50 results about "Knowledge framework" patented technology

The invention provides a wisdom education method and system based on student behavior sequence recommendation, and relates to the technical field of wisdom education. According to the method and the system, through an AI autonomous identification knowledge graph construction and innovative knowledge fusion method, a course knowledge system is structurally and hierarchically displayed, and a knowledge framework is clearly presented, so that knowledge graph framework construction of all-platform courses is facilitated, pre-repaired courses and subsequent courses are efficiently associated, and the construction efficiency is improved. And a clear and complete course learning path is provided for students. By adopting a recommendation algorithm based on a student behavior sequence, combining SASRec and introducing a self-attention mechanism of a sparse attention module, behavior data of students can be deeply analyzed, a learning path is adjusted according to real-time learning data, and personalized learning suggestions are provided for the students. According to the method and the system, multi-dimensional functions such as knowledge graph construction, a student behavior sequence recommendation algorithm and intelligent learning path optimization are integrated, and the method and the system have wide coverage and strong comprehensiveness.

The invention relates to the technical field of artificial intelligence education, in particular to an intelligent homework tutoring system and method based on multi-modal interaction and adaptive learning, and the system comprises a multi-modal input analysis module, a cognitive state dynamic evaluation module, an intelligent decision explanation engine, and a learning effect visualization closed-loop module. The method has the beneficial effects that a three-dimensional adaptive system of explanation granularity-presentation form-interaction frequency is used, and teaching strategies such as visual derivation, concept metaphor and the like can be automatically matched according to cognitive styles of students. Secondly, a'backtracking reinforcement-lateral expansion 'double-intervention mechanism is innovatively used, and the recurrence rate of similar errors is effectively reduced through error real-time detection and correlation knowledge contrast teaching. And finally, constructing a dynamic knowledge graph and an interactive learning report, realizing visual tracking of a learning path, and helping students to establish a systematic knowledge framework.

The invention relates to the technical field of crime abnormity early warning, and particularly provides a crime abnormity early warning method based on a legal knowledge framework and a star graph neural network. The method comprises the steps of obtaining crime determination abnormal data through a judgment document; obtaining normal crime determination data through the legal data set; constructing two types of legal knowledge frameworks of offender name exclusiveness and cross-offender name universality; a Qwen3-14B model is utilized to construct a structured legal element data set with double aligned frames; the method comprises the following steps of: embedding legal elements into dense vectors through a bert-based-history model, and constructing a star graph neural network crime determination model; based on the two crime abnormity early warning mechanisms, performing crime abnormity early warning to obtain a crime abnormity early warning model; according to the method, the problem of the existing AI-assisted law application technology in crime name false complaint recognition is solved, and the recognition accuracy of crime name application errors in judicial scenes is improved.

The embodiment of the invention discloses a method and device for constructing a field consanguinity tree, a storage medium and a terminal. Firstly, a target field, a database query statement of the target field and upstream blood relationship information are obtained, the target field serves as a root node, a first executable calculation function packaging calculation logic of the target field is generated, and the calculation process is dominant and modularized. By analyzing input parameters of the function, an upstream field which directly depends on is automatically identified and determined to serve as a child node. And then recursively processing each sub-node, generating a corresponding calculation function, analyzing the dependency of the calculation function, tracing layer by layer until all leaf nodes (namely original bottom table fields), outputting a complete blood relationship tree containing all the nodes and the corresponding calculation function, and displaying the complete blood relationship tree through a visual interface. According to the method, automatic and accurate tracing from a target field to an original data source is realized, a computable and reusable blood relationship knowledge framework is constructed, and a solid foundation is provided for data understanding, problem investigation and intelligent data service.

The invention belongs to the technical field of computer aided process design (CAPP), and particularly relates to a body-based shaft part fixture scheme automatic generation method. The method specifically comprises the following steps: (1) constructing a shaft part clamp scheme generation body; (2) establishing a shaft part clamp scheme to generate an SWRL inference rule; (3) extracting relevant feature constraint information of the part; (4) constructing and generating an instantiated ontology model; and (5) combining a Drools inference engine with an SWRL rule, and carrying out inference selection and adjustment on a shaft part fixture scheme. Constructing an ontology reasoning knowledge framework by utilizing ontology pair shaft part clamp scheme domain knowledge, and reasoning implicit knowledge according to dominant domain knowledge; and reasoning an optimal clamp scheme according to the feature constraint condition of the geometric product in combination with a semantic network rule language rule base. According to the method, a consistent knowledge description framework generated by the shaft part clamp scheme can be provided, so that a computer can automatically select a proper clamp scheme and the like according to various constraints of geometric product parts, and a quick and effective method is provided for intelligent selection of the shaft part clamp scheme.

