49 results about "Automatic Data Processing" patented technology

The present application relates to the technical field of automatic data processing, in particular to a dynamic resume evaluation method based on a multi-modal large model. The method comprises: receiving a file containing the resume information of an applicant and a portfolio containing one or more visual works of the applicant; based on the file containing the resume information of the applicant, analyzing the file using a resume analysis module to generate a resume score; based on the portfolio of the applicant, analyzing the portfolio using a portfolio analysis module to generate a portfolio score; based on the file containing the resume information of the applicant and the portfolio of the applicant, calculating a consistency score using a consistency analysis module; and fusing the resume, portfolio and consistency scores to generate a final matching degree score to determine the matching degree of the applicant and the post. The present application solves the problem that traditional methods cannot evaluate visual materials and verify the authenticity of resumes, and provides a more accurate, objective and comprehensive candidate screening scheme.

The application relates to the technical field of visual lane modeling, and discloses a business data processing method and system based on visual lane modeling, wherein a multi-task prediction model of a graph neural network and a Transformer fusion is trained based on multi-source historical business chain data, a plurality of groups of lane layout and event configuration schemes are generated in combination with real-time operations of a user captured by a design interface, the optimal scheme is selected through simulation evaluation and filtering, and visual display is performed, and finally, visual lane modeling is executed according to user feedback, through automatic data processing, high-precision model prediction, real-time interactive optimization and visual modeling, the design efficiency and quality of a business chain are improved, meanwhile, model prediction based on historical data avoids artificial experience deviation, meets the comprehensive requirements of real-time performance, reliability and resource efficiency for complex equipment development, and provides efficient technical support for the landing of model-based system engineering in the field of complex equipment.

The present application relates to a semiconductor factory production data analysis and presentation method and system based on multi-agent cooperation. First, access to multi-source heterogeneous production data and standardization processing. Subsequently, a plurality of serial special ability agents are executed in turn: data agent classifies and stores data; aggregation analysis agent performs cross-system calculation to generate comprehensive indicators; scene recognition agent matches real-time data with preset rules to identify the current running scene; key parameter recommendation agent outputs a sorted key parameter list according to the scene. Finally, the scene, indicators and parameter list are integrated and visualized through a leadership cockpit with natural language interaction function, and can respond to user queries to trigger reanalysis. Through agent cooperation, the present application realizes automatic data processing, scene recognition and dynamic key parameter recommendation, and solves the technical problem of quickly focusing on core production problems from isolated data.

The application provides a complex document intelligent extraction method, system and device based on a multi-modal large model and a storage medium, and relates to the technical field of automatic data processing and information extraction. The disclosed technology provides an efficient, accurate and self-optimizing information extraction solution, which includes a combination of technologies: a two-stage extraction strategy using preliminary screening and deep information extraction balances the efficiency and depth of information extraction; a cross-page dependency graph construction mechanism solves the problem of information fragmentation; a context-aware overall information extraction method ensures that the model reasons in a complete visual context; dynamic task decomposition and dimension reduction techniques are also used to address performance challenges caused by complex tasks; and a prompt word self-optimization closed loop based on artificial feedback is used to achieve continuous learning and precision improvement of the system.

Computerized systems and methods are described for managing vendor-independent configuration-on-order (CTO) and offer-on-order (QTO) processes. A real-time data grid (RTDM) is provided for aggregating, normalizing, and normalizing real-time data from different sources. A single pane view user interface (SPoG UI) facilitates dynamic interaction and visibility to vendor performance. An advanced analysis and machine learning (AAML) module analyzes product compatibility, optimizes a pricing strategy and predicts a market trend. A vendor independent CTO / QTO integration module (VACIM) includes a process standardization engine and a vendor data conversion gateway to ensure consistency of different vendors. According to the method, automatic data processing and data conversion gateway integration are carried out to ensure data consistency, and decision making is carried out by adopting a rule engine driven by machine learning, so that a retailer process is simplified in an extensible and high-adaptability framework, the expandability is enhanced, and a pricing strategy is optimized.

