23results about How to "Improve logic" patented technology

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.

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.

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.

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.

The invention discloses a knowledge graph retrieval enhancement generation method and device. The method comprises the steps that query information is received, and an anchor point set is determined according to the query information and an index graph; determining potential search paths according to the anchor point set, wherein the potential search paths comprise at least one search path; calculating an average value of resources flowing through each search path in the potential search paths, and screening out K search paths with the highest average value as core search paths; converting each search path in the core search path into a natural language text by sequentially splicing description information on the search paths; generating enhanced information representation according to the natural language text, the starting node and a one-hop neighbor node of the starting node; and inputting the enhanced information representation and the query information into a second large language model to obtain an answer corresponding to the query information. By adopting the method, the calculation amount is reduced, and the accuracy of model generation is improved.

The invention provides a renal artery stenosis ultrasonic diagnosis report automatic generation method, and relates to the technical field of image data processing, and the method comprises the steps: inputting a plurality of renal artery ultrasonic images, carrying out the redundancy processing, calculating the semantic correlation between each image and a diagnosis text, and screening out a key image; performing feature extraction and enhancement on the screened key images, constructing a static medical knowledge memory matrix, and aligning enhanced visual features with the static medical knowledge memory matrix to generate knowledge alignment vectors; maintaining a dynamic memory network, and injecting the knowledge alignment vector into the dynamic memory network; and fusing the external knowledge token into a code, and finally outputting a structured renal artery ultrasonic diagnosis report based on the enhanced visual features, the external knowledge token and the dynamic memory network state. According to the method, redundant information processing and fine-grained feature extraction of the renal artery ultrasonic image are realized, and external medical knowledge is guided to generate a long ultrasonic diagnosis report which is consistent in logic and has clinical interpretability.

The application discloses a method and device for generating a data visualization narrative based on a voiceover, an equipment and a medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: generating a voiceover feature at a sentence level according to voiceover content input by a user; generating corresponding plot information for the voiceover feature; determining data feature information corresponding to a data feature name in the voiceover feature according to data input by the user; generating visualization structure information of the voiceover feature according to the data feature information; generating animation structure information of the voiceover feature according to the voiceover feature, the plot information corresponding to the voiceover feature, the data feature information and the visualization structure information; obtaining to-be-rendered data based on the voiceover feature and the plot information, the data feature information, the visualization structure information and the animation structure information corresponding to the voiceover feature, rendering the to-be-rendered data, and generating a data visualization narrative picture. The highly personalized visualization narrative content is generated by driving the visualization narrative generation according to the user-defined voiceover.

The application discloses a kind of intelligent pavement friction performance evaluation method and system, method includes multi-source data acquisition, pavement image depth feature extraction, vehicle-road interaction friction performance evaluation and pavement friction performance dynamic evaluation.The application relates to the field of data processing, specifically refers to a kind of intelligent pavement friction performance evaluation method and system, the scheme of the application innovatively proposes to carry out vehicle-road interaction friction performance evaluation first, then the vehicle-road interaction result is combined with environmental data to analyze, accurate and comprehensive pavement friction performance evaluation is realized;Active shape model is used to extract pavement macrostructure features, and improved local binary texture method is used to extract pavement microstructure features, to improve the accuracy of vehicle-road interaction friction performance evaluation results;The calculation efficiency is improved, and the accuracy of the model output result is improved by introducing probability sparse self-attention mechanism, distillation mechanism encoder and one-time generation decoder to improve the Transform algorithm.

The application aims to provide an argumentation knowledge graph and an automatic construction method thereof, a model for simultaneously encoding text and the graph and a pre-training method thereof, and a method for secondarily pre-training the model by using graph information. The method comprises the following steps: organizing argument and evidence sentences into intra-sentence semantic graphs and inter-sentence logical graphs, and automatically constructing the graphs from original argumentation corpus; combining a pre-trained language model and a graph neural network to encode text and graph information, and realizing the interaction of the two types of information in the model, and pre-training the corresponding model on argumentation corpus through multiple self-supervised tasks; and expanding the pre-training corpus by using the graph information, adding corresponding pre-training tasks, and applying the tasks to the secondary pre-training of the model or other pre-trained language models. The application can effectively integrate information of various argumentation corpus, and provide a language model with clear and accurate argumentation semantics and logical information, which is helpful for the model to complete argumentation scene tasks requiring complex reasoning and argumentation.

