88results about How to "Improve data utilization" patented technology

The invention discloses a cloud computer end-to-end fault root cause analysis method and system, and relates to the technical field of operation and maintenance data analysis, and the method comprises the steps: deploying data collectors on a virtual resource pool, a multi-stage cloud management platform and a cloud computer terminal, and monitoring and collecting various operation and maintenance data in real time; a unified operation and maintenance management center is introduced to complete the following work: providing management and configuration services of the data collector; receiving and summarizing operation and maintenance data; various AI models are accessed, operation and maintenance data from different sources are distributed based on the types of the AI models, the AI models perform deep analysis and diagnosis on the operation and maintenance data, and potential fault modes or abnormal behaviors are identified; customizing an alarm strategy, an alarm processing flow and an alarm notification mode; and introducing an AIOps service to generate an alarm and fault analysis report according to the analysis and diagnosis results of the AI model. According to the method, the service quality of the end-to-end full link of the cloud computer is monitored in real time, so that faults can be quickly positioned, early warning can be performed in advance, and the operation and maintenance cost and the fault risk are reduced.

The invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, and belongs to the technical field of data processing, and the method comprises the steps: 1, importing a preset BIM model into a preset intelligent monitoring platform, and carrying out the analysis of the preset BIM model; 2, creating a monitoring project in a preset intelligent monitoring platform based on a model analysis result, and configuring monitoring project information; step 3, acquiring a monitoring demand matched with the monitoring project, determining the type of a monitoring point, analyzing the information of the monitoring project, and creating a plurality of monitoring points; 4, the monitoring equipment based on the monitoring points collects monitoring data in real time, uploads the monitoring data to a preset intelligent monitoring platform through a network, and automatically associates the uploaded real-time data with the corresponding monitoring points; and 5, analyzing the real-time data, and optimizing the deployment of the monitoring points and the data of the monitoring points. And an efficient and intelligent solution is provided for application scenes such as engineering monitoring and environment monitoring.

The invention belongs to the technical field of power system informatization, and provides a multi-modal power knowledge base construction method and retrieval method based on an AI large language model.The method comprises the following steps that S1, classification and named entity extraction are conducted on an original database, and a data set and an entity set are obtained; s2, aligning the data set and the entity set based on an AI large language model; s3, extracting and complementing entity relationships, and constructing an electric power diagram database; s4, performing joint embedding on the entities and the associated data, mapping the entities and the associated data to the same vector space, constructing a multi-modal power knowledge base, and generating a data index; and performing retrieval by using the constructed multi-modal power knowledge base, and performing source file backtracking through a data index. According to the method, multi-modal data are fused, the constructed multi-modal electric power knowledge base is rich and accurate, and source file backtracking can be carried out.

The invention belongs to the technical field of deep learning, and discloses a drug and protein interaction prediction model construction method based on multi-modal fusion. Due to the lack of protein three-dimensional data, the three-dimensional data of the protein is subjected to high-accuracy complementation by using a prediction model AlphaFold 2, and for the modal fusion problem, the drug molecules are fused at the atomic level with finer granularity, so that the multi-modal fusion effect is further enhanced, and the multi-modal fusion effect is improved. Protein 1D data and protein 3D data are processed by using Cross-Attention, the processed protein 1D data and protein 3D data are subsequently fused by using Cross-Attention, multi-modal fusion is performed by using Cross-Attention, so that the interaction relationship between modals can be carefully captured, and in a word, the DTI task effect is improved by designing a multi-modal fusion mode.

The invention provides a robotic arm control method based on a video diffusion model and related equipment, and the method comprises the steps: extracting a plurality of sample historical image track features and sample target image track features from a robotic arm motion video, and carrying out noise diffusion to obtain sample track noise diffusion features; based on the sample historical image track features and the sample track noise diffusion features, taking noise diffusion features associated with the sample target image as a training target, and training a preset model; inputting the current image track feature of the robotic arm into a trained target video diffusion model to obtain a plurality of predicted noise diffusion features obtained by diffusion, and performing feature fusion based on the plurality of predicted noise diffusion features through a multi-layer cascaded action adapter contained in the target video diffusion model to obtain a plurality of predicted action features, the robot arm is controlled on the basis of the action control signals corresponding to the multiple predicted action characteristics, and therefore the accuracy of mechanical arm action prediction and control is improved.

