17results about How to "Fast and accurate analysis" patented technology

The invention belongs to the field of artificial intelligence, and particularly relates to a bill information extraction method and system and a storage medium, and the method comprises the following steps: obtaining an electronic file containing a bill, and preprocessing the electronic file into an image file facilitating information extraction; extracting bill information in the image file by using a multi-modal large model and an OCR model, and outputting text information in a structured format; and analyzing, correcting and comparing text information respectively output by the multi-modal large model and the OCR model through the large language model, and performing fine tuning or cue word engineering guidance on the large language model for different tasks, so that an output result conforms to expectation. Compared with the prior art, the problems that in the prior art, OCR recognition accuracy is not high, and extracted information is incomplete are solved, the bill information processing speed is increased, the precision of the automatic processing process is greatly improved, and in financial, auditing and other related business scenes, the working efficiency and the data reliability are remarkably improved.

The invention discloses a data set classification method and system based on unbalanced incremental learning, and the method comprises the steps: obtaining an original data set, and carrying out the preprocessing of the original data set, and obtaining a training set and a test set; determining an unbalanced incremental learning model, and performing model training and model optimization on the unbalanced incremental learning model according to the training set and the test set to obtain a data set classification model; and obtaining a current to-be-processed data set, inputting the current to-be-processed data set into the data set classification model, and outputting a data set classification result. According to the method, the non-equilibrium incremental learning model is trained and optimized to obtain the data set classification model, and the classification efficiency and accuracy of the data set can be effectively improved through the data set classification model.

The invention discloses an ODPS-based customer service data aggregation method and server, and relates to the field of electric digital data processing, and the method comprises the steps: carrying out the field classification of collected source data, and obtaining a plurality of source data sets; loading the source data sets to an ODPS platform, and performing first aggregation processing on the source data in the plurality of source data sets by using the ODPS platform to obtain a plurality of field wide tables; pulling the field wide table to a DRDS platform, and performing distributed data processing on the field wide table by using the DRDS platform to obtain a plurality of pieces of distributed data; determining a target data feature corresponding to a target report in a report request instruction according to the report request instruction sent by the user terminal; and extracting multiple pieces of target data corresponding to the target data features in the multiple pieces of distributed data, and performing second aggregation processing on the target data to generate a target report. By implementing the method, the data processing efficiency can be improved when the multi-azimuth data display of the customer service staff is carried out.

The invention provides an aluminum plate processing online monitoring analysis system based on machine learning, relates to the technical field of aluminum material processing, and adopts a multi-sensor data acquisition system, a real-time data processing and analysis algorithm, an anomaly detection and early warning system, an intelligent optimization algorithm and a remote monitoring and control system to form the whole system. According to the system, various data parameters in the aluminum alloy material machining process are monitored in real time through a multi-sensor data acquisition system, and rapid and accurate processing and analysis are carried out through a real-time data processing and analysis algorithm; the abnormity detection and early warning system can give an alarm in time and provide corresponding processing suggestions to help operators to quickly take measures, and the system can automatically adjust processing parameters through analysis and learning of historical processing data and real-time monitoring data by using an intelligent optimization algorithm, so that comprehensive monitoring and analysis of the processing process can be realized, and the processing efficiency is improved. Product quality and production efficiency are improved, and production cost and manual intervention are reduced.

The invention discloses a motor operation fault analysis method based on electrical variable analysis, and particularly relates to the technical field of motor fault analysis, which comprises the following steps: acquiring motor operation data, dividing the acquired motor operation data into a plurality of areas according to operation conditions, building a motor virtual simulation model corresponding to each operation condition, comparing the collected operation data with the simulation prediction result, comparing the simulation prediction result with the actual dependent variable to obtain abnormal information, and summarizing to obtain the abnormal information of each operation condition; inputting the abnormal information of each operation condition into a corresponding fault diagnosis model, outputting a fault type probability matrix and a preliminary fault type set, performing mutual verification on output results of each operation condition, and judging whether the preliminary fault type set needs to be corrected or not; the problem that existing motor operation fault analysis is not fast and accurate enough is effectively solved, and the accuracy and reliability of fault diagnosis are improved.

The invention relates to the technical field of HIL rack automatic testing, and discloses an HIL rack automatic fault testing system based on a CAN bus, which comprises an HIL rack, a PC upper computer and a fault injection module, the fault injection module is integrated through a CAN bus, and is connected with the HIL rack through the CAN bus. The PC upper computer is connected with the fault injection module, and sends an operation instruction to the fault injection module through the PC upper computer according to a test requirement; the fault injection module is used for executing fault type information according to an operation instruction and integrating the fault type information into the HIL rack; the HIL rack is used for generating a running HIL test environment; and the PC upper computer comprises a test module, and the test module is used for processing an HIL test signal and performing an automatic test. According to the invention, the integration difficulty is reduced, the universality and interoperability are improved, the test data management efficiency is improved, and the automatic HIL bench test is realized.

