42results about How to "Improve analysis" patented technology

The invention relates to a power grid business intelligent auditing method based on AI enhancement, and the method comprises the following steps: S1, collecting data from a power grid SCADA system and a financial system, integrating historical and real-time data, and constructing an auditing knowledge graph; s2, constructing an instruction recognition model by using RoBERTa to analyze a natural language instruction of an auditor, and combining knowledge graph information with a language model to improve the understanding and reasoning ability of an instruction background; s3, constructing a multi-task deep learning framework to realize processing and fusion of multiple auditing tasks; s4, on the basis of a natural language instruction input by an auditor, extracting query targets, conditions and parameters involved in the instruction according to the instruction recognition model, obtaining corresponding auditing data in the database, and obtaining intention classification; and S5, calling a corresponding auditing model based on the intention classification result and the auditing data, and obtaining an analysis result. According to the invention, efficiency and precision in power grid business auditing work are effectively improved.

The invention provides a multi-platform collaborative data acquisition and analysis method, and relates to the technical field of data processing and analysis. The method comprises the following steps: receiving and converting data from different platforms into a unified intermediate format through a data adaptation intermediate layer, and processing a data structure and an interface of a specific platform by using a plurality of adapters; the data abstraction layer defines a standardized interface, so that multiple platforms request data through a unified interface, and a mapping rule converts the request into calling of the adaptation layer; the data integration engine preprocesses the data to ensure data consistency and accuracy; the data warehouse adopts a distributed architecture to ensure the service continuity through load balancing, data partitioning, a caching mechanism and a redundant storage and fault transfer mechanism; and the data processing and analysis module converts the data into decision suggestions by using a visual tool and a data mining algorithm. According to the method, manual intervention is reduced, the data processing speed and accuracy are improved, the data mode and trend are deeply mined, and service decision is promoted.

The invention discloses a glacier jump prediction and data sample enhancement management method, particularly relates to the field of glacier remote sensing monitoring, is used for solving the problem of accurate identification and processing of surface moraine covering type glacier local albedo abnormal effect, and aims at solving the problem of accurate identification and processing of the glacier local albedo abnormal effect by comprehensively analyzing the coupling relationship between spectrum and temperature and the spatial sensitivity of temperature gradient. And accurate identification and processing of the moraine covering glacier local albedo abnormal effect are realized. According to the method, main causes of albedo abnormity can be effectively distinguished, and an abnormal area is corrected in a targeted manner by scientifically selecting an optical and thermal infrared remote sensing fusion mode or an optical and radar remote sensing fusion mode, so that the glacier identification precision and the jump prediction accuracy are remarkably improved. Through multi-level remote sensing data correction and feature extraction, a high-precision data foundation is laid for subsequent analysis; in the process of generating an enhanced sample and optimizing a prediction model, the reliability and response speed of glacier jump prediction are further improved.

The invention relates to the field of image processing, and discloses a visual question-answering method, system and device based on a remote sensing tampered image, and a medium, and the method comprises the steps: carrying out the visual feature extraction of the tampered image through a main branch network, and carrying out the feature extraction of the input features of each layer of encoder through an edge prior guide block; performing edge detection on the image through a prior branch to obtain an edge prior feature, fusing the edge prior feature with an input feature in each edge prior guide block, and outputting the fused edge prior feature as an input feature of a next edge prior guide block to perform visual feature extraction; carrying out feature extraction on an input text through edge prior feature guidance, and carrying out cross-modal fusion on extracted visual features and text features; performing multi-modal reasoning according to the fused features, and outputting a question and answer result; according to the method, the prior branches are introduced to provide the edge prior features for the main branches, so that the edge artifacts of the duplicated-moved tampered image are remarkably recovered and enhanced, and the accuracy of analysis and understanding of the tampered image is improved.

