19 results about "Applying knowledge" patented technology

The invention discloses a music stave sentiment classification method and system based on multi-level distillation, and belongs to the technical field of music information processing. The method comprises the steps of firstly collecting music stave data and converting the data into stave data vectors, then performing feature extraction by using a long short-term memory network, then constructing a teacher network and a student network for knowledge distillation, and realizing multi-level knowledge transmission through temperature scaling, KL divergence loss and mask feature distillation. And finally, training a lightweight classification model to complete sentiment classification. The knowledge distillation technology is creatively applied to staff sentiment classification, the classification accuracy is effectively improved through an online multi-level distillation mode, and the technical problems that a traditional method lacks semantic information and a self-supervised model is not suitable for sentiment tasks are solved. The method has the main advantages of high classification precision, light model weight, capability of effectively capturing music emotion features and the like.

The invention provides an automatic construction and semantic understanding enhancement method and device for a large model knowledge base. Internet multi-type data sources are integrated through multi-source data collection, and text data related to the large model application field is obtained; a knowledge extraction and fusion technology is utilized to extract key knowledge elements from the collected data and eliminate redundant contradictory information to form a unified knowledge representation system; based on the unified knowledge representation system, automatically constructing a knowledge base with rich semantic association according to a predefined structure mode, and introducing a data quality evaluation mechanism to guarantee the reliability of the knowledge base; and based on the constructed knowledge base, the deep semantic analysis technology is used to enhance the semantic understanding ability of the large model, including semantic coding, knowledge graph embedding, semantic reasoning and knowledge completion. According to the scheme, efficient, accurate and automatic construction of the knowledge base can be achieved, the semantic understanding ability of a large model is remarkably improved, the large model can understand and apply knowledge more accurately, and powerful support is provided for artificial intelligence application.

The application discloses a kind of based on IC level EMC data assets measure quantification evaluation method, comprising: constructing read-only measure sensitivity database, extracting the quantification contribution value of target IC from IC level EMC data assets as measure sensitivity data;According to design mode or diagnosis mode, measure sensitivity data is called to predict measure effect and generate optimization scheme;Verification data is stored to user local application knowledge base, and local correction coefficient is generated for subsequent secondary correction.The present application converts EMC optimization from experience trial and error to quantitative calculation, supports pre-design reference and diagnosis rectification, and realizes user EMC design knowledge self-growth.

The invention discloses a drug-target interaction prediction method combining link-perceived cross attention and link-level contrast learning, and introduces a link-perceived cross attention mechanism which allows global features of a sequence (such as protein) to serve as a query to pay attention to internal details of a paired sequence (such as a drug), so as to predict drug-target interaction. Context-aware local features are dynamically generated for each drug-protein pair. Besides, in order to cope with the challenge that information of a knowledge graph is difficult to apply to a cold start scene, a link-based contrast learning strategy (Link-based contrast learning) is designed, and local features generated by a sequence mode through cross attention are aligned with local features obtained through different relation links in a relation mode, so that the probability that the information of the knowledge graph is applied to the cold start scene is lowered, and the probability that the information of the knowledge graph is applied to the cold start scene is lowered. Richer and finer-grained supervision signals are provided for the model, and the sequence model is prompted to learn a'relation mode 'contained in the knowledge graph instead of memorizing a'relation instance' of a specific entity.

The invention provides a dynamic handwriting enhancement method and device based on learning content semantic analysis and electronic equipment, and relates to the technical field of electronic note processing, and the method comprises the steps: obtaining space-time dynamic data corresponding to an original note when a user uses an electronic pen to write; performing stroke unit segmentation on the spatio-temporal dynamic data, and performing feature extraction on the segmented stroke units to obtain a feature tensor of each stroke unit; and performing semantic analysis based on the feature tensor of each stroke unit, determining a target enhancement strategy based on a semantic analysis result, and enhancing the original note based on the target enhancement strategy to obtain an enhanced note. According to the dynamic handwriting enhancement method and device based on learning content semantic analysis and the electronic equipment, by understanding the knowledge essence of the written content, sensing the real-time cognitive state of the user and applying the optimization strategy matched with the knowledge type, the handwriting readability is remarkably improved, and meanwhile the personal writing style is strictly reserved.