The application relates to a TTV automatic test system and a test method, which comprise a single-channel heating power module, a single-channel temperature acquisition module, a 100-channel power control array, a 100-channel temperature acquisition module array, a system control industrial computer; the single-channel heating power module realizes independent heating control on a single heating unit of a test product; the single-channel temperature acquisition module independently acquires temperature signals of corresponding areas; the 100-channel power control array and the 100-channel temperature acquisition module array realize centralized management and synchronous control on more than 3000 heating channels and temperature acquisition channels. Through multi-channel power array control, array type temperature acquisition and industrial bus centralized control architecture, the application realizes controllable and adjustable heating power, real-time temperature acquisition and automatic data processing, improves data acquisition density, supports millisecond-level transient temperature acquisition, improves test efficiency and reduces test cost.

The invention discloses an SF6 multi-component mixed gas liquefaction temperature detection method and system based on a laser scattering method. The system comprises a high-pressure visual cavity, a temperature and pressure control system, a laser light source, an optical signal detection system and a data acquisition and processing unit. The design of the high-voltage visual cavity simulates the working condition of actual electrical equipment, and the measurement result has higher engineering reference value; non-contact measurement is realized by means of optical detection, and subjectivity of manual observation in a traditional method is avoided; the automatic data processing flow improves the measurement efficiency and repeatability, and is especially suitable for continuous monitoring requirements of multi-element mixed gas. The method provides a reliable technical scheme for solving the practical problem of SF6 mixed gas liquefaction temperature monitoring in power equipment.

The invention discloses a steel wire rope intelligent detection crawling robot and a detection method thereof. The steel wire rope intelligent detection crawling robot comprises a robot frame and a robot body. The robot body comprises a detection probe assembly, a plurality of crawling roller assemblies, a driving control unit, a data processing unit, a power supply unit module, an encoder assembly and a safety rope ring assembly, the driving control unit is used for controlling the crawling roller assemblies to crawl along a to-be-detected steel wire rope, and the detection probe assembly is used for conducting magnetic flux leakage detection on the to-be-detected steel wire rope; the data processing unit is used for collecting, analyzing and storing magnetic flux leakage detection data, the power supply unit module is used for supplying power, and the encoder assembly is used for obtaining crawling data of the robot body; the safety rope ring assembly is used for being connected with a safety rope to prevent the robot body from falling from the to-be-detected steel wire rope. Through intelligent detection, automatic data processing and data storage in the autonomous crawling process, intelligent detection of damage of the steel wire rope is achieved.

The invention discloses an intelligent prediction method and system for operation platform operation and maintenance data, and the method comprises the steps: firstly, widely collecting all kinds of operation platform operation and maintenance data closely related to a prediction object, and carrying out the cleaning and preprocessing of the collected data; secondly, for the processed data, automatically extracting features with key value for a prediction task from the original data through a machine learning algorithm; then, on the basis of the extracted data features and a specific prediction target, a prediction model is constructed on the basis of a machine learning algorithm; training the prediction model by using historical data to obtain a trained prediction model; inputting new operation and maintenance data into the trained prediction model for prediction and evaluation; and finally, performing real-time response processing on the generated prediction result. According to the scheme, the intelligent prediction model for the operation and maintenance data of the operation platform is formed based on fusion of automatic data processing and a machine learning model, and efficient and accurate prediction for the operation and maintenance data generated by the operation platform can be realized.

The invention relates to the technical field of visual media data backup, and discloses a visual media data backup method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the information of each high-speed write-in port and a common port through main equipment, and interacting with a specified terminal through a user-defined protocol, and then, on the basis of a write-in scheme generated by the reading instruction, a personalized processing scheme is formulated for each piece of to-be-backed-up data to ensure that the data is backed up. The method has the advantages that the data backup and transmission efficiency is improved through the user-defined protocol, the increasing image and video data requirements are met, compared with a traditional manual backup mode, the automatic data processing flow is provided, and the data loss or damage risk caused by manual operation is remarkably reduced.

