46results about How to "Improve data quality" patented technology

This invention discloses an automatic verification and risk warning method for international trade information based on smart ports. It collects multi-source document data, including customs declarations, bills of lading, transportation routes, payment vouchers, and certificates of origin, and converts them into a standardized format through field mapping rules. A cross-document verification rule base is established, and rule matching is performed on key fields such as invoice amount and declared price, and bill of lading quantity and packing list, using a weighted scoring mechanism to calculate consistency scores. A spatiotemporal graph of trade behavior is constructed, and risk characteristics are statistically analyzed using an improved random walk algorithm, outputting a dynamic risk feature matrix. This matrix is ​​matched with a historical risk case database, and the probability of risks such as smuggling, false declarations, and tax evasion is calculated in conjunction with regional risk benchmarks. Risk thresholds are dynamically adjusted based on port clearance flow, triggering adaptive warnings and generating warning information including warning level, risk type, and inspection measures, thereby improving the intelligence level of port supervision.

This invention discloses a method, apparatus, equipment, and medium for determining the uniformity of a converted-wave observation system. The method includes: determining the target layer depth parameter and the target layer conversion parameter; determining the original conversion point of each original shot-receiver pair in the target layer based on the original shot point position, original receiver position, and target layer conversion parameter of each original shot-receiver pair corresponding to the common shot point; selecting a common conversion point from among the original conversion points, and moving other original conversion points and their corresponding original shot-receiver pairs to obtain new shot-receiver pairs corresponding to the common conversion point; determining the average receiver distance of the common conversion point based on each new shot-receiver pair, and determining the reference common-shot point conversion distance of the common conversion point based on the average receiver distance and the target layer conversion parameter; and determining the converted-wave uniformity of the common conversion point based on each new shot-receiver pair, the average receiver distance, the reference common-shot point conversion distance, and the target layer depth parameter. Embodiments of this invention can determine the uniformity of the observation system and guide the optimization of the observation system layout.

The application relates to the field of data processing, and provides an insurance product recommendation method and device, computer equipment and a computer readable storage medium, which comprises the following steps: obtaining a professional classification standard file and internal professional information, and generating a spoken address of a profession and a spoken address of an industry according to the professional classification standard file; supplementing the internal professional information according to the spoken address of the profession and the spoken address of the industry, and obtaining an internal professional standard file; obtaining conversation information of a user and a claimant, and identifying the profession of the user from the conversation information according to the internal professional standard file; and recommending an insurance product to the user according to the profession of the user. According to the embodiment of the application, the profession and the industry information of the user can be accurately obtained under the premise of ensuring the data security, so that more accurate insurance product recommendation services can be provided.

A magnetic resonance-compatible programmable synchronized photostimulation system includes a main control unit and a stimulation execution unit. The main control unit is located outside the magnetic resonance scanning room, while the stimulation execution unit is located inside the scanning room and consists only of an LED light panel shielded by a magnetic field. The main control unit is configured to: receive photostimulation sequence parameters configured by the user in a modular manner through a graphical interface; receive a scan trigger signal from the magnetic resonance equipment, and automatically control the start and stop of the photostimulation sequence based on the trigger signal using hardware triggering; thereby achieving high-precision time synchronization between photostimulation and magnetic resonance scanning while maintaining magnetic resonance compatibility. This invention solves electromagnetic compatibility issues through a split architecture, achieves millisecond-level synchronization accuracy through hardware TTL triggering, and enhances experimental paradigm flexibility through modular programmable sequences, making it suitable for neuroscience research fields such as functional magnetic resonance imaging.

This invention belongs to the field of new energy meteorological monitoring technology, specifically relating to an adaptive correction method and system for new energy meteorological monitoring data. The method includes: preprocessing multi-source meteorological monitoring data collected from target monitoring stations to obtain processed data; determining the path code corresponding to the processed data using a pre-established multi-dimensional decision tree model; and correcting the processed data according to the path code and the corresponding model to obtain the final corrected meteorological monitoring data. The multi-dimensional decision tree model is constructed using multi-source meteorological monitoring data from historical monitoring stations. The technical solution provided by this invention achieves automatic matching of optimal correction strategies under different scenarios, significantly improving data quality and providing a more reliable data foundation for new energy power prediction.

This invention provides a nonlinear time-series estimation method for the state of health (SOH) of lithium batteries, comprising: collecting feature data during the charge-discharge cycle of the lithium battery and generating a multi-channel standardized time-series dataset; extracting features and compressing dimensions of the data through an encoder; reconstructing and denoising the data through a decoder to output sequence features; mining the global correlation between feature sequences to obtain a context vector; further obtaining the SOH estimation result and synchronously updating the parameters. This method effectively suppresses model overfitting while introducing an improved KL divergence to prevent posterior collapse, achieving high-precision and robust estimation of the SOH of lithium batteries.

