276 results about "Linked data" patented technology

The embodiment of the invention discloses an intelligent contract auditing method and device based on a multi-modal large model and a medium, and relates to the technical field of contract auditing, the method comprises the steps that a to-be-audited target contract document is acquired, multi-modal data in the target contract document is analyzed, and the multi-modal data comprises text data, table data and image data; performing feature extraction on the multi-modal data to obtain multi-modal feature data, mapping the multi-modal feature data to a unified dimension space so as to calculate an association weight between each modality through a cross-modal attention mechanism, establishing cross-modal bidirectional link data, and obtaining multi-modal data; the multi-modal feature data comprises any one or more of text semantic features, table relation maps and image visual features; and based on the multi-modal feature data and the cross-modal bidirectional link data, performing single-modal self-consistency verification and cross-modal contradiction detection on the target contract document to generate auditing information of the target contract document.

The invention relates to the technical field of data analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data consanguinity map processing method, device, equipment and medium, and the method comprises the steps: collecting metadata change information and data operation event information, and building a mixed consanguinity map model; monitoring a metadata change event and a data operation event, and triggering incremental updating based on an event type; and performing data change influence analysis based on the updated blood relationship map model, generating an analysis result, and outputting the mixed blood relationship map model and the data change influence analysis result. According to the invention, by collecting metadata, incrementally updating the blood relationship map and carrying out data change influence analysis, full-link data tracing and influence analysis are realized, the problems of blood relationship breakage and insufficient real-time data tracing in traditional data blood relationship analysis are solved, and the accuracy of data tracing and the timeliness of business decision are improved.

The invention discloses a process parameter tracing and quality collaborative management system for ceramic production, and relates to the technical field of ceramic production, the management system comprises a step of obtaining a plurality of process data and quality data in a ceramic production process, and each group of process data comprises a raw material ratio, a forming pressure, a firing temperature and a firing time. According to the process parameter tracing and quality collaborative management system for ceramic production, a specific process parameter deviation link can be quickly positioned by reversely tracing to a raw material source from a finished product quality problem and combining batch association identifiers and data records of all links; the quality problem solving efficiency is improved, and batch loss caused by traceability lag is avoided; besides, a process quality association relationship constructed by the module is established, a quality detection result is closely associated with process parameters, and multi-dimensional analysis is performed, so that a quality problem can be fed back to process parameter adjustment in time, and targeted measures can be taken according to an analysis result.

The invention discloses a distributed supply chain traceability system and method supported by a block chain, and relates to the field of data analysis, and the system comprises a collection module which is used for capturing the original data of a supply chain in real time through an Internet of Things sensor, and cleaning the data; the processing module is used for constructing a distributed account book based on a HyperledgerFabric architecture, performing hash encryption on the data, performing uplink storage on the data, packaging the data into blocks according to a time sequence, and completing data verification between nodes through a BFT consensus mechanism; according to the invention, full-link data of production, logistics and the like are captured in real time through the Internet of Things sensor and encrypted and chained, so that the data cannot be tampered and can be traced in the whole process, and the problems of easy data tampering and many breakpoints in traditional traceability are solved.

The invention relates to the technical field of underground pipe gallery digital management, and discloses a BIM-based underground pipe gallery asset digital management platform, which comprises a data acquisition module, a dynamic digital twinborn body module, an event processing engine, a self-iteration data bus module, a spatial topology driving module and a self-healing logic triggering module. For the industry pain point of insufficient data real-time performance under the long-distance and multi-node scene of the underground pipe gallery, geometric attributes of a BIM model and sensor data are deeply bound through a dynamic digital twinborn body module, twinborn nodes synchronously bearing space coordinates, physical attributes and real-time state labels are constructed, the limitation of traditional BIM static visualization is broken through, and the real-time performance of the underground pipe gallery is improved. Second-level state synchronization and full-link data traceability of high-risk events such as leakage and equipment faults are achieved, collaborative design of lightweight architecture and edge computing is achieved, the low-cost modification requirement of the old pipe gallery is met, and the real-time performance and reliability of asset management are remarkably improved.

