain data, and join and aggregate one or more sets of cleaned supply chain data.

The burden of answering questions regarding a supplier in a company evaluation is eased by a company evaluation processor system that includes a memory that stores, for each predetermined target company, master data in which at least one or more questions are associated with an answer, and stores a predetermined score allocation for each question in the master data for each evaluation company. The system receives a questionnaire acquired by the target company from a questionnaire distribution source and a questionnaire answer, when any of the questionnaire questions and any of the questions in the master data related to the target company are similar to each other. The questionnaire answer is stored in the memory as the answer in the master data related to the target company, and the answer in the master data of the target company is scored using the score allocation according to the evaluation company.

Methods, systems, and devices for wireless communications are described. A first network entity may obtain one or more request messages for collecting data reports associated with one or more user equipment (UEs). The first network entity may output a first message requesting that a second network entity provide the data reports associated with the one or more UEs after the data reports are collected. The first network entity may output, via the user plane and to the one or more UEs, a second message including data collection information (e.g., a data collection configuration, information associated with autonomous data collection) for collecting the data reports from the one or more UEs. The first network entity may obtain, from the second network entity, reporting messages including the data reports, where the data reports may be sent from the one or more UEs to the second network entity via the user plane.

This application discloses a master data integration method, apparatus, electronic device, and storage medium. The method includes: acquiring multi-source heterogeneous master data; standardizing and cleaning the multi-source heterogeneous master data to generate multiple block keys and inverted index lists, and constructing a candidate comparison set; extracting and normalizing the multi-level similarity of each pair of candidate records in the candidate comparison set, and constructing a standardized feature vector; inputting the standardized feature vector into a learning model to obtain the matching probability of each pair of candidate records, and making hierarchical decisions based on the matching probability and at least one matching threshold; constructing an undirected matching graph and generating entity clusters from the undirected matching graph; determining the data quality score of each field of each candidate record within the entity cluster, determining the optimal value of each field based on the data quality score, and generating the integrated master data. This application significantly reduces the number of comparisons, improves comparison efficiency, eliminates the need for manually writing matching rules, and can adapt to various data types and new data sources.

The invention provides a main data automatic governance method and system based on a large model and Dores, and belongs to the field of data governance. The method comprises the following steps: constructing an RAG main data management knowledge base stored in Dores, and exploring multi-source heterogeneous data by utilizing federated query and MCP service of the Dores; driving an end-to-end process from main data automatic identification, asset checking, source layer modeling and data access, and main data standard model definition and gold attribute set generation by using a large language model; deep cleaning is carried out on the data by adopting a rule and AI cooperation mode, and heterogeneous data mapping and ETL task generation and arrangement are completed; finally, in combination with a graph database and an authority system, intelligent data security sharing based on dynamic desensitization is realized. According to the method, automation, intelligence and adaptive optimization of the whole process of main data management are realized, and the problems of dependence on manpower, low efficiency, incomplete coverage and insufficient safety compliance of a traditional scheme are solved.

The invention belongs to the technical field of data processing, particularly relates to a railway main data verification method, system and equipment based on a multi-rule engine and a storage medium, and aims to solve the problems that rule verification in a railway data base is solidified, the expansibility is poor, and the railway data base cannot adapt to complex business scenes in the prior art. The method comprises the steps that a rule base containing multiple rules is configured, input of data to be verified is received, and verification is executed by a verification engine based on preset groups and priorities. When rule conflicts occur, a multi-strategy conflict resolution mechanism is used for processing, and finally a verification report is generated and displayed. According to the method and the system, configurable management and flexible scheduling of the verification rules are realized, the defects of rule solidification, poor expansibility and the like in a traditional hard coding mode are effectively overcome, and the adaptability and the accuracy of data verification in a complex railway service scene are remarkably improved.

The present application provides an online task transaction full-link identity authentication and dynamic authorization method and system, wherein the method comprises: establishing a full-link task flow conversion session and assigning an initial operation permission matching the current task node when the initial identity verification is passed; continuously monitoring the risk state parameters of the environment where the terminal is located during the execution of the current task node process; blocking the main data submission channel and adjusting the flow conversion state machine to a suspended waiting mode when the risk state parameters reach the set threshold; synchronously opening an isolated virtual shadow buffer pool to push an asynchronous authentication challenge package with a virtual offset address sequence to the terminal; receiving the temporary task processing data sent by the terminal and storing it in the buffer pool; obtaining the identity authentication feedback data of the terminal; restoring the operation permission and mapping and merging the data in the buffer pool to the main channel to wake up the state machine when the feedback data is verified to be compliant. The present application can intercept abnormal risks while maintaining the continuity of normal business data flow conversion.

In some examples, a system receives a representation of a honeypot pattern and information about a honeypot object containing the honeypot pattern injected into primary data. The system examines backup data created by a backup management system by identifying an instance of the honeypot object within the backup data and determining whether the data of that honeypot object instance deviates from the honeypot pattern. Based on the finding that the data of that honeypot object instance deviates from the honeypot pattern, the system triggers a corrective action regarding the backup data.