The invention relates to an automatic driving and automatic charging fusion application scene evaluation method and system, and the method comprises the steps: searching an association path between an automatic driving entity and an automatic charging entity in a fusion application knowledge graph, and summarizing an entity combination which accords with the logic and is higher in association degree as a candidate fusion application scene; extracting a numerical feature vector of each candidate fusion application scene from the fusion application knowledge graph; obtaining potential scores of each candidate fusion application scene under different evaluation indexes; obtaining an overall potential score according to the potential score of each candidate fusion application scene under different evaluation indexes; and obtaining a posterior mean value of each candidate fusion application scene according to the overall potential score and the potential scores under different evaluation indexes, sorting all the candidate fusion application scenes according to the posterior mean values, and identifying the most potential automatic driving and automatic charging fusion application scene. Compared with the prior art, the fusion application scene recognition accuracy is improved.

The invention provides an image recognition optimization method and system based on deep learning, aims to solve the problems of large algorithm error, low efficiency and the like of a traditional machine learning algorithm, and aims to realize rapid and accurate recognition of disease types of corn leaves and promote healthy development of the corn industry. The method comprises four core parts: 1) adopting MobileNetV3 to replace CSPDarknet53, and optimizing a lightweight backbone network structure; and 2) a Biform module is introduced, and the image processing efficiency is improved through a double-layer routing attention mechanism (BRA). In the coarse granularity stage, key-value matching items irrelevant to the path are removed, only important path segments are reserved for fine granularity operation, the calculation and storage amount is reduced, and a path weight dynamic adjustment mechanism is added to avoid the situation of characteristic connection loss; (3) key information is selected in real time through a double-layer routing attention mechanism, and the operation burden is relieved; and 4) a knowledge distillation technology is applied, the calculation amount is reduced through a teacher-student model compression method, and the prediction precision is maintained.

The present application relates to the technical field of resource scheduling, in particular to a digital intelligent unaccompanied service management scheduling system, comprising: a service baseline construction module, which instantiates service items into standard plan event streams; a state portrait generation module, which collects service object data streams, applies a recurrent neural network model to establish dynamic time series signals, and quantifies the occurrence probability of future abnormal events through multi-step prediction; when an abnormality is triggered, a knowledge graph is applied to perform bidirectional collision positioning of causes; a task load calculation module integrates the plan event streams into plan task components, combines the predictive risk score, and further combines the real-time load of staff containing a fatigue index and spatial accessibility analysis to generate a staff resource list; the system further comprises a task allocation module, which determines the optimal staff and generates closed-loop scheduling instructions by minimizing a composite cost function containing a predictive load cost, thereby realizing digital intelligent unaccompanied service management from prediction to scheduling.

The invention relates to the technical field of intelligent operation and maintenance of mechanical equipment, in particular to an incremental equipment health assessment method based on dynamic perception sensitivity assessment, the core is to construct a dynamic perception sensitivity assessment perception domain expansion framework, the framework integrates a multi-dimensional parallel attention mechanism and a dynamic perception sensitivity assessment mechanism, and the dynamic perception sensitivity assessment perception domain expansion framework is established. And respectively constructing a representation learning and evaluation sub-network and a perception domain expansion sub-network. According to the multi-dimensional parallel attention mechanism, by applying an attention mechanism on four dimensions, the multi-scale feature extraction capability is enhanced; according to the dynamic perception sensitivity evaluation, knowledge anchoring is applied to mean square error loss by calculating parameter perception sensitivity contribution degree and acts on subsequent tasks, so that important knowledge of historical tasks is protected. According to the method, the low-weight parameters can be properly forgotten in the continuous learning process, and the sensitivity is perceived and the model performance is improved under the condition of not carrying out time-consuming de novo training.