The invention relates to the technical field of three-dimensional modeling, and particularly discloses a method and system for quickly generating a building three-dimensional white model, and the method comprises the following steps: obtaining an image region range, and determining a target region in a manner of manually dragging a base map of a map and inputting a place name or latitude and longitude coordinates by a user; extracting the contour of the building and obtaining space coordinate information, analyzing the remote sensing image of the target area by using an AI image recognition algorithm, automatically extracting the contour information of the building, and reading the corresponding space coordinate data to obtain the contour data of the building; and cleaning the contour data, endowing the contour data with a height value, and cleaning the extracted building contour data. Through an AI image recognition algorithm and an automatic data processing flow, the generation time of the building three-dimensional white mold is greatly shortened, and the problem that a traditional method is long in consumed time is solved.

The invention relates to an automatic data processing method and system. The method comprises the following steps: S1, constructing an ETL task generation rule base; s2, automatically generating an ETL task according to a user request; s3, deploying and executing an ETL task; and S4, performing configuration information persistence and state monitoring feedback. According to the method, the ETL task generation rule base can be constructed to realize logic rule solidification, the XML file is automatically generated, deployed and executed according to a user request, meanwhile, the information is persistently configured, the task state is monitored, automatic generation, execution and log monitoring of the ETL task are realized, end-to-end and whole-process ETL templating and automation are realized, and the efficiency is improved. The problems of repeated configuration, high technical threshold and lack of intelligent multiplexing in a traditional development mode are solved, and the method has the advantages of improving the development efficiency, reducing the error rate and enhancing the deployment standardization degree.

The invention relates to the technical field of data processing, in particular to an automatic data processing system and terminal equipment, and the system comprises a multi-source data collection module which is used for collecting a multi-source data by using a preset dynamic start-end time screening strategy and employee basic information obtained from a first structured database; screening historical attendance data obtained from the second structured database to obtain effective attendance data; the identification module is used for identifying the shift type of each employee in the effective attendance data according to the week characteristics and the on-off time characteristics; the data integration module is used for integrating each piece of employee information, the corresponding shift type and the corresponding effective attendance data in the employee basic information to obtain to-be-processed employee information; the determining module is used for determining follow-up personnel corresponding to each shift type in the target shift arrangement period from the to-be-processed employee information according to the position requirements and the multi-rule cooperation principle, and generating a shift arrangement table, and the problems of low shift arrangement efficiency, poor reasonability and poor adaptability can be solved.

The invention provides a complex document intelligent extraction method, system and device based on a multi-modal large model and a storage medium, and relates to the technical field of automatic data processing and information extraction. According to the technology disclosed by the invention, an efficient, accurate and self-optimized information extraction solution is provided, and the method comprises a group of technical combinations: a two-stage extraction strategy of preliminary screening and depth information extraction is utilized to balance the efficiency and depth of information extraction; the problem of information fragmentation is solved by utilizing a cross-page dependency graph construction mechanism; a context-aware overall information extraction method is utilized to ensure that the model carries out reasoning in a complete visual context; a dynamic task decomposition and dimension reduction technology is utilized to deal with performance challenges brought by complex tasks; and a cue word self-optimization closed loop based on manual feedback is utilized, so that continuous learning and precision improvement of the system are realized.

The invention discloses a method for automatically searching a crane station, which belongs to the technical field of cranes, is used for crane station selection, and comprises the following steps: carrying out preliminary selection on the crane station according to vectorized field photogrammetric data to obtain a selectable set of the crane station; detecting the feasibility of the optional set of the filtered crane stations on the basis of the field site cloud data to obtain feasible crane stations, and performing collision check detection during secondary hoisting on each feasible crane station on the basis of the field site cloud data and crane parameters in the feasible crane stations to obtain a unique feasible crane station. According to the method, a standardized decision-making model and a quantitative analysis system are established, so that the dependence on personal experience is effectively reduced, and the scientificity and consistency of a station scheme are improved; automatic data processing and an intelligent algorithm are adopted, site parameters and equipment information are rapidly analyzed, and the scheme making period is greatly shortened.

