38results about How to "Improve analytical performance" patented technology

The invention discloses a multi-dimensional training method and device for a support vector machine, electronic equipment and a computer readable storage medium, and the method comprises the steps: carrying out the discretization of a training sample data set, and obtaining a discretized data set, the discretized data set comprises a plurality of different attributes, and each attribute corresponds to a plurality of feature vectors; calculating a classification contribution parameter of each attribute feature vector to obtain a plurality of classification contribution parameters; performing data mapping on the plurality of classification contribution degrees by using a kernel function to obtain a target function; and optimizing and training the objective function by using a gradient descent algorithm to obtain a support vector machine model. According to the method, different dimension data are mapped through the kernel function, the data gain weight can be determined, the linear separable effect of the mapped dimension data can be improved, and then the classification and analysis capability of the SVM can be improved.

This invention discloses a method and system for predicting RNA binding protein binding sites on RNA without targeting, relating to the field of bioinformatics. The method includes the following steps: acquiring raw RNA structure data; expanding and filtering the preprocessed data; training an RNA analysis model using the filtered data; processing the RNA to be analyzed using the trained RNA analysis model, calculating the probability that each nucleotide is single-stranded, and obtaining a structure score; identifying low-accessibility regions based on the difference between the structure score output by the RNA analysis model and the structure score calculated by the original method; and identifying potential RNA binding protein binding sites by comparing the signal difference with that of the original method in a single RNA structure detection experiment. This invention utilizes the STONE method to improve the accuracy of RNA structure analysis in low-accessibility regions, and uses the improved difference to infer RBP binding sites, thereby improving the accuracy of RBP binding site identification.

The invention discloses a channel time-frequency joint extrapolation method based on a multi-layer perceptron hybrid architecture. Aiming at the problems of high acquisition cost of actual communication scene channel data, high time consumption of model online training and difficulty in covering the actual communication scene by simulation data, the method comprises the following steps of: making a pre-training model by using a large-scale simulation channel data set to extract channel change characteristics; and carrying out online rapid adaptation according to the trace data of the actual measurement scene. Aiming at the characteristics that the time delay spread of an indoor wireless channel is relatively small and the number of antennas is relatively small, a multi-layer perceptron hybrid architecture is applied to the time-frequency domain extrapolation of a single-carrier broadband channel. The model can quickly adapt to an actual communication scene through fine tuning of a small amount of data. The method has the advantages of being high in adaptation speed, small in data dependence, high in scene migration capacity, high in calculation parallelism degree and the like, and is suitable for edge deployment and rapid adaptation to actual scenes.

The invention discloses an AI visual identification-based front windshield support automatic detection system, and relates to the technical field of visual detection, and the system comprises a conflict rechecking module which locates a structure conflict region in a structure semantic conflict set through conflict judgment, carries out the targeted visual analysis processing of the structure conflict region in a structure alignment image, and carries out the automatic detection of a front windshield support. Generating a rechecking evidence item; and the causal judgment module is used for reversely deducing and rechecking the evidence item through causal, obtaining a minimum failure structure set, automatically detecting and judging the front windshield bracket image according to the minimum failure structure set, and outputting a grading detection conclusion and a structure tracing identifier. According to the method, by constructing a contradiction contrast rule and synchronously analyzing the support or negative relationship between the evidence and the initial assertion, high-sensitivity and high-reliability detection of correlation defects and hidden conflicts is realized, and the analysis capability and decision interpretability of visual detection for coping with a complex failure mode are enhanced.

The application provides a self-evolving intelligent fund analysis method, relates to the field of data processing, and comprises the following steps: receiving a natural language analysis instruction, combining a large language model decomposition task, extracting account fund flow characteristics and matching with historical cases, and generating a weighted execution instruction; identifying a transfer path based on a fund correlation graph, extracting a priority path subgraph structure according to a user identifier, and forming a directional analysis model; performing node risk scoring on the fund correlation graph, outputting a risk account identifier and an analysis report, and realizing intelligent and accurate fund risk identification.

