195 results about "Data interpretation" patented technology

The present invention discloses an artificial intelligence-based (AI-based) system and method for generating optimised operation planning and scheduling output. The AI-based system obtains at least one of: one or more data explanation videos, one or more process understanding videos, and unconstrained operational planning data, along with one or more prompts as an input. The AI-based system extracts one or more informative image frames and audio data, to train the one or more AI models and generate a planning standard operating procedure (SOP). The AI-based system processes the planning SOP, the constrained operational planning data, and the one or more prompts to generate the optimised operation planning and scheduling output based on an optimised function with a continuous feedback loop in response to at least one of: the one or more prompts, updated planning SOP, and real-time changes in the constrained operational planning data.

The invention provides a hydraulic fracturing fracture net distribution form near-field monitoring method, which comprises the following steps of: arranging a plurality of roof monitoring holes in roadways on two sides of an underground coal mine working surface, installing electromagnetic signal sensors in each roof monitoring hole and on anchor rods of roadway stoping sides, and constructing a full-space monitoring network covering a hydraulic fracturing layer; a data interpretation processing module of the electromagnetic signal time-shifting imaging analysis system comprises a multi-field coupling mathematical model for analyzing and deducing real-time imaging of electromagnetic signals; in the hydraulic fracturing process, an electromagnetic signal sensor is used for monitoring a current field, an electromagnetic field and an ion diffusion field in the rock mass fracture development and expansion process, a monitoring result is substituted into an electromagnetic signal time-shifting imaging analysis system for calculation and feature value extraction, and the fracture net distribution form is deduced through the feature value. According to the invention, the expansion path of the fracturing fluid in the rock stratum can be monitored in real time, the three-dimensional distribution form of the hydraulic fracturing fracture network can be inverted, and a new technical support is provided for the inspection of the hydraulic fracturing pressure relief effect of the underground coal mine.

The invention provides a low-code enterprise-level intelligent assistant development platform, and relates to the field of intelligent assistant development. The low-code enterprise-level intelligent assistant development platform comprises an application layer, a platform layer, a capability layer and an underlying service. According to the low-code enterprise-level intelligent assistant development platform, a low-code enterprise-level intelligent assistant development platform is realized, and by means of the platform, a user can develop enterprise-level intelligent assistants suitable for various industries, thereby helping enterprises to realize cost reduction and benefit increase; a user can complete the creation of a knowledge base, a workflow and an intelligent agent only through simple configuration or arrangement, a low use threshold is achieved, enterprise internal services or third-party MCP services can be accessed by means of a plug-in tool and a platform, uploading and publishing of user-defined tools are supported at the same time, high expandability is achieved, and the method can be widely applied to the field of enterprise management. The platform supports high-order functions such as multi-person collaborative development, multi-modal knowledge base construction, multi-modal data interpretation and processing and construction of multiple types of agents, and can meet complex enterprise-level application scenes.

The invention relates to the technical field of pipeline anomaly detection, in particular to a petroleum pipeline network anomaly recognition and early warning system which comprises a pressure fluctuation monitoring module, a flow anomaly analysis module, a state association optimization module, an early warning strategy adjustment module and an anomaly early warning generation module. According to the method, deep correlation analysis is carried out on the relation between pressure fluctuation and flow abnormity, the change trend of pipeline pressure and flow can be accurately captured, the abnormal state in pipe network operation can be recognized in time, the abnormity early warning structure is dynamically adjusted, the early warning strategy can be flexibly optimized along with the change of the working condition, and therefore the detection precision and the response speed are improved; the state can be mastered in real time in a complex pipe network environment, missing detection and false alarm phenomena caused by insufficient data interpretation or early warning strategy solidification are effectively reduced, the adaptability of the system to emergency situations is enhanced, and the problems of response lag and insufficient sensitivity caused by single data source under variable working conditions are avoided.