The invention discloses a digital integrated system of an LNG receiving station. Wherein the data fusion module comprises a multi-source heterogeneous data fusion engine and is used for collecting, storing and preprocessing data of an LNG receiving station; the model building module comprises a self-adaptive digital twinning modeling framework and is used for building digital twinning models of the LNG receiving station, and the models comprise a physical model, a machine learning model and a 3D visual model; the service module comprises an intelligent scene engine and a decision support subsystem driven by a knowledge graph; and the application module comprises an application development framework of a micro-service architecture and is used for developing an application oriented to a terminal user. Thus, efficient fusion and processing of multi-source heterogeneous data are realized through fusion of a multi-layer module architecture design and an advanced technology, and the data utilization rate and the real-time performance are greatly improved. And meanwhile, the intelligent decision-making capability of the system is remarkably enhanced, and various complex operation scenes and emergency situations can be effectively handled.

The invention provides a method for rapidly evaluating the residual life of a high-temperature material for active service equipment, and belongs to the field of life evaluation in the field of materials. The method comprises the following steps: firstly, obtaining high-temperature creep relaxation rates and high-temperature creep endurance life information of two or more samples at different temperatures, and determining main curve equations and corresponding parameters ZSRT of different time sequence tissue states; thirdly, main curve equations of different time sequence organization states and corresponding parameters Zc are obtained, and the incidence relation between the ZSRT and the Zc is established; 5, for the to-be-evaluated alloy, obtaining a high-temperature creep relaxation rate main curve equation and a corresponding parameter ZSRT of the to-be-evaluated alloy; and 6, calculating the parameter Zc of the to-be-evaluated alloy by using the incidence relation between the ZSRT and the Zc, and establishing a high-temperature creep endurance life main curve equation through the parameter Zc to realize life evaluation. The method has the advantages of short test time, less material loss, high data utilization rate and the like, and is particularly suitable for evaluating the endurance life caused by the change of the structure state.

The invention discloses a fire coal information data management method and system. The method comprises the following steps: S1, recording and storing fire coal management data for calling; s2, recording and storing fire coal transportation data for calling; s3, recording and storing coal sample data for calling; s4, selecting a coal source according to the blending combustion scheme; and calling fire coal management data, fire coal transportation data and coal sample data, automatically calling the fire coal plan, purchase plan data and as-fired coal source information data in the data analysis module according to the interval of the load value corresponding to each time period of the coal-fired unit in the actual fire coal machine account information, and automatically calling the fire coal plan, purchase plan data and as-fired coal source information data according to the blending combustion scheme matched with the power generation load of the unit. Forming a decision; s5, generating fire coal plan and purchase plan information data; and S6, confirming and issuing a fire coal plan and purchase information data. The system executes the method. The method has the advantages of being simple in principle, easy to implement, capable of improving working efficiency and operation management level and the like.

The invention belongs to the technical field of data analysis, and relates to a power grid data analysis and prediction method and system, and the method comprises the steps: obtaining original data, carrying out the analysis and processing of the original data, carrying out the data feature selection, constructing the data meeting the data features into a data set, and storing the data set; business scene building is carried out, and a data set is selected according to manual building or intelligent building; and constructing an interaction component, wherein the interaction component calls the selected data set to output interaction information. The method has the beneficial effects that a set of software with high flexibility and high operability is provided, the data utilization capability is improved, the operation efficiency and reliability of a power grid are optimized, and the response capability to emergencies is also improved, so that a power grid enterprise is supported to efficiently extract valuable information from mass data for scene planning, and the power grid enterprise is enabled to have a good development prospect. Strategic decision and daily operation management are assisted.

