33results about How to "Solve data silos" patented technology

The invention belongs to the technical field of nuclear power station site selection, and provides a nuclear power station site selection-oriented geographic knowledge graph construction method and system, and the method comprises the steps: obtaining related data of a nuclear power station site selection field, the related data comprising field knowledge data and geographic space data; a nuclear power station site selection geographic knowledge graph mode layer is constructed by adopting a top-down method, a nuclear power station site selection core ontology is determined, a hierarchical ontology system is defined, subordinate entities of the core ontology and attributes and data types of the subordinate entities are defined and constrained, a concept layer and the entities and an incidence relation between the concept layer and the entities are constructed, and the core ontology is established. A nuclear power station site selection geographic knowledge graph data layer is constructed by adopting a bottom-up method, entity and relation extraction is carried out respectively, and knowledge extraction is realized; and performing knowledge fusion on the nuclear power station site selection field entity, relationship and attribute information obtained by knowledge extraction to form a final geographic knowledge graph for nuclear power station site selection. According to the invention, the tightness of site selection and domain knowledge is improved.

The application provides a traffic classification method and system based on federal semi-supervised learning. The method comprises the following steps: constructing an unlabeled traffic dataset and a labeled traffic dataset; a center server decomposes a global model into supervised learning parameters and unsupervised learning parameters and initializes the parameters; the parameters and an auxiliary agent are sent to each client; each client performs unsupervised training based on the supervised learning parameters, the unsupervised learning parameters and the auxiliary agent by using a local unlabeled traffic dataset, uploads the difference between the unsupervised learning parameters to the center server; the center server aggregates and updates each unsupervised learning parameter; supervised training is performed by using a local labeled traffic dataset, and the difference between the supervised learning parameters and the difference between the unsupervised learning parameters are sent to each client; a new auxiliary agent is obtained based on nearest neighbor search, and the new auxiliary agent is sent to each client when a set sending condition is met; the two steps are iteratively executed until a stop condition is met.

The invention provides an adjustable load capacity analysis, regulation and control method for a virtual power plant, and relates to the technical field of power system operation and control, and the method comprises the following steps: S1, multi-source data collection and processing: collecting electrical parameters, non-electrical parameters and power grid dispatching instructions of various adjustable loads; s2, classifying adjustable loads, and dividing the adjustable loads into a plurality of grades; s3, constructing a multi-dimensional adjustable load capacity evaluation index system; s4, weighting calculation: carrying out weight calculation on the multi-dimensional adjustable load capacity evaluation index system based on an improved particle algorithm; s5, establishing a dynamic hierarchical collaborative regulation and control strategy, and establishing the dynamic hierarchical collaborative regulation and control strategy according to the power grid dispatching demand type; s6, a prediction model is constructed, and a load response prediction model is constructed; s7, executing an instruction; according to the multi-dimensional characteristic, powerful support is provided for accurate regulation and control, a multi-dimensional evaluation index system is constructed, the load capacity is comprehensively evaluated, and optimal configuration of resources is achieved.

A feature library construction method, the method comprising: obtaining a first file sample set, the first file sample set comprising a plurality of file samples and a cleaning level of each file sample; extracting a feature word that can affect the cleaning level of each file sample from each file sample in the first file sample set; filtering the extracted feature words to obtain a plurality of effective feature words; setting a weight of each effective feature word, and constructing a feature library for recommending the cleaning level of a file according to each effective feature word and the weight of each effective feature word.

This invention provides a deep learning-based intelligent analysis method for cardiovascular medical images, comprising the following steps: S1, heterogeneous data fusion and acquisition of multimodal medical images and physiological signals; S2, intelligent extraction and quantitative analysis of vascular structural features based on deep learning; S3, hemodynamic parameter modeling and risk region segmentation based on fused structural features; S4, texture feature mining and intelligent identification of lesion types in high-risk areas; S5, cardiovascular disease risk stratification prediction based on multi-dimensional feature fusion; S6, federated learning optimization and closed-loop update of the model. This invention's deep learning-based intelligent analysis method for cardiovascular medical images achieves, for the first time, heterogeneous fusion of medical images, millimeter-wave radar-PPG physiological signals, and clinical molecular data, and achieves precise spatiotemporal alignment through GPS-controlled crystal oscillators, overcoming the limitations of single image analysis and providing a more comprehensive feature foundation for subsequent feature extraction and risk prediction.