A lubricating oil LIBS analysis and detection method based on an oil liquid film comprises the following steps: spraying micro oil drops on a metal substrate in a single drop manner, exciting the oil liquid film through an LIBS system to generate plasma, and collecting a plasma spectrum and analyzing spectrum data to realize analysis and detection. According to the method, the supply and movement of the aluminum foil are controlled, the operation time sequence of the aluminum foil, the oil drops and the LIBS system is regulated and controlled, the laser-induced plasma emission spectrum of the oil drops on the surface of the aluminum foil is collected, and then the element composition of the oil liquid to be detected is analyzed. According to the method, the oil pretreatment requirement of oil detection is reduced, the number of parameters of oil online detection is increased, and the oil analysis speed is increased.

The application discloses a portable chromatographic detection device and application thereof, and belongs to the technical field of detection devices. The device comprises a top cover, a base, a test strip and a flexible PCB circuit board. The test strip comprises a sample pad, a binding pad, a nitrocellulose membrane, a water absorption pad and a supporting back plate. The nitrocellulose membrane has a patterned electrode as an electric channel, and is divided into a clamping area and a detection area for solution flow. The clamping area is connected with the flexible PCB circuit board through a magnetic attraction connector. One end of the detection area is connected with the water absorption pad in a top-overlapping manner, the other end of the detection area is connected with the binding pad in a top-overlapping manner, and the other end of the binding pad is connected with the sample pad in a top-overlapping manner. Hollow windows are formed in the top cover at the sample pad and the nitrocellulose membrane. The detection device is convenient to carry, has various functions, is high in sensitivity and integration, can be used in combination with computers, mobile phones and other equipment, and is particularly suitable for use in families, fields and other places.

The present application relates to the technical field of power market data analysis, solves the problem that the prior art lacks a verification mechanism for the credibility of generated reports and the traceability of evidence, and is also prone to generating false content that contradicts the actual physical operating state, thereby affecting the credibility of the report, and particularly relates to a power regulation report credibility verification method based on data traceability, which collects and preprocesses multi-modal operating data of the power market to obtain baseline operating data, then performs multi-dimensional feature extraction thereon to generate a multi-modal basic feature vector, assigns a traceability identifier to each valid data entity in the multi-modal basic feature vector, and synchronizes the data entity and its corresponding data feature abstract to a distributed tamper-proof network to construct a traceability credential library. The present application improves the evidence traceability of the analyzed text, reduces the risk of tampering with the operating data during circulation, and improves the reliability of the analysis and periodic regulatory evaluation results of the market operation.

The embodiment of the application discloses a kind of sentence translation method, device, computer equipment and storage medium, belong to computer technical field.The method comprises: based on translation model, first sample sentence and second sample sentence, obtain first prediction result, obtain third sample sentence, fourth sample sentence and first sample relationship label, based on translation model, third sample sentence and fourth sample sentence, obtain first prediction relationship label, based on first prediction result, first prediction relationship label and first sample relationship label, translation model is adjusted.The method provided in the embodiment of the application, when training translation model, in the case where guaranteeing that translation model has translation capacity, the analysis capability of sentence with correlation of translation model is promoted, so that subsequent based on translation model, when sentence is translated, it can be combined with the analysis capability of sentence with correlation of translation model, to improve the accuracy of translation model.

This application relates to a method, system, and storage medium for extracting structured data from literature. The method includes: acquiring information about the literature to be processed and information about the domain of interest; generating intent recognition routing results by calling a corresponding domain intent recognition model based on the information about the domain of interest; constructing prompt words based on the information about the domain of interest and the intent recognition routing results, and extracting first target literature information from the information about the literature to be processed; constructing a corresponding domain similarity function based on the intent recognition routing results, and generating second target literature information by removing similar literature from the first target literature information; and outputting experimental information, experimental knowledge graphs, and source relationships in a structured form based on the second target literature information. This application determines the intent recognition routing results through domain intent recognition, adaptively selects the extraction strategy and domain similarity function, and combines multimodal parsing and block processing to achieve high-completeness extraction, cross-domain adaptive fusion, and traceable structured output of experimental information from cross-domain literature.