The invention discloses a drilling pump pressure prediction method based on an artificial neural network, and the method comprises the following steps: S1, data collection; s2, data preprocessing; s3, constructing a neural network model; s4, model training and optimization; and S5, model verification and deployment. According to the method, multi-channel data fusion and time synchronization optimization are innovatively adopted, so that the data quality and consistency are improved; in combination with a deep neural network and a self-adaptive optimization strategy, the precision and generalization ability of pump pressure prediction of the drilling pump are improved; and an online updating mechanism is introduced, so that the model can be dynamically optimized according to real-time data, the defects of low prediction precision, poor adaptability and difficulty in real-time updating of a traditional method are overcome, and an efficient and reliable prediction means is provided for intelligent drilling control.

The invention provides a wheel-rail contact load decoupling prediction method, electronic equipment and a storage medium, and the prediction method comprises the steps: building a wheel-rail contact three-dimensional simulation model, and determining a strain collection radius and a collection point on a wheel; setting boundary conditions, running speed and traction torque of the wheel-rail contact three-dimensional simulation model; solving and calculating the wheel-rail contact three-dimensional simulation model to obtain a strain value and a contact force of each strain acquisition point of the wheel; inputting the obtained strain value and contact force of each strain acquisition point of the wheel into a neural network model for training, and establishing a wheel-rail contact neural network model of a strain-contact force relationship; unknown strain data are input into the wheel-rail contact neural network model of the strain-contact force relation, and wheel-rail contact force is obtained; according to the method, the wheel-rail contact force can be accurately identified and predicted.

The invention discloses a wool fineness determination method and system based on deep learning, and belongs to the field of wool fineness determination, and the method comprises the following steps: S1, collecting a wool fiber image, and obtaining a data set; s3, obtaining fineness characteristics of wool; s4, constructing a convolutional neural network model, inputting the training set into the convolutional neural network model for training, inputting the verification set into the trained convolutional neural network model, and verifying and evaluating the convolutional neural network model by using cross validation; s5, inputting the test set into the verified convolutional neural network model, predicting the fineness of the wool fibers by using the convolutional neural network model, and outputting a fineness evaluation report; and S6, displaying and storing a result. By adopting the wool fineness determination method and system based on deep learning, wool fineness determination can be efficiently and accurately completed, the advantages of simple operation, non-contact measurement, reliable result and the like are achieved, and the automation level of wool detection is greatly improved.

The invention relates to an ETC (Electronic Toll Collection) system based on an Internet of Things mode, which comprises a distributed database, a processing center, a service registration center, a service governance center, a monitoring platform and an application center, the AI middle station comprises a data access unit, a data processing unit, a data storage unit, a label extraction unit, a model training unit and a model application unit. According to the system, multi-scene and full-process application of ETC data is realized, the ETC value can be deeply mined, the data utilization rate is high, the data analysis effect is ideal, the usability of ETC is improved, and the value increment of the ETC data is promoted.

The application relates to the field of fault detection, and discloses a device fault detection method based on small sample learning and acoustic feature migration, a device and a medium, the method comprising the following steps: acquiring an acoustic signal of a target device; extracting a fusion feature vector; generating device-independent features based on a DANN network; generating a synthesized fault acoustic signal by using a Mel-CGAN; mixing the synthesized fault acoustic signal with an actual acoustic signal, and then training the DANN; and obtaining a fault detection result by using the trained DANN. The application can significantly reduce the dependence of fault diagnosis on labeled data, and can reduce the accuracy in a cross-device scene. The application can be widely applied in the fields of wind power and intelligent manufacturing.

The present application discloses a sequencing method, sequencing reagent and application for Small RNA. The present application is a sequencing method for Small RNA, comprising adding a normal sequencing primer and a blocking sequencing primer to a primer solution when sequencing a circular library of a mixed sample, and using a mixed primer for sequencing; the blocking sequencing primer is formed by chemically modifying the 3' terminal hydroxyl group of the normal sequencing primer. The sequencing method of the present application, by adding a blocking sequencing primer, enables Small RNA sequencing of a circular library of a mixed sample, and can effectively ensure the sequencing of the label sequence, thereby improving the data utilization rate and obtaining a good resolution rate. The Small RNA sequencing method and sequencing reagent of the present application provide a new idea and solution for Small RNA mixed sample sequencing, solving the current problem of sequencing resource waste in Small RNA sequencing of circular libraries.

