45results about How to "Improve logic" patented technology

The invention relates to the technical field of artificial intelligence, and discloses a man-machine dialogue history and semantic retrieval-based prompt automatic construction method, which comprises the following steps of S1, performing text preprocessing on historical dialogue data and user input, including dynamically generating query keywords, verifying and analyzing a file uploaded by a user, and extracting core contents; s2, utilizing a pre-training language model to map the historical dialogue and the user request into semantic vectors, matching in combination with the keyword weight, and constructing a historical vector set and a user request vector; and S3, extracting a context fragment most relevant to the user request from the historical dialogue vector based on a convex optimization method. The context information highly related to the user request is accurately extracted through the sparse optimization technology, and the background information, the user request and the generation instruction are organically integrated in combination with the dynamic Prompt template design, so that the semantic correlation and context coherence of the generated reply are improved, and the interference of redundancy and irrelevant information is avoided.

The invention relates to the technical field of space positioning and path planning, in particular to a three-dimensional space positioning and path planning method for an unmanned aerial vehicle tower. The method comprises the following steps of performing accurate longitude and latitude height data acquisition based on a laser range finder to generate accurate longitude and latitude height data; obtaining tower equipment parameters; performing inspection point three-dimensional coordinate calculation according to the tower equipment parameters and the tower three-dimensional coordinate system to generate inspection point three-dimensional coordinate data; cruise point comprehensive constraint range data are obtained according to the unmanned aerial vehicle flight association data and the polling area terrain data, and cruise point comprehensive constraint range data are generated; performing cruise node three-dimensional coordinate design on the three-dimensional coordinate data of the inspection point according to the cruise point comprehensive constraint range data to generate cruise point three-dimensional coordinate data; and performing optimized cruise path planning processing according to the cruise point three-dimensional coordinate data to generate optimized cruise path data. According to the invention, more accurate unmanned aerial vehicle tower space positioning and path planning are realized.

The invention relates to a text semantic conflict detection method for constructing context association based on a large model. The method comprises the following steps: S1, segmenting an original text by using a segmentation function; s2, performing triple information extraction on each segmented text by using a large model to generate a data set of an entity corresponding relationship; s3, extracting data groups of all entity pairs and relationships, and performing confidence evaluation; s4, aggregating all triads, constructing a affair graph, and pointing out a contradictory relationship with relatively low confidence in the affair graph; and S5, for the found contradictory relationship, performing semantic conflict repair in the text in combination with the large model. According to the method, the accuracy of information generation of the large language model is remarkably improved in a context association mode.

The invention provides a big language model-based intelligent analysis system for a newly created enterprise, and the system comprises a data collection module which is used for collecting data of the newly created enterprise; the data preprocessing module is used for preprocessing the data of the initially created enterprise; the vector database and knowledge graph construction module is used for constructing a vector database and a knowledge graph based on the preprocessed initially created enterprise data; the context information construction module is used for carrying out deep semantic analysis on related information, input by a user, of the to-be-analyzed enterprise, and fusing a semantic analysis result with data of the vector database and the knowledge graph to obtain context information; and the intelligent analysis module is used for carrying out intelligent analysis on the context information based on the initially created enterprise research analysis auxiliary large model, obtaining development state data of the to-be-analyzed enterprise, and completing initially created enterprise intelligent analysis based on the large language model. And comprehensive and efficient retrieval and processing of related data of the initially created enterprise are realized.

The invention provides a power industry knowledge question-answering method and system based on a large language model, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining power industry data; constructing a knowledge graph with node weights; acquiring a training set taking the power industry question as a feature and taking the power industry answer as a label; constructing a generative question and answer model with knowledge graph embedded information; establishing a generation loss function; taking the generation loss function value smaller than a preset generation loss function value as a target, training the generative question-answer model to determine an optimal hyper-parameter of the generative question-answer model; obtaining a to-be-processed power industry problem; and inputting a to-be-processed power industry question into the trained generative question and answer model, and outputting an expected power industry answer corresponding to the to-be-processed power industry question. And in combination with the knowledge graph with the node weight and the generative question and answer model, the interference of training set noise on model output answers is reduced, and the consistency and accuracy of the generated answers are improved.

