818 results about "Coursework" patented technology

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 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 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 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 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 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 relates to the technical field of online education, and discloses an online course learning management method based on a knowledge graph. The method comprises the following steps: acquiring multi-modal learning behavior data of a learner, and extracting a deep learning state vector reflecting knowledge understanding depth, learning input degree and cognitive confusion through semantic fusion; and dynamically calculating and updating the logical relationship strength among the knowledge points in the course knowledge graph by using the vector, so that the knowledge structure can adaptively evolve along with the actual cognitive state of the learning group. And generating a real-time personalized learning path based on the updated knowledge graph and the current state vector of the learner. Meanwhile, according to cognitive confusion features in the state vector, intervention measures such as pushing of remedial resources, adjusting of content sequence or starting of self-adaptive testing are triggered in real time. According to the method, the dynamic optimization of the knowledge graph and the accurate and immediate response of learning intervention are realized, and the adaptability and management efficiency of online learning are improved.

The invention provides an intelligent learning plan generation method and device based on a large model, and belongs to the technical field of educational informatization. Identifying entities and key terms; constructing a course knowledge graph; carrying out matching calculation with courses through mixed retrieval on the basis of a course knowledge graph and key information features, and carrying out dual-channel collaborative multi-channel retrieval in combination with vector similarity and keyword matching; the multi-target course arrangement information is optimized based on a genetic algorithm, course arrangement is carried out in combination with a time planner of a constraint satisfaction problem model, and a course time arrangement plan is generated; and related lecturers and students are informed of confirmed results. According to the method, the curriculums and student requirements are matched, the comprehensiveness and accuracy of curriculum retrieval are improved through weighted sorting, the curriculums meeting the learning plan requirements can be found more accurately, and the curriculum matching efficiency is improved. A better course arrangement scheme is found through global search, the resource utilization rate is improved, and a scientific and reasonable course time arrangement plan is generated.

The invention belongs to the technical field of artificial intelligence and natural language processing, and discloses an NL2SQL model training and storing method and device based on a GRPO reward function. According to the method, a high-quality training data set is constructed, and the training effect and the generation performance of the model are effectively improved in combination with grammar verification, execution verification and semantic consistency screening; according to the method, the GRPO is adopted as a basic framework, and a combined reward function with multiple dimensions of coverage execution accuracy, grammar legality, semantic similarity, mode link and the like is designed; according to the method, a staged course learning strategy is designed, the sub-reward functions are activated and adjusted in a staged mode, the model is guided to be gradually transited to semantic understanding and execution optimization from structure specifications, and the generalization ability is improved; meanwhile, a reward weight dynamic adjustment mechanism is introduced, the weight of a reward function is automatically adjusted when SQL query execution fails, and the stability and sensitivity of training feedback are enhanced.

The invention provides an artistic course personalized learning path method and system based on deep learning. Firstly, an artistic course learning basic information set of a learner and a preset artistic course module library are acquired; calling a pre-trained deep learning course dynamic association model to generate association strength description of the learning basis of the learner and each course module; screening and sorting based on association strength description to form an initial learning module sequence; acquiring a real-time learning behavior data set of the first course module learned by the learner, and generating a module adjustment signal; and adjusting the initial learning module sequence according to the module adjustment signal to obtain a personalized learning path adaptive to the real-time learning state of the learner, and generating a personalized learning path document comprising a course module learning sequence and module connection guidance, thereby realizing accurate customization and dynamic optimization of the learning path, and improving the learning efficiency. And the learning effect of artistic courses is improved.

The invention belongs to the technical field of education course recommendation, and discloses a personalized learning path recommendation method based on a knowledge graph, which comprises the following steps: acquiring a knowledge point set according to a learning target, and constructing a basic knowledge graph; matching the first similar group, and obtaining learning records of the first similar group to form a first reference record set; marking difficulty values for the knowledge points according to the mastery degree in the first reference record set, and generating a knowledge point learning sequence; taking the first reference record set as a target reference set, and matching an optimal learning track for each knowledge point to obtain a comprehensive learning path; after learning records of the learner are obtained, matching a second similar group, and obtaining a second reference record set; and optimizing the comprehensive learning path according to the second reference record set, and adding review nodes for the knowledge points with low mastery degree. According to the method, knowledge point difficulty values are marked based on actual learning data, a learning sequence of cognitive load balance is generated, a dynamic recommendation process from general to individuation is realized, and learning efficiency and experience are improved.

