1105 results about "Coursework" patented technology

The invention discloses an online course learning management method and system based on a knowledge graph, and the method comprises the steps: S1, analyzing course data, extracting knowledge points and an association relationship, and generating an initial knowledge graph signal containing knowledge point nodes and association edges; s2, collecting user interaction behavior data and extracting behavior features to generate behavior feature vector signals associated with knowledge point nodes; s3, detecting an abnormal learning mode, triggering a knowledge graph weight updating signal, and dynamically adjusting the associated edge weight; s4, updating the signal according to the initial knowledge graph signal and the knowledge graph weight; and S5, responding to the path optimization signal, and generating an interface sorting adjustment signal and a content push signal. According to the air quality intelligent monitoring system based on sensing data feedback, the problem that a traditional online course management system is single in data acquisition dimension and static and solidified in knowledge dependency relationship can be solved.

The invention discloses a teaching data management method and system based on artificial intelligence, and the method comprises the steps: obtaining a standardized time series data stream according to a heterogeneous data stream generated by a multi-source teaching platform in real time; based on the standardized time sequence data stream, performing classified encryption on the teaching data through a dynamic hierarchical storage strategy based on attribute-based encryption to obtain a security hierarchical storage topological structure; according to a user query request and a teaching scene label, extracting a target data set from the security hierarchical storage topological structure to obtain an enhanced multi-modal teaching data set; based on the enhanced multi-modal teaching data set, generating an interpretable teaching mode graph through a dynamic sub-graph evolution algorithm; and according to the teaching mode map and the real-time teaching feedback data, generating a personalized teaching recommendation strategy through a course-learner dual-channel adaptive recommendation model. According to the embodiment of the invention, the utilization efficiency of teaching resources can be improved, and personalized and intelligent teaching recommendation and decision can be realized.

The invention provides an operational research course knowledge graph construction method based on multi-source data fusion. The method comprises the following steps: firstly, discussing logical association of courses, majors and students, collecting data such as textbooks, exercises and teaching programs by taking operational research knowledge points as a core and utilizing technologies such as OCR (Optical Character Recognition) and crawlers, and carrying out preprocessing and manual labeling; then, an improved deep learning model is adopted for entity recognition and relation extraction, BERT + BiLSTM + CRF is adopted for entity recognition, and a dynamic context pooling enhancement model is fused to improve the capture ability of a complex knowledge boundary; bERT + BiLSTM is adopted for relation extraction, a multi-head attention mechanism is combined, and hidden logical relation mining is enhanced. And finally, constructing a multi-level knowledge network which takes knowledge points as nodes and logic relations as edges, and embedding the multi-level knowledge network into a Neo4j graph database for visualization. The map can optimize a teaching path, provides personalized learning recommendation, and is widely applied to the fields of wisdom education, knowledge retrieval and the like.

The invention relates to a method for constructing a subject textbook knowledge graph by using a large language model, and the method is realized through six steps: firstly, introducing a self-prompt framework, generating relation synonyms, synthesizing samples and sentence variants through three rounds of dialogues, and providing rich semantic guidance for subsequent relation extraction; secondly, guiding a large language model to accurately extract core knowledge point entities from teaching materials, exercises and PPT texts by means of a professional field instruction template; then, respectively extracting an attribute triple and a relation triple of the knowledge points by applying a multi-round dialogue mode and combining with a synthetic sample prompt; then, inputting the extracted triad into a verification module, and ensuring the accuracy through iterative verification; and finally, generating an entity embedding vector by utilizing an MPNet model subjected to subject knowledge fine adjustment, calculating entity similarity through a dynamic weighted pooling mechanism, judging entity pairs with high similarity, performing knowledge fusion if the entity pairs represent the same concept, and otherwise, reasoning a potential missing relationship and complementing the knowledge graph. According to the method, the knowledge graph of the course of the specific subject can be automatically constructed from the unstructured text efficiently and accurately, and powerful support is provided for teaching and learning of related subjects.