The invention belongs to the field of non-intrusive load monitoring, and discloses a household electricity consumption analysis method based on non-intrusive load monitoring and related equipment, firstly, non-intrusive monitoring is realized by using household total electricity meter data instead of a single equipment electricity meter, and the complexity and cost of household electricity consumption data collection are reduced; secondly, an unsupervised learning method is adopted to cluster the power load state data, data does not need to be marked in advance, and different load events are automatically identified and classified, so that the problems of load type diversity and high identification difficulty are effectively solved, and the accuracy and efficiency of load event detection are improved; in combination with a depth-first search algorithm and load event constraint conditions, fine search is carried out on load event labels, an undetermined event sequence set is generated, the logicality and continuity of event sequences are enhanced, electricity utilization behavior analysis of the electric equipment is deeper and finer, home users can better understand and optimize own electricity utilization modes, and the user experience is improved. And energy conservation, emission reduction and efficient utilization of energy are promoted.

The embodiment of the invention discloses a page component layout method and device, computer equipment and a storage medium. The method comprises the steps that a target layout resource and a target layout rule of a target low-code development platform are obtained, the target layout resource comprises a plurality of components carrying different element features, and the element features comprise size features, hierarchical relation features and element priority features; based on a grid layout algorithm, a target layout rule, the size features corresponding to the components and the hierarchical relation features, the multiple components are placed in a grid system of the current page, and a preliminary layout page of the target layout resources is obtained; based on a simulated annealing algorithm, a target layout rule, the hierarchical relation characteristics of the components and the element priority characteristics, performing position optimization on the components in the preliminary layout page to obtain a first target layout page; and outputting the first target layout page. By implementing the method provided by the embodiment of the invention, the layout of the middle page component of the low-code development platform can be automatically adjusted.

The invention relates to the technical field of information processing, and particularly discloses a geological text generation method, system and device based on a large language model, and the method comprises the steps of text data collection, data set construction, data preprocessing, large model construction, model fine tuning and the like. And intelligent text generation of stratum lithology description based on the deep learning large language model is realized. According to the method, the ChatGLM big language model is trained, fine tuning is carried out based on the P-Tuning v2 technology, and meanwhile, the geotechnical field knowledge is fused, so that the model can understand and apply the professional knowledge of the geological lithology, the stratum lithology description text meeting the requirements of the engineering geological segmentation specification is intelligently generated according to the known lithology description information, the continuity is good, the logicality is high, and the method is suitable for being used in the field of engineering geology. The whole method is simple to operate, can efficiently improve the efficiency of manual data arrangement and analysis, and is convenient to popularize and apply.

The invention relates to the technical field of data processing, in particular to a large transport vehicle identification method and system based on radar and vision fusion. The method comprises the following steps: collecting a monitoring radar signal on the way of a transport vehicle through radar monitoring equipment; carrying out transport vehicle characteristic analysis on the radar signals according to the transport vehicle on-the-way monitoring radar signals, and generating transport vehicle characteristic data; collecting on-way visual data of the transport vehicle by using high-resolution monitoring equipment; carrying out transport vehicle on-way visual feature analysis according to the transport vehicle on-way visual data, and generating transport vehicle on-way visual feature data; and according to the transport vehicle characteristic data and the transport vehicle on-way visual characteristic data, carrying out transport vehicle external heterogeneous data mapping and transport vehicle dynamic characteristic mode identification, and respectively generating a transport vehicle external heterogeneous data matrix and transport vehicle dynamic characteristic mode data. According to the method, the vehicle identification of the large transport vehicle is carried out based on radar and vision fusion, and the vehicle information of the large transport vehicle is identified more comprehensively.

