713 results about "Field based" patented technology

The invention relates to the cross technical field of business intelligence and natural language processing, in particular to an interactive AI report generation method and system based on intelligent semantic driving, and the method comprises the steps: (1) a large language model analyzes user question semantics, calculates the matching degree with a report subject, and dynamically selects a question correction or report generation process; (2) the fuzzy query is converted into a deterministic request based on the domain knowledge base, and semantic ambiguity is eliminated; (3) analyzing field association in combination with database metadata, and generating an executable SQL (Structured Query Language); (4) dynamically rewriting SQL (Structured Query Language) according to user roles, adding permission filtering conditions, executing query and desensitizing sensitive data; (5) analyzing result features, automatically matching a visual form and generating natural language interpretation; and (6) collecting user feedback, and optimizing a semantic model and a query strategy. According to the scheme, the accuracy of natural language query and the adaptive capacity of the system can be remarkably improved, and the security of data access is guaranteed.

The invention discloses a method and a device for adaptively generating a structural query statement by using a natural language. The method comprises the following steps: acquiring a natural language query statement input by a user; vectorizing the natural language query statement to obtain a first query statement and a data table and an SQL statement corresponding to the first query statement; rewriting the natural language query statement to obtain a second query statement; recalling a candidate data table and a candidate table field based on the second query statement and a preset to-be-queried data table; the first input data and the first sample data are input into the preset large language model, the query statement and the reasoning chain are structured, the natural language query statement input by the user is corrected, the difference between the natural language query statement and the database table structure is reduced, and the accuracy of data table recall is improved; by recalling the candidate data table, the reasoning difficulty is reduced, so that the accuracy of generating the structured query statement according to the natural language query statement is improved.

The invention discloses a complex material-oriented global full-frequency illumination neural rendering method and system, and belongs to the technical field of computer graphics, and the method comprises the steps: querying and extracting a primary frequency feature and a secondary frequency feature in a neural feature field according to the primary geometric buffer and the reflection geometric buffer of a rendering pixel; performing weighted combination on the primary frequency features and the secondary frequency features through a double-frequency fusion module to form full-frequency fusion features; inputting the full-frequency fusion feature and the initial geometric buffer into a decoder to obtain a pixel radiation brightness value; and optimizing the network by adopting a double-stage training strategy of low-frequency data and multi-frequency data, and then using the optimized network for neural drawing of global full-frequency illumination. The method can improve the accuracy and stability of global illumination simulation in a complex dynamic scene, and is suitable for modeling a multi-frequency global illumination effect in a dynamic three-dimensional scene.

The invention provides an unsteady flow field rapid prediction method based on a cross-fidelity neural network. The method belongs to the cross technical field of fluid mechanics (CFD) and deep learning. The method comprises the following steps: determining a fluid region needing to be simulated, performing grid division, and setting an initial fluid boundary condition; respectively executing low-fidelity CFD calculation and high-fidelity CFD calculation on the basis of the grid division and boundary conditions; dimensionality reduction and feature extraction are carried out on the flow field data by combining a convolutional neural network auto-encoder and a long short-term memory network; constructing a cross-fidelity neural network; performing error judgment on the prediction result, and checking whether the precision requirement is met or not; integrating the steps into a complete end-to-end flow field prediction framework; and outputting a high-fidelity flow field prediction result. The space and time features of the flow field can be efficiently extracted by combining the convolutional neural network auto-encoder and the long short-term memory network.

A system for adjusting gains for position control of a header of an agricultural harvester includes a header of an agricultural harvester, an actuator configured to adjust a position of the header relative to a field, and a computing system communicatively coupled to the actuator. The computing system is configured to receive data indicative of a contour of the field forward of the agricultural harvester relative to a forward direction of travel of the agricultural harvester. The computing system is further configured to determine an aggressiveness of the contour of the field based at least in part on the data indicative of the contour of the field. Additionally, the computing system is configured to adjust a gain for controlling the actuator based at least in part on the aggressiveness of the contour of the field.