The present application provides a kind of urban rail vehicle electronic history data processing method, the method establishes vehicle operation configuration in combination with vehicle structure design, and vehicle configuration data structure is structured using database technology;Vehicle history data granularity refined to the smallest maintainable unit is used, the correctness and timeliness of data are guaranteed through data analysis and integration technology based on business process, the whole life cycle stage document of vehicle is stored using data storage technology, and preliminary vehicle knowledge framework is established in combination with full-text search technology, to provide basic data support for later subway vehicle procurement, technical reform, operation.In history data management, block chain technology is added to ensure the confidentiality and integrity of data flow, to prevent sensitive data from being leaked and tampered with during transmission, and to enhance the security and correctness of vehicle history data, to provide reliable data security guarantee for subsequent history sharing.

The invention relates to a multi-source information knowledge fusion and intelligent retrieval method and system, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the unified knowledge modeling of the multi-source heterogeneous data, and obtaining a unified knowledge framework; and based on the unified knowledge framework, calling a large language model to perform structured preprocessing on the multi-source heterogeneous data to generate a structured knowledge triple. And performing structured processing on the structured knowledge triple through the attribute graph, and performing vectorization storage on each entity associated data through the embedded model to obtain a fused knowledge body. And receiving a user query request, and in response to the user query request, performing deep analysis on the user input data to obtain a user query intention and query elements. According to the method, the condition graph is constructed according to the query intention and the query elements of the user, and the condition graph is mapped into the knowledge network for entity positioning and condition graph matching to generate the structured retrieval result, so that the network overhead and scheduling delay are reduced, and the retrieval response speed and accuracy are improved.

The invention discloses a knowledge graph construction method and device, a storage medium and electronic device.The method comprises the steps that a comprehensive data set is constructed, accurate semantic relation representation is determined by recognizing entities in the comprehensive data set and relations between the entities, similar entities in the comprehensive data set are fused on the basis of the semantic relation representation, and the knowledge graph is obtained. In combination with heterogeneous data fusion, a basic knowledge framework is constructed, new knowledge is generated by utilizing an inference engine based on airspace monitoring requirements and the basic knowledge framework, and an airspace target knowledge graph is constructed in combination with an anomaly detection mechanism. The accuracy, reliability and safety of airspace target monitoring are improved, and intelligent development of airspace monitoring is promoted.

Current generative AI systems using large language models (LLMs) face challenges including non-deterministic outputs, hallucinations, outdated information, and resource-intensive training. This disclosure introduces topic maps for constrained retrieval augmented generation to address these issues. The technique leverages existing LLMs while constraining outputs to specific, user-defined content domains. Topic maps, composed of topic names, descriptions, and relevant resource references, create a curated knowledge base that guides agent responses. This approach reduces hallucinations, improves consistency, and allows for dynamic updates without model retraining. The method involves receiving a query, identifying relevant topic maps, transmitting the query and references to an AI agent, and generating constrained responses. By providing a structured, updatable knowledge framework, this method enhances the accuracy, reliability, and adaptability of generative AI systems.

The invention provides a chemical material data processing method and system based on a large language model, and relates to the technical field of semantic processing, and the method comprises the steps: obtaining chemical material data; activating a first channel layer in the double-layer data processing structure to standardize the text data to obtain a standardized text; activating a second channel layer in the double-layer data processing structure to standardize the numerical data to obtain a standardized numerical value; and evaluating and analyzing the standardized text and the standardized numerical value according to a material quality evaluation mechanism to obtain a data quality index. By means of the chemical material data processing method and device, the technical problem that in the prior art, due to the fact that a standardization processing method depends on expert rules or template matching, information missing or wrong matching is prone to occurring when an unstructured text is handled can be solved, and standardization processing is conducted on chemical material data through a double-layer data processing structure; semantic consistency of heterogeneous data under the same knowledge framework is guaranteed, and flexibility and automation level of data processing are improved.