The invention belongs to the field of artificial intelligence, and relates to an abstract text generation method which comprises the following steps: acquiring a to-be-processed text, and encoding the to-be-processed text to obtain a plurality of segmented words of the to-be-processed text and encoding information corresponding to each segmented word; determining key segmented words from the plurality of segmented words, and constructing an adjacent matrix of the to-be-processed text based on the key segmented words; performing feature fusion on the coding information and the adjacent matrix to obtain a node feature matrix of the to-be-processed text; performing feature optimization processing on the node feature matrix to obtain a target node feature matrix of the to-be-processed text; and generating an abstract text of the to-be-processed text based on the target node feature matrix. The invention further provides a device, equipment and a medium. In addition, the invention also relates to a block chain technology, and the to-be-processed text, the plurality of segmented words, the coding information, the adjacency matrix, the node feature matrix, the target node feature matrix and the abstract text can be stored in the block chain. The abstract text generation accuracy can be improved.

The invention relates to the field of mechanical arm trajectory tracking, in particular to a robot tracking control learning method based on postmortem screening experience playback, which comprises the following steps: initializing target network parameters and an experience pool, and storing a state transition tuple in the experience pool; when the number of the state transition tuples in the experience pool is larger than the preset number of the state transition tuples, the action complex state is determined according to the track smoothness and the node covariance degree of the robot mechanical arm; determining an experience pool optimization mode according to the complex state of the action; selecting experience of which the estimated position deviation is greater than or equal to the standard estimated position deviation as screening experience, and determining an experience selection mode according to the number of the screening experience; updating the cr-it ic network and the actor network, and carrying out soft updating on the target network of the cr-it ic network and the target network of the actor network respectively; and according to the reward evaluation value, soft update weights of the target network of the cr-it-ic network and the target network of the actor network are adjusted. According to the method, the tracking effect and the tracking stability of the model are improved.

The application discloses a modal balance and semantic enhancement training method of a full-modal safety guardrail, which significantly reduces the missed detection and false rejection of cross-modal queries by improving the training data and designing the training target, and improves the safety and reliability of the full-modal guardrail. The application efficiently constructs modal de-biased data, constructs a multi-modal query quadruple based on semantic pairing of a text seed query, and enumerates combinations of modal subsets and safety semantic labels to construct risk variant query samples. The data construction method can efficiently expand the training data based on a small amount of text samples, so that the safety and unsafe states of various modal combinations are comprehensively covered, the model is prevented from learning modal shortcuts during the training process, the dependence of the guardrail on different modal safety semantics is rebalanced, fine-grained safety supervision signals are efficiently expanded, and the cross-modal safety semantic understanding of the guardrail model is improved.

The invention discloses a power load characteristic multi-scale analysis method based on multi-source data and dynamic clustering, and relates to the technical field of power analysis, and the method comprises the following steps: fusing power, production, policy, environment and other multi-source heterogeneous data, and carrying out space-time alignment; cleaning the load data and identifying a characteristic time period influenced by the specific factor according to the auxiliary information; decomposing the load curve and recombining the load curve into components with definite physical significance according to auxiliary data such as a production time sequence; performing context-fused dynamic clustering and mode correlation analysis on each load component according to the time scale; and extracting refined characteristics and adjustable potential indexes, and generating a visual resource map capable of supporting scheduling. According to the method, by introducing a polymorphic load component separation and multi-scale collaborative clustering mechanism, cause decoupling and refined mode recognition of complex industrial loads can be realized, and the problems that in an existing method, mode confusion is likely to occur, and load characteristics under different driving mechanisms are difficult to distinguish are solved.

The application discloses a semiconductor thin film deposition process intelligent recommendation system and method based on a knowledge graph, belongs to the technical field of semiconductor manufacturing processes, and comprises the following: a semiconductor thin film deposition process knowledge graph construction module; a prompt word template generation module: used for receiving process requirements and constraint conditions input by a user, performing semantic analysis on the process requirements and constraint conditions, filling the analysis results into a preset prompt word template, and outputting standardized instructions meeting the calling requirements of a large language model; and a large language model reasoning module: used for receiving the standardized instructions, performing semantic retrieval and logical reasoning on the standardized instructions within the constraint range of the semiconductor thin film deposition process knowledge graph, generating a plurality of candidate thin film deposition process schemes meeting process constraint conditions based on entity relationships and causal associations in the semiconductor thin film deposition process knowledge graph; and the application constructs a complete process recommendation solution and exhibits significant advantages in multiple aspects.