The invention discloses a construction method, a recognition method and a system for simulating lung cancer metastasis cells in cerebrospinal fluid based on diffraction imaging and a deep learning technology. The construction method comprises the following steps: culturing a plurality of lung cancer cell lines which are easy to cause brain metastasis, collecting whole blood of a healthy person and splitting red blood cells; the method comprises the following steps: respectively acquiring diffraction images of cultured cells through a polarization diffraction imaging flow cytometry with a TDI camera to obtain a pair of diffraction image pairs with S polarization and P polarization; performing image processing on the obtained diffraction cell image pair, and establishing a training image data set and a test image data set; and establishing a convolutional neural network, and training the convolutional neural network according to the training image data set to obtain a model for identifying the lung cancer metastatic cells in the cerebrospinal fluid. When in use, a cerebrospinal fluid sample of a patient is collected, S polarization and P polarization image pairs are obtained, and the trained model is input to respectively extract S polarization and P polarization image features of cells for feature recognition, so that whether the current sample contains lung cancer cells or not is predicted.

The invention discloses a delay optimization method and system for cooperative video analysis of an unmanned aerial vehicle group, and the method comprises the following steps: constructing an unmanned aerial vehicle cooperative video analysis system comprising a plurality of unmanned aerial vehicles, collecting real-time videos from the ground by each unmanned aerial vehicle, and generating a reasoning task; generating a directed acyclic graph (DAG) for each unmanned aerial vehicle according to the reasoning task, initializing a segmentation point of the DAG of each unmanned aerial vehicle, and performing task unloading at the segmentation point by each unmanned aerial vehicle; and establishing an optimization objective function by taking minimization of task processing delay as an objective, solving the optimization objective function, and updating an unloading decision, communication resource allocation and a DAG segmentation point of each unmanned aerial vehicle according to a solving result. According to the method, the problem of delay minimization of video analysis in the centerless unmanned aerial vehicle group is solved, and task scheduling, communication resource allocation and DAG segmentation point selection can be optimized at the same time.

The present invention relates to the field of automatic design optimization technology, and in particular to an automatic arts and crafts design optimization method based on intelligent data. The method comprises the following steps: obtaining user demand input data; performing part-of-speech analysis on the user demand input data to generate user demand input part-of-speech data; performing sentence segmentation on the user demand input data using the user demand input part-of-speech data to generate user demand input sentences; performing design creative description analysis on the user demand input sentences to obtain a creative description keyword cloud map; obtaining an arts and crafts database; performing an initial arts and crafts map design on the user demand input sentences based on the arts and crafts database to obtain an initial arts and crafts demand map; performing simulated iteration replacement on the initial arts and crafts demand map according to the creative description keyword cloud map to obtain a first arts and crafts demand update map. The present invention improves the intelligence, accuracy and efficiency of arts and crafts design optimization through intelligent data processing and analysis.

The invention discloses a construction method of a dam multi-measuring-point deformation deep learning monitoring model, and the method comprises the steps: carrying out the clustering analysis of dam deformation data through employing a K-means algorithm, so as to recognize a spatial mode of a monitoring region; in each clustering region, based on the interaction of dependent variables between the monitoring points, a long-short-term memory network of a coupling attention mechanism is adopted to construct a deformation prediction model; sequencing and evaluating the measuring points and the influence factors of the measuring points in different areas by applying an interpretability analysis technology; according to the method, clustering analysis, an attention mechanism, deep learning and other algorithms are effectively integrated, and a dam multi-measuring-point deformation monitoring model which is high in prediction precision and high in explanation capacity is successfully constructed. The model provides a new thought and method for reasonably analyzing and predicting the overall deformation characteristics of the dam. The application of the method not only improves the prediction accuracy, but also enhances the transparency and credibility of the model.

The invention relates to a problem feedback information processing method and device, equipment, a storage medium and a product. The method comprises the following steps: determining at least one problem to be processed at the current moment; wherein the to-be-processed problem is used for describing a problem in a video playing process; determining a target analysis mode corresponding to each to-be-processed problem according to the problem number of the at least one to-be-processed problem and the problem details of each to-be-processed problem; and for any to-be-processed problem, analyzing the to-be-processed problem based on the target analysis mode corresponding to the to-be-processed problem to obtain a problem analysis result of the to-be-processed problem. By adopting the method, the problem feedback information processing timeliness can be improved.