The invention relates to a key point abstract video generation method and device, equipment and a computer readable medium. The method comprises the steps that a target video is acquired, text data of the target video is extracted, and the target video is a knowledge-intensive video; constructing a knowledge graph of the target video based on the text data; core knowledge nodes in the knowledge graph are determined, viewpoint edges associated with the core knowledge nodes are positioned in the knowledge graph, the core knowledge nodes are used for recording core knowledge of the target video, and the viewpoint edges are used for recording viewpoints for the core knowledge; shot fragments of core knowledge and viewpoints are intercepted from the target video, and a title card is generated for each shot fragment by using a knowledge graph; and synthesizing and rendering the shot clip and the corresponding title card to obtain a key point abstract video of the target video. The technical problem that it is difficult for audiences to obtain a complete knowledge framework through existing key point abstract videos is solved.

This invention relates to a method and system for multi-source information knowledge fusion and intelligent retrieval. The method includes: acquiring multi-source heterogeneous data and performing unified knowledge modeling on the multi-source heterogeneous data to obtain a unified knowledge framework. Based on the unified knowledge framework, a large language model is invoked to perform structured preprocessing on the multi-source heterogeneous data to generate structured knowledge triples. The structured knowledge triples are then structured using attribute graphs, and the associated data of each entity is vectorized and stored using an embedding model to obtain a fused knowledge body. User query requests are received and responded to by performing deep analysis on the user input data to obtain the user's query intent and query elements. A condition graph is constructed based on the user's query intent and query elements, and the condition graph is mapped to a knowledge network for entity localization and condition graph matching to generate structured retrieval results. This reduces network overhead and scheduling latency, and improves retrieval response speed and accuracy.

The invention provides a transformer fault maintenance virtual training method and system based on multi-modal cooperation. The method comprises the following steps: constructing a multi-modal knowledge graph of transformer fault data; pre-testing the trainee by adopting a preset collaborative training system, determining the initial proficiency of the trainee, and determining an eye movement triggering threshold of the trainee by adopting a preset fault complexity optimization algorithm and a preset eye movement triggering threshold algorithm; according to the eye movement data of the trainee and the eye movement triggering threshold value of the trainee, fault associated data in the multi-mode knowledge graph are called, a preset virtual-real feedback intensity algorithm is adopted, fault interaction data of the trainee and the collaborative training system are obtained, and then a personalized training scheme is generated. According to the invention, trainees are assisted to construct a complete fault maintenance knowledge framework, knowledge fragmentation is avoided, association understanding is enhanced, the problem of missing practical operation is solved, a personalized training scheme is generated, the training pertinence and efficiency are improved, and the skill improvement period is shortened.

The invention provides a normal school student teaching ability evaluation method based on a affair knowledge graph fused with a big language model. The method comprises the following steps: S1, acquiring graphic and text information of teaching content and video information of a target object; s2, extracting knowledge content from the graphic and text information, and constructing a subject knowledge graph according to the knowledge content; s3, according to the video information and the subject knowledge graph, event elements in the video information are extracted, and event coding data are obtained; s4, constructing a affair knowledge graph based on the event coding data; s5, calculating a teaching ability score of the target object according to the event coding data; and obtaining personalized ability evaluation feedback of the target object based on ability interpretation of the large language model. According to the method, the behavior data and the image-text data of the target object are collected, and the teaching behavior and the knowledge framework in the teaching process of the normal school students are used as characterization of the teaching ability of the normal school students, so that the teaching ability can be evaluated more comprehensively and accurately.

The invention relates to the field of multi-modal modeling, and provides a three-dimensional enterprise knowledge base construction method and system. The method comprises the following steps: classifying and marking knowledge assets to be constructed according to a preset dimension system, and extracting semantic feature information corresponding to multiple dimension knowledge nodes to obtain a multi-dimensional cross knowledge framework; knowledge nodes in the multi-dimensional cross knowledge framework are stored in a graph database, a knowledge association network is established, and a dynamic knowledge graph is obtained; collecting user feature data, and generating a user knowledge demand portrait according to the user feature data; performing matching calculation on the user knowledge demand portrait and knowledge nodes in the dynamic knowledge graph, and screening matching results to obtain a target knowledge push list; and determining an access permission level according to the role permission attribute of the user, performing permission verification on the knowledge nodes in the target knowledge pushing list based on the access permission level, and generating personalized knowledge content. According to the invention, the knowledge management efficiency, the delivery accuracy and the security are improved.