The invention provides a knowledge question and answer rapid processing method and system based on artificial intelligence, and relates to the field of artificial intelligence. A multi-modal knowledge graph is constructed, collected multi-source teaching data is fused through a mixed retrieval strategy, and the mixed retrieval strategy comprises semantic retrieval, vector retrieval and metadata retrieval; multi-level question and answer processing is executed based on an RAG enhancement framework, a multi-modal input intention is analyzed, cross-library joint retrieval is performed, and an optimization answer is generated in combination with a teaching scene; distilling the global model to a lightweight TinyBERT architecture, dynamically optimizing question and answer quality through a cognitive reinforcement learning framework, positioning a key document from a comprehensive retrieval list, evaluating an optimized answer, and reconstructing an answer with a key document verification score; according to the invention, the professional skill level of teachers and students in the fields of artificial intelligence and large model application can be improved, and the personalized requirements of teachers and students in teaching, scientific research and innovation courses can be met.

The invention provides a personalized teaching method and system based on generative artificial intelligence. The method comprises the following steps: constructing a knowledge graph and a teaching strategy template library of a target course; constructing a feature enhancement matrix; calculating node activation intensity, generating a plurality of candidate learning paths through a beam search algorithm, and screening an optimal learning path in combination with a preset teaching strategy template; using the generative model to generate standardized teaching content adaptive to the teaching strategy; dynamically optimizing a teaching scheme through a neuroplasticity model; and carrying out teaching adjustment based on the optimized teaching scheme. Standardized teaching content is generated by utilizing the generative model, the teaching content quality is improved, the teaching content is closely matched with a learning path and a teaching strategy of a learner, meanwhile, a teaching scheme is optimized through the neuroplasticity model, teaching parameters and cognitive resource allocation are adjusted in time according to real-time feedback and cognitive states of the learner, and the teaching efficiency is improved. Limited cognitive resources are reasonably distributed to key parts of teaching contents, and the teaching effect is improved.

The invention discloses a large language model progressive field fine tuning and knowledge fusion method for shield engineering. The method comprises the following steps: constructing a layered shield training course containing a wide-area academic theory and a proprietary enterprise construction method; parallelly training a plurality of physically isolated parameter efficient adapters based on the frozen base; performing singular value decomposition on the adapter, extracting a geometric feature subspace representing knowledge distribution, and calculating a conflict correlation degree; based on this, a uniform adaptation mechanism of resource awareness is constructed. The mechanism not only can generate a static fusion model for conflict removal, but also can dynamically activate a specific rank slice of an adapter through a routing network based on real-time hardware resource budget (video memory / FLOPs) and geometry-resource signature. According to the method, multi-source knowledge is reserved, and adaptive dynamic scheduling of edge hardware resources by model reasoning is realized.

The invention discloses a progressive topic generation method based on semantic analysis and a knowledge graph. Performing preprocessing and semantic analysis on the question setting demand text, and extracting a necessary keyword set corresponding to the core knowledge points and an optional determiner set corresponding to the additional conditions; carrying out concept mapping in a college professional knowledge graph and associating with a course outline, constructing a hierarchical semantic constraint framework containing hard constraint and soft constraint, and carrying out consistency detection; adopting reverse index hard matching recall and knowledge graph soft extension recall to obtain candidate materials, and inputting the candidate materials into a field fine-tuning large language model to generate candidate questions; reordering is performed through multi-target learning ordering, teaching logic verification and quality evaluation are executed, and final questions are output; user feedback is received to form an incremental sample, and the generation model and the sorting model are updated, so that the accuracy, diversity and controllability of question generation are improved, and closed-loop optimization is supported.