The application discloses a kind of based on semi-supervised learning's shield machine tunneling parameter optimization prediction method and system, and relates to engineering construction control technical field.The method includes: data preprocessing and feature generation;Adopt multi-algorithm fusion to carry out geological clustering;Execute feature optimization and multi-model construction;Through the collaborative training in geological cluster, noise enhancement and iterative learning carry out semi-supervised enhancement;Based on comprehensive score selection optimal model;Uncertainty evaluation is output by using cluster center distance to carry out routing prediction.The application effectively solves the problems of poor data quality, label scarcity, geological distribution deviation and low calculation efficiency in shield construction, improves the accuracy, generalization ability and engineering applicability of tunneling parameter prediction.

A deep learning-based battery health and remaining life prediction system includes: a database storing a first set of data including multiple battery data collected from an input unit; a processor preprocessing the first set of data based on a first artificial intelligence model, learning a second artificial intelligence model by inputting the preprocessed first set of data, and predicting battery state health and remaining battery life based on the learned second artificial intelligence model. The processor includes: a preprocessing unit that performs preprocessing by converting the first set of data into a spectrogram and removing noise based on the first artificial intelligence model; a learning unit that extracts features from the input preprocessed first set of data and learns the second artificial intelligence model to estimate battery state health based on the extracted features; and a prediction unit that estimates battery state health from a second set of data input by a user based on the second artificial intelligence model learned by the learning unit, and predicts the remaining battery life.

This invention discloses a method for constructing a digital twin for a water electrolysis hydrogen production system, relating to the field of digital twin technology. The method includes: collecting multi-source operational data from the water electrolysis hydrogen production system, extracting all sampling points, constructing proximity relationships between all sampling points to form a proximity connection graph; traversing the proximity connection graph, organizing the shortest path distances between all pairs of sampling points to form a high-dimensional spatial distance matrix, using multi-dimensional scaling transformation to map the sampling points to a low-dimensional space, establishing a correspondence between the low-dimensional coordinate vectors and the high-dimensional original data, forming a digital twin state representation model; based on the state representation model, constructing a probabilistic graphical model containing three types of nodes: components, sensors, and state variables; setting initial node confidence and information transmission rules, iterating until the confidence stabilizes; performing corrections based on node state deviations; and iterating in real-time according to the correction cycle to achieve real-time synchronization between the digital twin and the water electrolysis hydrogen production system.

The application relates to the technical field of health medical big data processing, and discloses a health medical data management method. The management method comprises the following steps: performing first hierarchical classification on collected data to obtain a first hierarchical classification result; performing data preprocessing on the first hierarchical classification result, and determining a second hierarchical classification result and a third hierarchical classification result based on the preprocessing result; performing data mining processing on the second hierarchical classification result, and performing fourth hierarchical classification to obtain a fourth hierarchical classification result; performing first data storage processing on the third hierarchical classification result and the fourth hierarchical classification result, and performing fifth hierarchical classification to obtain a fifth hierarchical classification result; performing data quality verification processing on the fifth hierarchical classification result, and performing sixth hierarchical classification on the data quality verification result to obtain a sixth hierarchical classification result; and storing the sixth hierarchical classification result. The management method can improve the quality of health medical data.

This invention provides a method and apparatus for mining quality inspection rules through multi-dimensional modeling of indicators. The method includes: extracting multi-dimensional data from a pair of indicators to be judged, wherein the multi-dimensional data is related to time, data lineage, and data meaning; determining the similarity of the pair of indicators to be judged in each dimension based on the multi-dimensional data; calling a pre-built target model to process the multi-dimensional data and the similarity to obtain a judgment result characterizing whether the pair of indicators to be judged is a valid association pair; and generating quality inspection rules based on the indicator pairs whose judgment result is a valid association pair. The method described in this embodiment can quickly and accurately generate effective quality inspection rules.

This invention relates to the field of engineering data processing, and more particularly to an intelligent data acquisition and processing method and system for dredging engineering projects in tunnel drainage systems. The method includes the following steps: receiving processing tasks based on a cloud-based dredging management platform; setting acquisition constraints based on the processing tasks to obtain an acquisition constraint set; driving a pipeline detection robot to perform operational detection based on the acquisition constraint set, outputting a first dataset; adaptively optimizing the first dataset to output a second dataset; performing index-based analysis on the second dataset to output multi-dimensional analysis indicators; and planning the dredging operation path based on the multi-dimensional analysis indicators, synchronously uploading the data to the cloud-based dredging management platform to complete the data acquisition and analysis operation. This invention optimizes dredging operation planning, improves dredging efficiency, and enhances the accurate structural analysis of the drainage system in the early stages through precise data acquisition and processing.