In embodiments, an apparatus includes a user data interpretation circuit structured to interpret user data corresponding to a first user; a bootstrap circuit structured to: match the first user to a second user via querying one or more databases based at least in part on the user data; retrieve historical time sequence data associated with the second user via querying the one or more databases; extract a time sequence trend from the historical time sequence data; identify a portion of the historical time sequence data corresponding to the extracted time sequence trend; and generate, based at least in part on the identified portion, time sequence data corresponding to the first user; and a time sequence data provisioning circuit structured to transmit the time sequence data.

The invention provides an AI-based ChatBI platform design and implementation method, which comprises the following core steps of: constructing a first semantic layer database, receiving a natural language problem of a user, and generating an executable SQL (Structured Query Language) statement through an NL2DSL engine; extracting keywords and performing vector similarity calculation on the keywords and the first semantic layer database to generate a second semantic layer knowledge base; judging query complexity according to the quantity of the knowledge bases, and scheduling corresponding processing engines; checking the user access authority, and automatically generating a chart configuration template and recommending an adaptive chart type when the user access authority exists; based on user selection, data interpretation and a core conclusion are generated through a large model in combination with SQL statements and a knowledge base, and an output result is integrated. According to the invention, by optimizing the platform architecture, accurate data insight can be obtained by non-technical personnel in a dialogue manner, the use threshold is reduced, terms are unified, and the result accuracy is improved; meanwhile, operation efficiency is improved, and waiting time is shortened; and the data security is enhanced through permission setting.

The invention mainly relates to the technical field of spectral analysis, and provides a white spirit brewing spectral analysis method and system based on a multi-modal large language model in order to efficiently and accurately obtain white spirit brewing information according to multi-modal spectral data of all links of white spirit brewing. The method is characterized by comprising the following steps of: collecting multi-mode spectrum data including gas chromatography data, infrared spectrum data and Raman spectrum data in each link of white spirit brewing; converting the multi-modal spectral data and the natural language instruction into a feature embedding vector which can be recognized by a large language model to train the large language model which is finely adjusted based on professional knowledge in the field of white spirit, and receiving a question of a user in a natural language instruction form by the trained large language model; wine making analysis tasks such as flavor compound identification, key microorganism detection, fermentation state evaluation and base wine quality grading are carried out, and finally analysis results and wine making process optimization suggestions are output in a natural language form, so that the accuracy and efficiency of spectrum data interpretation in the white wine making process are improved.

The invention provides a strike-slip fracture derived fracture parameter prediction method and system, and belongs to the field of oil and gas field exploration and development. The method comprises the following steps: based on three-dimensional seismic data interpretation data, establishing a three-dimensional geologic model; carrying out mechanical parameter assignment and gridding processing on the three-dimensional geologic model in sequence based on predetermined rock mechanical parameters to obtain a three-dimensional grid model; based on the three-dimensional grid model and predetermined geological model boundary conditions, solving stress data corresponding to each grid in the three-dimensional grid model to obtain stress distribution data corresponding to a strike-slip fracture formation period; and based on the exploration data and the stress distribution data corresponding to the strike-slip fracture formation period, predicting the occurrence and development degree of the strike-slip fracture derived tectonic fracture to obtain a strike-slip fracture derived fracture parameter prediction result. The purpose of quantitatively predicting the occurrence and development degree of the derived fracture in the strike-slip fracture forming process is achieved, and the problem of parameter prediction of the derived fracture in the strike-slip fracture forming process is solved.

The invention discloses a land subsidence area permeable channel identification and cause analysis method based on a high-density electrical method, which overcomes the limitation of traditional single profile data interpretation by arranging a three-dimensional observation grid and fusing and collecting multi-source data. Through combination of finite element three-dimensional inversion and the improved convolutional neural network model, accurate description of the three-dimensional spatial morphology, the extension direction and the connectivity of the permeable channel is realized, and the accuracy and the spatial resolution of low-resistance abnormal region identification are improved. Compared with the prior art, the method has remarkable advantages in the aspects of permeable channel recognition accuracy, cause analysis comprehensiveness, result application practicability and the like, and effective support can be provided for long-acting prevention and control work of geological disasters in land subsidence areas.

