469 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.

The invention relates to the technical field of embedded data storage, and discloses a method and a system for prolonging the service life of a FLASH simulation EEPROM (Electrically Erasable Programmable Read-Only Memory) in a single chip microcomputer, and the method comprises the following steps: firstly, initializing the FLASH, dividing the FLASH into a main data area, a mapping table area, a backup area and a transaction log area, selecting a physical block through a dynamic wear leveling algorithm when data is written, and writing the data into a database; the method comprises the following steps of: starting a backup area or a transaction log area, safely updating a mapping table by using a transaction mechanism, performing cyclic redundancy check when reading data, if the cyclic redundancy check fails, starting cascade recovery from the backup area or the transaction log area, and finally, updating a transaction log to record operation so as to form a closed-loop management process. According to the invention, through a set of dynamic wear leveling and data classification management mechanism, global and uniform distribution of the write load in the storage array is realized, the physical block with the least erasing times is selected during writing, the access frequency of the data can be identified, and cold data which is not changed frequently can be actively migrated.

A system and method for adaptive data processing combining compression and encryption. The system analyzes input data characteristics, compares probability distributions, and creates a transformation matrix to convert data into a dyadic distribution. It generates a main data stream of transformed data and a secondary stream of transformation information. The system dynamically selects and applies processing techniques, including transformation, encoding, compression, and encryption algorithms, based on analyzed characteristics and real-time performance metrics. It compresses the main data stream using Huffman coding and implements security measures to protect the output. A feedback loop monitors technique effectiveness, updates a knowledge base, and influences future selections. The system can operate in lossless, lossy, or modified lossless modes, adapting to different application requirements. This approach offers an efficient solution for scenarios where both data reduction and security are critical concerns.

The invention discloses a data analysis visualization processing method and device based on index management and a server, and belongs to the technical field of data processing, and the method comprises the steps: extracting related original data from main data and business data storage positions according to the data source information of each index in a predefined index dictionary, processing the extracted original data into standardized data; for the standardized data, calculating each index value according to a predefined calculation formula in an index dictionary; and obtaining visual configuration content operated by a user, automatically generating a report containing a visual chart according to the calculated index values according to the visual configuration content, and receiving a user operation instruction to export the report of the visual chart. According to the invention, the enterprise data analysis efficiency is improved, the labor cost is saved, the data visualization operation is realized, and convenience is provided for the use of the user.

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 discloses a cross-border e-commerce service data management method, and relates to the technical field of service data management, and the method comprises the steps: obtaining commodity service data from a plurality of overseas e-commerce platforms, and caching the commodity service data to an edge data pool according to the attributes of a data source; based on source attributes, updating time and historical accuracy of the data sources and supervision rating of judicial jurisdictions, comprehensive credible weight values of the data sources are calculated, and main data channels are screened out according to the weight values; inputting commodity service data in the main data channel into a multi-language semantic encoder, generating semantic vectors, calculating cross-language semantic alignment, and comparing logic consistency of key structured fields; generating a consistency score according to a comparison result of the semantic alignment degree and the structured field, when the score is lower than a preset threshold value, marking as abnormal and entering a rechecking process, and otherwise, entering a compliance review stage; and calling the regional compliance rule base to carry out regulation matching on the data passing the consistency verification.

The invention discloses an optimization method and device for an embedded linked list container, electronic equipment and a storage medium, and relates to the technical field of computers, the method comprises the steps that a linked list node is embedded into a host data structure to serve as a member variable, extra memory occupation of a pointer domain in a traditional linked list is omitted, and the optimization efficiency is improved. Physical storage of the nodes and the host data structure is continuous, so that cache missing can be reduced; meanwhile, the stability of the linked list under high concurrency is guaranteed through atomized insertion and deletion operations, and a traditional independent node structure and a non-atomized operation are not adopted; the technical problems that in the prior art, a traditional linked list node pointer domain occupies an extra memory, so that expenditure is increased, discontinuous node physical distribution causes cache missing, access delay is increased, and system abnormal safety and operation reliability are affected by iterator failure under high concurrency can be solved. And the technical effects of reducing the memory overhead, reducing the access delay and improving the abnormal safety and the operation reliability of the system are achieved.