The invention provides a tunnel deformation monitoring method and system, a readable storage medium and a computer, and the method comprises the steps: randomly obtaining sample set data from a data collection result, and carrying out the deviation processing of the sample set data and the data collection result through a deviation calculation algorithm, and obtaining the current point cloud data of a tunnel; performing point cloud splicing and point cloud preprocessing on the current point cloud data of the tunnel to obtain a curved surface model of the tunnel, and splitting the curved surface model into multiple pieces of section data according to a preset standard; calculating the gradient according to the width of each monitored target object and the corresponding current settlement data; and calculating the deformation amount corresponding to each section data according to each section data, the corresponding gradient and the current settlement data, and judging the current deformation state of the tunnel based on the determined abnormal section data. According to the method, the current deformation state of the tunnel is judged according to the deformation of the section data, the data utilization rate is increased while the working efficiency is improved, and the engineering safety is guaranteed.

The application provides a ship-vehicle-person virtual image establishment method and system, which comprises the following steps: obtaining ship, personnel and vehicle data as source data for preprocessing, calculating and mining labels according to the preprocessed source data, and producing ship, personnel and vehicle labels; defining hierarchical labels; defining group labels; extracting basic attributes of the ship, personnel and vehicle; defining personalized labels, adding personalized labels to the ship, personnel and vehicle according to their basic attributes and respective source data; defining correlation relationship labels among the ship, personnel and vehicle; creating a label table in a database, adding the calculated labels into the label table, and then performing correlation analysis on the respective source data of the ship, personnel and vehicle and the label table according to business requirements to obtain index data; and providing the index data to a ship-vehicle-person virtual image system for drawing charts. The application can improve traffic operation efficiency, safety and risk prediction.

The application provides a traditional Chinese medicinal material hyperspectral anomaly detection classification method and system and a computer device, relates to the traditional Chinese medicinal material quality inspection field, and the method comprises the following steps: reading hyperspectral data, performing foreground detection on the hyperspectral data by adopting a multiscale spatial spectrum fusion method to obtain an abnormal area foreground mask; collecting N three-dimensional Patch arrays of the foreground mask area through three-dimensional Patch automatic sampling; setting a three-dimensional convolutional neural network of a wave band self-attention mechanism, inputting the foreground mask and the three-dimensional Patch arrays into the neural network to generate a high-level semantic feature vector; and finally outputting a classification result through a neighborhood spatial spectrum self-supervision aggregation attention module of the high-level semantic feature vector. The traditional Chinese medicinal material hyperspectral anomaly detection classification method, system and computer device are used, a new efficient, accurate and automatic traditional Chinese medicinal material mildew and insect damage detection method is realized, and the detection quality and the practical value of the system are significantly improved.

The invention relates to the field of video display, and provides a video display method and device, electronic equipment and a storage medium. The method comprises the steps that in response to the fact that a service request of an application is triggered, configuration information of the service request is obtained, and the configuration information indicates whether the service request relates to use of real-time features or not; when the service request relates to the use of the real-time features, service parameters in the service request are updated, the updated service parameters represent the features of the operation performed by the user on the application, and the features are related to the service request; the updated service request is sent to a server, a service response from the server is received, and the service response comprises a plurality of videos to be displayed related to the updated service parameters and a recommendation sequence of the videos to be displayed; and displaying the to-be-displayed videos in the recommendation sequence. In this case, the server can obtain real-time user data and give a recommendation result with better quality, thereby improving the user experience.

The present invention discloses a method, device, equipment and storage medium for predicting transaction technical indicators based on neural networks, which includes: obtaining historical indicator data, performing preprocessing including data division and data feature extraction on the historical indicator data, and obtaining a training data set, wherein the historical indicator data includes average response time, transaction success rate, and number of transactions; inputting the training data set into a model constructed based on an LSTM network for training, obtaining an indicator prediction model, and determining upper and lower baselines in the obtained indicator prediction model; inputting the indicator data to be predicted into the indicator prediction model for prediction, and obtaining the prediction result for the future time period. The method can more effectively identify periodic fluctuations, and can dynamically adjust the threshold, which is more in line with actual application scenarios and improves the accuracy of prediction.