The invention discloses an agent development and safe operation method and system based on a private cloud. The method comprises the following steps: firstly, constructing a multi-level security virtual private cloud architecture; secondly, deploying a containerized agent platform in each security domain, creating an application through a micro-service architecture, binding an exclusive knowledge base, and establishing an application-knowledge base one-to-one security channel based on digital signature; thirdly, implementing a fine-grained permission management and control mechanism, dynamically verifying a data security level label by adopting an access control model, and guaranteeing the security of sensitive information in combination with a privacy technology; then integrating a cue word optimization engine to improve the interaction performance of an agent, and applying a dynamic load balancing algorithm to realize efficient scheduling of a large model cluster; and finally, constructing an end-to-end security protection chain through an AK / SK authentication system. According to the method, the secret-related intelligent agent is managed and controlled from multiple angles, safe development and efficient operation of the intelligent agent in the secret-related environment are achieved, and it is guaranteed that secret-related knowledge information is safe and controllable.

The invention discloses a high-readability industrial drawing AI intelligent identification and generation method, and relates to the technical field of industrial drawings, and the method comprises the steps: S1, constructing a function map and a drawing map; s2, on the basis of the double maps, constructing a three-chain cooperation mechanism of a function chain, a drawing chain and an application knowledge chain; s21, the function chain takes the function map as a core basis, and the dispersed post requirements are converted into coherent collaborative links; s22, the drawing chain constructs a logic chain of the full life cycle of the industrial drawing through the dynamic knowledge graph; S23, the knowledge chain is applied, and according to the demand weight difference of different posts, the generated drawing report is interpreted based on the dynamically updated knowledge graph; according to the method, through the AI technology, the drawing processing efficiency and readability are remarkably improved, and personalized drawings can be generated and interpreted and matched on the basis of post roles.

The application relates to the technical field of image data processing, and provides a chip visual design defect detection method and system based on deep learning, wherein the chip visual design defect detection method based on deep learning comprises the following steps: constructing a special search space and a hardware performance evaluation model; implementing a differentiable structure search optimization network structure; implementing mixed precision quantization optimization model storage and calculation efficiency; performing multi-objective joint optimization to simultaneously optimize the network structure and bit width configuration; and applying a knowledge distillation technology to maintain high detection precision. Through joint optimization of the network structure and precision configuration, the detection speed is significantly improved, the real-time detection requirement of a chip production line is met, the model size is greatly reduced, the storage space and transmission bandwidth are greatly saved, the demand for hardware resources is reduced, the hardware cost is reduced, lower configuration equipment can also be used for efficient detection, and the universality and economy of the method are improved.

This invention discloses a method, apparatus, electronic device, and storage medium for content detection of a model-associated knowledge base. The method includes: responding to a content detection request for the associated knowledge base of a target model, acquiring a target associated knowledge base for the target model, wherein the target associated knowledge base includes at least a test knowledge base and a scenario application knowledge base, the test knowledge base storing preset security detection knowledge, and the scenario application knowledge base storing industry security content; determining candidate risk content in the target associated knowledge base based on target prompt information and the target model, and generating test data based on the candidate risk content; testing the target associated knowledge base based on the test data, candidate risk content, and the target model, and calibrating the corresponding target risk content in the target associated knowledge base based on the test results, thereby achieving accurate risk mining across industry scenarios and dynamic optimization of knowledge base content, significantly improving the security detection capability and generalization of the target model.

The invention discloses an innovation ability management method and system, and relates to the field of human capital management and application. The method comprises the following steps that S1, a CoE department obtains data and information from an intelligent or application knowledge database and pushes the data and the information to employees or prompts the employees to actively obtain the data and the information, and therefore multidisciplinary skills, knowledge and experience are established; s2, the innovation ability management system gradually converts the multidisciplinary skills, knowledge and experience into interdisciplinary skills, knowledge and experience through the CoE department, thereby establishing the innovation ability of the employees and realizing innovation results; s3, the innovation ability management system transmits the innovation result to the wisdom or application knowledge base according to the file type of the innovation result for the CoE department to use; and in cycles, spiral growth of application knowledge and continuous improvement of quality are formed. According to the technical scheme, the problems of creation and retention of knowledge and innovation ability management of employees in the prior art are solved.