This invention relates to the field of tobacco procurement information technology, and discloses an intelligent processing system for weighing information and a weighing and sealing device for tobacco leaf pre-inspection. The system includes data acquisition, processing, interaction, and execution control modules. The data acquisition module performs time-series alignment of continuous weight data and identity data. The data processing module uses a peak-valley range algorithm to determine stability and lock the net weight, while simultaneously performing tare deduction and compliance verification. The data interaction module, based on visual anchor point positioning technology, establishes a non-intrusive transmission channel with external business systems, automatically corrects control coordinates, and simulates data entry. The execution control module, based on a finite state machine, drives seal generation and data entry when verification is successful, and triggers an audible and visual alarm and forcibly blocks the entry signal and sealing command when verification fails. This invention solves the problems of difficult integration with heterogeneous systems and error-prone manual data entry, achieving automated and accurate acquisition of weighing data and closed-loop quality control.

The application provides a container-oriented exception evaluation and repair method, which comprises the following steps: obtaining configuration information of a container, constructing a scenario-adapted rule for evaluation, and performing configuration risk evaluation; performing multi-tool collaborative scanning on a container image to generate multiple scanning results, converting the multiple scanning results into a single trusted result, and performing image risk evaluation; collecting service base information of the container to identify a CPE component, collecting runtime indicators of the container, and performing service risk evaluation; extracting non-compliance item information, constructing a local security knowledge base, performing knowledge matching in the local security knowledge base to generate a repair decision prompt word, guiding a large language model of private deployment to make a repair decision, and performing container repair. The method realizes the organic integration of three-dimensionally independent evaluation results, and fundamentally solves the data island and alarm fatigue problems existing in the prior art. Moreover, the repair process is changed from manual driving to intelligent and automatic driving.

The invention discloses a railway precast beam full-period digital delivery and disease tracing method and system, and the method comprises the following steps: S1, collecting and sorting precast beam data, and building an IFC entity model based on the data; s2, pre-burying a two-dimensional code plate and an RFID tag on the precast beam, and recording the digital identity of the precast beam; s3, obtaining the completion coordinate of the precast beam, and binding the digital identity of the precast beam with the completion coordinate at one time; s4, performing block chain evidence storage on the application data of the precast beam; s5, developing a front-end APP, and judging whether the precast beam meets a traceability condition or not; s6, all data of the precast beam meeting the traceability condition are called, and root cause judgment is conducted based on the IFC entity model. According to the method, by establishing the IFC standard data link which runs through design-production-operation and maintenance, data barriers of links of design, production and operation and maintenance can be broken through, data run-through is achieved, data islands are avoided, and comparison time consumption is greatly shortened.

The application relates to the technical field of financial automation processing, and discloses an archive multi-backup storage device for preventing financial fraud, which comprises a workbench, a conveying belt is installed on the upper side of the workbench, the conveying belt is used for carrying and conveying original bills and pasting sheets, a processing mechanism is used for processing the original bills and the pasting sheets on the conveying belt, a collecting mechanism is used for grabbing the pasted voucher sheets from the conveying belt and placing the pasted voucher sheets in a collecting box, a computer is used for receiving image backup data and electronic data backup generated by the processing mechanism and performing multi-backup data cross inspection, and the computer is electrically connected with the conveying mechanism, the processing mechanism and the collecting mechanism. The application solves the technical problems that the prior art relies on manual operation, is low in efficiency and cannot perform closed-loop checking on final entity vouchers, realizes full-process unmanned operation, and effectively prevents financial fraud.

This invention relates to the field of enterprise digital management technology, and particularly to an enterprise digital operation management method and system. The method includes: acquiring business activity data, extracting the interaction strength between business activities, and clustering multiple business activities into multiple intelligent agents; extracting technical metadata from the target system's data dictionary and interface documents, constructing a metadata knowledge graph using the technical metadata, and providing data interfaces for the intelligent agents using the data knowledge graph; based on the data interfaces, each intelligent agent uses a probability prediction model to generate a probability distribution of future external variables, and makes individual decisions based on the probability distribution; determining whether there is shared resource coupling in the individual decisions of two intelligent agents, and if so, introducing a collaborative mechanism to correct their independent decisions through shadow prices so that the individual decisions reach global optimality.

