515 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 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.

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 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 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 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 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.

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.

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 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.

The invention discloses a method and device for generating mass database table test data based on configuration, and the method comprises the steps: obtaining table building information, extracting table metadata from the table building information, and carrying out the standardization processing of the table metadata, and obtaining the standardized table metadata; obtaining a data generation template based on the standardized table metadata; modifying the data generation template according to a target demand to generate a target data generation template; extracting global variable information from a global configuration node of the target data generation template; analyzing a data generation rule corresponding to each field based on the global variable information, and generating test data based on the field generation rule; and based on the test data, generating an insertion statement, and writing the insertion statement into a preset file to obtain a final test data file. According to the embodiment of the invention, full-process automation from table metadata extraction to test data files can be realized, real association of data is guaranteed, and test effectiveness is improved.

The invention provides a method and device for constructing an intelligent agent, electronic equipment and a medium, and relates to the field of artificial intelligence, in particular to the field of large language models and intelligent agents. The method can comprise the steps that first user input is obtained, wherein the first user input is a natural language text containing agent construction requirements; determining one or more fields based on the first user input; obtaining a second user input associated with the one or more fields; obtaining a prompt statement based on the one or more fields and the second user input; and constructing an intelligent agent according to the prompt statement.

The invention provides a point table data checking method and system based on a knowledge vector library and a large model, and relates to the technical field of point table data checking, and the method comprises the steps: constructing a knowledge vector library storing a plurality of historical point table data standardized vectors, and adding a checking rule for each standardized vector based on domain knowledge; performing structured analysis on the to-be-processed point table data to obtain a target field and an unstructured field, performing word segmentation processing and entity recognition on the unstructured field by using a preset BERT-CRF model to obtain a processing result, integrating the target field and the processing result into a multi-tuple, and generating a target vector according to the multi-tuple; and calculating the similarity between the target vector and each standardized vector in sequence, recording the checking rule corresponding to the similarity greater than a preset threshold value as a target rule, calling the target rule to check the to-be-processed point table data and outputting a result, and the application can process unstructured data in the point table data.

A three-dimensional temperature gradient field visualization method comprises the following steps: after analyzing a part model and storing a point-surface relationship and temperature information, calculating a data point temperature gradient and carrying out point cloud, grid and entity model visualization; after creating temperature field color mapping and calculating a temperature field contour line, representing a temperature gradient field based on a point icon, representing a temperature gradient field based on a streamline and representing a temperature gradient field based on texture; through design of a data visualization core algorithm and model building, visualization of three-dimensional data of various vector fields for an unstructured grid model is realized.

The invention relates to the technical field of network security, and discloses a data desensitization and access control method based on data consanguinity and sensitivity identification, which comprises the following steps of: constructing a data consanguinity graph for describing derivation, circulation and dependency relationships among fields; identifying a sensitivity level of each field based on a field consanguinity context provided by the graph; in response to a data access request of a user, dynamically generating an access control strategy for the target field in combination with a service scene, a user role and the sensitivity level of the target field, the strategy specifying access permission, access denial or access after desensitization; and executing the strategy through a data interceptor deployed in a data access key layer, releasing and shielding the data or outputting the data after real-time desensitization by calling a specified algorithm. According to the invention, accurate identification and dynamic and consistent fine-grained access control of the sensitivity of the network data are realized, and the security protection capability of the network data is improved.

Certain aspects of the disclosure provide for information retrieval that exploits context derived from document structure. Source documents can be preprocessed to identify fields and determine context attributes related to each field based on the structural layout of a source document. Resource documents can also be preprocessed to segment a resource document into passages and determine context related to the passages based on structural layout. Queries pertaining to a field can be enhanced by adding context metadata associated with the field. A query embedding can be generated and compared with previously generated passage embeddings to locate candidate matches based on similarity. A machine learning model can be provided with the top-ranked passages and tasked with re-ranking the passages based on relevancy to the original query. The highest re-ranked passage or set of passages can be output in response to the query.