The invention discloses an electronic component device reliability evaluation system and method based on big data, and relates to the technical field of reliability evaluation, and the evaluation method comprises the following steps: a monitoring system is installed to carry out the performance data collection of any electronic component device, and carries out the analysis of the use performance of the electronic component device; capturing the abnormal root causes, and dividing the types of the root causes; establishing a causal relationship chain among root causes; performing relation chain connection on the root causes contained in the electronic element appliance, and performing contribution degree analysis on the direct root causes which directly generate abnormities; analyzing influence conditions of indirect influence anomalies of other root causes in the relation chain; based on the influence and contribution condition of each root cause, carrying out reliability evaluation on the electronic component and appliance, and carrying out abnormity identification on the electronic component and appliance; the method breaks through the limitation of a traditional method in data scale and real-time performance, achieves the early recognition and dynamic tracking of abnormal conditions, and effectively reduces the fault risk of a system.

The invention discloses a test data generation method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring online behavior data and online behavior event information obtained by aggregating the online behavior data; and creating an online behavior knowledge graph according to the online behavior data. And fusing and generalizing the associated key statements extracted from the description information of the online behavior event information and the associated graph information extracted from the online behavior knowledge graph to obtain online behavior data to generate background information. And inputting the online behavior data generation background information into the logic prediction model, and determining intermediate data in the online behavior data generation background information through the logic prediction model. And fusing the data included in the intermediate data to obtain target data. And performing mask operation on the sensitive words in the target data to obtain mask operation data. The test data is determined based on the mask operation data, and the quality of the test data can be improved.

The application provides an interview question divergent generation method, comprising the following steps: S1, obtaining current question data and candidate's current answer data in the interview interaction process, and simultaneously loading interview investigation target data, post image, candidate background auxiliary reference data; S2, performing dimension analysis and path coverage analysis on the current question data and the current answer data, identifying the opened investigation direction of the current question, the covered investigation direction of the current answer, and marking the to-be-investigated direction which is not expanded; the application realizes systematization and multi-level divergent generation of the interview question by obtaining the interview interaction data, performing path coverage analysis, constructing a dynamic update investigation space expansion diagram, generating and evaluating candidate questions, outputting optimal questions and correlation update, has systematization and multi-level divergent capacity, and significantly improves the coverage, pertinence, logic, dynamic adaptability and traceability of the interview evaluation.

This invention relates to the field of permanent magnet motor design and optimization technology, specifically a high-efficiency integrated permanent magnet rotor magnetic circuit optimization design system, comprising: a configuration module, a topology generation module, an analytical calculation module, an index evaluation module, a parameter analysis module, and a scheme output module. The system generates multiple candidate magnetic circuit schemes based on initial parameters and performs rapid magnetic field analysis and comprehensive performance scoring using the equivalent magnetic circuit method. The parameter analysis module performs parameter perturbation analysis on the preferred schemes, accurately identifying the key design variables that have the most significant impact on performance; the scheme output module then iteratively optimizes these sensitive variables as the main optimization objects. This technical solution transforms traditional high-dimensional global optimization into low-dimensional directional search targeting key variables, reducing computational complexity and optimization time, effectively improving the efficiency and accuracy of permanent magnet motor rotor magnetic circuit design, and ultimately outputting optimal configuration parameters and a complete magnetic circuit design model.

This application relates to the field of automotive parts testing technology, and particularly to a method for detecting the clearance angle of an automotive brake adjustment arm control arm. The method includes: obtaining the forward and reverse contact points by bidirectionally rotating the control arm in both directions, achieving accurate identification of the contact points; based on the forward and reverse contact points and the forward and reverse force limit positions corresponding to the same preset force limit threshold, obtaining the total forward clearance angle, the forward force limit angle, and the reverse force limit angle, improving the accuracy of the testing data; enhancing the logicality of the method by constructing a condition set; and finally, accurately obtaining the geometric clearance angle and elastic torsion angle parameters based on the condition set, achieving separate detection of the two core parameters. This method solves the technical problem in existing testing schemes where the elastic torsion angle and geometric clearance angle of the adjustment arm and control arm cannot be separated in real time under industrial production line conditions, resulting in low clearance angle measurement accuracy and a high rate of false positives for qualified products.