The invention provides a building energy consumption analysis method and system based on an MCP service model and an intelligent agent, and relates to the technical field of building energy saving and artificial intelligence, and the method comprises the steps: constructing and packaging an MCP service model, and abstracting the MCP service model into a standardized function interface after packaging; acquiring and storing operation equipment and equipment operation parameters in the building; an input request of a user is obtained, a task intention in the input request is recognized, the task intention is converted into specific API calling according to the task intention, and the API obtains structured energy consumption data by calling an MCP function interface; combining the structured energy consumption data returned by the MCP with the cue word template, transmitting the combined Agents to the large language model, and outputting to obtain a report; and visualizing or exporting the report. According to the invention, full-automatic generation from complex building energy consumption data to a structured report containing natural language analysis and visual charts is realized.

The present application discloses a method, device and system for testing an operating system integrating multiple chips, belonging to the field of chip testing technology. The method includes: receiving input data from a user in a running panel of a test script; the test script is used to test multiple different chips with at least partially identical functional instructions; selecting a target chip for testing according to the input data; using the test script to test the operating system of the target chip using a target command parameter set. The present application establishes the same test script for multiple different chips with at least partially identical functional instructions, tests different chips by changing input data and command parameters, and realizes integrated testing of multiple chips with a single test process. Different chips can also interact with each other, and different command parameters can be tested by modifying the value of the command parameter. There is no need to write code for each command parameter test, which reduces code redundancy and improves test efficiency.

This invention relates to the field of expected functional safety testing technology for autonomous vehicles, specifically to an accident scenario screening and matching device and method for SOTIF testing requirements. The device includes a processing system comprising an acquisition module, an analysis module, a risk modeling module, and a matching module. The risk modeling module uses Fault Tree Analysis (FTA) to perform a structured decomposition of SOTIF risk causative factors, quantifies the importance of risk elements through a combination of subjective and objective weighting, and outputs a comprehensive weight for each risk element. The matching module combines the comprehensive weight of risk elements to weight and enhance the accident scenario text, filtering out accident scenarios that match the test requirement text. This invention achieves precise matching between test requirements and accident scenarios by semantically associating risk elements with accident scenario text in an accident database.

The invention relates to the technical field of government affair informatization, in particular to a government affair knowledge graph construction method and system based on artificial intelligence, and the method comprises the steps: obtaining multiple pieces of multi-source government affair data, carrying out the format unification of the multiple pieces of multi-source government affair data, obtaining multiple unified texts, carrying out the cleaning segmentation of the multiple unified texts, obtaining multiple text segments, and carrying out the segmentation of the multiple text segments; performing subject recognition on the plurality of text segments to obtain a plurality of government affair entities, performing normalized alignment on the plurality of government affair entities to obtain a plurality of unique entities, obtaining a plurality of semantic relationships of the plurality of unique entities, and constructing a triple set by using the plurality of unique entities and the plurality of semantic relationships, and constructing a government affair knowledge graph by utilizing a plurality of triads in the triad set, and automatically generating a rule required by the rule engine by the government affair knowledge graph. The method can solve the problem that intelligent application of government affair knowledge is difficult due to dispersion of multi-source government affair data and low manual integration efficiency.

The invention provides a time sequence-based children drug-induced liver injury data fusion and prediction method and system, and relates to the technical field of children drug-induced liver injury information process.The method mainly comprises the following steps: collecting historical medical data of hospitalized children as a longitudinal data set, transversely arranging the data according to a time sequence, and constructing a clinical database; performing entity relationship labeling on unstructured information in the research data set based on related event phenotypes of the child drug-induced liver injury; an MUIRFTI model is constructed and trained, and is used for carrying out structured mapping reconstruction based on time sequence information on the unstructured text; a machine learning prediction model is trained to predict whether the drug-induced liver injury is attacked or not; and inputting medical data of a detected child patient into the machine learning prediction model, and predicting whether the child patient suffers from the drug-induced liver injury or not. According to the scheme, the information amount and logicality of the research data set can be greatly improved, so that the machine learning prediction model which has a better effect and is used for predicting the drug-induced liver injury of children is trained.