This application discloses a method, apparatus, equipment, and medium for industrial drawing recognition and error correction, relating to the field of drawing recognition technology. The method includes: acquiring and preprocessing industrial drawings; performing recognition and extraction based on the drawing data to generate a structured candidate set; calculating the alignment between the recognition coordinates and the coordinates of the drawing tool object based on anchor point features to obtain alignment parameters, errors, and confidence levels; performing quality verification based on the confidence level and errors to obtain compliant alignment parameters; mapping the structured candidate set to coordinates based on the compliant alignment parameters to obtain the positioning target; controlling the drawing tool to perform positioning and measurement to generate deterministic evidence objects; constructing a binding relationship graph and performing consistency constraint detection; executing a minimum-cost action sequence when errors occur to complete closed-loop error correction; and encapsulating the results to obtain structured results, evidence chains, and replayable data. This method can achieve highly reliable closed-loop error correction of drawing recognition results.

This application discloses a method and system for spot market bidding decisions in combined heat and power (CHP) power plants, relating to the technical field of CHP power plants. The method includes: acquiring operating data of the CHP unit and market data of the electricity spot market; constructing a heat-power coupling and coal consumption characteristic model based on the operating data and the market data, with rigid heat load supply parameters as the primary constraint; inputting the cleaned operating data and market data, along with energy conversion efficiency, energy consumption characteristics, and feasible domain of CHP output under different operating conditions, into a chaotic hierarchical gray wolf optimization algorithm to initialize the population and implement a hierarchical search strategy; defining a fitness function with profit maximization as the objective, solving for the optimal power output or optimal bid price, and guiding the CHP power plant in making bidding decisions regarding power generation or price. This application aims to solve the problems of unscientific bidding decisions and low profitability of CHP power plants in the electricity spot market.

The application provides a large model-based vertical field data construction method, and belongs to the technical fields of data processing and artificial intelligence, and comprises the following steps: converting a vertical field source document into an intermediate format text and dividing the intermediate format text into multiple text blocks; inputting the text blocks into a pre-trained generative language model, guiding the pre-trained generative language model to generate a plurality of candidate questions according to the contents of the text blocks according to a pre-designed prompt word, and performing preliminary screening and fine screening on each candidate question to obtain a question set; predefining a mode of a knowledge graph according to the field characteristics of the vertical field, processing all the text blocks based on an information extraction model, and constructing a field knowledge graph; and performing local context retrieval based on the text blocks of the source of each final question in the question set, performing global knowledge retrieval based on the field knowledge graph, and generating a final answer and a final thinking chain. The application is suitable for different vertical fields, and can effectively improve the quality of data and ensure the accuracy of question generation.

This invention discloses a data compliance dynamic detection method and system based on deep learning, belonging to the field of data security processing technology. It addresses the problem that existing methods, which focus primarily on text data processing when performing compliance detection based on extracted comprehensive semantic words, lack the ability to comprehensively process multimodal data and comprehensively evaluate complex compliance issues, leading to inaccurate compliance assessments. The method includes preprocessing the original data stream, extracting features from the preprocessed data stream based on data stream type, and using a compliance analysis model to identify and analyze multimodal feature sets based on a standard rule base. In this invention, preprocessing the original data stream filters and reduces noise in the multimodal data, ensuring the authenticity and validity of the data. Furthermore, the compliance analysis model's identification and analysis of multimodal feature sets ensures comprehensive processing capabilities for multimodal data and comprehensive evaluation capabilities for complex compliance issues.

This invention relates to the field of vehicle driving safety monitoring technology, and more particularly to a driver behavior monitoring device and electronic equipment based on multimodal visual recognition. The device includes a physical device and a control device. The control device is integrated into an embedded computing platform and includes a data acquisition module that acquires vehicle dynamic parameters through a vehicle bus interface and controls a multispectral imaging unit to acquire video streams; a feature extraction module that fuses spatial features and temporal motion features from the video stream to generate a spatiotemporal feature tensor; a behavior analysis module that generates standard behavioral features through a generative adversarial network and calculates statistical deviations; a risk decision module that uses a dynamic weighted algorithm to output a comprehensive risk level and confidence assessment; and a control output module that executes early warning operations and sends feedback data to the behavior analysis module for model optimization. This invention reduces the false positive rate and improves the timeliness of early warnings through multimodal data fusion and closed-loop optimization mechanisms.