The invention relates to the technical field of agriculture, and discloses an intelligent agricultural greenhouse environment monitoring system, which comprises an intelligent sensor, monitors indoor environment parameters of an agricultural greenhouse in real time, collects environment parameter data, calculates an environment state index and a sensing induction index based on the environment parameters, and sends the environment state index and the sensing induction index to the intelligent sensor. And the display module is used for displaying the environmental parameters collected by the intelligent sensing module and the calculation results of the environmental state indexes and the sensing induction indexes, extracting the environmental state indexes and the sensing induction indexes to judge the current environmental state and the current sensor state, and sending out feedback signals. The system constructs a comprehensive environment monitoring system for environment monitoring of the greenhouse, guarantees comprehensiveness, real-time performance and accuracy of environment parameter monitoring through multi-sensor cooperative acquisition and automatic data processing, avoids hysteresis and errors of manual monitoring and decision errors caused by monitoring failure, and improves the monitoring efficiency. The labor management cost is greatly reduced, and the intelligent and refined management level of agricultural greenhouse planting is improved.

The invention discloses an unmanned aerial vehicle intelligent receiving party system considering highway engineering project receiving party rules, and belongs to the technical field of constructional engineering, the system is used for uploading basic data through a preparation center module, the basic data comprises an organized road center line, a designed cross section and a construction red line, regulation setting can be preset, and the design cross section and the construction red line can be preset; subsequent data processing conforming to rules is facilitated; the processing center module is used for uploading and processing the picture data uploaded by the unmanned aerial vehicle, can realize remote and unmanned data acquisition, is suitable for an engineering environment, and can reduce the cost; and the application center module is used for carrying out business operation on the scene unit, scenes corresponding to the scene unit comprise original ground remeasurement, roadbed earthwork calculation amount and plotting receiving amount, automatic data processing can be provided, and the data processing efficiency is improved.

The application discloses a kind of MEMS accelerometer nonlinearity batch testing device and method, including test tooling, the test tooling is fixed on centrifuge, and install multiple MEMS accelerometers on test tooling, each MEMS accelerometer is connected to the input end of multichannel analog-digital converter respectively, is converted into digital signal, and the digital signal output by analog-digital converter sequentially passes through microcontroller, serial port level conversion chip and is converted into serial port data.The application installs test tooling on test station, increases the number of testing devices, converts device output signal and the mode of automatic data processing improves test efficiency, reduces test cost, realizes single multiple devices synchronous testing, positive and negative synchronous testing batch testing mode.The application is divided into two kinds based on MEMS accelerometer sensitive axis, designs two different test toolings, to adapt to different MEMS acceleration test.

The multivariate heterogeneous data integration method about the smart city comprises the following steps that S1, data sources are configured and connected, and a user carries out database configuration according to different data sources; s2, accessing a data acquisition standard module, and performing automatic data processing and verification; and S3, writing the data into a data warehouse of the big data platform through the platform integration task. The invention provides a multivariate heterogeneous data integration method for a smart city. The method can perform semi-automatic data cleaning and perfect data standards according to pre-collected data. For a smart city, the smart city relates to many industries, and the storage of the same type of data of different data sources can be merged, classified and unified through the method. Clean and normative data are provided for subsequent big data processing analysis, artificial intelligence training data and the like, and analysis accuracy and algorithm fitting are improved.

The invention discloses a bridge point cloud data automatic extraction and elimination method and system based on a river center line, and relates to the technical field of point cloud data processing, and the method comprises the following steps: building an automatic data processing flow, remarkably reducing the demands of manual intervention, and improving the efficiency of point cloud data processing. The method comprises the steps of data acquisition, preprocessing, feature extraction and elimination, adopts an intelligent algorithm and a data processing technology, achieves efficient and automatic point cloud data processing, and solves the problems that an existing point cloud processing technology is low in efficiency, insufficient in precision and much in manual intervention in a complex water area and bridge intersection area.