The application provides a kind of laser physical experiment ultrafast image measurement system and method based on end side coordination architecture, it is related to laser physical experiment image measurement technical field, comprising the following steps: laser target shooting generates plasma, captures the X-ray signal image of plasma, generates original data stream;Through lightweight convolutional neural network model, the original data stream is handled, and the feedback control signal for adjusting the laser parameter of the next laser is generated;Obtain the laser parameter related to each physical parameter characteristic value, adjust the laser parameter related to the physical parameter characteristic value;Store the adjustment data each time, periodically train lightweight convolutional neural network model based on adjustment data.The advantage of the application is to realize the instant, reliable experimental parameter control in the experiment process.

The invention relates to the field of access control, in particular to an access control method and system based on the ultrahigh frequency RFID technology, and the method comprises the following steps: S1, setting a multi-antenna array through distributed layout according to the size, shape and personnel flow condition of an access control area; s2, according to a Hyperledger Fabric block chain platform, nodes are deployed at different positions through a distributed node architecture; s3, the artificial intelligence model is trained by collecting data in the access control system, the trained artificial intelligence model analyzes and detects the data in the access control system in real time, and abnormal behaviors and events are recognized; and S4, displaying each data in the access control system on a management platform in a visual mode, and providing data analysis and optimization management according to an authority management mechanism. The safety of the access control system can be improved, the reliability of the access control system is improved, the intelligent level of the access control system is enhanced, and the management efficiency of the access control system is improved.

The present invention belongs to the technical field of magnetic resonance imaging, and specifically discloses a companion field measurement method, system and program product for eliminating the influence of residual magnetism. By setting multiple groups of bipolar gradients for testing, and performing corresponding combined calculations on the test results to distinguish the second-order companion field from the first-order residual magnetism, the residual magnetism accumulated phase can be effectively removed, and the influence of residual magnetism during the measurement process can be eliminated, so as to more accurately measure and evaluate the companion field situation, such as determining the phase distribution and corresponding symmetry coefficient of the companion field, which is helpful to further compensate the companion field. At the same time, the present invention can also expand the analysis of the residual magnetism situation under a certain gradient.

The invention relates to a method for carrying out industrial internet time sequence anomaly detection based on multiple scales and multiple features, and belongs to the technical field of time sequence anomaly in the industrial internet field. The method comprises the following steps: establishing an industrial internet time sequence anomaly detection model based on a time sequence period conversion network, a multi-feature extraction network, a data feature aggregation network and a frequency feature aggregation network; the method comprises the following steps: firstly, capturing various kinds of periodic information in a time sequence through a periodic extraction module, secondly, respectively extracting features in a time sequence period, features between channels of different dimensions and features of a time sequence in a period through a multi-feature extraction network, thirdly, aggregating the various features based on a data feature aggregation network, and finally, extracting the time sequence from the aggregated features. And performing aggregation based on a frequency feature aggregation network, outputting frequency aggregation features of the time sequence data sequence in a time sequence period, and detecting abnormality of the time sequence data by using the frequency aggregation features.

The application discloses a kind of assembly simulation analysis methods of hole shaft group, which comprises the following steps: S1: using Monte Carlo method to convert the design parameters of hole shaft group into hole shaft simulation data;S2: based on hole shaft simulation data, the mathematical model of hole shaft movable circle intersection angle is established, the assembly possibility of hole shaft group is predicted, when hole shaft movable circle contains actual intersection, it can be determined that the simulation data can be assembled, enter S3 step;Otherwise, there is no possibility of assembly, after adjusting hole shaft group design parameters, return S1 step;S3: using GRO gold panning optimization algorithm, obtain the assembly simulation position point of suitable assembly hole shaft group.The method provides a simulation result of hole shaft group assembly by evolutionary algorithm, to simulate the actual assembly result;Further, by calculating simulation data, the distribution of assembly error and the limit assembly condition are provided, the tolerance analysis process is accelerated, the design cycle is reduced and the yield is improved.

The present invention relates to a query system based on data analysis, and in particular to the technical field of database management. The query system comprises a user information management module for implementing dynamic authority management for users, a data source management module for managing user requests, caches, data sources and data security, and a database management module for managing a number of databases. The present invention analyzes the query request of the user to determine whether the user request exists in the cache, so as to determine the selection of the query mode for the query request, thereby making flexible selections in the query process, and setting different supervision strengths in the corresponding query mode to supervise the query process, thereby improving the accuracy of control over the data query process.