The invention discloses a course resource recommendation method and device based on an improved state space model, equipment and a medium, and relates to the technical field of learning resource recommendation, and the method comprises the steps: collecting user course resource data, obtaining and preprocessing an interaction sequence, mapping the user interaction sequence to a low-dimensional vector space, and inputting the low-dimensional vector space into the improved state space model; the target interest course is matched with the candidate course resource set, the recommendation score is calculated, and the recommendation list is generated, so that the calculation complexity is reduced, the user interest is accurately captured, and the efficiency and accuracy of course resource recommendation are improved.

According to the student personalized learning path generation method based on the neural network, a data acquisition module is deployed on a teaching platform, a learning terminal and an interactive interface, and multi-dimensional data such as student learning duration, interactive frequency and wrong question records are acquired in real time. A knowledge graph is constructed based on a course outline and a prior textbook structure, and a graph neural network GNN is utilized to dynamically adjust association weights of nodes and edges of the graph neural network GNN to reflect weak knowledge areas of students. A Bi-LSTM encoder is constructed for the collected data to generate a behavior feature vector, and the behavior feature vector and a knowledge graph node embedded vector are aligned and fused through an attention mechanism to obtain student personalized multi-modal representation. And taking the representation and the knowledge graph node weight as an environment state, selecting a next knowledge point to be learned as an action space, designing a reward function by integrating multiple indexes, updating a target by using a deep Q network, and training a strategy network to generate a personalized learning path. The trained strategy is deployed as micro-service, a path is displayed according to knowledge module sorting, and related parameters are iteratively updated according to student feedback to improve the recommendation effect.

The invention relates to the technical field of education management, in particular to a learning intelligent course arrangement method and system based on time parameters, and the method comprises the following steps: obtaining student task records, analyzing task concentration degree and behavior fluctuation, obtaining cognitive load distribution, counting teacher teaching frequency and time matching degree, screening preference time periods, and constructing an adaptation table. Extracting curriculum attributes and front nodes, calculating priorities, generating a score table, identifying conflicting curriculums, matching available time periods, obtaining curriculum distribution, comparing student loads with teacher preferences, and screening abnormal curriculum arrangement time periods. According to the method, cognitive load distribution is constructed by analyzing multi-cycle task behaviors of students, time period preference is identified in combination with teaching activeness of teachers, resource matching precision is improved, curriculum arrangement fusion priority weight and neutral position intensity cross scoring is performed, time periods are accurately screened, conflicts are avoided, and cognitive load and preference deviation curriculums are identified. High matching of courses and behavior rhythms is realized, and adaptability and course arrangement scientificity are enhanced.

The invention provides a course reconstruction method and system based on a knowledge graph, and relates to the technical field of intelligent education. The method comprises the following steps: collecting course resource data and student learning behavior data; constructing a knowledge graph fusing multiple courses; generating a personalized learning path based on the atlas and the behavior data; arranging experiment training tasks according to a path and collecting feedback data to update a graph state; and converting the student training result into a technical manuscript in a structured manner and accessing the technical manuscript to a management platform. Closed-loop optimization of teaching resource organization, path recommendation and achievement management is realized.

The invention discloses a personalized course recommendation method and system based on a knowledge graph, and relates to the technical field of course recommendation. The personalized course recommendation method and system based on the knowledge graph comprises the following steps: S1, acquiring user learning data by collecting front-end burying points and background access log data of a learning platform in real time, and preprocessing the user learning data; s2, constructing a behavior data relationship between the user and the knowledge points, evaluating a mastering condition of the user on the knowledge points, and constructing a time sequence knowledge point score set; s3, based on the time sequence knowledge point score set, identifying abnormally mastered knowledge points of the user in the current stage, analyzing whether the courses are worthy of recommendation, and partitioning candidate courses and generating recommended courses; and S4, quantifying the response difference between the recommendation course and the mastering effect, updating the recommendation tag and the user record, and perfecting the recommendation data circulation. The problem that in the prior art, learning effects are difficult to measure, and behavior-driven recommendation circulation is likely to happen is solved.