A method and system are disclosed for improving the accuracy, efficiency, and scalability of data interpretation and extraction of structured data from unstructured documents utilizing Large Language Models (LLMs). Applicable in finance, healthcare, legal, and government contexts, the disclosed invention addresses limitations of conventional Optical Character Recognition (OCR), machine learning, and LLM-based methods. In particular, the system and method incorporate feedback loops for continuous learning and leverage pre-processing, contextual tagging, customized prompt engineering, and post-processing to achieve robust data extraction. By integrating data enrichment techniques, the invention manages the inherent complexities of multilingual documents and evolving content standards.

The invention discloses a phased array, TOFD and magnetic powder integrated detection system and method for medium and large diameter pipeline welded joints, the detection system comprises a control system, a liquid storage and supply system and a scanning system, the control system is connected with the liquid storage and supply system, the control system is connected with the scanning system, and the scanning system is connected with the control system. The liquid storage and supply system is connected with the scanning system through a flexible water pipeline and a flexible magnetic suspension pipeline; the liquid storage and supply system comprises a magnetic suspension tank and a water tank which are respectively used for storing magnetic suspension and water; the scanning system is a data acquisition center, is provided with a phased array probe, a TOFD probe, an electromagnetic yoke and a video sensor in a centralized mode, operates in a sub-control mode, is driven by a magnetic attraction wheel driving mechanism to achieve automatic scanning of a welding joint, and transmits detection data information to the control system. The device is compact in structure, convenient for later maintenance and low in manufacturing cost; according to the invention, the parameter control is accurate, the detection data can be analyzed in real time, the data interpretation is convenient, the detection precision is high, the detection speed is fast, and safety and reliability are realized.

The invention discloses a method and a device for predicting an anode copper refining reaction endpoint, and relates to the technical field of anode copper refining process management. The prediction method comprises the following steps: S1, sensor deployment and data acquisition; s2, data preprocessing; s3, constructing a feature project; s4, model training and optimization strategy implementation; s5, carrying out online prediction and decision support; and S6, continuously improving closed-loop feedback. According to the method, the gradient boosting decision tree, the LSTM + Attention hybrid architecture and the Stacking integration are adopted, and the data interpretability and the nonlinear fitting capability are considered; bayesian hyper-parameter optimization and an early stop method are combined to prevent overfitting, and the generalization performance of the model is ensured.

The application discloses a well logging data interpretation method and device based on large model fine tuning, equipment and a storage medium in the field of computer technology. The method comprises the following steps: connecting a gating selection unit of a fine tuning module to a geophysical logging large model to obtain a scene large model; obtaining a label data set for well logging interpretation in a target environment; dynamically determining a subnetwork matched with the label data set from a classification subnetwork and a regression subnetwork of the fine tuning module by using the gating selection unit, and fine tuning the scene large model to adapt to various well logging interpretation tasks; obtaining well logging data to be interpreted corresponding to the target environment, and interpreting and processing the well logging data to be interpreted by using the fine tuned scene large model to obtain at least one well logging interpretation content corresponding to various well logging interpretation tasks. The application can efficiently process various well logging interpretation tasks, does not need to construct independent models for different well logging interpretation tasks, has a high data feature reuse rate, and has a higher complex scene generalization capability.

A simulation system for hands-on training for medical diagnostic and treatment techniques is provided. The simulation system is provided with a manikin of a human body having a first side and a second side. The second side has a transparent simulated skin, an internal organ simulated model, and a sensor module configured to collect and transmit data based on an input by a user. In addition, the system has a processor with an implemented computer program configured to receive and interpret the data from the sensor module and provide a feedback to the user based on the interpretation of the data.