The present application proposes a method for constructing a digital twin flight system of a drone cluster, which belongs to the field of digital twin technology. The method includes the following steps: constructing a system model of a drone cluster flight system, the system model includes multiple drone geometric models, a drone cluster geometric model, and a flight environment geometric model; constructing a digital twin system, loading the system model into the digital twin system, and completing the recording, simulation, retrieval and control of all real drones in the digital twin system to obtain a digital twin flight system; using the digital twin flight system, constructing a parameter optimization scheme for the system model, and dynamically adjusting the parameter optimization scheme based on real-time data and historical data. The present application can improve the flight efficiency of drone clusters and optimize the flight trajectory and maneuverability of drone clusters.

The invention relates to the technical field of gyroscope north seekers, in particular to a signal processing method, device and equipment, a storage medium and a computer product, the method comprises the following steps: acquiring N groups of orthogonal position gyroscope output values of a fiber-optic gyroscope north seeker, and determining N to-be-north-seeking resolving data according to the N groups of orthogonal position gyroscope output values, N being a natural number greater than 1; performing cross pairing recombination on the to-be-north-seeking resolving data according to the N to obtain N2 to-be-north-seeking resolving data; determining N2 north-seeking resolving values according to the N2 data to be subjected to north-seeking resolving, and performing consistency verification processing according to the N2 north-seeking resolving values to obtain M north-seeking resolving values; and carrying out mean value processing according to the M north-seeking solution values to obtain a final north-seeking calculation value of the fiber-optic gyroscope north seeker. The invention aims to improve the north-seeking precision of the fiber-optic gyroscope north seeker on the premise of not increasing the equipment cost and the operation time.

The present application relates to a magnetic resonance (MR) imaging method and system, the method comprising: acquiring imaging data of an object collected by an MR imaging device within a scanning time; determining multiple reference images corresponding to multiple times within the scanning time based on at least part of the imaging data; determining time information of a dynamic image corresponding to the object within the scanning time based on the multiple reference images; determining spatial information of the dynamic image based on the time information and the imaging data; and reconstructing the dynamic image based on the time information and the spatial information.

The invention provides a civil aircraft configuration management method based on a data statistics and analysis technology, and relates to the technical field of civil aircraft data management and application, and the method comprises the following steps: S1, data extraction: extracting metadata from structured data and unstructured data in different business systems through ETL to obtain meta-incremental data; and S2, data storage: processing the meta-incremental data, and synchronously storing the processed meta-incremental data to a source pasting layer to realize integration and backup storage of the meta-incremental data of different data sources. Through integration of metadata of different sources, the data utilization rate is improved, through a data source pasting layer, full-link tracing influenced by configuration change and configurable billboard design of a visualization layer can be achieved, a configuration management user can generate an analysis view according to needs by himself / herself, increment synchronization only updates change data, redundant storage is reduced by 60% compared with full-amount copying, and the system reliability is improved. And minute-level rapid generation of configuration management related lists and reports can be realized.

The present invention provides an adaptive load matching method and device for a radio frequency power supply, which relates to the field of power supply management technology, including: establishing a load data set of a load, establishing a load feature extraction result, and building a steady-state prediction network for the load; performing random frame sampling on the load, establishing a frame sampling result, performing steady-state prediction, and generating a steady-state prediction result; performing time interval clustering of the load, establishing a time interval clustering result, and re-collecting data within the time interval clustering result to generate a data collection result; configuring a comprehensive evaluation formula to generate an impedance feature comprehensive evaluation result; establishing an impedance matching model for the radio frequency power supply, and generating matching parameters for the radio frequency power supply; and controlling the radio frequency power supply to perform power supply control management for the load. The present invention solves the technical problem in the prior art that the radio frequency power supply has a slow response speed and cannot adjust the matching parameters in time when facing dynamic load changes, resulting in low energy transmission efficiency.