This invention discloses a system for converting tacit knowledge into explicit knowledge based on knowledge graphs and large models. It includes a knowledge system ontology modeling and management module for constructing and maintaining the knowledge system ontology model in large model applications; a data access and knowledge graph construction module for mapping and loading multi-source heterogeneous data onto the knowledge system ontology; an ontology virtual business environment engine module; a tacit knowledge mining and explicit knowledge generation module that, in a virtual environment, uses graph analysis and large model reasoning to mine tacit knowledge and generate explicit knowledge; and a business strategy planning and workflow orchestration module that transforms explicit knowledge into executable business strategy plans and workflow orchestrations. Through the knowledge system ontology modeling and management module, a systematic knowledge framework is constructed, achieving systematic accumulation of enterprise tacit knowledge, effectively preventing knowledge loss, and improving knowledge reusability and scalability.

The invention relates to the field of financial science and technology, and discloses a bank customer portrait knowledge graph construction method and system based on multi-source heterogeneous data fusion, and the key point of the technical scheme is that the method comprises the following steps: collecting bank customer data; obtaining a hierarchical classification rule of clients and products, constructing a knowledge framework of client portraits, and sorting the collected client data through the knowledge framework; based on the sorted customer data, through an entity extraction rule, extracting entities from the structured data; extracting entities from the unstructured data through a deep learning model; based on the extracted entity, extracting an entity relationship from the client data through an entity relationship extraction rule; meanwhile, multi-dimensional features related to entities are extracted and input into a machine learning model with a confidence coefficient threshold value, and entity relations higher than the confidence coefficient threshold value are output; and according to the entity, the entity relationship and the knowledge framework, constructing a customer portrait knowledge graph, and setting a dynamic updating mechanism.

The invention discloses an interactive encyclopedia implementation method and device based on artificial intelligence, and a terminal, and belongs to the technical field of artificial intelligence education. The method comprises the following steps: constructing an encyclopedia knowledge framework in advance according to a preset classification system, collecting parent-child education videos and setting a home page display mode; accessing an artificial intelligence video understanding interface into an encyclopedia knowledge framework, and carrying out content understanding and segmentation on parent-child education videos; a user operation instruction is received to play the video, a midfield interaction link is added in the playing process, each segment node is controlled to correspond to one knowledge point, and an interaction problem is displayed; receiving feedback information of the user for the interaction problem and reminding whether the feedback information is correct or not; and collecting user behavior data, constructing a user portrait, and carrying out personalized sorting and content recommendation on encyclopedia videos in combination with a knowledge graph. The interaction of parent-child education videos is enhanced through the artificial intelligence technology, the learning effect is improved, personalized learning experience is achieved, and the learning interest and knowledge absorption efficiency of children are effectively improved.

The invention discloses a knowledge graph construction method and system, and the method comprises the steps: carrying out the feature extraction of obtained multi-source operation and maintenance text data, and generating a context vector feature, a static word vector feature and a statistical text feature; carrying out weighted fusion on the three types of features to generate text representation; wherein the time sequence and causal ontology serves as a weight adjustment basis, the larger the similarity between the multi-source operation and maintenance text data and the ontology is, the larger the weight coefficient corresponding to the context vector feature is, and the time sequence and causal ontology is a structured knowledge framework about a time sequence rule and a causal mechanism; recognizing an entity and an entity association relationship based on the generated text representation, and generating a target result through entity alignment and information fusion processing; and finally, constructing the knowledge graph according to the target result. According to the embodiment of the invention, through multi-dimensional feature extraction and in combination with the weighted fusion strategy guided by the time sequence and the causal ontology, accurate identification of the entity and the entity association relationship is realized, and then the accuracy of knowledge graph construction is improved.