The invention relates to the technical field of intelligent recommendation systems, and discloses a personalized resource recommendation method and system.The personalized resource recommendation method comprises the steps that real-time interaction logs and course resource data are collected, and preprocessing and feature extraction are conducted on the real-time interaction logs; performing structured processing on the course resource data; performing occupational skill mapping based on the layered resource data; performing feature extraction and semantic coding; performing multi-level user interest analysis; constructing a curriculum-to-skill mapping model and training, and deducing pre-repair dependence between curriculums; strategy mixed recommendation is carried out, and weighted fusion is carried out on mixed recommendation results; performing user diversity preference analysis, evaluating the diversity of the personalized recommendation candidate set, and performing optimization in combination with an evaluation result; performing multi-dimensional interpretation and interpretation quality optimization on the final recommendation result; in combination with a scene adaptive fusion mechanism, the learning requirements of the user in different time dimensions can be accurately captured, and the recommendation accuracy is improved.

The invention discloses a knowledge point extraction and question knowledge point matching method based on a large language model, and relates to the technical field of natural language processing. Firstly, a knowledge point extraction test set and a candidate baseline large model are constructed, an average recall score is calculated through matching, and an optimal baseline model is selected; a model adaptive to the teaching field is obtained through supervised fine tuning, then knowledge points are disassembled through the model, classification is carried out according to a Bloom learning model so as to clarify a teaching target, and finally the mastering degree of students is analyzed by associating questions with the knowledge points. According to the method, the course analysis efficiency can be improved, and full-automatic or manual intervention flexible analysis is realized; the accuracy and generalization ability of knowledge point extraction and matching are guaranteed, and subjective errors are reduced through semantic matching and evaluation schemes; teaching objectives are defined, and Bloom type labels are marked for knowledge points; a detailed question and knowledge point corresponding relation is constructed, the importance degree of each knowledge point is represented through a weight vector, and support is provided for student portrait and teaching progress analysis and the like.

The invention discloses a robot obstacle avoidance assembly method and system based on a curriculum learning strategy, and the method comprises the steps: setting a plurality of subtasks according to the curriculum learning strategy, and generating a task scene which comprises barrier-free assembly, static obstacle avoidance assembly and dynamic obstacle avoidance assembly; based on the task scene, action strategy training is conducted on the robot through a reinforcement learning algorithm, an HER mechanism and a parameter migration mechanism are introduced for optimization, and system state information is obtained; and carrying out feature coding on the system state information, then splicing the system state information, constructing a course switching condition to carry out loop training, and outputting a robot obstacle avoidance assembly result. According to the invention, the learning efficiency and generalization ability of the robot in complex tasks can be improved. The robot obstacle avoidance assembly method and system based on the course learning strategy can be widely applied to the technical field of robot intelligent control.

The invention discloses a dynamic space-time diagram flow prediction method and system based on course learning, and relates to the technical field of supply chain logistics data analysis, and the method comprises the steps: building a space-time matrix based on historical multi-source data, generating a dynamic adjacent matrix through learning, and carrying out the smooth fusion through combining a static diagram, and forming a dynamic diagram structure. And then space and time features are respectively extracted by using a graph convolutional network and a gating loop unit, and deep interaction and fusion are realized through a bidirectional cross attention mechanism. A multi-dimensional difficulty estimator is innovatively introduced, the prediction difficulty of each training sample is quantified from three dimensions of space, time and time-space coupling, the selection sequence of the training samples is dynamically adjusted based on an adaptive course scheduler, and progressive learning is realized. And finally, feature representation is obtained through global pooling, and multi-step traffic prediction is realized by adopting a parallel independent decoder, so that error accumulation is avoided. According to the invention, prediction precision and model training efficiency in a complex supply chain logistics scene are effectively improved.