The invention relates to the technical field of informatization management systems, and particularly discloses an AI-aid decision-making product full-life-cycle informatization management system which comprises a full-link data sensing module, a digital main line construction module, an AI decision-making center module, a service execution interface module and a man-machine collaborative interaction module. The method comprises the following steps: collecting multi-source heterogeneous data of each stage of a full life cycle of a product through a full-link data sensing module to form a full-amount data sequence with a space-time stamp; and in combination with a data fusion engine, an association graph construction unit and a twinborn model driving unit of the digital main line construction module, data cleaning, alignment, semantic fusion and cross-stage association are realized, a dynamically updated product digital twinborn model is constructed, real-time mapping of a physical entity and a virtual model is achieved, and the real-time mapping of the physical entity and the virtual model is realized. Unified and complete data support is provided for full-link decision making, and the problems of data fragmentation and prominent collaborative barriers of a traditional system are thoroughly solved.

The invention relates to the technical field of network security, and discloses a network attack tracing and evidence fixing method and system based on full-link data, and the method comprises the steps: collecting multi-source heterogeneous data; cleaning and standardizing the collected data, and extracting attack feature information; constructing a full-link data association model, and connecting scattered attack behaviors in series into a complete attack chain through time sequence analysis and node relation graph technologies; based on the attack chain, determining an attack source and a potential motivation thereof by using a multi-level threat intelligence matching algorithm; and the traceability result is in butt joint with an evidence fixing module, a network attack electronic evidence file is generated, and the evidence is stored in a non-tampering manner through a block chain technology. The system corresponds to the method. The method and the system have remarkable technical advantages in the aspects of data integration, traceability precision, evidence fixation, storage optimization, visual display and the like, and can effectively cope with security challenges in a complex network environment.

The invention discloses a fresh food supply chain full-link traceability and loss early warning method based on a block chain, and the method comprises the steps: 1, carrying out the lightweight collection of full-link data, processing the data, and obtaining a standardized data source, and 2, carrying out the construction of a block chain bottom-layer architecture based on the standardized data source; step 3, establishing a dynamic reputation consensus mechanism, and establishing a trusted environment for data sharing through reputation score driving node collaboration based on a block chain bottom layer architecture; 4, constructing a federated learning network; step 5, risk prediction modeling: performing multi-dimensional risk prediction by utilizing model output of collaborative modeling and combining real-time data; step 6, graded early warning response: based on a risk prediction result, realizing graded automatic response through a grading rule; step 7, dynamic decision optimization; the beneficial effects of the invention are that the dynamic decision optimization function adjusts the strategy of each link of the supply chain according to the early warning result, achieves the intelligent scheduling of logistics, inventory and production, and reduces the loss and cost.

The invention relates to the technical field of grain safety management, and particularly discloses a grain safety traceability system and method, and a distributed data collection module obtains data in real time through an Internet of Things sensor network. And the multi-source data fusion engine performs standardization processing on the heterogeneous data by adopting a space-time alignment algorithm to generate a traceability data packet. And the block chain evidence storage node adopts fragmentation storage, and the traceability data packet is stored in a multi-chain parallel structure in a distributed manner. And the risk early warning analysis platform constructs a quality safety evolution model based on deep reinforcement learning, establishes an automatic triggering mechanism, and automatically sends a risk early warning data packet with a block chain evidence to the supervision platform when it is detected that grain is abnormal. According to the method, standardized processing, space-time consistency calibration and cross-link data collaborative analysis of heterogeneous data are realized by utilizing a multi-source data fusion engine; and the risk early warning analysis platform sends a risk early warning data packet to the supervision platform in time by using an automatic triggering mechanism, so that a powerful means is provided for grain safety risk prevention and control.

The invention relates to the technical field of catch safety tracing, and discloses a block chain verification method for catch quality safety tracing. The method comprises the following steps: acquiring environmental parameter data of catches in the links of fishing, transportation and storage through a distributed sensor network, and encrypting according to a timestamp sequence to generate a catches environmental data chain; key environment indexes are extracted based on the data chain, an abnormal deviation degree is calculated by using a dynamic weight distribution algorithm, and a catch quality risk score is generated; dividing risk levels according to the risk scores, mapping data of different risk levels to a block chain differentiation storage layer, and constructing a fragment storage structure; performing redundancy verification on the high-risk data through an intelligent contract triggering mechanism, and calling a consensus node to perform cross verification to generate a verification result; and performing association analysis on the verification result and the risk score, updating the risk tag and synchronizing the risk tag to a block chain distributed account book. According to the method, full-link data tracing and verification of the catches are realized, and the data credibility and the quality control accuracy are improved.

