62 results about "Automated data processing" patented technology

The present invention relates to a method for automated data processing to assess the state of multiple filtration membranes used in liquid filtration. The method involves receiving data from state sensors positioned within or near a set of membranes, which process incoming water into permeate and concentrate flows. This data, collected as time series at predefined frequencies, pertains to external physical parameters. An operating indicator is determined from this data, forming a second time series. Both the first and second time series are recorded as point clouds over a specified acquisition period. An intermediate operating indicator is generated, representing the state of new, clean, or cleaned membranes, using a learned normalization model. Finally, a normalized operating indicator is produced, characterizing membrane fouling and aging, independent of environmental variations, and forming a third time series. This method enhances the accuracy of membrane state assessment in filtration systems.

The invention relates to the technical field of visual lane modeling, and discloses a business data processing method and system based on visual lane modeling, and a multi-task prediction model based on multi-source historical business chain data training graph neural network and Transform fusion. A plurality of groups of lane layout and event configuration schemes are generated in combination with user real-time operation captured by a design interface, an optimal scheme is screened through simulation evaluation and then is visually displayed, finally, visual lane modeling is executed according to user feedback, and through automatic data processing, high-precision model prediction, real-time interaction optimization and visual modeling, the optimal lane layout and event configuration scheme is obtained. According to the method, the service chain design efficiency and quality are improved, meanwhile, artificial experience deviation is avoided through model prediction based on historical data, the comprehensive requirements of complex equipment development for real-time performance, reliability and resource efficiency are met, and efficient technical support is provided for landing of model-based system engineering in the field of complex equipment.

The application discloses a kind of intelligent park infrastructure intelligent monitoring system, it is related to the technical field of intelligent monitoring, including fusion module, prediction module and optimization module, set state related parameter, characteristic data is obtained by fusion, state prediction model is obtained by training, state prediction model is optimized.The application fusion module fuses the characteristic value of multiple state related parameters, reduces the complexity and delay of data processing, improves the monitoring efficiency through automated data processing and model optimization, the characteristic value of multiple related parameters is fused by fusion module, avoid the limitation of single parameter, optimize state prediction model, can dynamically adjust model parameters according to actual operation data, further improve the prediction accuracy of model, can early warning potential failure risk, take measures in time to maintain and repair, reduce the probability of failure occurrence, improve the reliability and stability of power supply facilities.

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 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 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 present invention relates to the technical field of operation and maintenance management, and specifically relates to an integrated operation and maintenance management system and method based on AIOps; the method includes: collecting multi-source IT operation and maintenance data and completing standardized preprocessing to form operation and maintenance data input in a unified format; classifying and storing the preprocessed data using a hybrid storage architecture, and implementing data synchronization through a cross-database synchronization tool to break data silos; performing intelligent analysis on the data based on the TensorFlow framework to generate fault diagnosis results and performance trend prediction data; generating operation and maintenance decisions according to the analysis results, and performing automatic repair, work order allocation or resource scheduling operations; outputting operation and maintenance management results, presenting data and decision execution situations through a visualization interface, and providing report generation and management configuration functions; through a full-process automated data processing and intelligent operation and maintenance decision-making mechanism with multi-module collaboration, the technical problems of slow fault response, weak data integration, low accuracy of risk prediction and low operation and maintenance efficiency in the traditional operation and maintenance mode are solved.

Systems, methods, and devices for automating data processing tasks that typically rely on specialized software without API-level access. A virtual machine environment may be created on a cloud server for automated interaction with a target software application. Configuration instructions specifying one or more automation tasks for execution within the virtual machine environment may be received. Automation tasks may be executed within the virtual machine environment by emulating user interactions with the target software application. Data generated by the target software application may be received in response to the emulated user interactions and an execution status associated with the one or more automation tasks may be determined based on the received data. The execution status may be transmitted to a designated external system.