The application discloses a data intelligent service method and device under a mixed model, equipment and a storage medium, relates to the technical field of artificial intelligence technology, and comprises the following steps: acquiring user question and answer information and user search information; identifying at least one of a corresponding query intention category, an inquiry intention category, an exploration behavior category and a search behavior category based on the user question and answer information or the user search information, and determining at least one of query intention category information, inquiry intention category information, exploration behavior category information and search behavior category information; predicting a corresponding service demand based on at least one of the query intention category information, the inquiry intention category information, the exploration behavior category information and the search behavior category information, and determining service demand prediction information; and analyzing a corresponding data resource based on the service demand prediction information, and determining a data resource analysis result. The application identifies query, inquiry, exploration and search intentions, performs data interpretation, and adapts intelligent service.

The application discloses a two-dimensional distributed optical fiber sensing full waveform simulation method based on a discrete elastic lattice, and belongs to the technical field of seismic wave field simulation and distributed optical fiber sensing. The application comprises the following steps: receiving seismic wave data of a target geological medium layer; setting a two-dimensional calculation region according to the spatial distribution of the longitudinal wave velocity, the transverse wave velocity and the density parameters of the seismic wave, and taking the two-dimensional calculation region as a numerical simulation carrier of seismic wave propagation; discretizing the two-dimensional calculation region into a plurality of particle nodes, and connecting adjacent particle nodes through elasticity to form a discrete elastic lattice, so as to represent the mechanical properties of the stratum medium; and according to the density parameters, the longitudinal wave velocity, the transverse wave velocity or the equivalent elastic parameters of the stratum medium, the elastic connection between the particle nodes is endowed with corresponding elastic coefficients. The application provides a reliable numerical tool for DAS deployment design, data interpretation and seismological inversion, and can be widely applied to the fields of earthquake monitoring, oil and gas exploration, geological disaster warning and the like.

The application relates to a method for fracturing a fractured shale zone by using a pre-bridging body, and relates to the field of shale gas fracturing. The method comprises the following steps: establishing a shale fracture development degree judgment model, judging the natural fracture development degree of a target layer according to the shale fracture development degree judgment model; calculating different discharge wellhead pressure values according to well logging data interpretation and small pressure test data, and establishing a fractured shale zone body reconstruction construction pressure judgment standard; adopting a pre-bridging technology for construction in the early stage of fracturing implementation; and adjusting the discharge, liquid viscosity and in-joint bridging process parameters to realize reservoir reconstruction. The method for fracturing a fractured shale zone by using a pre-bridging body can help field engineers to judge the fracture development degree of a formation and evaluate whether fracturing reconstruction is effective, and on this basis, systematic pre-bridging technology and field construction control means are provided, so that the reconstruction effect of the fractured shale zone is effectively improved.

This invention relates to the technical field of seismic data interpretation, and discloses a method and computer equipment for identifying the locations of beaded reflections in carbonate rocks. The identification method includes: S1, obtaining a two-dimensional seismic migration profile; S2, extracting typical beaded reflections, determining their dimensions, statistically analyzing their structural morphology, and constructing adaptive hit-and-miss structural element pairs; S3, using a hit-and-miss transformation to identify locations that match the adaptive hit-and-miss structural element pairs. This invention's identification method extracts multiple typical beaded reflections, constructs adaptive hit-and-miss structural element pairs, and only identifies locations that match these pairs. Based on a hit-and-miss transformation algorithm, this algorithm has low hardware requirements, high computational efficiency, and high accuracy.

The invention discloses a geologic horizon calibration method, device, equipment and medium based on virtual drilling, and is applied to the technical field of seismic data interpretation, and the method comprises the steps: determining an initial interpreted seismic amplitude profile based on a pre-stack time migration seismic amplitude profile of a research region and a reference interpreted seismic amplitude profile of an adjacent region; solving wellhead coordinates in the pre-stack time migration seismic amplitude section to generate a virtual drilling well of the research area based on the two-way travel time of the entity drilling well of the adjacent area in the geologic horizon bottom boundary in the reference explained seismic amplitude section; based on the interval transit time curve and the volume density curve of the entity drilling well, generating synthetic seismic response characteristics of the virtual drilling well; and correcting the initial interpreted seismic amplitude profile based on the synthetic seismic response characteristics to obtain a target interpreted seismic amplitude profile, thereby synthesizing the seismic response characteristics by using the interpreted geologic horizon of the adjacent region and the virtual drilling well under the condition of no well or few wells in the cross-work area, and realizing high-precision identification of the geologic horizon of the research area.