The invention relates to the technical field of supply chain management, in particular to a multi-dimensional data visualization supply chain process optimization method and system based on big data, and provides a supply chain dynamic optimization thought based on multi-source heterogeneous data fusion and intelligent decision closed loop. By constructing an end-to-end multi-source heterogeneous data real-time cooperative processing and intelligent decision closed-loop architecture, dynamic integration, intelligent analysis and automatic response and update optimization of full-link data of the supply chain are realized, so that the real-time analysis capability and dynamic adjustment capability of the supply chain are remarkably improved.

The invention discloses a toothpaste production raw material tracing method and system based on a block chain, and relates to the field of block chains, and the method comprises the steps: distributing a unique identifier for a raw material, binding basic information to form an initial data packet, taking a supplier as an alliance chain node, uploading the initial data packet, and verifying the qualification through an intelligent contract; the transportation data is encrypted in real time through the Internet of Things and edge calculation and is uploaded, and a detection report deviation value is verified during warehousing; raw materials and finished product batches are associated in a production link, and process parameter compliance is automatically checked and then written into a block; in the warehouse logistics link, environment data and transportation information are integrated, and a tracing sub-block is generated after a goods receiver is verified through code scanning; an improved PBFT consensus mechanism and a cryptographic algorithm are adopted to ensure data security, and transparent tracing of consumers, supervisors and enterprises is realized through hierarchical authority management. The method has the advantages that the raw materials and the finished product full-link data are bound through the unique traceability identifier, non-tampering traceability of the toothpaste raw materials from supply to the terminal is achieved, and multi-party hierarchical query requirements and product safety are met.

The invention discloses an industrial building business service management method and device and a medium, and belongs to the technical field of building service management, and the method specifically comprises the steps: constructing a multi-dimensional data fusion model; defining cost, progress, quality and security dimension data ranges and acquisition nodes, and storing the data ranges and the acquisition nodes in a model database after standardization; based on the standardized data of the multi-dimensional data fusion model, analyzing a dimension internal relationship by using an association rule mining algorithm, defining a dimension mapping relationship, and integrating to form an association rule base; establishing a multi-dimensional linkage analysis module; the module is established by taking a model database as a data source and a multi-dimensional data association rule base as a logic support, and a threshold triggering mechanism and a cross-link data calling function are set; constructing a business collaborative decision support module; business requirements of multiple departments are integrated; a multi-dimensional data visualization monitoring platform is built; and designing a linkage trend chart based on a result of the multi-dimensional linkage analysis module.

Methods and systems for generating, publishing, processing, and consuming linked data events and streams being shared by complex systems in order to construct a semantic web of the data and thereby form a consolidated coherent view of the data are provided. A method includes: receiving a set of metadata that relates to a set of events; generating, based on the metadata, an ordered node hierarchy map and a modern data event schema; receiving a set of tabular data; generating, based on the tabular data and the ordered node hierarchy map, the set of events; streaming the set of events over a streaming pipeline; processing the streamed set of events to generate a linked data media format; and outputting the linked data media format.

Disclosed in the present invention are a method and apparatus for performing full-link reconciliation on the basis of a snowflake algorithm, and a device and a medium. The method comprises: responding to a cross-border business reconciliation request, and acquiring bill source data from a business terminal; parsing the acquired bill source data, and converting same into target bill data; calling a reconciliation rule engine to acquire a corresponding reconciliation rule and select a corresponding reconciliation algorithm, and executing a full-link reconciliation task for the target bill data, so as to obtain a reconciliation result; transmitting reconciliation imbalance data to a corresponding exception handling system for checking processing of abnormal data; and on the basis of an optimized snowflake algorithm, generating a unique target ID to associate a corresponding bill with the reconciliation result, automatically penetrating full-link data of an information flow and a fund flow on the basis of the corresponding unique target ID in the reconciliation result, and on the basis of the unique target ID, tracing the whole process of the circulation of a corresponding bill between the fund flow and the information flow, so as to achieve a complete reconciliation closed loop. In this way, the reconciliation working efficiency and data accuracy of the full-link reconciliation of a cross-border business are improved.