This invention discloses a multimodal emotion intervention data protocol and a collaborative repositioning evaluation method based on neural mapping parameters. The method defines a standardized Emotional State Data Structure (ESDS), atomically binding Role ID, Embodied Action Code, and Narrative Tag based on self-distance theory, thus addressing the problems of unstructured intervention instructions and the disconnect between cultural prototypes and machine instructions in existing systems. Simultaneously, this invention constructs a multimodal collaborative repositioning evaluation algorithm, quantifying the degree of emotion calming using the formula Q_return=alpha × Norm(S_bio) + beta × Norm(S_audio) + gamma × Sync(S_visual) + delta × Decay(S_time), where the weight alpha of the biological signal derived from real physical electrical signals (HRV / GSR) is ≥ 0.5. This invention establishes an industrial-grade emotion intervention interface, achieving cross-device interoperability, and ensures the objectivity and scientific rigor of the repositioning evaluation through high-weighted physiological indicators.

The application discloses a kind of monitoring method and device based on ring network cabinet full life cycle twinborn data, it is related to equipment management technical field.The method includes: in production stage, key manufacturing data is collected for ring network cabinet to form production gene bank, combined with unique digital identification to build digital twin initial model, while real-time acquisition various operating data, then rely on manufacturing execution system to individualize correction to initial model, match physical entity parameter, use five-layer collaborative fusion algorithm to deeply fuse production and operating data, diagnose early abnormality and typical fault, carry out ring network cabinet intelligent monitoring and operation and maintenance.The present application can establish complete digital archives for each equipment, realize the whole link data through from production to process optimization, improve early fault identification sensitivity, reduce the repeated incidence of similar fault.

This invention relates to the field of bioinformatics management technology and discloses a high-throughput enzyme mutant data screening and management system, comprising: generating a unique identifier for each physical experimental sample and associating it with the location coordinates recorded in the experimental container; communicating with high-throughput experimental equipment to collect raw experimental data, binding it in real time with the corresponding unique identifier to generate standardized experimental data records; integrating and storing the standardized experimental data records, location coordinates, and experimental process metadata in a central database to establish a complete traceability data chain; performing multi-dimensional analysis on the standardized experimental data records to identify target mutants; generating an analysis report based on the location coordinate information and performance indicators of the target mutants, and outputting it to a designated experimental device or interface, significantly improving the data consistency, process traceability, and intelligent decision-making efficiency of high-throughput enzyme mutant screening experiments.

The application discloses a multi-source heterogeneous data correlation retrieval method and system based on a unified identifier, a terminal and a medium, and relates to the technical field of information retrieval.The technical solution points are as follows: the application uses a unique identifier to perform strong correlation mapping between all scattered and heterogeneous physical storage and logical indexes at a logical level, and constructs a unified data view centered on an entity, so that data originally stored in object files, metadata tables, structured records, relationship graphs and retrieval indexes can be stably and accurately correlated and traced.Especially, the two-stage retrieval mode of keyword preliminary screening and graph expansion first efficiently locates a target through a retrieval engine, and then uses graph mining to find the correlation network.The application effectively solves the disadvantage that the existing retrieval scheme cannot perform correlation resource expansion, upgrades data query from single finding to network discovery, and improves the data correlation discovery rate and query completeness in a complex business scenario.

The invention discloses a new energy battery recovery system and method based on the Internet of Things. The system comprises a data acquisition layer, an edge calculation layer and a cloud platform layer. The data acquisition layer acquires key parameters of a battery in a running state, a charging state and a standing state in real time through an integrated embedded sensor, and uploads the key parameters to the edge calculation layer and the cloud platform through the Internet of Things; the edge calculation layer adopts a dynamic self-adaptive strategy, filters noise in combination with a TS-SFEA algorithm, analyzes voltage-temperature collaborative change by a Pearson's correlation coefficient, constructs a battery health state parameter matrix, and inputs a pre-training model to predict residual life and health level; the cloud platform layer integrates production and use historical data, records full life cycle information of the battery through a block chain technology, and generates an echelon utilization score and a recovery value index based on an AI evaluation model; and when the cycle count is greater than or equal to 500 times, the system triggers a health state reestimation mechanism, and dynamically updates an OCV-SOC mapping table and a parameter matrix to ensure the timeliness of an evaluation result.