The application provides a database upper-layer application analysis optimization method and relates to the technical field of data analysis. The database upper-layer application analysis optimization method specifically comprises the following steps: S1. forming a topological sequence according to a workflow; S2. marking nodes; and S3. performing materialized calculation on a node to be optimized. Through the database upper-layer application analysis optimization method, the problem that Pandas lacks analysis classification for functions when driving a database to execute an SQL statement operation according to an application function, and the problem that when the number of application functions is large, the interaction between Pandas and the database is frequent and the communication resources are excessively occupied are solved. Meanwhile, the occupation of communication resources is reduced, and the use of computing, storage and other resources is also reduced based on the replacement optimization processing of multiple nodes, so that the database upper-layer application analysis efficiency and performance are improved.

The application relates to a public opinion intelligent monitoring and analyzing method, device and computer equipment for multi-platform interaction. The method comprises the following steps: obtaining current public opinion monitoring data of each platform and platform user distribution information of each platform, and extracting public opinion feature information of each platform through a public opinion feature extraction strategy based on the current public opinion monitoring data of each platform; identifying user population proportion information of each platform based on the platform user distribution information of each platform, and identifying public opinion change trend information of each platform through a public opinion change analysis strategy based on the public opinion feature information of each platform and the user population proportion information of each platform; constructing a comprehensive public opinion change correlation graph through a multi-platform public opinion interaction correlation strategy based on the public opinion change trend information of each platform, and predicting public opinion risk information through a public opinion risk analysis strategy based on the public opinion change correlation graph. The method can improve the accuracy of public opinion monitoring and analysis of multiple platforms.

The application discloses a multi-modal fusion spatial intelligent scene instance segmentation method and system, relates to the field of spatial intelligence, solves the problem of poor segmentation accuracy of scene instance segmentation technology, and comprises the following steps: S1, key word interception audio collection is carried out on a target space scene, effective interception audio in the key word interception audio is extracted through audio index analysis, and the effective interception audio is collected for a to-be-segmented instance to obtain segmented instance collection data; S2, spatial feature extraction is carried out on the to-be-segmented instance, consistency analysis is carried out on the extraction result and scene objects in the target space scene, an instance segmentation model is obtained according to an analysis result, and scene instance segmentation data is obtained; and S3, position rationality verification is carried out on a position of the instance segmentation model in the target space scene, and instance position early warning is carried out according to a verification result, so that the scene instance segmentation accuracy and segmentation efficiency can be improved.

This invention discloses a method for measuring Rydberg atoms using micro-Doppler agility under a polarization-multiplexed microwave field, belonging to the field of quantum precision measurement technology. The method includes generating a dynamic coded sequence and an interleaved coded microwave field by aperiodically pairing and jumping the microwave polarization state with the carrier frequency; acquiring the target echo signal and applying it to the Rydberg atom ensemble with a reference microwave field to generate a quantum coherent state; applying a synchronous laser pulse to acquire a time-series optical response signal; obtaining the signal's fundamental frequency, envelope spectrum, and initial phase component through time-frequency transformation and feature separation; and performing parameter mapping and synthesis to generate three-dimensional micro-motion parameters. This invention uses an aperiodically agile polarization-frequency-multiplexed microwave field for active target detection and utilizes the Rydberg atom ensemble for quantum coherent detection and optical readout of the echo signal, enabling high-sensitivity, high-precision, and high-anti-interference measurement of the target's three-dimensional micro-motion parameters.

The invention discloses a mountain water system distribution visualization analysis method, and relates to the technical field of geographic information systems.The mountain water system distribution visualization analysis method comprises the steps that multi-source geological and hydrological data of a target area is obtained; based on the multi-source geological and hydrological data, generating at least one thematic visualization layer, the thematic visualization layer being used for expressing spatial distribution characteristics of geological or hydrological attributes of the target area; and fusing the at least one thematic visualization layer to generate a mountain water system comprehensive visualization view. According to the method, multi-source geological hydrological data of a target area is acquired, a plurality of thematic visual layers including a water system distribution visual layer, a geological risk marking layer, a climate distribution layer and the like are generated based on the data, and then the layers are superposed and fused with a three-dimensional terrain base model; the mountain water system comprehensive visual view is formed, and the problem that in a traditional method, all thematic layers are independently displayed, and effective fusion is lacked is solved.