The invention discloses an information interaction system and method and electronic equipment, and relates to the field of large model technology and application development. The system comprises a client used for detecting a query request in an interactive interface, and the query request at least comprises query content to be replied; the text processing end is used for retrieving a text associated with the query content based on the query content in the query request; the guide information processing end is used for generating guide information based on the retrieved text and query content, and the guide information is used for generating reply information corresponding to the query content; and the information reply end is used for calling a machine learning model to analyze the guide information and outputting reply information. The technical problem of low accuracy of information interaction is solved.

This invention provides a method and system for compressing power messages, relating to the field of power system control technology. The method includes: acquiring power messages via a first FPGA; timestamping the acquired power messages; caching the timestamped messages in a first shared memory; extracting messages from the first shared memory; parsing the messages according to their type and classifying and storing them in a second shared memory; extracting messages from the second shared memory by control block using a second FPGA; compressing the compressed packages corresponding to the control blocks; storing the compressed packages in a third shared memory; packaging and secondary compression according to a set time period; generating a total compressed package for that time period; and storing it on an SSD for dual-disk storage. This invention effectively reduces the storage space requirements for historical messages, improves the storage time of historical messages, and solves the problem of frequent failures in storing historical messages for post-event analysis of power systems.

The invention provides a hot water force multi-field monitoring analysis method and system suitable for a TEHB test system, and the method comprises a model framework building step, a steady-state initial field solving step, a hot water force coupling solving step, and a multi-field response data collection and recording step. The invention further provides a hot water force multi-field monitoring and analyzing system suitable for the TEHB test system, a three-dimensional model containing the six-direction waveguide rod and the rock sample is established, a mechanical, seepage and temperature steady-state initial field before impact loading is obtained, hot water force coupling dynamic calculation is carried out under the action of triaxial six-direction synchronous impact, and the test result is obtained. And curves and statistical indexes of key results such as stress, fracture, pore pressure, temperature and the like are automatically output. According to the invention, the impact process simulation can be realized under the hot water-force coupling loading condition, and multi-field response data can be monitored and analyzed, so that the dynamic mechanical behavior of the rock under the hot water-force coupling effect can be revealed, and a basis is provided for the scientific design of deep engineering.

The invention belongs to the technical field of power transmission equipment maintenance, and particularly discloses a bird self-adaptive expelling method and a bird expelling rod mechanism for an electric power tower area, and the method comprises the steps: arranging terminal devices with image collection and expelling execution functions at a plurality of electric power towers, and recognizing the types and body types of birds through the image data collected by the terminals; the identification result is sent to the upper computer; the upper computer calls an expelling parameter combination according to the identification information, generates a control instruction and issues the control instruction to a corresponding terminal; the terminal adopts at least one preset expelling mode to execute expelling operation; after expelling is completed, recording parameters and expelling data and returning the parameters and the expelling data to the upper computer; and the upper computer disturbs at least one feature in the expelling parameters based on the recorded data to generate a new parameter combination, and the new parameter combination is applied when birds of the same type and body form appear again. According to the scheme, dynamic updating and continuous optimization of the repelling strategy can be realized, and the method is suitable for bird repelling management in a multi-tower scene.

The invention relates to the technical field of power fault diagnosis, and discloses a power fault diagnosis method and system based on cross-modal semantic alignment and agent coordination decision, and the method comprises the steps: obtaining multi-modal data needed in the operation and maintenance process of power equipment, and constructing a multi-modal power professional knowledge base; extracting multi-modal features contained in the multi-modal electric power professional knowledge base based on a feature representation technology, calculating dynamic weights of the multi-modal features, and generating an intelligent agent in combination with the electric power fault diagnosis instruction data set; an electric power fault diagnosis task is input, the modal weight of the electric power fault diagnosis task is calculated, and the intelligent agent outputs a fault diagnosis result of the electric power fault diagnosis task and a corresponding processing flow according to the modal weight; and generating a fault diagnosis report based on the fault diagnosis result of the power fault diagnosis task and the corresponding processing flow. According to the invention, intelligence and real-time performance of decision making are greatly improved, and dependence on artificial experience is reduced.