This invention discloses a method for rapidly extracting and classifying interglacial lakes in the Antarctic Cosmonaut Sea. The steps include: quality control of sea ice concentration data, identification and extraction of interglacial lakes, classification of interglacial lakes, and data storage. This method improves the speed and accuracy of extracting spatial distribution, spatial morphology, and area parameters of interglacial lakes. It enables the extraction of interglacial lakes by category, facilitating research on the formation mechanisms of different types of interglacial lakes. It also improves the degree of automated data processing, reduces manual intervention and workload, standardizes data processing criteria, improves data quality, and obtains long-term data sequences. Furthermore, it helps researchers to promptly grasp the near-real-time status of interglacial lakes during the sea ice growth season (May-August) in the Antarctic Cosmonaut Sea, enhancing understanding of the formation and dissipation processes of interglacial lakes, further strengthening scientific understanding of the Cosmonaut Sea, providing data support for future scientific expeditions, and serving my country's national strategic interests in Antarctica.

This application discloses a quality inspection and appeal method, apparatus, electronic device, and storage medium, relating to the field of automated data processing technology. The method includes: receiving a quality inspection plan task request configured by an operations planning platform; obtaining object data according to the quality inspection plan task request; uploading the object data to an analysis platform and receiving the quality inspection results returned by the analysis platform; and, in response to detecting a violation in the quality inspection results, initiating a quality inspection appeal process through the operations planning platform. This application can reduce labor costs and unify, automate, and standardize the quality inspection and appeal process.

The invention discloses an automatic processing system and method for electric power information communication scheduling operation data. The system comprises a man-machine interaction module, a data capture module, a report processing module, a file management module and a data verification module. The man-machine interaction module is used for realizing data interaction between the system and an operator; the data capturing module is used for automatically extracting work order data through a cross-communication scheduling core business system; the report processing module is used for realizing monthly report updating, daily report generation and end-of-month data archiving; the file management module is used for managing template files, result files and log files related to the system; and the data verification module is used for verifying the accuracy of the data processing result. The method disclosed by the invention is high in data processing efficiency, good in integrity and relatively high in accuracy.

The invention discloses a remote sensing big data automatic processing method based on cloud computing, and relates to the field of remote sensing big data processing, and the remote sensing big data automatic processing method comprises the steps: (S1) collecting remote sensing data; (S2) uploading remote sensing data to a cloud computing platform; (S3) processing and analyzing the remote sensing data by adopting a cloud computing processing model; (S4) storing the remote sensing data compression package to a cloud computing platform, and debugging a data processing algorithm by adopting a container cloud integrated model; (S5) displaying and analyzing a processing result by adopting a data visualization platform; according to the invention, remote sensing big data automatic processing is realized based on cloud computing, and a low-orbit remote sensing constellation-oriented transmission routing task planning algorithm and a container cloud integrated model are fused at the same time, so that the efficiency and security of remote sensing big data automatic data processing and data transmission are improved.

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.

According to the automatic data processing system and method based on artificial intelligence, a client provides inquiry information to an artificial intelligence platform, so that the inquiry information is provided to a large language model through a decision agent to generate at least one task and select a corresponding task agent; the task agent executes the selected task to generate a task result and a task completion instruction and feeds back the task completion instruction to the decision agent, the decision agent selects the task agent corresponding to the continuous task, and the answer agent generates an answer result from the task results of all the tasks by using an artificial intelligence generation technology and feeds back the answer result to the client; therefore, the technical effect of providing convenient and automatic data processing based on artificial intelligence can be achieved.

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 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 relates to the field of mineral resource evaluation, in particular to a mineral economic value prediction method based on multi-source data fusion. Comprising the following steps of data collection and analysis, representative drill hole selection, rapid detection data acquisition, key segment chemical analysis, data verification model construction, historical data reliability evaluation, ore body spatial feature analysis, resource quantity re-verification and grading and economic value dynamic prediction. Through a multi-source data fusion and intelligent analysis technology, existing exploration and production data are fully utilized, and dependence on newly added field exploration is reduced; a representative sample grading collection and intelligent space modeling method is adopted, the precision of resource reserve assessment is effectively improved, the assessment process is accelerated through an automatic data processing platform and a dynamic feedback mechanism, the problem of data reliability and cost-benefit balance in undeveloped mine right assessment is solved, and the method is suitable for popularization and application. And a scientific basis is provided for mine right circulation and resource development.