The invention provides a course dynamic optimization method and system, an electronic device and a storage medium, and relates to the technical field of online education, and the method achieves the quantitative association of a course unit-knowledge fragment through a soft mapping matrix, enables the perplexity to be bidirectionally transmitted between the course unit and the knowledge fragment, and achieves the quantitative traceability. The comprehensive confusion degree is obtained by fusing the confusion degrees of the course unit layer and the knowledge fragment layer, so that the high-confusion course unit can be positioned more accurately and explainably, and fine optimization of the course can be realized; through a course optimization decision with cost constraint, automatic generation of a course optimization scheme with controllable cost is realized; besides, personalized learning path optimization of a course structure layer is realized, and a course optimization strategy continuously acts on subsequent learners, so that dynamic evolution of course contents and personalized learning paths is realized.

The invention discloses an educational resource recommendation method and system based on artificial intelligence. The method comprises the following steps: acquiring audio data, interaction data and task data acquired by a user terminal; performing feature extraction on the audio data, the interaction data and the task data to obtain an emotion feature vector, a learning rhythm vector and a content feature vector; inputting the emotion feature vector and the learning rhythm vector into a pre-constructed emotion recognition model to obtain a psychological state vector; the cognitive load is calculated based on the learning rhythm vector and the content feature vector, and then the cognitive load is corrected through the psychological state vector; matching a state interval of the corrected cognitive load according to a preset threshold interval; and according to the state interval, adjusting a difficulty coefficient of the recommended course, rearranging a course content sequence and an auxiliary learning prompt, generating structured data, and outputting the structured data as an intelligent auxiliary learning recommendation result. According to the invention, online learning interactivity and teaching quality in rural and remote areas are effectively improved.

The invention discloses an adaptive education textbook generation method and device based on a large model, and a medium, and relates to the technical field of intelligent education, and the method comprises the steps: determining a knowledge point set which needs to be expanded currently by a learner through a knowledge graph retrieval and course outline matching algorithm based on a learner portrait data set, and generating a teaching task list; according to the individualized evidence Prompt script, generating individualized teaching content by using a controlled decoding mode of a large language model, performing content consistency verification, and rearranging the content into a customized teaching material; carrying out conflict discovery-retrieval supplement-local regeneration-format recombination on the customized textbook through a ReAct reasoning framework to obtain a high-adaptability education textbook; the teaching material can be continuously corrected and optimized in the use process, so that the generation of the high-adaptability personalized education teaching material which is correct and smooth in teaching, fit and sustainable in evolution is realized.

The invention discloses an expert intelligent recommendation method and system based on CTR estimation and multi-feature fusion. The method comprises the following steps: acquiring multi-source heterogeneous case data through data acquisition and standardization; case semantic features are extracted through text preprocessing and LDA topic modeling; feature engineering and quantification are carried out on expert basic information, academic backgrounds, historical behaviors and matching degrees with cases; a GBDT + LR fusion model is adopted to realize multi-feature fusion and CTR estimation; generating an expert recommendation list based on CTR scores in combination with rigid filtering and post-processing strategies, and constructing a continuous learning mechanism through user feedback; the system comprises a function module corresponding to the method to realize accurate and fair expert recommendation. The problems that in the prior art, recommendation accuracy is low, the model structure is single, the feature utilization rate is low, and labor-dependent efficiency is low are solved, expert recommendation accuracy, efficiency, fairness and universality are improved, and the method is suitable for college teaching competition, course review, project acceptance and other scenes.