The invention relates to a big language model data conversion method and system based on model structure analysis, and belongs to the technical field of automatic data processing and artificial intelligence. Aiming at the problem that a simulation model is stored in a binary format and is difficult to directly understand by a large language model, the invention provides a conversion method for automatically analyzing structural data of the simulation model and generating a structured text. The method comprises the steps of reading a model file, analyzing a component structure and a connection relation in a model, tracking variable references and replacing the variable references with corresponding actual values, and further generating a structured document adaptive to a context window of the large language model. Through the technical scheme, the large language model can understand and reasone the simulation model, the simulation analysis efficiency is improved, manual intervention is reduced, and the method is suitable for application scenes such as power electronics, control system design and fault diagnosis.

The invention discloses a temperature control process diagram batch generation method and system, and belongs to the field of glass manufacturing, and the method comprises the steps: setting a column title of a temperature control process table according to the type of temperature control process data, and obtaining the temperature control process table; importing the temperature control process data into a temperature control process table; and generating temperature control process diagrams in batches based on the data in the temperature control process table. Compared with a traditional rapid method for manually generating the temperature control process diagram, the automatic data processing mode is adopted, interference and errors of human factors are reduced, and the speed and accuracy of generating the temperature control process diagram are improved.

The application discloses a kind of composite insulator core rod and sheath interface fatigue test and life prediction method and system, belong to insulator life assessment technical field.The method of the present application comprises: obtaining the hourly wind speed data of the running area environment of composite insulator, based on the hourly wind speed data to determine the stress amplitude of glue joint layer;Based on the stress amplitude of the glue joint layer, electro-hydraulic servo fatigue test and accelerated test are carried out for the core rod and sheath interface of composite insulator, and test data are obtained;Introducing environmental correction factor to correct the test data, obtain correction data, based on the correction data, the life of composite insulator is predicted.The present application significantly improves the evaluation efficiency and objectivity through automatic data processing and multi-source information fusion, reduces the interference of human factors, and provides innovative technical means for power equipment anti-aging design, differentiated operation and replacement cycle optimization.

The invention discloses an intelligent monitoring and evaluation method for the network security situation of an electric power unmanned aerial vehicle, and relates to the technical field of intelligent monitoring of the network of the electric power unmanned aerial vehicle, and the method comprises the steps: collecting the network operation data of the electric power unmanned aerial vehicle; carrying out formatting and preprocessing on the collected data; extracting characteristic parameters related to network security based on the collected data; constructing a power unmanned aerial vehicle network security situation index system according to the characteristic parameters, and performing quantitative expression on each index; performing real-time monitoring on the operation state of the power unmanned aerial vehicle network by using the situation awareness model, and generating corresponding security situation data; evaluating and analyzing the security situation data to obtain a security situation level of the power unmanned aerial vehicle network; and generating safety early warning information according to an evaluation result. According to the invention, real-time monitoring of the network security state is realized by using the situation awareness model and automatic data processing, and the network protection capability is improved by constructing a security situation index system and a grading evaluation method.

The invention discloses a big data e-commerce platform operation and maintenance management system for financial economy, and the system comprises a data collection and preprocessing module, a financial economy dynamic analysis module, an intelligent operation and maintenance decision module, a visualization and decision support module, and a security and compliance guarantee module. And the data acquisition and preprocessing module accesses a financial database, third-party payment platform data and user financial behavior data in real time. The invention relates to the technical field of big data. According to the big data e-commerce platform operation and maintenance management system for financial economy, through automatic data processing and intelligent decision making, manual intervention is reduced, the operation and maintenance efficiency is improved by more than 30%, the operation and maintenance efficiency is greatly improved, the operation risk can be reduced, the financial risk identification accuracy is greater than or equal to 95%, the inventory turnover rate is improved by 20%-40%, the market adaptability can be enhanced, and the economic benefit is improved. Financial economic changes are dynamically responded, the strategy adjustment delay is shortened to the second level, the personalized recommendation conversion rate is improved by 15%-30%, and the user satisfaction is remarkably improved.