The application discloses a kind of city platform and method based on national network identity authentication public service, it is related to wisdom city field, it first utilizes national network identity authentication public service to complete user identity authentication to establish trusted access foundation, when user handles business, constructs data request contract and is authorized after user confirmation and is stored, forms transparent authorized mechanism of traceability. Then, based on the authorized range of user, the data token of limited access is generated, based on the unified data aggregation service, the required cross-source data is obtained, the on-demand aggregation and safe flow of data are realized, and the audit result is obtained by automatically auditing the aggregated data through business logic. Finally, the audit result is returned to the user. In this way, the business process can be simplified, the problem of repeated authorization and data silos can be solved, while the user data control right is guaranteed, and the intelligence and efficiency of city platform business handling are improved.

The application relates to the technical field of artificial intelligence and intelligent decision-making, and provides a business process optimization and intelligent decision-making method and device.The method comprises the following steps: determining multi-modal fusion features according to multi-modal business data and a business scenario; inputting the multi-modal fusion features and timestamp data into a target time sequence fusion transformer model to obtain a business demand prediction result output by the target time sequence fusion transformer model; determining a target decision threshold according to the business demand prediction result and a preset threshold adjustment strategy; and determining a target business process decision scheme according to the multi-modal fusion features, the business demand prediction result and the target decision threshold.The business process optimization and intelligent decision-making method and device provided by the application can realize business process optimization and high-efficiency, high-precision intelligent decision-making.

The invention relates to the technical field of medical equipment data processing, in particular to a medical equipment-oriented digital twin system implementation method and a related device. The method comprises the following steps: collecting multi-source fusion data; the operation data and the image data of the medical equipment are input to a data verification module, consistency and validity verification is carried out on the operation data and the image data of the medical equipment, and a verification result is obtained; inputting the operation data and the image data of the medical equipment into a digital twin model, so that the digital twin model outputs analog data corresponding to the operation data and the image data of the medical equipment; and analyzing analog data output by the digital twin model through an analysis and prediction module to obtain a failure rate analysis result and a residual life prediction result. According to the method and the device, the problem of data islands caused by lack of relevance of data in the existing scheme can be effectively solved, all-around periodic management can be effectively carried out on the medical equipment, and closed-loop management driven by data is completed.

The invention relates to a federal cooperative training optimization method and system for project achievement reward classification, a client extracts image features of a project achievement by using an image encoder and a text encoder of a CLIP model, and converts a reward classification label into a natural language template to generate a semantic vector, thereby realizing unified spatial alignment of visual and text features; the attention adapter screens task related features; knowledge distillation is adopted to migrate knowledge of the teacher model to the student model, and classification loss and distillation loss are jointly optimized; the server aggregates only the adapter parameters of the clients to update the global model. Compared with the prior art, the classification accuracy is improved by more than 5%, the cross-client generalization performance is improved by 8%, meanwhile, the convergence speed is increased by 20%, and the federal learning efficiency and the model performance are remarkably improved; scenes such as project achievement reward classification can be effectively supported, privacy and confidentiality of local client data are ensured, and low-delay and high-precision distributed reasoning is realized.

The application discloses a kind of digital twin-oriented lamps and lanterns total element data acquisition and modeling method, comprising the following steps: step 1: construct lamps and lanterns four-dimensional integrated data system;Step 2: multi-source heterogeneous data synchronous acquisition and timestamp is covered;Step 3: data cleaning, fusion and time sequence alignment;Step 4: construct lamps and lanterns dynamic behavior model;Step 5: digital twin encapsulation and double-drive update;The application realizes that lamps and lanterns are from physical entity to digital twin high-fidelity mapping, provides standardization data and model basis for predictive maintenance, energy efficiency optimization, system simulation and other advanced applications, with the advantages of low cost, strong scalability etc..