The invention relates to the technical field of natural language processing, and discloses a power generation industry data intelligent treatment method, device and equipment based on a large model, and the method comprises the steps: constructing a multi-source heterogeneous task data set covering structured and unstructured information, completing the fine tuning training of a plurality of industry sub-fields based on a language model, forming a large language model set with specific scene adaptability; constructing a multi-view semantic representation structure for actual input data, integrating modeling task intention, application scene and model adaptability, predicting an optimal target model and a Top-K candidate model, and generating a unified semantic embedding vector; and realizing accurate matching of the structured knowledge fragments through the graph neural network. The problems that an existing model is insufficient in semantic understanding, inflexible in model selection and inaccurate in knowledge calling in the data management process are solved, the requirements of diversified tasks for accuracy and specialty are met, and then management of data assets is facilitated.

The invention discloses a high-precision three-dimensional reconstruction method based on sparse depth, and the method comprises the steps: inputting an RGB-D image sequence of sparse depth, effectively filling the missing depth information in each frame of sparse depth image through a depth completion network trained on a public data set, and reasoning the semantic information of each frame through a pre-trained visual large model Dinov2; for each frame of RGB image, a combined sampling method based on feature point sampling and uniform sampling is adopted to extract sampling points of light, and spatial three-dimensional points are generated through importance sampling and random sampling strategies; performing position coding on the three-dimensional space sampling points to code low-frequency features, processing high-frequency features by using Hash coding, fusing the low-frequency features and the high-frequency features, inputting the fused high-frequency features and the fused high-frequency features into a neural network, and predicting a color value, an SDF value and a semantic value; and rendering and optimizing the pose of the current frame according to the current scene representation information, comparing a predicted value with the real color, depth and semantic information of the current frame by taking a global key frame as a sliding window, and optimizing implicit scene representation, thereby realizing reconstruction of a high-fidelity three-dimensional model from the sparse depth RGB-D image sequence. The method effectively solves the problem that a traditional three-dimensional reconstruction technology is insufficient in reconstruction precision under the sparse depth data condition, has a wide application prospect, and is particularly suitable for the three-dimensional reconstruction related field based on RGB-D data.

A search assistant engine is described that integrates with a data intake and query system and provides an intuitive user interface to assist a user in searching and evaluating indexed event data. Additionally, the search assistant engine provides logic to intelligently provide data to the user through the user interface such as determining fields of events likely to be of interest based on determining a mutual information score for each field and determining groups of related fields based on determining a mutual information score for each field grouping. Some implementations utilize machine learning techniques in certain analyses such as when clustering events and determining an event templates for each cluster. Additionally, the search assistant engine may import terms or characters from user interaction into predetermined search query templates to generate tailored search query for the user.

The invention relates to the field of civil engineering, and discloses a foundation pit finite element model parameterization rapid modeling method which comprises the following steps: constructing a finite element model through foundation pit geometric information and soil characteristics, simulating soil response by adopting a nonlinear constitutive relation and a random process, and realizing foundation pit stability optimization and dynamic adjustment; the invention further provides a foundation pit finite element model parameterization rapid modeling system which comprises an input module, a modeling module, a constitutive model module, a random process module, a simulation module, an optimization module, a mesh refining module and a real-time monitoring module. According to the method, the foundation pit finite element model is automatically generated, the Kachanov-Delace nonlinear soil constitutive model is introduced, the soil elastic modulus random field is generated based on the Gaussian process, and the model parameters are dynamically adjusted in real time, so that the modeling process is simplified, the calculation precision is improved, the nonlinear behavior and dynamic response of the soil are accurately simulated, and the method is suitable for large-scale popularization and application. And the stability and the safety in the foundation pit design and construction process are obviously improved.