The invention relates to the technical field of new media operation, in particular to a new media operation content recommendation system based on big data. The system comprises a creator feature extraction module, a vertical knowledge graph construction module, a content potential prediction module, a dynamic strategy optimization module and a visual decision support module, and accurate operation recommendation of a new account is realized through multi-dimensional data fusion and an intelligent algorithm. The method comprises the following steps: firstly, carrying out deep processing on registration data, and constructing a structured feature model comprising content styles, professional fields and creation capabilities; establishing a domain knowledge framework based on the feature data, and forming a dynamically updatable content association network; an innovative dynamic balance algorithm is adopted to calculate the matching degree between the characteristics of the creator and the market demand in real time; and generating a staged operation scheme according to a prediction result. According to the method, a complete decision chain from feature analysis to strategy optimization is established, so that individuation of content positioning is realized, and the growth efficiency and market competitiveness of new accounts are remarkably improved.

The invention provides an intelligent international education management method and system, and the method comprises the steps: obtaining term paraphrasing text contents and corresponding related knowledge point lists in teaching resources of all language versions, and recognizing the paraphrasing difference degree and semantic deviation degree of terms in different teaching systems, obtaining a preliminary evaluation result of the meaning offset of the term among different languages; obtaining a term application instance and a context expression mode in student homework, extracting an actual application scene of a term in a homework text, matching the actual application scene with a related knowledge point list, and determining a semantic offset distance in combination with a meaning offset preliminary evaluation result; and dynamically updating a reference relationship of terms in teaching resources among teaching material chapters and a hierarchical structure among knowledge points according to a bifurcation classification result and a paraphrasing version of the unified semantic benchmark to obtain an aligned knowledge framework.

The application provides an intelligent international education management method and system, comprising: obtaining term interpretation text content and corresponding involved knowledge point list in each language version teaching resource, identifying interpretation difference degree and semantic deviation degree of the term in different teaching systems, and obtaining preliminary evaluation result of meaning deviation of the term between different languages; obtaining term application instances and context expression methods in student homework, extracting actual application scene of the term in the homework text and matching with the involved knowledge point list, combining the preliminary evaluation result of the meaning deviation, and determining the semantic deviation distance; according to the interpretation version of the divergence classification result and the unified semantic benchmark, dynamically updating the reference relationship of the term in the teaching resources between the chapters and the hierarchical structure between the knowledge points in the teaching materials, and obtaining the aligned knowledge framework.

The invention provides a dialysis patient management system and method based on mobile medical treatment, and the system comprises a multi-dimensional data collection module which is used for collecting the multi-dimensional data of a dialysis patient; the dynamic data display module is used for displaying the collected multi-dimensional data; the self-management module is used for analyzing the physiological index data of the patient and giving a self-management suggestion for the dialysis patient according to an analysis result; the cloud database is used for storing knowledge architecture of related diseases of the dialysis patient, so that the dialysis patient can perform self-retrieval according to requirements; and the grading alarm system is used for analyzing the physiological index data and the self-management data of the dialysis patients, dividing alarm grades according to analysis results and crisis degrees, and giving an alarm according to the alarm grades. According to the invention, comprehensive and multi-dimensional evaluation and systematic intervention on the dialysis patient can be realized, the self-management ability of the dialysis patient is improved, and the dialysis sufficiency and life quality of the dialysis patient are further improved.

The invention discloses an automatic bid invitation file generation method and system based on a large model, and the method comprises the steps: obtaining and analyzing a bid invitation demand document, and determining a plurality of document logic units in the bid invitation demand document; analyzing each document logic unit to determine a key semantic entity, and analyzing the key semantic entity to determine a core demand of each document logic unit; retrieving a template knowledge framework matched with the core demand from a preset knowledge database based on the core demand, and constructing a retrieval data set based on the template knowledge framework and the core demand; the retrieval data set is input into a preset language large model for text output, and an initial bid invitation file is obtained; and verifying and optimizing the content of the generated initial bid invitation file to obtain a formal bid invitation file. According to the method, the big language model serves as an intelligent brain and is combined with a professional bid invitation knowledge base and a rule engine, the intention of a user can be accurately understood, high-quality and compliant texts are generated, and professionals are liberated from tedious and repeated document work.

The application discloses an AI-assisted experimental teaching system, which organically integrates a teaching process with artificial intelligence technology, constructs a whole-process intelligentized auxiliary teaching of 'pre-class preparation-in-class operation-post-class review', and preliminarily constructs a knowledge framework for students through man-machine intelligent interaction and virtual two-dimensional simulation experiment operation, and completes intelligent preparation; the system realizes auxiliary guidance and error avoidance in the operation process through real-time monitoring and intelligent judgment; the system provides post-class homework correction assistance for teachers through a photographing automatic evaluation system, and reduces single repetitive work; the system provides accurate weakness evaluation and improvement suggestions for students through dynamic review question generation and individualized evaluation reports, and is characterized by AI assistance as a core, full-fledged teaching, teaching individualization, high efficiency and intelligence.