This invention discloses an automatic extraction and removal method and system for bridge point cloud data based on the centerline of a river, relating to the field of point cloud data processing technology. The method includes the following steps: By establishing an automated data processing workflow, this invention significantly reduces the need for manual intervention, covering all stages from data acquisition, preprocessing, feature extraction to removal. Employing intelligent algorithms and data processing technologies, it achieves efficient and automated point cloud data processing, solving the problems of low efficiency, insufficient accuracy, and excessive manual intervention in existing point cloud processing technologies in complex waterways and bridge intersection areas.

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 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 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.

This paper relates to the field of automated data processing and provides an experimental data processing method, apparatus, and system. The method includes: sending a task preparation sequence instruction to a data computing system, causing the data computing system to create task-related computing processes according to the task preparation sequence instruction, create data objects within the computing processes, and assign initial values; sending a data acquisition instruction to a device control terminal, receiving data acquired by the device control terminal through operation of the experimental equipment; generating a data modification instruction based on the acquired data and sending it to the data computing system; sending a data calculation instruction to the data computing system, causing the data computing system to perform calculations on the data of the relevant task computing processes according to the data calculation instruction; and sending a data output instruction to the data computing system, causing the data of the relevant computing processes to be stored in a data object database according to the data output instruction. This paper can realize the entire business process and has the advantages of ease of use, ease of learning, and versatility.

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.

This utility model discloses an offline maintenance device for continuous casting rolls, relating to the field of mechanical manufacturing technology. It includes a base and a continuous casting roll body. A U-shaped plate is positioned above the base. Extended support plates are located on the top edges of the vertical sections on both sides of the U-shaped plate. Support structures and fixing components are located at the upper ends of both extended support plates, and movable adjustment components are connected to the lower ends of both extended support plates. A leveling sensor is fixed to the upper end of the horizontal section of the U-shaped plate. A water tank is located at one corner of the upper side of the base, and a flow measurement component is connected to the water tank. A detection host is fixed to the upper surface of the base. This utility model achieves high-precision measurement and accurate evaluation of the water flow performance of the continuous casting roll; it is easy to operate, facilitating the installation and disassembly of the continuous casting roll; it automates data processing, improving detection efficiency and accuracy; it can adapt to the detection needs of continuous casting rolls of different specifications; and it improves the quality of offline maintenance and inspection of continuous casting rolls, thereby extending their service life.

The invention relates to a method of ophthalmic ultrasound in which: - the ultrasound probe is positioned (S1) relative to an eye, - the ultrasound probe emits and receives ultrasound waves (S21), generates data, and transmits the image data to the automated data processing unit (S22), - the inertial measurement unit determines (S23) positioning data of the ultrasound probe and transmits said positioning data to the automated data processing unit, - the automated data processing unit selects (S24) a predetermined spatial configuration from a finite set of predetermined spatial configurations, - the automated data processing unit determines (S3) display data associating the image data with a visual reference comprising visual indicators representative of the previously selected predetermined spatial configuration.- The automated data processing unit transmits the display data to the screen, and the screen displays the display data (S4).

This invention discloses a method, system, device, and storage medium for predicting and analyzing diesel vehicle fuel consumption based on machine learning. It relates to the field of diesel vehicle fuel consumption prediction and analysis technology, and includes constructing a fuel consumption prediction model; using intelligent interpolation to impute missing values ​​in data from different sources; using Gaussian filtering to suppress noise in data from different sources; extracting fuel consumption features from the imputed data and generating simplified features using PCA; constructing a multi-algorithm fusion prediction framework; evaluating the performance of the trained fuel consumption prediction model; and using a weighted integration strategy and Stacking method to construct real-time fuel consumption prediction accuracy for the simplified features. The method of this invention achieves high-precision prediction of diesel vehicle fuel consumption, feature optimization and dimensionality reduction, automated data processing, and real-time visualization analysis, improving prediction accuracy, ease of operation, and system stability, and adapting to complex driving conditions and multi-scenario applications.