The utility model belongs to the technical field of gas detection, and particularly discloses a cloud embedded gas detection system based on a sensor array, which comprises a plurality of sensor node systems, a cloud server and a terminal system, the sensor node system comprises a gas sensor array, a signal acquisition module, a microcontroller unit MCU and a wireless communication module; the output end of the gas sensor array is electrically connected with the input end of the signal acquisition module, and the output end of the signal acquisition module is electrically connected with the input end of the MCU; the MCU is in communication connection with the cloud server through the wireless communication module; the terminal system is connected with the cloud server through a network. Through multi-node wireless networking and cloud cooperative processing, remote monitoring is supported, the flexibility and expandability of the system can be improved, and the maintenance cost and deployment difficulty can be reduced.

The invention discloses a fluorescent probe microplastic fluorescence detection method based on boron-fluorine dipyrrole, and relates to the technical field of fluorescence detection. The invention discloses a double-targeting ratio type fluorescent probe micro-plastic fluorescence detection method based on boron-fluorine dipyrrole, and relates to rhodamine spirolactam boron-fluorine dipyrrole organic fluorescent molecules as a fluorescent color developing agent combined with micro-plastics, and the fluorescent color developing agent can rapidly detect polyester and polystyrene micro-plastics in a water phase and an organic phase. The method is easy and convenient to operate, does not need complex pretreatment, has the advantages of being high in detection sensitivity, high in anti-interference performance and high in function integration degree, and is suitable for rapid detection and risk assessment of environment water samples and food contact material migration liquid.

The application discloses a binary program dynamic analysis method based on a process hollowing technology, utilizes a system kernel module to further expand the ability of a traditional binary dynamic plug-in tool when performing fine-grained analysis, constructs a brand-new fine-grained analysis environment in a new hollowing process by combining a code decoupling analysis thought, can reduce the influence on the execution of a target program, improves the application range and analysis reliability of binary analysis, and can reuse the function of an application layer dynamic plug-in tool. The application utilizes the operating system characteristics to realize more flexible and efficient analysis function, gives a hollowing process environment and an analysis task construction method, and can realize fine-grained data flow analysis on special binary programs such as a vulnerability exploitation program and a countermeasure malicious program. The application method can effectively improve the application range of binary program automatic analysis, simplifies the complexity of analysis memory management, and improves the performance and stability of dynamic analysis on binary codes.

The embodiment of the invention provides a new energy station active power acquisition, analysis and processing system, and the system comprises a data acquisition module which is disposed at a grid connection point of a new energy station, and is used for collecting a voltage signal and a current signal in real time; the active power calculation module is connected to the data acquisition module and is used for calculating active power based on the voltage signal and the current signal; the frequency detection module is connected to the data acquisition module and is used for extracting frequency information from the voltage signal; the data processing and analysis module is connected to the active power calculation module and the frequency detection module and is used for carrying out preprocessing and statistical analysis on the active power and frequency information; the data storage and management module is connected to the data processing and analysis module and is used for storing the processed data; and the communication interface module is connected to the data storage and management module and is used for carrying out data interaction with an external system. High-precision data acquisition, real-time power calculation and frequency monitoring are realized through modular integration, and the data processing speed and accuracy are improved.