The invention belongs to the crossing field of statistical data processing technology and artificial intelligence, and particularly relates to a statistical data intelligent early warning and report urging system and method based on a time sequence large model, in particular to application of AI time sequence analysis and natural language processing in statistical data report urging, anomaly detection and early warning in advance. The method is suitable for a full-process intelligent management scene of statistical data of a statistical bureau, an industry supervision department and the like for large-scale enterprise groups (tens of thousands of families), and aims to overcome the defects of high manual dependency, low processing efficiency, poor report urging accuracy and early warning lag in the prior art through automatic data processing, TFT model multi-task analysis, BERT semantic analysis and law enforcement feedback closed loop. High efficiency, precision and intelligence of statistical data management are realized, labor cost is reduced, and data quality and early warning timeliness are improved.

The application discloses an automatic data processing system and method based on an artificial intelligence model, and relates to the technical field of data processing.The application comprises a data acquisition module, a data preprocessing module and a system state evaluation module.The data acquisition module is used for acquiring an acquisition interval, acquiring real-time business data according to the acquisition interval, and batch importing offline business data.The data preprocessing module is used for preprocessing the offline business data and the real-time business data.The system state evaluation module is used for acquiring current load data of the system.Through comprehensive analysis and dynamic adjustment of the acquisition interval, business and system data, the application constructs an intelligent closed loop of data acquisition.Data fluctuation and system state double-dimension driving interval optimization can accurately capture business abnormal details, adapt system load capacity, dynamically match data acquisition amount and system processing capacity during data processing, avoid data overload or insufficient acquisition, improve preprocessing efficiency and abnormal detection accuracy, and reduce invalid calculation consumption.

According to the LLM and LSTM-driven intelligent business and financial classification and identification management method provided by the invention, the LLM large model has strong language understanding and information extraction capabilities, sales orders in various formats can be accurately analyzed, and errors caused by manual analysis are avoided. The LSTM industry financial intelligent classification driving model is trained based on a large amount of historical data, customer attributes and prediction rules can be identified more accurately, and the accuracy of order settlement and delivery is improved. Interference of human factors is reduced in the automatic data processing and delivery process, and accuracy and consistency of data are guaranteed. The automatic business process reduces the dependence on manpower and reduces the labor cost. Meanwhile, due to the fact that errors and delay are reduced, extra cost expenditure is avoided, and economic benefits of enterprises are improved.

The invention discloses an abnormal transaction recognition method and system based on deep learning, and relates to the technical field of automatic tasks, and the method comprises the steps: firstly obtaining historical transaction data, a data label and a target deep learning model designated by a user, and achieving the collection of a data recognition task; then diversified coding individuals are generated by adopting a vertical and horizontal reverse chaos strategy, and the task processing efficiency is improved; according to historical transaction data and data labels, a triangular walk strategy, a multi-strategy oscillation learning strategy and a bidirectional search strategy are adopted to carry out collaborative optimization on coding individuals, and the task identification accuracy is improved; and finally, deploying the target deep learning model to a cloud end through the target coding individual obtained through optimization, so as to identify to-be-identified transaction data uploaded by the user and realize an automatic data processing task for the user. Through the clustering collaborative optimization strategy, the global exploration and local development capabilities of the algorithm are balanced, and the optimization efficiency of hyper-parameters and the recognition precision and generalization capability of a final model are remarkably improved.

This invention belongs to the field of data processing, and specifically relates to a method and system for recompressing data files based on a large model. The system includes a data warehouse storage management platform; the data warehouse storage management platform includes a data file storage module for storing data files; a data asset ledger storage module for storing a data asset ledger containing data files, the data asset ledger including physical attribute information and business attribute information of the data files; a preliminary disposal plan generation module, which calls a processing plan generation model, takes the data asset ledger as input, outputs a preliminary plan for the data files, and sends it to the business end; a final disposal plan generation module, which generates a final processing plan based on the plan manually confirmed by the business end; and an automatic data processing module, which automatically processes the data according to the final processing plan. This invention solves the problems in existing technologies where outdated big data clusters do not support tiered cold and hot storage, and where business personnel misuse compression algorithms leading to storage waste.