An embodiment of the present application provides a method and device for sending data information, a storage medium, and an electronic device, wherein the method includes: during the process of operating system startup, obtaining a target processor that communicates with the operating system, wherein the target processor includes N levels of processors connected in sequence, N is an integer greater than 1, and the target processor is used to implement step-by-step processing of data information; according to the category of the data information and the processing rules corresponding to the target processor, the data information is sent to the target processor step by step, wherein the processing rules are used to indicate the category of the data information to be processed by the target processor, and each time the data information is sent, the messages to be sent are filtered according to the processing rules of the lower-level processor as the recipient, thereby solving the problem in the related art that the operating system may receive a large number of irrelevant messages, resulting in a waste of operating system resources.

This application relates to the field of retrieval enhancement and generation technology, and in particular to a method and system for collaborative construction of multi-source knowledge bases and text question answering in a professional domain. The construction method includes acquiring professional domain text data and performing block processing on the professional domain text data; constructing an initial knowledge graph, a text knowledge base, and a question answering knowledge base; binning the entity set in the initial knowledge graph by type, extracting connected components to obtain candidate entity clusters, employing a two-layer scheduling strategy for semantic determination using a large language model, and progressively solidifying the determination results and performing targeted supplementary judgments on unresolved nodes; updating the initial knowledge graph to form a knowledge graph library; and obtaining a multi-source heterogeneous knowledge base. The method and system for collaborative construction of multi-source knowledge bases and text question answering in this professional domain can solve the problems of insufficient quality in vertical domain knowledge graph construction, low entity disambiguation accuracy, and high computational cost, and also features high stability and adaptive adjustment.

The utility model discloses a compact single-action rotary clamping air cylinder, and belongs to the technical field of air cylinders. Comprising a rotating cylinder which is provided with a rotating shaft capable of rotating around the axis of the rotating shaft; the linear air cylinder is provided with a piston rod, and the piston rod can do linear motion independently in the axial direction; the rotating cylinder is detachably connected with the linear cylinder through a fastener, the rotating shaft is matched with the piston rod through a key groove, and when the rotating shaft is static, the piston rod can independently execute linear motion; when the rotating shaft rotates, the piston rod is driven to rotate synchronously. Due to the fact that the rotating action and the clamping action can be independently controlled, an operator can flexibly adjust the operation sequence according to different working condition requirements, the operation flexibility and logicality of the device are greatly improved, the device can better adapt to complex working scenes, and the production efficiency and the product quality are improved.

The application discloses a kind of laser radar point cloud prediction method and system based on Transformer, it is related to three-dimensional environment perception technical field.The method first converts distance image by spherical coordinate projection conversion for historical point cloud sequence with semantic label, and utilizes semantic embedding module to map discrete semantic label into high-dimensional continuous feature;Subsequently, the geometric feature and semantic feature are spliced in channel dimension, to form the integrated composite feature tensor;The tensor is input into the improved Transformer network for training, the network adopts encoder-double-branch attention-decoder architecture, and is supervised using the composite loss function combining geometric loss and semantic loss;Finally, the network synchronously outputs future distance image and semantic probability distribution, and generates future three-dimensional point cloud with semantic attributes through inverse projection and color mapping.The application significantly improves the modeling ability of the motion law of different categories of objects and the prediction accuracy in complex dynamic scenarios by explicitly integrating semantic information.

The invention provides a large-scale generation method and system for interpretable question and answer pairs for the vertical field, and relates to the technical field of data processing, and the method comprises the steps: 1, constructing a cross-modal heterogeneous meteorological knowledge graph; heterogeneous graph neural network modeling is carried out on the cross-modal heterogeneous meteorological knowledge graph, an embedded vector of each node is learned, a knowledge graph with node embedded vectors is obtained, semantic community division is carried out on an entity based on the embedded vectors, and an optimized knowledge graph is generated. According to the method, through knowledge graph optimization, conical semantic space construction, structured evidence chain retrieval and pre-training large language model generation, large-scale generation of interpretable meteorological question and answer pairs in the vertical field is realized, and interpretability and credibility of question and answer results are improved.