The invention discloses an Internet of Vehicles data analysis method and device, electronic equipment and a storage medium, and relates to the technical field of Internet of Vehicles. The method comprises the steps of determining attribute information of a target user question in response to the target user question related to Internet of Vehicles data; determining an embedded representation of the attribute information, and determining a target table structure matched with the target user question based on the embedded representation of the attribute information; based on the target table structure, reconstructing the target user question to obtain a target query statement; and in response to an execution instruction of the target query statement, obtaining target Internet of Vehicles data related to the target query statement, and determining an analysis result of each target Internet of Vehicles data based on a large model. According to the scheme provided by the embodiment of the invention, the acquired Internet of Vehicles data can be quickly and accurately analyzed, so that help is provided for research, development and design of vehicles.

The invention discloses a construction project quality whole-process monitoring method and system based on the Internet of Things, and relates to the technical field of intelligent supervision. Comprising the following steps: S1, determining a quality monitoring target and requirement, and formulating a quality monitoring scheme based on the Internet of Things; s2, implementing the formulated quality monitoring scheme; s3, performing quality analysis processing on the construction data based on a machine learning algorithm, and performing corresponding quality early warning; and S4, carrying out whole-process collaborative management by utilizing the whole-process collaborative management platform. A large number of sensors are deployed in combination with the Internet of Things technology, engineering quality related data can be obtained in real time in the whole construction process, the data collection speed and precision are high, data processing and analysis are conducted in combination with a machine learning algorithm, and the quality problem in engineering can be rapidly and accurately analyzed; corresponding quality early warning is carried out; and a whole-process collaborative management platform is further established to realize information sharing and collaborative decision among all parties, so that the quality problem processing efficiency is improved.

The invention belongs to the technical field of computer network monitoring, and particularly relates to a service end-to-end monitoring method and system based on eBPF. Comprising a data collection module, a service topology snapshot generation module, a service template generation module and a service index calculation module, flow message data are collected and called from multiple applications and a K8S cluster through an eBPFAgent, and the collected data are transmitted to the service topology snapshot generation module; the service topology snapshot generation module receives data collected by the eBPFAgent, and obtains data from the eSpan; the service call chain template marking module manually marks nodes and paths of a panoramic topology snapshot generated by the service topology snapshot generation module; a service topology association and index calculation module obtains a panoramic topology snapshot generated by a service topology snapshot generation module, call chain data and a service template generated by a service call chain template marking module; the invention aims to provide an accurate, efficient and comprehensive end-to-end monitoring solution for an enterprise-level complex business system.

The invention relates to the technical field of new energy station monitoring, in particular to an intelligent station operation optimization control system based on big data analysis, which comprises an anomaly detection unit, an image recognition unit and a monitoring large screen unit, and further comprises a video monitoring unit, a data processing unit and a data processing unit, the video monitoring unit is used for carrying out video monitoring on a photovoltaic area and booster station equipment in a photovoltaic energy station to obtain a station monitoring video, and transmitting the station monitoring video to the anomaly detection unit. Through the combination of the video monitoring unit and the abnormity detection unit, the system can monitor the operation state of the photovoltaic station in real time and rapidly detect abnormal conditions, so that the safety and reliability of the station are effectively guaranteed, and through the station inspection unit, the inspection robot is utilized to carry out comprehensive inspection, so that the inspection efficiency is improved. Meter data and equipment state information can be efficiently obtained, the operation parameters of equipment can be rapidly and accurately extracted and analyzed in combination with the image recognition technology, the inspection efficiency is improved, and the labor cost is reduced.

The invention provides an insertion and deletion mutation data analysis method, device and system and a readable storage medium, and relates to the field of high-throughput next-generation sequencing. The method comprises the following steps: acquiring a sequence comparison file and extracting an intersection sequence; generating a target fragment according to the CIGAR value in the intersection sequence; carrying out primary screening and removing treatment on the target fragment; re-comparing the target fragment to obtain a comparison result; mutation site information is obtained according to the comparison result. According to the method, the sequence near the InDel breakpoint is processed based on the comparison result, the InDel representative sequence with high consistency in different sequences can be accurately found, the target InDel is rapidly obtained through local re-comparison, and accurate frequency statistics of the target InDel is achieved by adopting a BAM re-comparison mode. And the problem of analysis of extremely low frequency or complex type mutation is solved.