The present application belongs to the technical field of data management, and particularly relates to a data management system for a twisted wire automatic detection platform, to solve the technical problem that the prior art cannot identify periodic defects and the traceability analysis process cannot correlate defects. The management system comprises: a data acquisition and synchronization unit for forming a longitudinal data profile; a main data modeling unit for constructing a pedigree main data model containing twisted wire product, single wire raw material and production process data; a data warehouse hierarchical processing unit for establishing a hierarchical data warehouse; an identification unit for constructing a data theme layer above the data detail layer; and an application service unit for constructing a data application layer above the data theme layer. The present application realizes traceability from quality problems to production, and provides data decision basis for process improvement and quality control.

Embodiments of the present application provide a read-write separation method, an electronic device and a storage medium. The read-write separation method comprises: in response to receiving a service request, intercepting an interface in the service request marked with a first annotation, and obtaining a second annotation marked on an interface method of the interface; the first annotation is used to indicate that the interface is a database interface, and the second annotation is used to indicate a data source required by the interface method, the data source comprising a master data source and a slave data source; and based on the second annotation, a target data source used when executing an SQL statement in the service request is selected. In the embodiments of the present application, the interface method in the database interface can be intercepted without concerning the business logic, so even if the business logic of the master data source and the slave data source exists in the interface method at the same time, the data source required for use can be accurately selected based on the second annotation when executing the SQL statement in the interface method, so that the read-write separation can be more accurately implemented.

To provide a history data management device excellent in flexibility and versatility by allowing a user to arbitrarily set timing for acquiring history data in a master in loan examination work.SOLUTION: The history data management apparatus includes: a master in which a case status of a creation timing of history data is registered; an application acquisition unit that acquires the latest application data and stores the application data in the storage unit when a loan examination application is made; a history registration unit that determines whether or not the case status of a transition source is registered in the master when the case status of the application data transitions, and if the case status of the transition source is registered, stores the application data as history data in the storage unit; and a difference check unit that refers to the history data and the application data stored in the storage unit, and displays and outputs a difference item with respect to a comparison source case status and a comparison destination case status specified for a target case according to an operation of an operator on a confirmation screen.SELECTED DRAWING: Figure 3

A new methodology for describing business processes and permissible process variants is introduced. Additionally, an allowed system status and involved master data objects with read and change authorization can be defined for process step. If a user wants to run the defined process in a BPM product, the system connection may be made from an authorized central solution manager system (connected to all systems in a landscape) so it is possible to run predefined processes. As soon as a process step shall be executed, a central cloud service can be executed. The central cloud service can access and analyze selected objects. The execution system, which may be a completely different system than the system that defined the business process, can then execute an associated process step, and a process pointer will store the data in involved systems and set a new status.

The application discloses a touch device and a data processing method thereof, and a display device. The display device comprises a touch screen, which is divided into multiple touch areas; multiple touch chips in cascade, each of which corresponds to a touch area and is used for scanning the corresponding touch area in a preset order to obtain touch data of the corresponding touch area through a main data channel. Each touch chip is also used for obtaining partial touch data of a touch area adjacent to the corresponding touch area through an auxiliary data channel. The partial touch data is touch data of an adjacent position of the adjacent touch area and the corresponding touch area, so that the finger touch position can be accurately confirmed without increasing the area of a main touch chip random access memory.

The present application relates to the technical field of equipment life cycle management, and specifically includes an aviation equipment full life cycle management method and system based on digital threads, the method comprising: acquiring process parameters of each stage, determining first digital thread master data and a first layer of associated test sequences, second digital thread master data and a second layer of associated test sequences, mapping the deviation of the physical equipment state and the virtual simulation state, and identifying the performance difference point under the cross-stage data correlation link; setting a management strategy for collaborative optimization. The technical problem of being unable to identify the cross-stage performance difference point equipment performance fluctuation root cause positioning in real time, causing the reliability of aviation equipment to be limited is solved, the first and second digital thread master data and the associated test sequences are constructed, the deviation of the physical equipment and the virtual simulation state is accurately mapped, the management strategy is set in combination with the double thread data and the performance difference point, and full life cycle collaborative optimization is performed, and the technical effect of effectively improving the equipment reliability is achieved.

This invention relates to the field of drug monitoring, specifically a drug monitoring and traceability method and system based on traceability labels. During the drug production stage, traceability labels are generated for each level of packaging, and traceability codes, anti-counterfeiting information, and basic drug information are linked and written into master data. At the production line, fixed scanning equipment completes the initial reading and consistency verification, enabling label activation or cancellation management. In the circulation and usage stages, various nodes collect public codes and business event information through terminals and upload them to the monitoring and traceability platform. The platform maintains the label's lifecycle from pending activation to dispensing, returned, and cancelled states. At high-risk nodes, concealed anti-counterfeiting information and electronic tag identification are collected simultaneously. A first monitoring indicator is constructed based on activation behavior, geographical distribution of scanning, and anti-counterfeiting consistency. A second monitoring indicator is constructed by combining state migration trajectories, cross-regional circulation, and return / cancellation behaviors. Finally, a comprehensive monitoring score is calculated, triggering early warnings from enterprises and regulators.