The invention discloses a basin and peripheral area digital modeling method and system based on big data analysis, and relates to the technical field of digital modeling, and the method comprises the steps: connecting a GIS system, and obtaining the geographic distribution information of a target basin area; building a digital space, carrying out grid division on the digital space, and determining a digital grid; acquiring detection data of each exploration zone in the target basin area, performing data interpretation and preprocessing respectively, and determining geological feature information; based on the digital grids, in combination with the geological feature information, performing three-dimensional modeling on the digital grids in parallel, and respectively determining grid models; and splicing the grid models, determining a digital basin periphery model, performing macroscopic quantization, and generating a macroscopic digital basin periphery model. According to the method, the technical problem of low model building efficiency in linkage modeling of the basin combined peripheral mountain-building belt in the prior art is solved, and the technical effect of improving the efficiency and the accuracy of exploration and analysis of the basin and the peripheral area is achieved.

The present disclosure provides a fully integrated autonomous wearable ultrasound patch system (USoP) that includes a miniaturized flexible control circuit designed to interface with an array of ultrasound transducers for signal pre-processing and wireless data communication. Artificial intelligence (e.g., machine learning) may be used to track mobile organization targets and assist in data interpretation. In one implementation, USoP allows continuous tracking of physiological signals from tissue up to 164 mm deep. For moving subjects, USoP may continuously monitor physiological signals, including central blood pressure, heart rate, and cardiac output, for example for 12 hours. This result enables continuous autonomous monitoring of deep tissue signals.

This invention discloses a training method and apparatus for a 3D meandering river recognition model, and a method for identifying meandering rivers. The method includes: acquiring several interpretation results and corresponding seismic data volumes in a target area; initializing a preset label data volume based on the interpretation results and the seismic data volume; masking the label data volume to update it; inputting each seismic data point in the seismic data volume as sample data into a pre-constructed image segmentation model for training; calculating a loss function value based on the output of the image segmentation model and the updated label data volume; adjusting the parameters of the image segmentation model inversely based on the loss function value; iteratively training until the loss function reaches a preset convergence condition, stopping the training, and obtaining a trained 3D meandering river recognition model. This model enables the identification of meandering rivers in a target area using sparse labels, improving the efficiency of seismic data interpretation.

The invention discloses an axial strain rate field simulation method of a distributed optical fiber DAS-VSP based on strain tensor projection under a non-vertical well layout condition, and belongs to the field of exploration. Aiming at the problem that a traditional vertical well simulation method is difficult to accurately simulate wave field propagation characteristics in a curved well trajectory, high-precision wave field simulation under a non-vertical well type condition is realized by establishing a parameterized well trajectory model based on a curvilinear function and combining a first-order speed-stress elastic wave equation and a finite difference algorithm. A tangential direction projection algorithm is introduced to convert a velocity field into optical fiber axial strain rate response, and a dynamic scale distance segmentation method is adopted to truly simulate the spatial sampling characteristics of an actual DAS system. By constructing PML absorption boundary conditions and a stability control mechanism, the numerical stability of highly-deviated well section wave field simulation is ensured. According to the method, a reliable numerical experiment method is provided for DAS-VSP data interpretation and forward modeling under the non-straight well type condition, and important technical support is provided for non-straight well optical fiber earthquake monitoring in oil-gas exploration.