The invention relates to the related technical field of equipment life cycle management, in particular to an aviation equipment full life cycle management method and system based on a digital thread. Determining main data of a first digital thread and a first-layer associated test sequence and main data of a second digital thread and a second-layer associated test sequence, mapping the deviation between a physical equipment state and a virtual simulation state, and identifying a performance difference point under a cross-stage data association link; and setting a management strategy for collaborative optimization. The technical problem that the reliability of aeronautical equipment is limited due to the fact that a cross-stage performance difference point equipment performance fluctuation root positioning fuzzy cannot be recognized in real time is solved, first and second digital thread main data and an associated test sequence are constructed, the deviation between physical equipment and a virtual simulation state is accurately mapped, and the reliability of the aeronautical equipment is improved. And a management strategy is set in combination with dual-thread data and performance difference points, and full-life-cycle collaborative optimization is performed, so that the technical effect of equipment reliability is effectively improved.

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.

A method includes ingesting by a set of computing nodes of a parallelized database, a dataset for storage therein. The method further includes formatting, by a lead computing node of the set, the dataset in a primary data format to produce a primary data formatted dataset, and storing the primary data formatted dataset in system state data. The method further includes receiving, by a first computing node of the set, a query request involving the dataset and a first data function, determining that the first data function triggers a first data conversion optimization, accessing the primary data formatted dataset from the system state data, converting the primary data formatted dataset from the primary data format to a secondary data format to produce a secondary data formatted dataset, and processing the secondary data formatted dataset in accordance with the first data function to produce a first function output.

To provide a fixed asset management device, etc., that can improve the listability of items missed during correction when confirming correction requests by enabling the display of whether items that should be corrected as a set with items corrected in the correction request have been corrected.SOLUTION: In one embodiment, the input values of each data item are compared between the first registration data and the second registration data to detect modified data items whose input values were changed in the second registration data (step S11), a set of configuration values associated with a key value that matches an input value of a key item in the second set of registration data is collated with the set of input values associated with a combination of related data items in the data items of the second set of registration data (step S12), a screen requesting confirmation containing the detection result from step S11 and the matching result from step S12 is displayed (step S13).SELECTED DRAWING: Figure 1

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 discloses a big data real-time calculation abnormal value filtering method and a terminal. The method comprises the following steps: acquiring scene basic data, establishing a relevance rule of master data and slave data in the scene basic data, determining upper and lower limit parameters of the master data, the slave data and the scene basic data according to data attributes, and calculating a basic data value of the scene basic data; determining a preset range of the scene basic data by using machine learning in combination with upper and lower limit parameters of the master and slave data and the scene basic data; if the basic data value of the scene basic data is within the preset range, calculating the fluctuation range and the fluctuation value, and if the fluctuation value exceeds the fluctuation range, determining that the scene is abnormal; if the fluctuation value does not exceed the limit, the deviation proportion is calculated, and if the deviation proportion exceeds a preset abnormity judgment coefficient, abnormity is judged; if the deviation proportion does not exceed the limit, finally judging the deviation proportion through an abnormal rule in a preset decision tree; abnormal data are eliminated, filtered data and basic data values thereof are obtained, and the adaptability and reliability of abnormal value detection are improved.

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.

The invention relates to the technical field of data synchronization, in particular to a data synchronization method in a disaster recovery backup system, which comprises the following steps of: based on a data block of a to-be-synchronized transaction in a main data center, segmenting the data block into node sets according to a preset byte length, and a directed edge set among the nodes is defined according to the adjacent position relation of the nodes in the original data block, and a data graph is obtained. According to the method, byte-level segmentation is carried out on the data block, the node set is constructed, the directed edge set is defined in combination with the adjacency relation of the nodes in the original data block, the data graph with the structured characteristics is formed, and the linear storage form of the original data block can be converted into an information structure with the topological level. Explicit mapping is carried out on the connection relation by means of a topological adjacency matrix, a global topological check value is generated in cooperation with one-way Hash calculation, and high compression and consistency expression of the relation between cross-structure nodes can be achieved.

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.