The present invention discloses a sentiment warning method based on a public figure public opinion knowledge graph and XGBoost multi-feature fusion, and adopts a combination of XGBoost gradient boosting decision tree and knowledge graph to analyze the public opinion warning value from a multi-feature perspective. First, microblog texts are obtained in real time, and each microblog text is processed. Then, topic extraction is achieved through LDA, XGBoost performs microblog sentiment analysis, public figure public opinion knowledge graph is constructed, and the heat of public opinion events is calculated. Finally, the sentiment warning value is calculated and graded. The method of combining XGBoost gradient boosting decision tree and knowledge graph is adopted, and the heat value and sentiment warning value calculation formula of topics and events are designed and proposed at the same time. The public opinion warning value is calculated from the perspective of multi-dimensional communication features such as forwarding, commenting, likes and sentiment polarity, and different grade division mechanisms are adopted for topics and events, so that the public opinion analysis is more accurate and faster.

The invention discloses a measuring device and method for measuring normal stiffness of concrete foundations of different specifications. The measuring device comprises a concentric-square-shaped metal frame, a load loading system, a displacement measuring system and an information collecting system. Wherein the concentric-square-shaped metal frame is integrally designed. A hollow force sensor is arranged in the load loading system, real-time monitoring and collecting of loading can be achieved, a pressing block at the tail end of the loading system can achieve local uniform distribution and transmission of force, and actual foundation supporting load is simulated. The displacement measurement system takes a laser displacement sensor as a core component, and finally realizes non-contact measurement and accurate analysis of normal deformation of the grouting concrete foundation with the anchor hook through the information acquisition system, so that the overall operation is simple, the measurement is quick, and the accuracy is high. The result is reliable, and the method has important development significance for subsequent research on the normal static rigidity of the grouting concrete foundation support with the anchor hook.

The application discloses a kind of optical fiber spectrometer and spectral detection method, it is related to spectral detection technical field, the optical fiber spectrometer includes optical signal receiving module, transmission optical fiber and integrated in integrated shell optical fiber end face miniature optical splitting device, signal acquisition module, spectral information processing module.Optical signal receiving module is converted into parallel light signal after light signal, transmission to the optical fiber end face miniature optical splitting device that binary Fresnel diffraction lens is etched in transmission optical fiber core using photolithography method, obtain circular diffraction spot, and it is irradiated to signal acquisition module;Signal acquisition module is converted into electrical signal by circular diffraction spot, and transmission to spectral information processing module, obtain spectral information.The application directly integrates optical splitting element in optical fiber end face, simplifies optical path design, realizes the miniaturization and portable of equipment by integrated shell, so that compact structure, stable performance, can realize high-precision real-time spectral detection, applicable to a variety of spectral analysis scene.

A method for identifying a bacterium in a biological sample, and a method for testing antibiotic susceptibility of a bacterium in a biological sample, comprising: step (1), centrifugally separating the bacterium from the biological sample; step (2), staining the bacterium obtained in step (1); step (3), fixing the stained bacterium in step (2) by electrostatic adsorption; step (4), injecting an extraction solution into a capillary tip, and then extracting metabolites of the single living bacterium by in-situ extraction; step (5), inserting an electrode into the capillary obtained in step (4), applying a voltage, and performing mass spectrometry. The method does not depend on incubation, and the identification and the antibiotic susceptibility testing of a bacterium in a biological sample of an early bacterial infection can be realized, and the method has the advantages of being rapid, having high sensitivity, capable of perfroming a non-targeting analysis, etc., and has good application prospects.

The invention relates to the technical field of motor vehicle detection, in particular to a motor vehicle steering wheel angle intelligent detection method and system based on machine learning, and the method comprises the steps: constructing a motor vehicle steering wheel angle data analysis model through collected motor vehicle steering wheel angle sample data, and carrying out the preprocessing of the collected target motor vehicle steering wheel angle data; generating standard target motor vehicle steering wheel angle data, performing data analysis according to the motor vehicle steering wheel angle data analysis model and the standard target motor vehicle steering wheel angle data, generating target motor vehicle steering wheel angle data, and performing numerical comparison with a set angle threshold value; and generating target motor vehicle steering wheel angle calibration analysis result data according to the numerical value comparison result, if the target motor vehicle steering wheel angle calibration analysis result data is required to be calibrated, constructing target motor vehicle steering wheel angle real-time detection data, and executing target motor vehicle steering wheel calibration operation. And intelligent detection and accurate calibration of the steering wheel angle of the motor vehicle are realized.

Embodiments of the present application provide a log processing method, an electronic device, a storage device and a storage medium, the method comprising: transmitting a target timestamp to the storage device, the target timestamp comprising a time corresponding to a first running log of the electronic device; recording, by the storage device according to the target timestamp, a second running log of the storage device, wherein the second running log comprises a time corresponding to the second running log; sending a log reading request to the storage device, wherein the log reading request is used to read the second running log recorded by the storage device; and saving the second running log recorded by the storage device. Embodiments of the present application can quickly correspond the running log of the electronic device and the running log of the storage device without a logic analyzer, and can quickly analyze and locate problems, quickly and accurately analyze and locate performance optimization, kernel log abnormalities, lag and card logo problems and the like of the electronic device.