Interactive Computer Device for Real-Time Machine Learning Driven Data Interpretation

Systems and methods for improving graphical user interfaces and data interpretation are provided. A method may include determining, using a generative artificial intelligence (GAI) model, an ontology defining a plurality of interconnected nodes of a dataset, generating at least one textual element and one or more visual elements corresponding with the interconnected nodes, providing the at least one textual element and the one or more visual elements to a graphical user interface (GUI), receiving, via the GUI, a selection corresponding with the at least one textual element or the one or more visual elements, and updating, via the GUI, at least one of the at least one textual element or the one or more visual elements based on the selection. Updating the textual element may cause the visual element to update, and updating the visual element may cause the textual element to update.

The invention discloses an interpretation method and system for mixed DNA diallelic SNP data based on WGA-WGS, and relates to the technical field of forensic genetics, and the specific steps are as follows: obtaining intersection SNP sites based on WGA-WGS typing data of a mixed sample and a target individual sample, and generating an SNP site set; determining the number N of mixed contributors and a single allele typing error rate; calling a preset integrated typing error observation probability model based on the application scene, and calculating a total likelihood ratio based on the number N of mixed contributors and a single allele typing error rate on the basis of the SNP site set; the mixed sample is explained based on the total likelihood ratio, the whole process is operated based on a preset mathematical model and objective parameters, and potential bias caused by manual intervention and subjective judgment is reduced to the maximum extent; the scientific preciseness of evidence explanation is ensured, and the repeatability and verifiability of an analysis result are also ensured.

The invention discloses a transmission method of an Esp32 serial port communication system, which is applied to the technical field of communication, the J1939 communication technology of a Can bus is improved, any one value of a PGN number can be selected from 0-65565, a client defines the function of the PGN number, each time an ESP32 unvarnished transmission module is powered on, and a plurality of PGN numbers of a user form only need to be transmitted respectively for the first time, so that the PGN numbers can be transmitted to the client, and the PGN numbers can be transmitted to the client. The ESP32 module memorizes the PGN number at the first time, data can be successfully transmitted in a transparent manner at the second time, and memory is emptied in case of power failure; by adopting the method disclosed by the invention, intelligent memory can be quickly realized, the master machine and the slave machine completely upload and download data in a form form, unvarnished transmission is completely realized, automatic splicing is realized during multi-packet data transmission, and after complete data unvarnished transmission is successful, a data explanation right is automatically handed over to an upper-layer user interface, so that a user does not need to care about an internal implementation process.

A system includes at least one wearable device having a housing, at least one sensor disposed within the housing, at least one output device disposed within the housing, and at least one processor operatively connected to the sensors and output devices, wherein one or more sensors are configured to detect electrical activity from a user's facial muscles and to transmit a data signal concerning the electrical activity of the user's facial muscles to one of more of the processors. A method of controlling a wearable device includes determining facial muscular electrical data of a facial gesture made by a user, interpreting the facial muscular electrical data to determine a user response, and performing an action based on the user response.

The application discloses a kind of quantitative evaluation method and device of oil and gas along fault to river channel sand body filling capacity, first, according to fluid inclusion analysis, oil and gas reservoir forming period is identified, and oil and gas reservoir forming period and time are determined;Then, according to the result of seismic data interpretation, the configuration relationship of fault and hydrocarbon source rock is analyzed, and gas source fault is determined;Then, in combination with the configuration relationship of fault and sand body, its evolution and the recovery result of porosity evolution, and the historical data of porosity-permeability relationship, the filling capacity evaluation parameter of key reservoir forming period is obtained;Then, in combination with the relationship between filling capacity evaluation parameter and sand body gas content, the weight coefficient of different parameters on the influence of natural gas filling is determined, and then the filling capacity coefficient of each river channel sand body is calculated, so that the selective filling strength of oil and gas along fault to each river channel sand body is obtained.The application realizes the quantitative evaluation of oil and gas along fault to river channel sand body filling capacity, and based on the evaluation result, favorable gas-bearing river channel sand body can be further predicted.