An AI-based platform for enabling intelligent orchestration and management of power and energy is provided herein. The AI-based platform includes a set of adaptive, autonomous data handling systems, wherein each of the adaptive, autonomous data handling systems is configured to collect data relating to energy generation, energy storage, energy delivery, and / or energy consumption, wherein the data is collected from a set of edge devices that are in operational control of a set of distributed energy resources; and a set of intelligent agents configured to, by robotic process automation, autonomously adjust, based on the collected data, a set of operational parameters for such operational control.

A multi-source heterogeneous chronic disease data quality detection system comprises a data source acquisition module, a data conversion and aggregation module, a quality rule maintenance module, a data quality detection module, a data exception processing module and a data quality analysis module. Main data and metadata information are obtained, data quality detection rules are mined, chronic disease long-term monitoring sequence data and diagnosis related information of multiple groups of patients are obtained, the similarity of the sequence data is measured through a dynamic time warping algorithm, and the quality problem of the chronic disease long-term monitoring sequence data is found through a clustering method. According to the method, comprehensive and systematic quality detection and management are carried out on the current chronic disease data from multiple dimensions, low-quality data existing in a current medical system are found, the overall quality of the medical data is improved, and therefore data quality support is provided for subsequent medical application such as medical quality analysis, personalized diagnosis and disease prediction.

The invention discloses a high-speed rail business travel service personnel scheduling method based on data analysis, and relates to the technical field of personnel scheduling management. Comprising the following steps: data acquisition and integration: acquiring high-speed rail station operation data, passenger information, service personnel information and environment data; streaming data processing and space-time alignment are carried out on the collected data, and a dynamic main data matrix is constructed; service demand prediction: extracting passenger multi-dimensional features from the dynamic main data matrix, and generating passenger multi-dimensional feature tags; and performing bimodal demand prediction based on the passenger multi-dimensional feature tag in combination with the environmental data to obtain an initial passenger service demand. According to the invention, through establishing the bimodal service demand prediction method, extracting the multi-dimensional features of the passengers, combining environment data and predicting the demands of the passengers, passive response is converted into active prediction, the response speed is accelerated, the waiting time of the passengers is reduced, and the service quality and efficiency are improved.

The invention relates to the technical field of power equipment monitoring, and discloses a power distribution cabinet internal environment monitoring method, which comprises the following steps: selecting a low-power-consumption, high-precision and wireless temperature and humidity sensor, a gas sensor, a partial discharge sensor and a laser dust sensor, installing the sensors on a power distribution cabinet, reasonably deploying a plurality of data acquisition nodes in the power distribution cabinet, the data acquisition nodes acquire data of peripheral sensors and transmit the acquired data to the main data receiving device. According to the method for monitoring the internal environment of the power distribution cabinet, the temperature, the humidity, the gas concentration, the dust content, the electrical parameters and the multi-dimensional parameters of partial discharge in the power distribution cabinet are comprehensively monitored, and advanced sensors and data acquisition and transmission technologies are adopted, so that the limitation of traditional monitoring is avoided, and the accuracy of monitoring data is improved; through real-time data analysis and historical data modeling, operation and maintenance personnel can know abnormal changes of the internal environment of the power distribution cabinet in time and take measures in advance to prevent faults.

The invention relates to the technical field of data processing, and discloses a material warehouse-in and warehouse-out processing method and system based on big data. The method comprises the steps that material information is collected through RFID and converted into assembly main data in a standardized mode; analyzing the main data to generate material storage mapping; forming a distribution instruction according to the mapping sorting distribution demand; recording component circulation and environment parameters to establish a quality data link; constructing a multivariable model to identify key indexes to form a threshold list; and calibrating inventory parameters by combining historical data to construct a management matrix. The technical problem that intelligent decision support is lacked in material warehouse-in and warehouse-out management in an industrial tablet personal computer production line is solved, and the material quality traceability and the inventory management accuracy are improved.

Techniques are provided for object store mirroring. Data within a storage tier of a node may be determined as being data to tier out to a primary object store based upon a property of the data. A first object is generated to comprise the data. A second object is generated to comprise the data. The first object is transmitted to the primary data store for storage in parallel with the second object being transmitted to a mirror object store for storage. Tiering of the data is designated as successful once acknowledgements are received from both the primary object that the first object was stored and the mirror object store that the second object was stored.

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.