The application relates to an energy data processing method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: obtaining original energy data based on space division multiplexing transmission energy data; sequentially performing denoising processing and data quality evaluation processing on the original energy data to obtain denoised energy data and a quality evaluation result of the denoised energy data; performing denoising processing on the denoised energy data again according to the quality evaluation result to obtain target energy data; and inputting the target energy data into a trained energy scheduling processing model to obtain target scheduling information of the target energy data. The method can improve the energy data processing efficiency, utilize the target scheduling information to improve the scheduling effect of energy equipment, and realize intelligent management of the energy equipment.

The application discloses a multi-factor short-term power load prediction system based on adaptive weight sharing of a graph structure, and comprises the following steps: multi-dimensional power load data preprocessing, implicit graph structure and dynamic space correlation modeling, adaptive weight sharing graph convolution design, multi-factor enhancement module fusion, and end-to-end space-time graph network training.The application solves the problem of poor data quality after new energy access through multi-dimensional data preprocessing; through the cooperation of dynamic implicit graph and weight sharing graph convolution, the problem of the inability of a static graph to capture dynamic implicit correlation and the loss of space-time characteristics is solved; through weight sharing and multi-factor fusion, the problems of model parameter redundancy and insufficient feature fusion are solved, high-precision and efficient short-term power load prediction is realized, and the needs of users are met.

This invention discloses a method, apparatus, device, storage medium, and product for constructing a knowledge base. The method includes: preprocessing original business materials to obtain initial business data; performing semantic parsing on the initial business data using a large model to generate candidate business data; if preset indicators of the candidate business data do not meet preset conditions, revising the candidate business data multiple times according to a preset revision strategy until the preset conditions are met; wherein the preset indicators include at least one of semantic accuracy, semantic novelty, and semantic stability indicators of the candidate business data; determining target business data based on the revision results, and constructing a business process knowledge base based on the target business data. This invention can construct a business process knowledge base based on original business materials in the initial stage of intelligent system construction, thereby supporting the initial deployment of the intelligent system.

The invention discloses a locomotive servicing size measuring and scheduling method, system and device and a storage medium, relates to the technical field of locomotive maintenance management, and aims to solve the problem that manual measurement data and duration are not standard. After the locomotive enters the field, generating a measurement work order, associating the measurement work order with the measurement tool, the train number and the measurement guidance process, updating the measurement work order, and distributing the measurement work order according to the priority; a measurement work order is bound with the worker, a measurement tool is distributed after the identity of the worker is identified, the worker is guided to a measurement station, and the worker is guided to carry out a measurement task; receiving the uploaded field measurement data, predicting the current measurement data based on the historical measurement data and the standard reference data, comparing the field measurement data with the prediction data, and storing the field measurement data conforming to a deviation threshold value; and guiding a worker to carry out supplementary measurement on data exceeding the deviation threshold. According to the method and the system, the measurement data quality is ensured, and the measurement time is shortened.

This invention discloses a pre-trained online Gaussian process device and method for predicting hydrogen fuel cell voltage, applicable to marine propulsion systems. Addressing the problems of traditional models relying on real-ship data, weak generalization ability, and inability to adapt to battery aging, the device includes a sensor module, a memory, and a processor. The sensor module collects pressure, current, temperature, and voltage data; the memory stores the model and program; and the processor performs data processing and prediction calculations. The method pre-trains a Gaussian process regression model using experimental data, updates it online according to preset rules using real-time ship data, and outputs the prediction results after data preprocessing, model construction, updating, and prediction. This invention achieves high-precision prediction in zero-sample / few-sample stages, dynamically adapts to complex operating conditions and battery aging, operates efficiently without redundancy, and ensures reliable operation of marine hydrogen fuel cell propulsion systems.

This application discloses a damage detection method, electronic device, storage medium, and computer program product, relating to the field of nondestructive testing technology. The method includes: scanning a composite material to be tested using a terahertz time-domain spectroscopy system to obtain the original time-domain echo signal of the composite material, wherein the composite material to be tested is a glass fiber reinforced polymer composite material; preprocessing the original time-domain echo signal to obtain target image data; extracting features from the target image data to obtain damage features; predicting the geometric offset of the damage features; and locating the damage target in the composite material to be tested based on the geometric offset and the damage features. This application solves the technical problem of poor detection effect in existing nondestructive testing technologies.