A ML-based method and system for automatically generating responses to inputs using a multi-tier technique, is disclosed. Initially, first inputs are obtained from electronic devices of first users. An information is retrieved from external sources based on first inputs, using RAG engine. The responses are generated based on optimized information, using ML model. The ML-based method determines whether queries of first users within first inputs are resolved through the optimized responses. Issues are categorized using ML-based issue classification engine when the queries are not resolved. Dynamic forms are generated with fields based on categorization of issues, using a dynamic workflow engine. Second inputs are obtained and processed to determine whether queries are resolved using a rule-based logic engine. The first users are adapted to interact with second users to resolve the issues through communication channels. The responses are provided as output upon resolving the issues, to first users.

The invention discloses a shock wave overpressure field reconstruction method based on physical information, and relates to the technical field of artillery launching, and the method comprises the following steps: building a multi-scene overpressure empirical formula library comprising a Henrych formula, an M.A.Sadovskyi formula and national defense design specifications, and embedding physical prior knowledge into a neural network for training through a weighted loss function; an attention module with a channel-space cooperative enhancement capability is formed by fusing the one-dimensional efficient channel attention, the extrusion excitation network and the dimension reduction CBAM; according to shock wave propagation characteristics, a forward shock wave field subtask and a muzzle rear wave front subtask are divided, and regional prediction is achieved through dynamic characteristic routing; and optimizing network parameters by adopting a low data volume training strategy, and outputting overpressure peak field distribution. According to the method, through construction of a physical data fusion constraint mechanism and an EC-SEAM mixed attention mechanism, network physical interpretation and near field mutation feature extraction capability are improved, and dependence on labeled data is remarkably reduced.

The invention provides a method and a system for constructing a time sequence knowledge graph in the water conservancy field based on a T-ATLOP model, which are applied to the water conservancy field, mainly comprise two key levels, namely a data layer and a knowledge extraction layer, and realize semi-automatic time sequence knowledge graph construction. The method comprises the following steps: carrying out batch document processing on water conservancy census data by utilizing a large language model through a prompt project, crawling water conservancy consultation of water conservancy halls of all provinces and cities by a web crawler, constructing a time sequence knowledge graph data set HE-DOCU through two-step method labeling of large language model assistance and heuristic strategy labeling, and carrying out a knowledge extraction layer part on the basis of an ATLOP model. A new model T-ATLOP is provided for knowledge extraction. And finally, storing the knowledge tetrad of the knowledge extraction layer by using a graph database to form a water conservancy field time sequence knowledge graph. According to the method, the more perfect water conservancy field time sequence knowledge graph is constructed by fully utilizing the water conservancy information, so that the accuracy and the intelligent level of querying related knowledge by hydrological employees can be improved.

The embodiment of the invention provides a data query method and system. The data query method comprises the steps that a data query request associated with a target field is received; identifying query intention information corresponding to the data query request, and constructing a query prompt word corresponding to the data query request according to the query intention information; the query prompt word is input into a target distillation model to be processed, a data query sequence corresponding to the data query request is obtained, and the data query sequence comprises a data query statement and a data query reasoning chain associated with the data query statement; and executing the data query statement to obtain data query information, and reading target data in a target data table associated with the target field based on the data query information to serve as a response of the data query request.

The invention belongs to the technical field of marine environment monitoring and numerical simulation, and relates to a three-dimensional marine element field reconstruction method based on multivariable empirical orthogonal function decomposition. Comprising the steps of S1, multi-source data collaborative preprocessing; S2, MEOF joint modal extraction; S3, vertical projection operator optimization construction; S4, three-dimensional element field dynamic reconstruction; according to the method, through organic combination of multivariable modal decomposition and physical constraint projection, limitation of precision, efficiency and adaptability in three-dimensional field reconstruction in the prior art is broken through, key technical support is provided for real-time monitoring of the ocean dynamic environment, and the method can be widely applied to the fields of ocean disaster early warning, underwater engineering safety guarantee and the like.