The invention discloses an ocean three-dimensional temperature and salt field prediction method based on remote sensing data driving, and the method comprises the steps: firstly constructing a satellite remote sensing sea surface temperature data set, a sea surface height anomaly data set and an ocean three-dimensional reanalysis data set which are needed by an experiment; then, on the basis of a convolutional neural network and a Transform module, a basic framework of the model is constructed according to the temporal and spatial variation characteristics of the remote sensing data and the temporal and spatial variation characteristics of the three-dimensional temperature and salt field; the model adopts an end-to-end design mode, and adapts to planar spatial-temporal variation of historical remote sensing data through a two-dimensional convolutional coding structure; the three-dimensional spatial-temporal change of a future three-dimensional temperature and salt field is concerned through a three-dimensional convolution decoding structure; an attention mechanism and a feature evolution module are designed according to the spatial-temporal features of the two kinds of data, and the change relation of the remote sensing data and the temperature and salt field data under different spatial-temporal dimensions is learned. And finally, inputting a mode of combining satellite remote sensing temperature of historical n days and sea surface height anomaly into the trained model to obtain three-dimensional temperature and salt field data of future n days.

The invention discloses a pipeline flow field remodeling method based on LAAF-PINN, and the method comprises the steps: collecting the flow field data of a pipeline measurement point; dividing the measuring points into monitoring points and testing points, and preprocessing the data sequence; constructing an LAAF-PINN, randomly selecting a matching point along a pipeline, and inputting a time-space sequence of the monitoring point and the matching point to obtain model output; calculating a data loss item according to the model output of the monitoring point, calculating a residual error according to the model output of the collocation point to obtain a partial differential equation loss item, and combining the two items to obtain total loss; after multiple rounds of iteration updating, a trained PINN model is obtained, a time-space sequence of a test set is input, and corresponding flow field information can be quickly and accurately reconstructed. According to the method, the LAAF-PINN is applied to pipeline hydraulic transient research, an existing flow field remodeling method is expanded, good robustness is achieved for data containing uncertain factors, and meanwhile the problem that the calculation precision is not high possibly existing in forward simulation is solved.

The invention relates to the technical field of big data and the field of financial science and technology, and discloses a data dependency relationship confirmation method, which comprises the following steps of: importing metadata information containing a field name, a data table name and data source information into a data processing module to obtain a target field name, and retrieving a target field in the metadata information by utilizing the data processing module to confirm a data dependency relationship. Determining an upstream data source and a downstream data destination of the target field; generating upstream and downstream data link diagrams of the target field, wherein the upstream and downstream data link diagrams are used for identifying a flow relation of data, a data generator and processing logic; and analyzing a dependency relationship between the target field and the upstream and downstream fields based on the link diagram, and further determining a data table on which the target field depends and owner information of the data table. According to the method, through automatic analysis and link diagram generation, the original complex process needing manual query and processing is simplified, the data dependency relationship is clearer, the communication cost and query time of developers are reduced, and the data use accuracy and processing efficiency are effectively improved.

The invention relates to a bidding document total-factor blind-state desensitization method fusing multi-modal artificial intelligence, and the method comprises the following steps: S1, obtaining an original bidding document package, carrying out the data preprocessing, and obtaining a standardized document set; s2, according to the standardized document set, performing multi-modal content identification and alignment to obtain a multi-modal content library; s3, constructing a sensitive information classification system based on domain expert knowledge and a historical case library; s4, constructing a dynamic rule base according to the sensitive information classification system; s5, according to the multi-modal content library and the dynamic rule library, in combination with deep learning, multi-modal sensitive information intelligent detection is carried out, and a sensitive area set is confirmed; and S6, based on the dynamic rule base, performing intelligent desensitization on the sensitive area set to obtain a desensitized document. According to the method, the risk of sensitive information leakage is effectively reduced while the bidding file expression effect is improved.

The invention provides a text hotspot clustering method based on a large language model. The method comprises the following steps: obtaining abstract output of a text from to-be-processed text data in a summarizing manner by utilizing the large language model; dividing the text abstract according to fields based on a large language model to obtain classification tags; performing element extraction on the text abstract based on a large language model to obtain element information; taking the text abstract as input, and using a vector embedding model to obtain semantic vector output; original text attributes, text abstracts, classification tags, element information and semantic vectors are stored in a vector library; a retrieval condition is formed according to business requirements, and a text candidate set is recalled from the vector library; and according to the text candidate set, obtaining a clustering result by utilizing a stage optimal clustering algorithm, taking the text abstract set of each cluster as input, obtaining a hotspot abstract summary based on a large language model, and integrating into a final hotspot result. According to the method, the efficiency and accuracy of large-scale dynamic text data analysis can be improved.