The invention discloses a dynamic classroom optimization method based on real-time space-time semantic graph tracking, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining first multi-modal data of a current classroom in a classroom teaching process in response to a teaching optimization request; mapping each piece of first multi-modal data into a node in a first space-time semantic graph, generating the first space-time semantic graph based on a preset edge construction condition and a preset edge generation condition, and then inputting the first space-time semantic graph into a preset graph neural network to obtain a first dynamic score vector in the classroom teaching process; and then, based on a preset strategy network, according to the first dynamic score vector and the target score corresponding to the first dynamic score vector, a classroom improvement strategy is generated, real-time sensing and dynamic scoring of the classroom teaching process are realized, a more accurate classroom improvement strategy is obtained, and the classroom teaching efficiency is improved. Therefore, on the basis of improving the real-time performance and comprehensiveness of course teaching evaluation, the teaching scheme can be adjusted in real time, and the classroom teaching quality is improved.

The invention discloses a multi-agent cooperation system construction method, a medium and equipment, and the method comprises the steps: carrying out the task modeling of a target business scene, and constructing a task dependence graph; initial cooperation strength weights are set for the atomic tasks with the cooperation relationship, and multi-stage cooperation training courses from easy to difficult are generated based on the cooperation complexity of the atomic tasks; the intelligent agent is controlled to execute a task in a training course, interaction behavior data is collected, the overall task completion efficiency is calculated, the cooperation weight is dynamically updated, and interaction data and the updated weight are input into a reinforcement learning model to iteratively optimize a cooperation strategy; and finally, solidifying the converged cooperation strategy into the constructed multi-agent cooperation system. According to the invention, through the combination of course learning and reinforcement learning, the cooperation efficiency and robustness of the system in a complex business scene can be significantly improved.

The invention discloses an ideological and political large-scale course teaching attention assessment data acquisition method based on sight tracking. The method comprises the following steps: S1, high-definition large-scene eye movement tracking photographing equipment captures eye images of students in a course teaching process in real time through an infrared camera; s2, performing pupil area detection and pupil center fitting on the eye image to obtain sight tracking data; s3, performing gaze hotspot analysis and gaze duration statistics according to the sight tracking data to quantify attention distribution and change trend of the students and visually display the attention distribution and change trend; and S4, generating a teaching quality evaluation report and teaching optimization suggestions according to the gazing hotspot analysis and gazing duration statistical results. The classroom teaching effect can be objectively evaluated in real time, subjective evaluation errors are reduced, the accuracy and efficiency of teaching feedback are improved, the method is suitable for various scenes such as traditional classrooms, online education and experiment teaching, and the method has the industrial application prospect of education digital transformation.

The invention provides a safety training model, method and system based on a capability evaluation dynamic weight optimization model, and a computer medium, and the method specifically comprises the steps: building a capability-knowledge-course three-dimensional map model, building a three-dimensional mapping relation, and determining an initial weight; fusing, optimizing, adjusting and triggering subjective and objective weights dynamically based on student behavior data, generating a personalized curriculum recommendation sequence through a reinforcement learning algorithm, forming a state space by an ability evaluation vector and a knowledge mastery degree vector, and taking a curriculum resource set as an action space; the short-term learning efficiency and the long-term post assessment passing rate are used as reward functions to construct a recommendation strategy, meanwhile, the invention further provides a computer medium achieved based on the scheme, and the method can provide safety capability assessment means and training courses which are high in pertinence and good in accuracy for chemical enterprise employees.

The invention discloses a teaching course recommendation method and system based on English learning data, particularly relates to the field of semantic processing, is used for solving the problem of poor pertinence of a traditional English learning course, and comprises the following steps: aiming at systematic difference of native language and English expression on a syntactic structure, constructing a language structure difference map and extracting a structure offset label; the method comprises the following steps: performing dependency syntactic analysis on semantic consistent sentence pairs of multi-language aligned corpora to generate a structural difference feature set; on the basis, a semantic-grammar dimension mapping graph is constructed in a classified mode, and a general structure expression vector is established for graph nodes. A teaching course is divided into knowledge point units in combination with a context label, and a mapping relation between a structure label and the course is established. The system performs structure analysis and semantic matching on sentences input by the learner, identifies structure migration type expression errors, and recommends accurate teaching content according to context and structure labels.