The invention relates to the technical field of data management, and discloses a dynamic partitioning and priority scheduling method based on time sequence data, which comprises the following steps: collecting time sequence characteristics of an industrial data source through an edge gateway and carrying out structured storage in Redis, the time sequence characteristics comprising collection frequency, historical peak time period and service priority; load indexes are calculated based on time sequence characteristics, and a distributed message system is driven to execute dynamic partition adjustment of pre-judgment type capacity expansion, hot spot precise splitting and low-peak intelligent capacity reduction; and full-link data priority scheduling is realized through production end route isolation, Broker end resource inclination and consumption end thread pool isolation. According to the invention, the dynamic adaptation of the distributed message system resource and the industrial time series data load is realized, the real-time performance of high-quality data is ensured, the integrity of data transmission and the system adaptability are improved, and the problems of poor load fluctuation adaptation, high-quality data delay and easy data loss due to faults in the prior art are effectively solved.

A system including a media data optimization engine (MDOE) and a method for automatically creating structured data objects for media content rendered in one or more languages in an electronic document of a business entity are provided. The MDOE identifies non-textual objects including media content rendered in one or more languages in the electronic document and generates textual objects in the corresponding language(s) therefrom. The MDOE transforms the textual objects into structured data objects based on configurable criteria and generates a dynamic index-oriented object for the structured data objects specific to the business entity. The MDOE connects the structured data objects to the dynamic index-oriented object by creating linked data nodes therefrom with the dynamic index-oriented object as a core. The MDOE connects the dynamic index-oriented object with the linked data nodes to the electronic document, thereby facilitating dynamic changes to the electronic document and dynamically optimizing the electronic document.

One or more data objects from at least one of HR data system may each contain data fields storing HR information. A graphical user interface (GUI) may present primitive coding blocks representing standard graphic programming functions and custom business blocks representing high-level operations customly directed to the one or more data objects. A visual workflow may be constructed using at least one block selected, by a user of the GUI, wherein the at least one block represents operations linking the data fields of the one or more data objects to corresponding input fields of the at least one block. The at least one block of the visual workflow may be translated into a custom data connector comprising executable instructions for obtaining values for the corresponding input fields from the linked data fields and generating, based on the values, at least one corresponding data object storing the HR information.

The invention relates to the technical field of intelligent supply chain management, in particular to an intelligent procurement source searching method and system based on multi-target dynamic optimization and a real-time feedback mechanism, and the method comprises the steps: collecting full-link data of a supply chain in real time, and enabling the data to comprise enterprise internal ERP system data, supplier database data, market public opinion platform data and Internet of Things equipment data; the integrity and credibility of the data are ensured through an API interface and a block chain technology; carrying out denoising and normalization processing on the collected multi-source data, eliminating data redundancy and contradiction, and constructing a unified data model; the method has the beneficial effects that operational research, machine learning, a dynamic game theory and a real-time data analysis technology are fused, and an intelligent source searching decision system is constructed through a multi-target dynamic optimization model, a real-time feedback closed-loop system and a multi-source heterogeneous data fusion framework. The core technology covers a multi-target weight dynamic allocation model based on reinforcement learning, and collaborative optimization of dimensions such as cost, quality, delivery cycle, risk and the like is realized.

A full-link data security protection method and a system are provided. The method includes: at a data creation and collection stage: building a data security identification; at a data transmission and storage stage: dividing the ciphertext file into blocks to generate ciphertext components; calculating a virtual index and a data label; transmitting the ciphertext components to a distributed hash table (DHT) network; uploading a tuple including the virtual index, the data block, and the data label to a cloud server; at a data processing and exchange stage: applying re-encryption based on a re-encryption key generation algorithm; performing decryption to obtain the signed identifier and a secret value; acquiring a tuple having a ciphertext component associated with the virtual index. Attribute-based proxy re-encryption is used to achieve fine-grained access control for the cloud storage. In the data destruction stage, the DHT network automatic updating utility is leveraged to realize data self-destructing.

