45results about How to "Solve data silos" patented technology

The invention relates to a transportation agent intelligent cargo tracking method, device and equipment and a storage medium. The method comprises the following steps: carrying out multi-source sensor data acquisition on goods and a transport vehicle to obtain an original data set, and carrying out time synchronization and Kalman filtering processing to obtain a fused state vector; performing feature extraction through a deep cargo tracking network to obtain a feature vector; performing distributed model training based on the feature vectors to obtain a global cargo state detection model; inputting the collected real-time data set into a global cargo state detection model, and performing multi-stage anomaly detection processing to obtain an anomaly detection result; and performing trajectory prediction and multi-target optimization by using a space-time diagram neural network model according to an anomaly detection result and a historical transportation route to obtain a target transportation route. According to the embodiment of the invention, comprehensive goods tracking of multi-source data and dynamic optimization paths can be integrated to cope with complex and changeable logistics environments, and more accurate, safer and more efficient goods tracking services are provided.

The invention discloses a satellite remote sensing target data identification method based on artificial intelligence, which belongs to the technical field of data processing, and realizes distributed model training and data mining of a multi-node joint model by performing satellite remote sensing target identification on an acquired spectral vector and adopting a model training architecture of distributed federated learning; carrying out manual annotation on the acquired spectral vector data; a generative adversarial network based on multi-scale spectral decomposition is adopted for sample generation, and then data expansion is achieved; and performing data identification by adopting a satellite remote sensing target data identification model. According to the method, the generative adversarial network based on multi-scale spectral decomposition is adopted for sample generation, and the data set is expanded. A self-encoder algorithm is used for carrying out feature dimension reduction, feature independence is enhanced through a nonlinear conversion layer, and redundant information between features is reduced. A kernel extreme learning machine algorithm based on conjugate gradient compensation is adopted, a kernel function is mapped to a high-dimensional space, and the classification accuracy and efficiency are improved.

The invention relates to the technical field of data processing, in particular to a cloud computing-based software development application data processing method, which comprises the following steps of: collecting original data generated by software development application from a plurality of heterogeneous data sources in real time through a distributed data acquisition module; cleaning, filtering and formatting the original data, removing noise data and redundant information, and generating a standardized data set; the standardized data set is fragmented according to a preset fragmentation strategy; analyzing the stored data in real time by using the parallel processing capability of cloud computing to generate an analysis result; a result storage and updating step; a data visualization step; a security and authority management step; and an anomaly detection and fault-tolerant processing step. According to the method, the data processing speed and the throughput of the system are improved, and the requirement for real-time feedback in an agile development environment is met.

The present invention provides a production traceability and transaction collaboration method and system for an MES system, which relates to the technical field of production traceability, including using graph database technology to construct a production process knowledge graph, mapping the fused multi-source heterogeneous data to the entities and relationships of the knowledge graph; based on a time series graph algorithm, establishing a dynamic time series association in the knowledge graph to form a traceable production process chain; based on a knowledge graph and anomaly detection model, constructing a transaction trigger mechanism to automatically identify production transactions that need to be collaboratively processed and obtain transaction collaboration records; using a reinforcement learning algorithm to establish a multi-agent collaborative decision-making model, which can adaptively allocate tasks and resources according to the current production status and historical experience; through a sequence-to-sequence model enhanced by an attention mechanism, converting unstructured collaboration requirements into executable workflows; and automatically generating a multi-dimensional production analysis report based on the traceable production process chain and transaction collaboration records.

The invention relates to a power enterprise data full-link fault positioning method based on data consanguinity. The method comprises the following steps: step 1, constructing a data consanguinity map; 2, based on the data consanguinity map constructed in the step 1, metadata consanguinity full links are collected and monitored in real time; 3, based on the data blood relationship map constructed in the step 1 and the real-time monitoring data collected in the step 2, a state transition matrix is constructed by analyzing state change information, and a Markov chain model is defined; by using the Markov chain model, the system can identify potential fault points and carry out accurate positioning. According to the invention, the problems of high data link complexity, difficulty in fault positioning, low troubleshooting efficiency and the like in the prior art can be solved.