Techniques disclosed herein implement an improved format for sending a call stack, referred to herein as a stack, from a machine to a stack analysis service. In addition, the techniques implement an improved way for stack analysis services to represent a large number of stacks. The improved format enables collection of a large number of stacks (e.g., millions or even trillions of stacks) to be more efficient. The improved representation of a large number of stacks enables analysis to be more efficient. Thus, the techniques described herein reduce the carbon footprint of an organization for operating a set of machines, since fewer networks, storage, and / or processing resources are required to collect and analyze a large number of stacks.

The present invention discloses a fault detection device and method for electric power equipment, including a tripod, a screw diameter adjustment assembly is installed at the end angle position of the outer wall of one side of the tripod, and a straight-mouth pressure arm is hinged on the outer wall of the tripod on one side of the screw diameter adjustment assembly, and a double-wheeled electric walking module for moving the tripod forward along the extension direction of the cable is installed on the outer wall of one side of the straight-mouth pressure arm, and the screw diameter adjustment assembly allows the straight-mouth pressure arm and the double-wheeled electric walking module to approach or move away from the cable. The present invention enables the double-wheeled electric walking module to be effectively pressed on the cable to be detected, and introduces a bevel gear pushing mechanism that works together with the screw diameter adjustment assembly, so that the device can effectively prevent foreign matter from entering the current transformer during the cable detection process, ensure the cleanliness of the cable detection area, and enable the current transformer and the partial discharge detector to accurately capture the real state of the cable, thereby improving the detection accuracy.

The invention provides a 3D scene arbitrary angle model segmentation method based on 3D Gaussian Splatting, which belongs to the field of machine perception technology and includes point cloud data extraction, 3D model training, model category feature extraction, model category feature training, model segmentation, and noise elimination; the system uses Colmap to reconstruct dense 3D point cloud information and camera trajectory from an image sequence; uses a 3DGS method to perform 3D reconstruction on the point cloud information; uses a DEVA model to perform image segmentation and image category preprocessing; trains a multilayer perceptron composed of multiple fully connected layers as a discriminator for segmenting the 3D model, which greatly reduces the discriminator training overhead; uses a KNN algorithm to calculate the average distance between each Gaussian kernel and several adjacent Gaussian kernels to judge the noise, so as to solve the detail problem that occurs during model segmentation, and can also solve the object boundary roughness problem; finally, the 3D model arbitrary angle segmentation method based on 3DGS is realized.

The application discloses a sea evaporation duct path loss adaptive prediction method based on small sample transfer learning, and belongs to the technical field of sea wireless communication. In view of the problem that the prediction accuracy of a pure simulation data driven model is reduced due to the difference between a simulation scene and a measured scene in actual sea area application, a transfer learning framework of'source domain pre-training-target domain fine-tuning' is constructed. First, a Transformer model optimized by a tree structure Parzen estimator is pre-trained by using large-scale simulation data, so as to extract general physical features of evaporation duct propagation; second, for sparse single-point measured data, the encoder parameters are frozen and the decoder is fine-tuned, and a point constraint loss function is introduced, so as to calibrate the full path prediction curve by using the measured path loss value of a link specific distance point. The application can reduce the demand for measured data, improve the path loss prediction accuracy and time sequence stability, and is suitable for sea over-the-horizon communication link modeling and performance evaluation.

The invention relates to the technical field of network security, in particular to a network security monitoring system based on artificial intelligence, which comprises a data preprocessing module, an anomaly detection module, a fault mode learning module, a threshold dynamic adjustment module, an attack source positioning module, a behavior analysis and identification module, a decision support module and a recovery strategy optimization module. According to the method, the abnormal state can be efficiently identified through the isolation forest algorithm and the self-encoder model, the learning of the fault mode is deeper through the application of the long and short-term memory network, and the precise attack source positioning is realized through the combination of the self-regression differential moving average model and the graph database query technology; the accuracy of attack pattern recognition is improved by introducing the convolutional neural network and the graph neural network, the security decision is more scientific and reasonable by applying the analytic hierarchy process and the support vector machine algorithm, and the resource allocation and recovery strategy are optimized by using the genetic algorithm and the particle swarm optimization algorithm.