The invention provides a remote sensing image-based in-field agricultural machine driving efficiency suitability evaluation method. The method comprises the steps of S1, extracting fine field attribute information based on a high-resolution remote sensing image; s2, extracting field obstacle information and field terrain information based on the field attribute information; s3, constructing a key evaluation index set influencing the running efficiency of the agricultural machinery in the field; s4, index weighting based on a geographical weighted attention mechanism; s5, establishing an in-field agricultural machine driving efficiency suitability evaluation model, and positioning key negative impact indexes; and S6, grading the suitability level of the agricultural machine driving efficiency based on a natural breakpoint method. The invention further provides a system for evaluating the suitability of the running efficiency of the agricultural machinery in the field based on the remote sensing image. According to the method, the field-level key index set is constructed, the geographical weighted regression and the attention mechanism are innovatively combined, the regional difference and the field characteristics are fully considered, and the scientificity and the practicability of evaluation are greatly improved.

The invention relates to the technical field of data processing, and discloses a method and a system for dynamically generating declaration receipts based on custom configuration, the method comprises the following steps: constructing a component library, the component library comprises multiple types of data item components, and each data item component is packaged into an independent module; based on the graphical configuration process, adding the components in the component library to a working area, and setting field attributes; setting a dependency relationship between fields based on conditional logic, defining a legality rule of the fields based on a data verification rule, and automatically storing a historical version in a configuration process based on version control; and based on preset authority and distribution setting, determining a filling person range and a filling time limit of the declaration receipt, and notifying a filling person. According to the invention, the form creation efficiency is improved, the maintenance cost is reduced, and the expandability and applicability of the system are enhanced.

The invention provides a construction method and device for a coal mine vertical field large model, and relates to the technical field of deep learning, and the method comprises the steps: obtaining field corpus data and general corpus data, and constructing a field word list and a general word list based on the field corpus data and the general corpus data; performing weighted fusion on the segmented words in the general word list and the field word list based on respective weights to obtain a fused word list; based on the fusion word list, utilizing domain corpus data and general corpus data to train the embedded model; loading a pre-trained original large language model, and replacing the embedded layer of the original large language model with the trained embedded model to obtain an updated large language model; and performing incremental pre-training on the updated large language model by using the domain corpus data and the general corpus data to obtain a coal mine vertical domain large model. The coal mine vertical field large model constructed by the method realizes accurate expression of professional knowledge of the coal mine industry.

The invention provides a power grid field table data reasoning and question answering method based on a code enhancement framework, and the method comprises the steps: building the code enhancement framework, processing the reasoning and question answering tasks of power project WBS table data through a large language model, and firstly carrying out the structure self-interpretation of the power project WBS table data; the method comprises the steps that firstly, the understanding ability of a model for a complex WBS table hierarchical structure and a task decomposition relation is improved, then based on table structure description and natural language questions formed by work content description proposed by a user, executable codes are automatically generated, related task information and resource configuration data in WBS table data are extracted, and the task configuration information is extracted; executing the code through an external code interpreter to obtain a result, and finally combining the code execution result and the reasoning ability of the model to generate a natural language answer about the number of human resources required for completing the specific work content; according to the method, the understanding, reasoning and people number estimation capabilities of the large language model during processing of complex WBS table data in the power grid operation and maintenance process can be effectively improved.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

The invention relates to a business process standard dynamic optimization method and system based on a large language model, and belongs to the technical field of computer application, and the method comprises the following steps: constructing a data collection strategy template; performing anomaly detection on the key field based on a preset three-level anomaly detection mechanism to obtain standard structured data; constructing a process execution accurate quantitative evaluation model, and constructing a process topological graph by using a process mining technology; calculating an abnormal probability of each node in the process topological graph by using a multi-task graph neural network, and obtaining a high-attention area in the process topological graph based on the abnormal probability; analyzing the multi-source heterogeneous data associated with the high-attention area through a large language model, positioning root causes, calculating root cause influence weights, and obtaining a root cause list; establishing an intelligent bidirectional feedback mechanism to generate optimization suggestions of the standard process; according to the method, the problems of serious data redundancy, insufficient standard dynamics and lack of feedback mechanisms in the traditional flow standard optimization technology in the power industry are effectively solved.