The invention relates to a comprehensive experiment training teaching method and platform based on large model progressive guidance and multi-agent collaborative customization, and belongs to the technical field of artificial intelligence education. The invention aims to solve the following technical problems: in the existing teaching, a theory-practice gap exists, and knowledge points are isolated and disjointed; the general large model provides a super-cognitive perfect answer; and deep personalized culture and systematic cognitive framework construction are lacked. The scheme provided by the invention comprises the following steps: 1) designing a gamification through-type large experiment to connect multidisciplinary knowledge in series; 2) providing progressive guidance adaptive to the level of a learner through a subdivided single agent; and 3) dynamically generating a personalized learning framework by using multiple agents and the knowledge graph. According to the method, an intelligent self-adaptive teaching framework taking the learner as the center is constructed, the defects of a general large model are effectively made up, accurate customization cultivation of'one-life-one-case 'is provided, and the ability and innovation potential of the learner in integrating application knowledge in a complex situation are remarkably improved.

The invention is suitable for the technical field of dialogue systems, and provides a dialogue state tracking method and device, terminal equipment and a medium, and the method comprises the steps: obtaining a current dialogue data set in a dialogue system, the current dialogue data set at least comprising current dialogue data and a to-be-predicted slot of the current dialogue data; the current dialogue data set is input into the target student model, a predicted slot value of the slot to be predicted and current reasoning data corresponding to the predicted slot value are obtained, and the current reasoning data are used for representing the reasoning process that the value of the slot to be predicted is the predicted slot value; and maintaining the conversation state of the conversation system based on the predicted slot value and the current reasoning data. According to the method, the predicted slot value of the to-be-predicted slot and the current reasoning data corresponding to the predicted slot value are output according to the target student model, so that the dialogue state of the dialogue system is maintained, the dialogue system can dynamically integrate and apply knowledge from various fields, disastrous forgetting is effectively relieved, and the dialogue efficiency is improved. And the adaptability and learning efficiency of the system are enhanced.

The application discloses a large model-based financial data analysis method and system and a medium, mainly relates to the technical field of financial data analysis, and is used to solve the problem of the isolation of each module in the prior art architecture, that is, the lack of a cooperative mechanism among the three links of data query, rule application and knowledge calling. The method comprises the following steps: acquiring initial financial problem data, disassembling the initial financial problem data into a financial problem string, a rule problem string and a query problem string; obtaining corresponding financial knowledge; obtaining corresponding financial early warning rules; integrating all corresponding query sub-SQL into a final query SQL by a large model; executing the final query SQL by an execution engine to obtain a query result; taking the financial knowledge, the financial early warning rules and the query result as input parameters of a financial data summary function, and then obtaining a prompt word composed of the financial knowledge, the financial early warning rules and the query result; inputting the prompt word into the large model to obtain a returned summary string.

The invention discloses a medical information learning auxiliary system based on a knowledge graph, and the system comprises a relation information construction module which is used for obtaining medical theoretical information, extracting the relation between entities, and constructing a theoretical knowledge graph; the perception aggregation module is used for inputting clinical case information, extracting a relationship between entities and constructing an application knowledge graph; the fusion module fuses the theoretical knowledge graph and the application knowledge graph based on the semantic features to generate a fused knowledge graph; and the information output module retrieves and outputs pre-selected information from the fused knowledge graph according to the information input by the user. In the technical scheme provided by the invention, repeated and redundant contents are effectively eliminated through semantic fusion.

This invention discloses a few-sample dynamic modeling method for composite material composite structures of electric aircraft based on attention meta-learning, including the following steps: Step 1: The dataset used comes from vibration experiments conducted on composite laminated wing box structures, composite laminated air beam structures, and composite conical-cylindrical composite shells under different temperature conditions. Given limited data, various composite material structures and ambient temperatures are divided into independent task samples. Each task includes vibration test data of the structure at its respective temperature. The data is divided into a meta-training set and a meta-test set. This invention relates to the field of dynamic modeling technology, innovatively integrating meta-learning with dynamic modeling of composite material composite structures of electric aircraft. The AMLT model can utilize MAML's rapid task adaptability and multi-task learning capabilities to quickly transfer and apply knowledge to new tasks, resulting in higher adaptability and generalization ability when facing new tasks.