The application discloses a kind of multi-level group ecological environment examination subsystem and examination method, it is related to ecological environment processing technical field. Including: an evidence graph construction unit is used to establish organization unit table, organization path table and index rule table in the relational database of same platform, and according to database view output generates evidence node and evidence relationship to form examination evidence graph;A fusion and calculation unit is used to generate hierarchical consistent score fragment set based on examination evidence graph;A flow unit is used to automatically generate reference score according to hierarchical consistent score fragment set, and flows according to the order of self-score, two-level audit and group audit.This application effectively solves the problems of low data processing efficiency, rigid weight configuration, difficult to trace results and insufficient system scalability in the prior art, and provides efficient, fair and practical technical support for the ecological environment management examination of multi-level groups.

This invention discloses an enterprise procurement supply chain collaborative management system, comprising: a perception layer deployed at each node of the procurement supply chain, used to collect data on the flow and processing of renewable resource materials through passive RFID tags built into heterogeneous devices, and to perform data verification and synchronization through an inter-device self-organizing network; a trust layer used to bind the operator's biometric information with their identity, operating permissions, and initial credit rating, and to dynamically manage credit and cross-enterprise permissions based on their operating behavior and device feedback; and an analysis and decision-making layer used to analyze and issue early warnings on the perceived data through an edge computing module, and to simulate the value transformation path under different working conditions through a virtual mapping model. By deploying computing modules at the network edge, this invention can process data locally, independently and quickly identify and issue early warnings for anomalies such as equipment failures and quality deviations, achieving a leap from post-event response to pre-event prediction and in-event intervention.

The invention discloses a special equipment inspection data intelligent analysis method based on natural language understanding, and belongs to the technical field of special equipment safety supervision and big data analysis. The method comprises the following steps: receiving and analyzing a natural language request, and extracting a target use unit name and a risk analysis dimension; searching and constructing a device analysis group and matching a risk evaluation model; for each device in the group, extracting related data from the multi-source database; carrying out quantitative calculation by utilizing the matching model, and generating a single-equipment risk score and a risk factor label; carrying out aggregation analysis on a total-amount equipment result, and identifying an overall risk level, a dominant risk type and a high-risk equipment distribution characteristic of the unit; and finally, differential supervision suggestions are generated on the basis. According to the invention, full-process automation from natural language instructions to intelligent risk assessment and decision suggestions is realized.

This invention relates to the field of smart space operation and maintenance technology, and discloses a smart space operation and maintenance optimization method and device based on data elements. The method includes: collecting operational data from IoT terminals, edge nodes, and a cloud platform within the smart space, and fusing the operational data to obtain fused data; identifying and encrypting sensitive information in the fused data to obtain encrypted data; inputting the encrypted data into the local models of each edge node to extract device operational features, and integrating the device operational feature set in a cloud aggregation server; identifying anomalies based on the device operational feature set to obtain device abnormal status information; and conducting anomaly cause tracing analysis based on the device abnormal status information to obtain a smart space abnormal situation report. This invention provides operation and maintenance personnel with an interpretable anomaly analysis path and a comprehensive situational awareness report, significantly improving fault handling efficiency and realizing intelligent operation and maintenance management of smart spaces.

The invention relates to the technical field of data processing, in particular to an enterprise master data governance and distribution method based on multi-system collaboration. The method comprises the following steps: synchronously acquiring main data of multiple systems about the same entity, and associating the main data with product data and real-time equipment metadata; generating a consistency label independent of each system rule based on a product constraint condition as an objective arbitration benchmark; performing collaborative analysis on the system data processing rules according to the labels to identify conflicts; calibrating the system master data with conflicts, and generating and distributing calibrated data matched with rules of the system master data; and finally, iteratively executing collaboration and calibration based on system feedback until the similarity of each system rule reaches a set threshold, and realizing quantitative convergence. According to the method, intelligent, dynamic and consistent collaborative governance of cross-system main data is realized by introducing an objective consistency label and a quantitative closed-loop mechanism.