The invention discloses a data processing system and method based on a vehicle-mounted CAN / LIN bus hybrid application layer, and the system comprises a data collection layer which is used for receiving and transmitting a CAN message, and receiving and transmitting an LIN message; the cache management layer comprises a cache strategy and cache partitions, the cache strategy comprises cache according to a matrix table and cache index counting, and the cache partitions comprise a CAN channel partition and an LIN channel partition; the network transmission layer comprises a communication protocol and frame header filling, the communication protocol comprises a UDP (User Datagram Protocol) and CAN / LIN (Controller Area Network / Local Interconnect Network) packaging and reassembling, the frame header filling comprises a filling format and filling content, and the method further comprises a CAN / LIN message circular cache management method, a dynamic template CAN data acquisition method and a timestamp synchronous calibration method. The method has the advantages that the CAN message processing efficiency is improved, the memory occupation is reduced, the timestamp synchronization precision is enhanced, dynamic configuration and template switching are supported, and CAN / LIN bus unified management is realized.

The application relates to the technical field of animal behavior analysis, and discloses an animal behavior analysis method, device, equipment and medium based on animal posture, which comprises the following steps: acquiring animal images under multiple perspectives; performing posture analysis on the animal images to obtain animal posture feature images, and performing action recognition according to the animal posture feature images to obtain action types; performing normalized coordinate system conversion according to three-dimensional coordinates of the animal posture feature images to obtain target position data; and analyzing the action types, time information corresponding to the respective animal images and the target position data to determine whether the behavior of the animals in the animal images is abnormal. The action types, time information and target position data of the extracted animal posture feature images can be accurately analyzed to determine whether the behavior of the animals in the animal images is abnormal, thereby improving the effect of analyzing the behavior of the animals.

This invention belongs to the field of radiative heat transfer technology, specifically relating to a method for obtaining the radiation characteristics of divergent suppressed particles. The calculation results of the absorption factor and scattering factor obtained by this method are in good agreement with those obtained by traditional Mie scattering calculations. When the shape factor increases to the point that traditional Mie scattering calculations diverge, the proposed divergent suppressed particle radiation calculation method does not diverge, effectively solving the divergence problem that occurs in traditional Mie scattering calculations when the shape factor increases. By introducing the divergent suppressed Riccati-Bessel formula and stepwise recursive calculations, the stable calculation of the absorption factor Qa and scattering factor Qs is ensured, avoiding divergence and thus improving the reliability and accuracy of particle radiation characteristic calculations.

The invention provides a power production data quality intelligent monitoring method, system and device and a storage medium, and the method comprises the steps: constructing a time sequence database of production core data, employing a self-adaptive preprocessing strategy to dynamically improve the data quality stability, fusing a deep learning model of multi-scale feature extraction to recognize the data trend and fluctuation features, and achieving the intelligent monitoring of the power production data quality. And dynamic self-optimization of an anomaly detection threshold is realized by combining a reinforcement learning algorithm of multi-objective decision, and accurate positioning and explanation of abnormal data are realized by introducing an anomaly traceability mechanism of fusion of causal test and a knowledge graph. Meanwhile, based on an edge-cloud collaborative architecture, abnormal preliminary screening and quick response are achieved on the edge side, fine analysis and model updating are completed on the cloud side, and the monitoring precision, the response speed and the deployability of the system in a complex distributed scene are remarkably improved.

The invention aims to provide an internal combustion engine fault data feature dimension reduction method based on data processing, and belongs to the field of internal combustion engine fault diagnosis. According to the method, secondary deep analysis, including multi-factor analysis and result analysis, is carried out on fault data, and the multi-factor analysis comprises the steps of calculating multiple statistical characteristics of basic characteristics of a fault data sample, utilizing an average value of multiple statistical factors as a standard, calculating a characteristic weight value and sorting the basic characteristics; and result analysis: sequentially importing a machine learning algorithm to construct a fault diagnosis model according to an arrangement sequence of basic features in multi-factor analysis, counting result accuracy rates of different features, and performing optimal feature sorting on a fault data set by taking the accuracy rates as result weight values. The internal combustion engine fault data analysis capability can be improved, and effective dimensionality reduction can be performed on the basic features of the fault data.