The invention provides an advertisement full-link data integration platform and method, electronic equipment and a storage medium, and relates to the technical field of data processing. The platform comprises a data acquisition module used for acquiring platform data from an advertisement platform and an attribution platform, the platform data comprising advertisement data, attribution data and user behavior data; the putting optimization module is used for analyzing the advertisement effect of the advertisement on each advertisement platform based on the advertisement data and optimizing the advertisement budget distribution of each advertisement platform according to the advertisement effect; the user interaction module is used for constructing user portraits based on the user behavior data, generating recommended advertisements for corresponding users based on the user portraits and pushing the recommended advertisements to the users; the effect evaluation module is used for analyzing the advertisement data and the attribution data based on an attribution model, and generating an advertisement putting strategy based on an analysis result; and the data display module is used for visually displaying the full-link data of the advertisement and generating a delivery effect report of the advertisement. According to the invention, the advertisement putting effect is optimized.

The invention relates to the technical field of carbon emission, in particular to an on-chain enterprise carbon emission accounting method and system. The method comprises the following steps: acquiring energy consumption, process flow and supply chain collaborative data of an enterprise in links of raw material acquisition, production processing, logistics transportation, equipment operation and recovery processing through Internet of Things equipment, and generating a carbon emission data set; based on a life cycle evaluation theory, boundaries of direct emission, indirect energy emission and supply chain associated emission are divided, and enterprise-level, product-level and supply chain-level carbon emission accounting models are constructed. A machine learning algorithm is utilized to analyze production fluctuation data in real time, carbon emission factor weights and accounting boundary parameters are dynamically adjusted, and an accounting result is optimized. And outputting a result to a green purchase decision-making system, a carbon asset management system and a supply chain collaboration platform. The system integrates industrial Internet of Things equipment, block chain nodes and enterprise resource planning system interfaces, collects and verifies full-link data in real time, solves the problem of data collection, and improves the accounting reliability.

The invention provides a full-link data tracing processing method and system for a cross-border home supply chain, and relates to the technical field of supply chain management, and the method comprises the steps: 3, dividing a plurality of data unit sets which are correlated with each other based on the process data of the supply chain, determining a reference data unit set, and obtaining two groups of different data correlation rules; analyzing an interaction relationship between the two groups of data association rules, establishing a data state identification range, and selecting two data verification reference points in the data state identification range; and according to the evolution relationship of the data verification reference point on the time sequence, the evolution span is quantified by calculating the comprehensive distance between two points on the time and data feature dimensions, a data state evolution path is constructed to generate a data calibration coefficient, and a data link track comprising a timestamp and link information is formed. According to the invention, full-link data tracing of the cross-border home supply chain is realized, and the collaboration efficiency of each link of the supply chain is improved.

The invention discloses a cloud computing bare metal hardware market distribution and management optimization system and method. The system comprises a resource access module, a distribution channel management module, an intelligent matching module, a transaction settlement module, a data analysis and optimization module, a safety and operation and maintenance management module and the like. Standardized aggregation of heterogeneous resources is realized through a resource access system, and the problem of fragmented management is solved; the distribution channel management system dynamically adjusts distribution rules and improves the channel cooperation efficiency; the intelligent matching system is combined with user behavior analysis to realize accurate supply and demand connection; the transaction settlement system integrates payment channels and marketing tools and simplifies fund circulation; the data analysis system precipitates full-link data to support decision optimization; the safety and operation and maintenance system guarantees safe and stable operation. According to the method, through the steps of resource access standardization, channel rule dynamic and the like, efficient, intelligent and standardized management of the whole distribution process is realized, and the market distribution efficiency, accuracy and safety are improved.

Computer-readable media, methods, and systems are disclosed for improving performance when dropping database snapshots by linking converter streams associated with adjacent snapshots in a database system. A request is received to drop a snapshot. The database system iterates a first converter tree associated with a first converter stream to identify a first physical page number to be loaded. A physical page corresponding to the first physical page number is loaded. The database system iterates a second converter tree associated with a second converter stream to identify a second physical page number to be loaded. The second physical page number is queried from an iterator associated with the first converter tree. Responsive to determining that the physical page associated with the second physical page number is not present in main memory, the in-memory database system loads from disk, the physical page associated with the second physical page number.