The invention discloses a course recommendation method and system for intelligently building an education platform, and relates to the technical field of intelligent education informatization, and the method comprises the steps: building a knowledge graph, and completing the multi-level association of tasks and knowledge points; analyzing the assessment data, identifying weak knowledge points of the trainees, and setting score threshold fusion post weights; generating a course recommendation result on the basis of recognizing weak knowledge points of students, and optimizing recommendation logic in real time by adopting feedback; according to the method, a knowledge graph model associated with three layers of tasks, knowledge points and posts is constructed, student examination performance and post skill requirements are fused, and a course recommendation path construction mechanism driven by a multi-dimensional weight is realized; a dynamic feedback and real-time scoring algorithm is adopted, closed-loop logic of recommendation, assessment and re-recommendation is established, recommendation individuation and post matching degree are effectively improved, and the problems that knowledge points in an existing platform are not traceable, post adaptability is weak, and a recommendation strategy is single are solved.

The invention discloses an information extraction data annotation and training system based on a large language model, and relates to the technical field of information extraction, and the system comprises a data set construction module which is used for constructing a pre-training data set and a fine tuning data set; the model continuous pre-training module is used for carrying out continuous pre-training on a preset general large language model based on the pre-training data set to generate a field adaptive pre-training model; the model fine tuning module is used for performing supervised fine tuning training on the domain adaptive pre-training model through a two-stage course learning strategy based on the fine tuning data set, and generating an information extraction model; the retrieval enhancement generation module is used for performing entity-semantic retrieval on an input text based on a preset knowledge base, outputting context information related to the input text, and outputting structured information of the input text based on the context information and an information extraction model, the problems of insufficient generalization ability, poor field adaptability and disastrous forgetting of a general large language model are solved, and the accuracy and robustness of information extraction are improved.

The invention relates to the technical field of course supervision, in particular to a course quality supervision analysis method and system based on big data. The method comprises the following steps: acquiring course data of a course teaching platform to carry out course teaching planning design, and generating course teaching planning data; obtaining course teaching log data of the course teaching platform to perform learning attention deviation evaluation and course response evaluation during teaching of each course, and generating response evaluation data corresponding to teaching of each course; and obtaining historical teaching information data to carry out course teaching result analysis and course teaching quality evaluation, carrying out course structure adjustment processing on the course teaching planning data, generating course teaching planning data after structure adjustment, and carrying out updating processing on the course data of the course teaching platform. According to the invention, real-time assessment of course quality and course optimization are realized through a whole-process supervision mechanism of behavior deviation, course response and teaching achievements.

The invention relates to the technical field of education, and particularly discloses an intelligent accounting teaching method combined with behavior recognition, which comprises the following steps: collecting behavior data of students in an accounting course learning process through a multi-mode sensor deployed in an accounting teaching environment, the behavior data comprises facial expression data, limb movement data, voice interaction data, handwriting writing data and eye gazing trajectory data, a comprehensive feature vector is generated according to a deep learning model, visual features are extracted by using CNN, voice time sequence features are extracted by using RNN, and multi-modal features are fused through an attention mechanism, so that the visual features are extracted, and the visual features are extracted. According to the method, complementary information of different modal data is effectively integrated, and compared with single modal feature extraction, the learning state evaluation accuracy is improved by about 30%, for example, when the understanding degree of a student on a loan accounting method is recognized, the learning state evaluation accuracy is improved by about 30% in combination with handwriting writing fluency and the professional term use frequency in voice interaction, and the learning state evaluation accuracy is improved by about 30%. And the knowledge point mastering degree can be judged more accurately.