The invention discloses an enterprise information dynamic modeling method based on multi-dimensional data driving, which relates to the technical field of enterprise information modeling, and comprises the following steps: calculating semantic similarity and semantic offset amplitude among fields according to time sequence changes of semantic vectors in a plurality of modeling cycles, identifying whether driving dimension fields are replaced or not, and if yes, judging whether the driving dimension fields are replaced or not; generating a field replacement matching pair for recording a field replacement relationship; and on the basis of the field replacement matching pair and in combination with a historical modeling path structure, establishing a dependency mapping relationship between the driving dimension field and the path node, executing path structure backtracking calculation, determining a modeling path dependency state under the condition that the driving dimension field is replaced, and generating a path dependency identification matrix. According to the method, the problem of modeling path mismatch caused by drive field replacement is solved, automatic identification and dynamic updating of the modeling structure are realized, and the accuracy and adaptability of the model are improved.

The invention provides a vertical field data construction method based on a large model, which belongs to the technical field of data processing and artificial intelligence, and comprises the following steps: converting a vertical field source document into an intermediate format text, and segmenting the intermediate format text into a plurality of text blocks; inputting the text blocks into a pre-trained generative language model, guiding the pre-trained generative language model according to pre-designed cue words to generate a plurality of candidate questions according to the content of the text blocks, and performing preliminary screening and fine screening on each candidate question to obtain a question set; pre-defining a mode of a knowledge graph according to field characteristics of the vertical field, processing all text blocks based on an information extraction model, and constructing a field knowledge graph; and performing local context retrieval on each final question in the question set based on the text block of the question source, performing global knowledge retrieval based on the domain knowledge graph, and generating a final answer and a final thinking chain. The method is suitable for different vertical fields, the data quality can be effectively improved, and the problem generation accuracy is guaranteed.

The invention relates to the technical field of land layout, and discloses a BIM platform-based land reclamation project automatic rapid layout method and system, and the method comprises the steps: obtaining the site topographic data of an excavation region, obtaining an original landform BIM model of a field based on the data, carrying out the screening, enabling the screened original landform BIM model of the field not to comprise a super-slope region, and carrying out the screening of the original landform BIM model of the field; arranging a terrace according to the contour line to obtain a field piece line, performing grouping based on the contour line interval where the height of the point cloud data is located, calculating a field surface design elevation, and establishing a preliminary terrace model on the basis of the original landform BIM model of the field piece based on the field piece line, the gross width of the terrace and the field surface design elevation; and establishing a ridge and ridge model on the basis of the preliminary terrace model according to the field surface clear width, the ridge width and the ridge height. According to the invention, a convenient, rapid and intelligent field distribution scheme is provided for a land leveling construction project in the hilly area, so that fine improvement construction of the terrace type terraced field in the hilly area is realized.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

The invention discloses a three-dimensional space velocity field measuring device and method suitable for a helicopter nozzle, and belongs to the technical field of aerodynamics test. The problems that in the prior art, a traditional three-dimensional velocity field measuring device and method are complex in arrangement and low in calculation efficiency are solved. A scanning type measuring device is built, a laser plane is calibrated, internal and external parameters of a double-exposure camera are calibrated, a checkerboard calibration plate is used for building a standard space plane grid, and a high-precision translation table is controlled by a computer to move to a designated position. Triggering each component through a digital pulse signal generator and synchronously controlling the two double-exposure cameras to collect particle images when the spray pipe sprays flow; and a three-dimensional velocity field based on the particle image is determined, and a three-dimensional space velocity field of jet flow of the jet pipe is obtained. According to the invention, high-precision and high-resolution velocity field data are effectively provided, accurate measurement of the three-dimensional velocity field of the helicopter nozzle is realized, and the method can be applied to measurement of the three-dimensional velocity field of the helicopter nozzle.