This invention discloses a distributed data processing method, device, and medium based on local storage, relating to the field of data storage technology. The method includes: acquiring real-time data streams to be processed; performing preprocessing and segmentation on the real-time data streams to generate a set of data blocks to be written to disk; acquiring the network topology and real-time load status of cluster nodes; constructing a two-layer weighted dependency graph by combining historical processing association information; calculating cross-domain transmission cost scores between data blocks based on the two-layer weighted dependency graph; and performing topology-aware co-location writing processing on the set of data blocks to be written to disk based on the cross-domain transmission cost scores to generate distributed processing location information. This invention achieves ordered management and traceable reuse for subsequent precise processing, thus improving the overall processing accuracy and practicality.

The application discloses a kind of holographic traffic perception method and system based on wide-area radar, belong to vehicle traffic control technical field, for solving the technical problems of existing traffic perception system, it is difficult to provide real-time, holographic traffic information service for mass vehicle, not conducive to optimizing traffic flow space-time distribution, and relieving congestion and reducing accident rate.Method includes: the coordinate data uniform processing of initial tracking data and initial driving data about latitude and longitude offset, respectively corresponding to obtain radar tracking data and target driving data;Radar tracking data and target driving data are calculated about the trajectory topology of target reference system, respectively to obtain first state estimation information and second state estimation information;And generate minimum topology distance optimization model;Based on minimum topology distance optimization model, relative position of multi-vehicle target is distinguished and identified, to obtain vehicle holographic information;Realize holographic traffic perception to global vehicle target.

This application provides a method and system for processing multi-source heterogeneous data, an electronic device, and a storage medium. The method includes: a data access layer initiating a mode request to a metadata service before sending a message to a message bus via a target component, wherein the target component is one of the following: an adapter or a processing component; the metadata service responding to the mode request sends constraint information corresponding to the target component to the target component, wherein the constraint information includes: field rules and type constraints; the target component verifies the acquired target source data according to the constraint information, and if the target source data satisfies the constraint information, pushes the target source data to the message bus; the message bus persistently stores the target source data. This application solves the technical problem of poor overall system stability and reliability in related technologies.

This invention relates to a multi-bus data fusion processing method and system based on a logging tool, belonging to the field of oil and gas drilling measurement data transmission technology. The method includes receiving logging data collected by the logging tool's measurement probe and different data packets sent by the system bus; performing preliminary processing on the received logging data, including self-identification, self-diagnosis, and self-correction; identifying and converting the different received data packets; performing real-time fusion processing on the self-identified, self-diagnosed, and self-corrected logging data and the identified and converted data packets; and packaging the real-time fused data according to different communication protocols, and sending the packaged data to the system bus and / or storage unit. This invention forms a multi-bus data fusion processing system, which is embedded in the logging tool connected to the drilling string, enabling data interaction between the logging tool and other instruments in the instrument string, and achieving parallel processing and self-diagnosis of multiple sets of data.

The invention provides a design method, system, equipment and medium based on a residual gas well underground combined mining three-dimensional observation system, and belongs to the technical field of petroleum geophysical exploration. According to the method, aiming at the problems of complicated geological conditions and insufficient resolution of a conventional observation system in residual gas exploration, observation points are arranged by comprehensively analyzing geological condition factors and technical parameters of the observation system and adopting a multi-dimensional optimization strategy. The method specifically comprises the following steps: constructing a fitness function based on wave field energy coverage maximization, reflection interface coverage and geological parameter mean square error minimization, and carrying out adaptive observation point position design; according to the dimension difference of well-ground combined mining, ground excitation points are finely adjusted to make up a well periphery covering density gap; and on the basis of a static correction principle, according to topographic relief and reservoir heterogeneity characteristics, excitation and receiving parameters are adjusted in a partitioned manner. The method can effectively improve the signal-to-noise ratio and imaging precision of seismic data under complex geological conditions, accurately depicts the residual gas distribution, and improves the exploration efficiency.

The application relates to the field of power distribution network data detection and repair, in particular to a power distribution network abnormal data detection and repair method based on a neural network, which comprises the following steps: judging the probability of data abnormality of load data in the same day through a convolutional neural network, classifying the probability through a preset adaptive detection threshold, and judging whether the load data is abnormal based on the classification result; for the load data judged as abnormal, determining the time when the abnormality occurs through clustering analysis; determining the hyperparameters of a fuzzy self-organizing neural network according to the time when the abnormality occurs, then inputting all normal values in the load data of the day into the fuzzy self-organizing neural network, and obtaining the repair result of the abnormal data. The application improves the efficiency of processing power distribution network data and the accuracy of abnormal data detection and repair, provides a data basis for the construction and operation of the power distribution network, improves the operation efficiency and saves the construction cost.