The invention relates to the technical field of network security, in particular to a malicious software evidence obtaining system and method and electronic equipment. The system comprises a data acquisition module, a data processing module, a result generation module and an automatic response module. The data acquisition module is used for acquiring software samples, flow data and log data; the data processing module is used for performing analysis based on the software sample, the flow data and the log data to obtain a feature analysis result, and determining a static scoring result, a dynamic scoring result and a model scoring result based on the feature analysis result; the result generation module is used for determining a software comprehensive score based on the static scoring result, the dynamic scoring result, the model scoring result and a preset scoring standard, and integrating and displaying the feature analysis result and the software comprehensive score; and the automatic response module is used for triggering safety response measures according to the feature analysis result and the software comprehensive score. According to the method, the malicious software identification efficiency can be improved.

The invention discloses a hospital information equipment closed-loop management system, which relates to the field of equipment closed-loop management. The technical scheme includes: collecting patient medical treatment data and equipment status data, extracting high-level value data categories related to detection time prediction through an ElasticNet regression model based on a lagged data set, and forming a correlation feature set; based on information data and the correlation feature set in a current time period, predicting the time conversion coefficient of a detection item in each detection equipment in the next time period; constructing a high-dimensional data set and a closed-loop fitting model, and training the closed-loop fitting model to obtain a trained closed-loop fitting model; importing the high-dimensional data set in the current time period into the trained closed-loop fitting model, predicting an adjustment plan for each patient in the next time period, and dynamically adjusting the patient's examination items and equipment according to the predicted adjustment plan, so as to realize efficient closed-loop management of hospital information equipment.

The invention relates to the technical field of ovality analysis and processing decision systems, in particular to a BIM-based tunnel segment ovality repair effect evaluation system and method, and the system comprises a data collection module which is used for collecting node data in the whole life cycle of a tunnel, and constructing a tunnel multi-source heterogeneous data set; the intelligent analysis module is in communication connection with the data acquisition module and is used for receiving the multi-source heterogeneous data sent by the data acquisition module; analyzing segment ovality based on the three-dimensional point cloud data; constructing an ovality change prediction model; the processing scheme recommendation module is in communication connection with the intelligent analysis module and is used for recommending an ovality processing scheme based on the expert knowledge graph; the man-machine interaction module is in communication connection with the intelligent analysis module and the processing scheme recommendation module and is used for interactively displaying the intelligent analysis result; the method supports the input and optimization of an analysis model, can improve the processing efficiency and quality of the tunnel segment ovality problem, and can provide a new idea and tool for the overall management of tunnel engineering.

The invention relates to the technical field of water pollutant collection, and discloses a water pollutant collection device for environmental monitoring, which comprises a mounting plate, a collection box is fixedly mounted on the lower part of one side of the mounting plate, a fixed plate A is fixedly mounted on the top of the mounting plate, and a fixed plate B is fixedly mounted on one side of the top of the collection box. A main power lifting adjusting part is installed between the fixing plate A and the fixing plate B. Rotary connectors are installed at the two ends of the main power lifting adjusting part correspondingly, multi-stage telescopic intercepting and gathering rods are installed on the upper portions of the two rotary connectors correspondingly, and a notch is formed in the upper portion of one side of the collecting box. A U-shaped baffle located outside the notch is fixedly installed on the upper portion of one side of the collecting box, and a collecting and conveying end used for conveying pollutants is installed in the U-shaped baffle. The water body pollutant collecting device is matched with a power boat for use, and can comprehensively collect and salvage pollutants in a water body in a large range, so that the water body pollutant collecting efficiency is effectively improved, and the water body pollutant collecting speed is effectively increased.

The present invention discloses a method for determining the abundance of metagenomic species based on GPU acceleration, first analyzing the needs of user input, and determining the mode of calculation according to the input parameters of the user; then initializing, checking the integrity and version of the file, to ensure the stable operation of the present invention; after ensuring the stability, data preparation is performed, and preparation is made for subsequent GPU calculations, and all data are stored in an array of numpy format; after the current data preparation is completed, the GPU will be called by the numba package of python, and the data will be first transferred to the GPU, and then the GPU call calculation will be performed twice, the former as an intermediate result, and the latter outputs the relative abundance of each final clade; finally, the present invention will select the file format of the output according to the parameters output by the user, and save it in the saved file input by the user. The present invention greatly improves the overall efficiency of metagenomic analysis, and the accuracy is also well performed in the same type of metagenomic analysis software, with strong practical significance.