The invention relates to an automatic education resource generation and dynamic adjustment system and method based on artificial intelligence, electronic equipment and a computer readable storage medium, and the system comprises a resource site management module which is used for integrating an open learning platform, a third-party professional site and college learning resources; the data acquisition module is used for acquiring multi-modal resources related to knowledge points; the digital person making module is used for generating a digital person model and a sound model of the teacher; the resource generation module is used for automatically constructing a PPT and generating a teaching video according to the course large model output; the dynamic adjustment module is used for performing real-time adjustment and intervention on the generated teaching resources according to teaching requirements; and the user interface module is used for interaction operation between students and teachers and the system. According to the method, the generation efficiency of the educational resources is remarkably improved, the individuation and interactivity of the resources are enhanced, the dynamic adjustment and optimization of the resources are realized, and powerful technical support is provided for online education.

The invention discloses a power industry large model continuous pre-training method and system based on dynamic self-constraint, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining industry pre-training corpora and instruction training corpora, dynamically adjusting the mixing ratio of the industry pre-training corpora and the instruction training corpora through a curriculum-type strategy, and obtaining an industry pre-training corpora and an instruction training corpora; obtaining a dynamic mixed data set; configuring a reference model based on the dynamic mixed data set, and training a target power industry large model by adopting a differential loss function and the reference model for different types of data in the dynamic mixed data set; according to the differential loss function, self-adaptive KL divergence is calculated according to inter-partition optimization logic, and the self-adaptive KL divergence is adopted to construct a loss function; and obtaining the probability of the reference model through an online reasoning framework, and enabling the training to be continuously carried out based on the probability of the reference model. According to the method, the knowledge conflict problem in professional field training is effectively solved, and the generality of the model is kept while the professional property of the power field is improved.

The invention discloses an optimization method and system based on intelligent learning condition analysis and interactive teaching and a medium thereof, and relates to the technical field of educational informationization, and the method comprises the steps: carrying out the recognition and analysis of a teaching aid data image, and generating structured homework data containing knowledge point labels; collecting classroom behavior data of students, calculating a concentration degree deviation value based on knowledge point labels, and triggering teaching strategy adjustment; constructing a student ability evaluation matrix, establishing a dynamic task grouping model based on the student ability evaluation matrix and the task attribute characteristics, and generating a task-learning progress report; collecting teacher physiological data, and combining course information and complexity indexes of the dynamic task grouping model to construct a teacher fatigue evaluation model; calling a course arrangement optimization algorithm to optimize a course arrangement scheme based on a fatigue evaluation result; acquiring student learning track data, generating a personalized learning plan by adopting machine learning, and dynamically adjusting learning parameter weights through a feedback mechanism; the interactive teaching quality and the learning efficiency of students are improved.

The invention relates to the technical field of recording and broadcasting teaching, and discloses a recording and broadcasting teaching course quality evaluation method based on cloud computing. The method comprises five steps of data item collection, data preprocessing, evaluation model construction, evaluation strategy optimization and quality optimization. Wherein the evaluation model construction adopts a dynamic heterogeneous improved graph calculation method, and evaluation reference data is obtained through the steps of construction of an evaluation heterogeneous graph, dynamic updating of a graph structure, construction of a three-layer layering weight mechanism and the like; the evaluation strategy optimization adopts a multi-objective guide improved multi-layer strategy optimization method, and optimal strategy reference data is obtained through the steps of improving a state parameter space, modeling a multi-layer strategy network and the like; and finally obtaining a course quality comprehensive optimization reference scheme. According to the method, multi-modal dynamic data can be comprehensively processed, correlation of each factor is deeply mined, multi-objective optimization is balanced, evaluation accuracy and effectiveness are improved, and specific guidance is provided for quality improvement of recording and broadcasting courses.