The application discloses a kind of intelligent magnetic control low-voltage switch device, including magnetic control primary module and blade type secondary module;The magnetic control primary module includes first bottom shell, first top shell, plastic shell baffle, bottom plate, the first bottom shell, first top shell, plastic shell baffle, bottom plate form entire shell frame;The application can realize automatic opening and closing under the size requirement of original manual plastic shell circuit breaker, directly replace the existing old switch of station area, the opening and closing speed is greatly changed fast, improve mechanical performance, effectively increase the space capacity of secondary control module, realize high intelligence, one, secondary integral modular design, secondary module heat insulation design, secondary control module can be plugged, can realize not power replacement secondary module, improve the operation and maintenance ability of product, the service life of primary and secondary module is same period, greatly improve the service life of device, secondary vulnerable components can be plugged alone, improve the flexibility of device, with higher scientificity, logic.

The present application relates to a multi-objective task question-answering enhancement method based on a large language model intelligent agent, including: constructing a multi-objective task question-answering model based on a large language model intelligent agent; inputting the target to be processed into the task decomposition unit of the multi-objective task question-answering model to obtain multiple task contents; inputting multiple task contents into the task result acquisition unit of the multi-objective task question-answering model to obtain multiple task results; inputting multiple task results into the target result unit of the multi-objective task question-answering model to obtain the target result corresponding to the target to be processed; saving the target to be processed, task content, task result and target result to the local history library of the multi-objective task question-answering model, so as to enhance the target result of the multi-objective task question-answering model based on the local history library. The present application supports multi-objective complex tasks, inputs historical context and document knowledge as the prior knowledge base of the current question and answer, and enhances the logic between multi-objective contents and the accuracy of question and answer.

The invention relates to the technical field of computers, and relates to a video generation method and device, a computer program product and electronic equipment. The video generation method comprises the following steps: acquiring process information of a game; in response to a current touch operation for a target game role of the game, real-time state information of a current frame is generated according to the current touch operation and the process information, and the real-time state information is used for reflecting real-time states of a plurality of event layers in the game; performing feature coding on the real-time state information, and determining a global feature vector of the current frame according to a coding result; and acquiring global picture information of a previous frame of the current frame, and performing picture generation processing based on the global picture information and the global feature vector to generate a target picture of the current frame. According to the method and the device, the game video quality can be improved, the game picture can smoothly display the expected dynamic picture under the real-time operation of the user, and the visual experience of the game is improved.

This invention provides a method, apparatus, electronic device, and storage medium for generating product recommendation copy. The method includes acquiring fields of multiple products input by a user; using the fields of the multiple products as input to a text generation model; processing the fields of the multiple products based on the text generation model; and outputting product recommendation copy arranged in a specific order. The text generation model is trained using sample data. In this embodiment of the invention, the text generation model trained based on sample data can predict the fields of multiple products and output product recommendation copy arranged in a specific order, thereby enabling recommendations for multiple products and improving the logical consistency of product recommendation copy.

The application discloses an audit literature mind map generation method and system based on feature refining and a large language model, first extracts object theme information, text information, reference object information and hierarchical information in target audit literature and converts them into structured audit text information, and then adopts a hybrid weight model to calculate the global importance of keywords in the structured audit text information and extract target text keywords; the application realizes the function of efficiently generating corresponding audit mind maps for audit literature, and through the adoption of a graph neural network, the logical coherence of text object information can be enhanced and the logical relationship between different nodes can be ensured to be rigorous, and through rule constraints, the text information can be ensured to conform to the audit field specifications, which not only can enhance the systematicness and logicalness of knowledge organization, but also can finely control the generated content, and at the same time, can provide accurate audit literature reading assistance for users and greatly improve the understanding and analysis efficiency of audit literature.