The invention provides a remote sensing image analysis method and device, and belongs to the technical field of remote sensing image processing. The method comprises the following steps: receiving multi-source multi-temporal remote sensing image data, and constructing a space-time spectrum multi-dimensional data cube based on a space grid engine; target grid data is dynamically scheduled according to a user request, real-time processing such as dynamic stretching, wave band combination and light and color uniformity is carried out, and the target grid data is published in a map service form. The device comprises a data receiving module, a scheduling module, a dynamic processing module and a service publishing module. Through gridding scheduling, dynamic processing and micro-service architecture, the slicing-free, multi-specification and high-concurrency online service of the remote sensing image is realized, the data processing efficiency, the storage utilization rate and the service stability are remarkably improved, and the method is suitable for real-time analysis and application of mass remote sensing data.

The invention relates to a WEEE recycling supply chain inventory optimization method based on evolutionary reinforcement learning, and belongs to the technical field of supply chain management and artificial intelligence. According to the method, a multi-level inventory cooperative control model is constructed, an inventory optimization problem is modeled as a Markov decision process, and an evolution reinforcement learning algorithm is utilized to train an intelligent agent to learn an optimal strategy. Core innovations include design state space capture inventory dynamics, action space processing continuation to discrete decision mapping, and cost-based minimization of reward functions. According to the algorithm, a population evolution mechanism and experience playback optimization are fused, and the learning efficiency and stability are improved through parallel exploration and sample screening. According to the method, the uncertainty of the recovery amount and demand can be effectively dealt with, the inventory cost is reduced, the supply chain robustness and sustainability are enhanced, and a self-adaptive intelligent solution is provided for WEEE management.

The invention is suitable for the technical field of model construction, and provides a construction system for a power grid health degree evaluation model, and the system comprises a data quality management module which carries out the cleaning, denoising, difference and anomaly detection of data, and processing of the data; the data integration and consistency management module is used for performing format consistency processing on the multi-source data and integrating the data; the self-adaptability and flexibility enhancing module is used for setting configurable algorithms and model parameters to automatically adjust and optimize the model; the security and privacy protection module is used for matching a power grid data processing standard through an encryption technology, data desensitization, access control and security audit, and ensuring the security of the data in the processes of acquisition, transmission, storage and processing; and the continuous maintenance and update management module is used for regularly updating the model and the algorithm by implementing an automatic monitoring and feedback mechanism so as to ensure that the model performance is continuously optimized and adapts to new technology and data optimization. Misjudgment caused by a data quality problem is avoided, and the accuracy of the evaluation model is improved.

The present invention discloses a method and system for detecting the quality of dynamic arrangement of industrial data, which relates to the technical field of data governance and quality detection, including formulating industrial data standards, performing metadata inspection of dropped tags, collecting data of various cities into a collection library according to different data types and integration methods; configuring data quality models and quality inspection rules, automatically generating a mapping relationship between data standards and data models, configuring and executing quality inspection plans, and performing data quality audits; diverting audit data, extracting correct data into a standard library, and marking data labels to form data assets, and returning data to the source of data to form a complete closed loop of data governance. The method described in the present invention improves the efficiency and accuracy of data quality inspections and reduces the workload and errors of manual inspections by performing data quality audits; forms closed-loop management through data reflux, establishes a continuously improved data governance mechanism, and ensures the long-term stability and optimization of data quality.

The invention discloses a method for automatically calculating the gear of an arc suppression coil in a power dispatching ticket, and the method comprises the steps: receiving a dispatching order, and storing the name of the arc suppression coil according to the dispatching order; according to the name of the arc suppression coil, searching the name of a corresponding transformer substation in a power grid operation mode database in a certain region, obtaining various data values in the circuit, calculating the capacitance current of the arc suppression coil by using the data values, obtaining the compensation degree according to the capacitance current of the arc suppression coil, and completing the optimal optimization of the circuit; the purposes of adjusting the gear of the arc suppression coil through the optimal target value, being safe and reliable, being high in data utilization rate and improving the working efficiency are achieved.