The application provides a control block method and system of a UI interface, comprising: obtaining geometric information of each control to construct a control information sequence; calculating geometric characteristics of any two controls; based on a Gaussian distribution model, calculating similarity probability of the controls in each geometric characteristic according to the geometric characteristics, and fusing to obtain block probability between the two controls; taking each control in the control information sequence as a node, and taking the block probability between the two controls as the weight of the directed edge to construct a weighted directed graph; determining a classification threshold based on control block information of a historical UI interface, converting the weighted directed graph into a weighted undirected graph according to a preset selection mode and the classification threshold; analyzing the weighted undirected graph through a graph connected component algorithm, extracting each connected component as a block, and obtaining block information of the UI interface, so that the method realizes intelligent conversion from a UI design draft to structured block information, and improves page performance and response efficiency.

The application provides a kind of multilingual source code automation verification method and system, it is related to computer technology field, method includes: obtaining multilingual source code;Through LLVM compiler, multilingual source code is compiled, and intermediate file is generated;Optimize intermediate file;Through IR resolver, the intermediate file after optimization is parsed, and symbolic execution model is generated;Simulate execution operation to symbolic execution model;After simulating execution operation, the safety verification of symbolic execution model is carried out, and verification report is obtained.The application can realize the unified verification of multilingual source code, greatly reduce the cost of verification environment building and development, improve the overall efficiency of verification;It can also strengthen the analysis ability of intermediate file, accurately capture the logical constraint relationship in code, effectively guarantee the comprehensiveness and reliability of verification work, meet the diversified software safety verification demand.

The application discloses a motion capture method based on sports equipment training and relates to the technical field of intelligent motion capture. The method comprises the following steps: extracting a skeleton key point set of a measured object from training field image data stream by using a human body key point detection algorithm, extracting a two-dimensional contour region of sports equipment by using a target segmentation algorithm, recording time index information of a corresponding image frame, synchronously comparing by using a time stamp of a training event data stream, establishing a time sequence mapping relationship, obtaining time sequence action data, taking the time sequence action data as a spatial anchor point, performing intermediate frame reconstruction on a high-speed motion stage based on the time sequence mapping relationship, restoring a sports equipment motion track, performing joint matching, and generating a time sequence fusion sequence; and establishing multi-view geometric constraints by using the time sequence fusion sequence and camera pose parameter information, restoring a human body three-dimensional skeleton point cloud and an initial six-dimensional attitude of sports equipment. The method effectively improves the space-time consistency and reconstruction accuracy of motion capture in a high-speed dynamic training scene.

The present application relates to a kind of multi-scale soft soil layer single pile P-Y curve correction method based on recurrent neural network, can accurately predict the deformation of single pile pile head after horizontal load, especially in the field of pile body horizontal load and deformation calculation.The present application obtains single pile micro P-Y curve by numerical simulation, obtains single pile mechanical P-Y curve by empirical formula, constructs multi-scale P-Y curve sequence data combined with pile and stratum environmental variable, compared with prior art, actual environmental influence is considered, and the model multi-factor analysis ability is improved.Then based on LSTM recurrent neural network, the P-Y LSTM model is constructed, the model comprehensively considers the influence of multi-scale mechanical derivation result and multi-modal experience data, greatly improves the accuracy of P-Y curve prediction, can accurately calculate the accurate value of load and pile deformation of single pile pile head in soft soil layer after horizontal load.

The invention discloses a train on-board equipment line fault simulation system and method, the system comprises an industrial personal computer, a fault simulation control box, a power supply module and an external terminal strip, and the method comprises the steps that a train on-board equipment line is connected to the external terminal strip of the system according to simulation requirements, and the fault simulation box is connected to the train on-board equipment line in series. Line faults such as open circuit, short circuit and line mixing of train on-board equipment lines are set in fault simulation control software of the system through a touch screen of the industrial personal computer, the industrial personal computer controls the fault simulation control box through CAN communication, the train on-board equipment lines are automatically detected, and fault removal reasons are displayed in the system software. When fault simulation is finished, the industrial personal computer controls the fault simulation control box through CAN communication after the fault simulation control software of the system is set to be recovered through the touch screen of the industrial personal computer, the train-mounted equipment line is automatically recovered to the initial state, and train maintenance personnel can be conveniently trained in the rail transit industry.