The present invention provides an order allocation processing method and system based on a decision tree algorithm. By determining an order allocation channel sequence based on the decision tree algorithm and constructing an order allocation knowledge network, it can effectively integrate the association information between service orders and order allocation channels, providing a comprehensive and systematic knowledge framework for order allocation. By screening out reference order allocation channels that conform to the service logic in the order allocation knowledge network and calculating comprehensive cost parameters in combination with predicted allocation instance data to determine the target order allocation channel, it realizes a more scientific and accurate order allocation, improves the rationality and efficiency of order allocation, reduces the allocation cost, and helps to improve the overall service quality and operational efficiency.

This invention discloses a concept graph-based method for enhancing astronomical computational reasoning instructions, comprising the following steps: obtaining seed problems labeled with subdomains and topics as basic input; extracting knowledge points from the seed problems using a large language model to establish a knowledge framework covering subdomains, topics, and knowledge points; and transforming problem instances into conceptual representations; constructing a concept graph based on the knowledge framework; executing a dynamic random walk algorithm on the concept graph to output a complete concept combination; and using the complete concept combination to guide the large language model in generating astronomical computational problems and corresponding reasoning processes through example matching, data deduplication, and thought chain distillation. This invention, by constructing a concept graph and employing a random walk strategy, improves the diversity and logical rigor of the generated data, providing a new path for optimizing astronomical computational reasoning instructions.

The invention discloses a spacecraft intention reasoning optimization method based on a multi-hop knowledge chain, and the method comprises the steps: firstly constructing a hierarchical knowledge framework containing spacecraft information, subsystems, components, behaviors and possible intentions, and extracting a dynamic sub-graph which fuses time sequence dependence and is associated with inherent semantics through a sliding window; the orbit dynamics constraint is embedded to ensure logic self-consistency; a hierarchical reasoning framework with rule guidance and graph neural network cooperation is adopted, an upper layer rapidly screens a high-confidence-coefficient intention candidate set based on predefined behavior pattern rules and orbital constraints, and a lower layer aggregates multi-hop semantic information along a knowledge chain through a lightweight graph neural network to generate fine-grained intention probability distribution; and finally, matching a historical behavior template through a dynamic time warping algorithm, and combining sequential logic constraint verification to realize intention dual verification. According to the method, the accuracy, the real-time performance and the engineering realizability of spacecraft behavior intention reasoning in a complex scene can be remarkably improved.

The application provides a single disease reporting method and system, a terminal and a medium. The method comprises the following steps: obtaining disease reporting standard information, constructing a disease knowledge framework according to the disease reporting standard information; constructing an event set according to the disease knowledge framework, and obtaining event questions of each disease event in the event set respectively; determining relevant text in a sample case according to the event questions, constructing pre-annotation training data according to the relevant text and the event set; training an event implication model according to the pre-annotation training data, determining to-be-reasoned data according to a to-be-reported medical record; inputting the to-be-reasoned data into the trained event implication model for event prediction, and obtaining single disease reporting information. Based on the disease reporting standard information, the disease knowledge framework representing the key information of the corresponding single disease can be automatically constructed, so that the event implication model can effectively learn the key information in the pre-annotation training data, and the accuracy of single disease reporting is improved.

The invention discloses a children early-stage nursing health science popularization method based on a knowledge graph. The method comprises the following steps: investigating the ability and knowledge demands of a caregiver; constructing a popular science knowledge framework; constructing a knowledge graph; according to the method, multi-field knowledge of early-stage nursing and nursing of children is integrated through a knowledge graph technology, a structured and visual science popularization system is formed, the time and region limitation of a traditional science popularization mode is broken through, and the knowledge acquisition efficiency is improved; multidisciplinary team cooperation guarantees scientificity and practicability of knowledge; personalized recommendation and interactive application enhance pertinence and interestingness of science popularization, effectively meet the needs of nursing knowledge of caregivers, and improve the ability of nursing.