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 invention provides a business processing method which can be applied to the field of big data or digital employees. The method comprises the following steps: in response to a request of a client for handling a target service, matching a target process chain corresponding to the target service from a plurality of pre-constructed initial process chains; based on the process nodes of the target process chain, sequentially obtaining service data provided by the client; calling an identity model of the customer in a pre-configured database, and processing the service data according to the identity model to obtain a first processing result of the target service; in response to the fact that at least one process node in the first processing result is not completed, synchronizing the target process chain to a client of the client, so that the client delays submitting business data corresponding to the uncompleted process node at the client; and obtaining a second processing result corresponding to the target service based on the service data submitted by the client in a delayed manner. The invention further provides a business processing device, equipment, a storage medium and a program product.

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.

The present application relates to the technical field of artificial intelligence, and provides a vehicle odometer risk detection method, device, equipment and medium, which can collect audio-visual data, working condition data and cloud behavior portrait data of a target vehicle, and correspondingly generate wear degradation index, physical working condition characteristics and portrait characteristic quantitative values, realize multi-source data transmission link connection, and solve the data island problem; the wear degradation index, physical working condition characteristics and portrait characteristic quantitative values are input into a pre-trained prediction model as a feature vector to obtain the real confidence mileage of the target vehicle under the current loss state, and the deviation degree of the odometer display mileage relative to the real confidence mileage is calculated to obtain the mileage deviation index, which can quantify the mileage deviation degree based on multi-dimensional data, effectively solve the problem of avoiding cross-validation through a single tampering method, and generate the odometer risk detection result of the target vehicle according to the mileage deviation index, thereby improving the accuracy of vehicle odometer risk detection.

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 relates to the technical field of bank intelligent anti-fraud, and discloses a TensorFlow Fedged-based bank anti-fraud model optimization method, device and equipment and a medium, and the method comprises the steps: determining participants and data standards of the participants; the method comprises the following steps of: establishing a TensorFlow Federated environment on the basis of a data standard of a participant; preprocessing local data of participants, and extracting key features; constructing a deep learning model of the key features, and initializing parameters of the deep learning model in a TensorFlow Federated environment; and executing the federated learning task, and performing updating iteration on the deep learning model parameters based on the federated learning task. Cross-mechanism data collaborative modeling is realized through a federated learning technology, so that model training samples and feature dimensions are enriched, and the accuracy of the anti-fraud model is remarkably improved; and the data privacy security is protected.

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.

The application discloses an industrial control system data standardization management method and device. The method comprises the following steps: formulating a four-level structured data naming specification composed of workshop codes, equipment numbers, parameter type codes and measuring point serial numbers, wherein the parameter type code is taken from a closed code set with complementary, mutually exclusive and hierarchical semantic relationship constraints; using a native alias mechanism to establish a decoupling mapping layer from original variable names to standard variable names in the configuration layer of the industrial control platform without modifying the control logic of the underlying programmable logic controller; performing standard compliance checking on the code and mapping layer based on the semantic relationship constraints; and according to the version revision, scanning the affected entries in the mapping layer, generating an update list and performing dynamic updating. The application realizes gradual standardization of data expression of multi-brand heterogeneous industrial control systems, and has the characteristics of version traceability and standard evolution without production stoppage and hardware replacement.

The embodiment of the invention discloses a data interaction method and device, a terminal, a storage medium and a program product, and relates to the technical field of terminals. The method comprises the steps that application data of a second application client side are obtained from a second application server side through a first application server side, the first application server side is used for providing services for a first application client side, and the second application server side is used for providing services for the second application client side; the first application client and the second application client run in an operating system, and the first application server and the second application server run in a trusted virtual machine; according to the application data of the second application client, through the first application server, calculating to obtain a calculation result required by the first application client; and returning a calculation result to the first application client through the first application server. By adopting the method provided by the invention, the problem of data islands between different applications in the terminal can be solved, and the data security can be guaranteed.

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..