The invention relates to the technical field of course analysis management, in particular to a course analysis management system based on deep learning. The method has the advantages that multi-modal data deep analysis realizes full-dimensional analysis of unstructured data by integrating a 3D-CNN model, a Transform architecture and a BERT model and synchronously extracting an attention hot area, a voice emotional state and a text knowledge point association network of a classroom video; nonlinear behavior modeling adopts an LSTM network and time convolutional network fusion model, a knowledge internalization path and forgetting curve prediction are dynamically generated, parameters are optimized in combination with incremental learning, and a transition rule across knowledge points is captured; a teaching scene-evaluation threshold mapping table is constructed based on a reinforcement learning algorithm through dynamic decision and resource collaboration, collaborative optimization under multi-campus data privacy protection is achieved in combination with a federated learning framework, GPU computing nodes are dynamically allocated through a heterogeneous resource scheduling engine, and the analysis efficiency is improved.

The invention discloses a new engineering course teaching evaluation method based on a knowledge-ability-quality triple atlas, and belongs to the technical field of intelligent education and intelligent control. Collecting data of three dimensions of knowledge, ability and quality of students to construct a unified state vector, and describing dynamic evolution of a learning process by combining a state updating and prediction model of a residual network; a multi-cell filtering method is adopted to carry out state set estimation, linear propagation and residual local linearization are combined in the prediction step, and a Lipschitz upper bound is utilized to carry out external connection on a nonlinear residual to ensure the safety and credibility of state interval estimation; in the updating step, set tightening is achieved through prediction strip intersection and generator contraction, coverage rate calibration based on quantiles is introduced, and the confidence level of set estimation is ensured. According to the method, the learning state of the student can be dynamically, stably and interpretably estimated, accurate and efficient course adjustment optimization is realized, and the method has a good application prospect and popularization value.

The invention discloses an end-to-end planning method fusing mixed trajectory representation and curriculum reinforcement learning. The method comprises the following steps: constructing a discrete-continuous mixed representation end-to-end pre-training network; constructing a course strengthening fine tuning framework based on interactive deduction; and designing a hard and soft constraint coupled hierarchical course award mechanism. According to the method, a discrete intention and continuous residual error coupling mixed trajectory characterization mechanism is introduced, and on the basis that a driving intention is quickly locked by using discrete primitives, subgrid-level geometric correction is performed on a coarse-grained trajectory through parallel regression branches. According to the invention, on the premise of not increasing the complexity of the search space, accurate trajectory planning with both long-time-sequence intention consistency and dynamics smoothness is realized. According to the method, the reinforcement learning training efficiency is effectively improved, catastrophic forgetting of a long-tail risk scene is prevented, a safety boundary is established in a strategy planning decoder, and the robustness and decision-making ability of an automatic driving system under extreme working conditions are improved.

The invention provides a recruitment post course system recommendation method and system based on a knowledge graph, and the method comprises the following steps: collecting recruitment post information data issued by recruitment enterprises in different regions, extracting recruitment post demand entities in the recruitment post information data to construct a post knowledge graph, and storing the post knowledge graph in a Neo4j graph database; according to professional course system teaching resources and course knowledge structures of vocational colleges, constructing a professional course system knowledge base through an LDA model, extracting professional course entities to construct a course knowledge graph, and storing the course knowledge graph into a Neo4j graph database; performing entity vectorization representation on the post knowledge graph and the course knowledge graph, combining similar entities, and constructing a complete post and course knowledge graph; constructing an on-duty recommendation model based on the on-duty knowledge graph, and in a recommendation stage, inputting a post name needing to be queried in the on-duty recommendation model and recommending a course system for the post; according to the method, the intelligent matching model of the post and the course is constructed based on the knowledge graph, so that the accurate butt joint of the post demand and the course system is realized, the intelligence, the accuracy and the matching degree of course recommendation are improved, and the pertinence of vocational education and the market adaptability of talent training are effectively improved.