The application discloses a network security threat event report generation method and system, and belongs to the network security field.The method comprises the following steps: acquiring a network security threat event log, pre-processing the log, extracting key information by using a pre-trained ERNIE-Gram model, and generating structured knowledge points; integrating the knowledge points into a feature vector, inputting the feature vector into a KAN model for nonlinear feature extraction and multi-level feature fusion, and generating fusion feature data; inputting the fusion feature data into an OverLoCK model, performing multi-stage feature extraction and aggregation through a dynamic context guiding mechanism of the model, and generating summary semantic analysis information; standardizing the semantic analysis information, inputting the semantic analysis information into a pre-trained federal decision tree model, matching a decision path of the federal decision tree model according to a global split rule, and outputting an interpretable security event report containing a confidence distribution.The application can improve the accuracy and interpretability of threat event report generation.

The application discloses a water conservancy project global situation awareness and collaborative early warning system and relates to the technical field of water conservancy project management, comprising: a multi-source perception acquisition module: collecting multiple types of original monitoring data generated in the operation process of the water conservancy project, and performing a preprocessing operation on the collected original monitoring data to generate a global perception dataset; a global digital twin deduction module: used for receiving the global perception dataset, extracting a target node historical output sequence, and constructing an exogenous input matrix including upstream outflow, water level, discharge, tributary flow, pump station state, gate opening and scheduling instruction according to a water network topology, performing multi-step prediction by using an NARX time series prediction algorithm, and outputting a virtual-real fusion situation sequence. In the application, the multi-source perception acquisition module is used to preprocess the multiple types of original monitoring data, the data island problem is solved, the comprehensive gathering and standardization of the water conservancy project operation data are realized, and a foundation is laid for subsequent analysis.

The invention discloses a user portrait generation method and system based on federal learning and differential privacy, and relates to the field of user portrait generation, and the method comprises the steps: formulating a collaborative privacy protocol, and initializing a global user portrait model. Each platform independently trains the model, generates a parameter update quantity, and applies adaptive differential privacy noise to the update quantity. And encrypting the parameter update quantity after noise addition, and aggregating the encrypted update iteration optimization global model of each platform through a security aggregation algorithm. The model is locally deployed, an accurate portrait is generated, personalized advertisement recommendation is driven by using the portrait, and the model is optimized based on physical feedback. According to the method, the problem of data islands between e-commerce platforms is solved, the balance between privacy protection strength and model accuracy is realized on the premise that the platforms do not share original data, an accurate user portrait capable of directly driving marketing is obtained, the model is continuously optimized by using delivery feedback, and the user experience is improved. And a complete privacy-safe and effect-measurable commercial closed loop is constructed.

The application discloses a kind of regional power grid cooperation methods based on knowledge graph driving, comprising the following steps: collecting regional power grid data, generating multi-source heterogeneous power grid data set;Construct regional power grid knowledge graph, initialize federation heterogeneous graph neural network and graph neural controlled differential equation joint framework;Perform federation heterogeneous graph neural network coding, generate local embedding, mode weight decoupling parameter and local coding parameter;Run graph neural controlled differential equation modeling, generate continuous time state trajectory and dynamics parameter set;Perform federation aggregation on client set results, generate global model parameters;Issue global model parameters and update local model;Based on knowledge graph data, control path input and updated model parameters, generate regional power grid cooperation result set.The application significantly improves the cross-regional power grid cooperation prediction accuracy and calculation efficiency, effectively guarantees the operation safety and dispatching reliability.

The application provides a linkage drawing implementation method and device for building electrical design and a medium, and belongs to the technical field of building design. The method comprises the following steps: constructing a power distribution topology database; wherein the power distribution topology database is used for storing power distribution node data, topology relationship data and electrical appliance and cable combination data. Based on the power distribution topology database, load linkage calculation is performed through a preset rule engine, and corresponding selection data is determined to update the power distribution topology database according to the selection data; in response to a drawing request, based on the updated power distribution topology database, a structured data set corresponding to the power distribution node is determined, and the structured data set is mapped to a preset drawing display platform to generate corresponding system drawings. When it is determined that the parameters in the system drawings are changed, the corresponding changed parameters are written back to the power distribution topology database, and the load linkage calculation is re-executed through the preset rule engine and the selection data is determined to update the power distribution topology database and the system drawings.