The invention relates to the technical field of electric data processing, in particular to a data blood relationship automatic tracking method and system based on a table in AI data, and the method comprises the steps: obtaining metadata in the table in the AI data, and analyzing a task log of a calculation engine to construct an initial directed acyclic graph; the initial directed acyclic graph comprises data nodes and explicit blood edges connected with the data nodes; and calculating a flow entropy coupling index between any two data nodes. According to the method, a flow entropy coupling index based on update time correlation and logic evolution divergence based on key entity distribution similarity are constructed, a graph comparison learning model is combined, under the condition that an explicit SQL grammatical structure is lacked, the implicit dependency of upstream and downstream nodes is automatically identified, and the flow entropy coupling index and the logic evolution divergence are combined. And end-to-end full-link data consanguinity complete tracking in a complex AI feature engineering scene is successfully realized.

Embodiments herein relate to data management and, more particularly, to collecting data from a plurality of sources, and linking the collected data to derive information and knowledge. The method includes defining at least one data model and asset by including data models, vocabulary, data quality rules, data mapping rules for at least one of, a particular data industry, a data domain, or a data subject area, importing data from a plurality of data sources, performing de-duplication of the imported data and data profiling of the imported data, and creating linked data either by semantic mapping, or by curating the data.

The invention discloses a traditional Chinese medicine whole industry chain big data construction method and system, and relates to the field of data links, and the method comprises the steps: generating a standardized metadata template for seven links of a traditional Chinese medicine industry chain; block chain nodes are deployed in each link of an industrial chain, timestamp fingerprints are generated for key process data, and the key process data are automatically chained for evidence storage; constructing a data fusion model based on a graph neural network, and generating cross-link associated data; constructing a traditional Chinese medicine ontology knowledge graph, and mapping the association relationship between the cross-link associated data in the graph; full-chain digital tracing from implantation to clinic is realized through a path reasoning algorithm; based on the hybrid collaborative storage architecture, access management is carried out through dynamic fragmentation strategy data points; and constructing a process prediction model, detecting process parameter deviation, and carrying out risk early warning. The method has the advantages that through block chain evidence storage and combination of the graph neural network and the knowledge graph, full-chain digital tracing and risk early warning from implantation to clinic are realized.

The invention relates to the technical field of data processing, in particular to a financial contract online editing system based on multi-person collaboration, and the method comprises the steps: capturing an operation feature vector with a space-time mark in real time through an acquisition unit; the analysis unit generates a regional operation intention set in combination with the contract semantic tag library; the conflict probability calculation unit fuses the space overlapping degree, the logic association chain and the time sequence overlapping window to quantify the conflict risk; the dynamic partition response unit triggers area locking, operation confirmation or background monitoring according to probability value grading; the legal rule verification unit implements compliance interception and outputs a forced interrupt signal; the business logic serialization unit reconstructs an instruction sequence; the multi-strategy synchronous execution unit carries out atomic broadcasting, transaction binding and batch asynchronous updating by differentiating a core term channel, a dependency group channel and a common term channel; and the closed-loop audit tracking unit aggregates full-link data to generate a verifiable influence chain. The risk of data inconsistency caused by concurrent operation of financial contracts is systematically solved.

The invention relates to the technical field of automatic driving, and particularly discloses an off-line evaluation and iterative optimization method and system for an automatic driving system, and the method comprises the steps: recording a task-level data packet, and binding a reproducible fingerprint for the task-level data packet; in the off-line stage, a consistent input data stream is reconstructed, and the consistent input data stream is input into a current online running old model in parallel to be compared with a to-be-evaluated candidate new model for evaluation; according to a comparison evaluation result, extracting a scene fragment data packet based on a preset multi-dimensional evaluation index and a triggering rule; the method comprises the steps that scene fragment data packets are obtained, failure type labels are automatically generated for the scene fragment data packets, bucket dividing and weighted sampling are carried out according to scene features, and scene fragment data are returned to a model training data pool, full-link data packaging is carried out with task-level data packets as the granularity, reproducible fingerprints are bound for all the data packets, and therefore the scene fragment data can be obtained. Therefore, the offline playback, the A / B evaluation result and the backflow training sample can be subjected to consistent indexing and tracing through the fingerprint.