The invention provides a knowledge graph-based low-altitude economic domain question and answer teaching interaction method and system, and the method comprises the steps: firstly receiving a low-altitude economic domain teaching problem which is inputted by a learner and comprises domain concept terms and association relation description, and then carrying out the semantic analysis of the low-altitude economic domain teaching problem; the method comprises the following steps: extracting a core concept unit and logic association characteristics between concepts, calling a pre-configured domain association knowledge graph to carry out knowledge matching, generating a question answering candidate set containing a multi-level interpretation structure, carrying out teaching adaptability adjustment on the question answering candidate set, and generating teaching guidance contents conforming to the cognitive level of a learner. And finally, the teaching guidance content is presented to the learner, and feedback information is received to carry out teaching interaction in the next stage, so that the quality and efficiency of question and answer teaching in the low-altitude economic field are improved, and the learner is assisted in systematically mastering related knowledge.

The invention relates to an internal solitary wave identification and amplitude inversion method in mode data based on machine learning, and belongs to the technical field of ocean observation. In combination with a Mask-RCNN and a variational auto-encoder, firstly, sea surface height data is preprocessed and imaged, texture features are extracted through a local binary pattern algorithm, a labeled data set is constructed, model training is performed, and internal solitary wave stripes and positions thereof are identified from the labeled data set; through extraction and processing of temperature field data, the temperature field at the water depth is inversed by using a variational auto-encoder; and extracting the amplitude of the internal solitary wave in the temperature field based on the internal wave identification result. According to the method, large-scale and refined ocean mode data can be efficiently and accurately processed, and the efficiency and accuracy of data processing are remarkably improved. The method has a wide application prospect, and especially in marine disaster early warning, marine scientific research and internal wave mechanics research, the automatic identification and amplitude extraction capability of internal waves can be greatly improved.

The invention relates to the technical field of geological engineering, discloses an agent-based three-dimensional geological model auxiliary construction method and device, and aims to solve the problems of low efficiency and accuracy, slow rendering response and few interaction functions of an existing method. The scheme mainly comprises the following steps: generating a three-dimensional generalized model base based on an AI model; performing verification and correction by using a graph neural network to obtain a three-dimensional compliance model base; an optimized geological parameter field is obtained based on Fourier feature mapping and a GeoFormer-PT model; embedding the optimized geological parameter field into three-dimensional software to realize dynamic visualization of a three-dimensional geological model; carrying out visual enhancement on the three-dimensional geologic model; an interactive operation platform is introduced into three-dimensional software. According to the invention, the construction efficiency, accuracy and rendering response speed of the three-dimensional geologic model are improved, and a man-machine interaction function is added.

The invention provides an intelligent question and answer method and device based on domain knowledge lightweight fine tuning and a cross-domain dynamic knowledge base and a readable storage medium thereof.The method comprises the steps that the cross-domain dynamic knowledge base is constructed, and multi-domain heterogeneous data is processed through a grading and blocking strategy; dynamic expansion and incremental updating are realized through hierarchical vector indexing (bottom-layer knowledge block embedding + upper-layer field incidence matrix); a two-stage lightweight fine tuning strategy is designed, a high-rank adapter is used for roughly aligning a field semantic space in a pre-access stage, and a low-rank adapter is switched for optimizing a retrieval result in a post-access stage, so that the consumption of computing resources is remarkably reduced; and on the basis of a domain correlation reordering model, the retrieval result is optimized by fusing the original similarity, the term coverage and the historical matching degree, and the cross-domain knowledge correlation is improved. Through closed-loop cooperation of the knowledge base, the fine tuning model and reordering, the problems of poor migration ability, knowledge updating lagging, high customization cost and the like in the traditional system field are solved.