The invention provides an electric power training knowledge graph construction method and device based on multi-source heterogeneous data, equipment and a storage medium, and relates to the technical field of knowledge graphs. The method comprises the following steps: acquiring and preprocessing multi-source heterogeneous post training data to obtain structured post description data and course description data; respectively extracting post feature vectors and course feature vectors; calculating the semantic similarity between the post feature vector and the course feature vector; comparing the semantic similarity with a preset matching threshold value, screening out candidate courses matched with the target post, and generating a triple; constructing and updating a post training knowledge graph based on the triple; and when a new post or course is added, generating a new triple to update the knowledge graph. Through unified processing of multi-source heterogeneous data, post-course semantic matching and dynamic triple updating, precision, structuring and real-time updating of post training content are realized, and training pertinence and management efficiency are improved.

The invention relates to a method and device for predicting online open course learner satisfaction and electronic equipment, and the method comprises the steps: predicting the online open course satisfaction of students through an MLP and RBF neural network model by using a virtual learning environment of a large-scale online teaching and learning platform and combining learning behavior data in a learning management system (LMS); the model comprises a data acquisition and processing module, a multilayer perceptron (MLP) module, a radial basis function (RBF) neural network module, a classification tree module and a control block, the data acquisition and processing module is used for generating a training and testing data set, and the MLP and RBF neural network model predicts the satisfaction degree of a learner. The MLP model carries out feature extraction through a multi-layer perceptron structure and different activation functions, the RBF model measures the distance between input data and a center by using a radial basis function to realize feature extraction, the classification tree is used for judging a prediction model to which a data point belongs, the control block integrates features from the MLP and RBF neural network models, and the RBF model is used for determining a prediction model to which the data point belongs. Experimental results show that the prediction accuracy of low-satisfaction-degree learners and high-satisfaction-degree learners can be improved at the same time through the combination scheme of the MLP and the RBF, the method can be applied to learner satisfaction degree prediction of various online open courses, an educational institution is helped to know the satisfaction degree condition of students in time, course design and teaching strategies are optimized, and the teaching efficiency is improved. And important support is provided for teaching reform and optimization in the field of online education.

The invention relates to the technical field of railway line planning, in particular to an improved SAC railway line planning method based on course learning, and the method comprises the steps: modeling a three-dimensional line selection environment; constructing an improved SAC decision model, and designing a 100-dimensional state space including topographic features, forbidden zone positions and unfavorable geological risks; constructing a reward function fusing engineering cost, terrain fitness and invalid action penalty; curriculum learning staged training is carried out; and generating and optimizing a line scheme. According to the improved SAC railway line planning method based on course learning, a continuous action space decision framework based on the SAC algorithm can effectively solve the high-dimensional continuous action optimization problem of railway line selection. By designing a multi-dimensional fusion state space, a continuous action space based on a proportionality coefficient and a reward function based on target guidance, the adaptability of the intelligent line selection model to topographic features is remarkably improved.

The invention discloses an intelligent adaptive personalized learning method and system based on AIGC and a knowledge graph. The technical problems that an existing online learning system is low in individuation degree, a knowledge system is fragmented, the evaluation dimension is single, the adaptive capacity is weak, and different requirements of teachers and students are not distinguished are solved. The method comprises the following steps: constructing and updating a domain knowledge graph library with weights, establishing a self-adaptive course resource library associated with knowledge entities, respectively generating multi-dimensional portraits of teachers and students, generating differentiated personalized learning / teaching paths based on an A * search algorithm, and realizing self-adaptive content recommendation, continuous self-adaptive evaluation and dynamic adjustment through a mixed recommendation algorithm. A closed-loop intelligent ecology is formed; the corresponding system comprises core modules such as a knowledge graph library, a self-adaptive course resource library and a teacher and student portrait module. According to the method, all-dimensional personalized support of teachers and students, dynamic self-adaptive adjustment and resource generation according to needs are realized, the teaching and learning efficiency is remarkably improved, and the method is suitable for practical subjects and has system self-evolution ability.

The invention relates to the technical field of teaching planning, in particular to a professional teaching planning analysis system and method based on production-learning fusion, and the system comprises a post capability adaptation module, a course coverage recognition module, a resource distribution statistics module, a time period scheduling optimization module and a multi-element linkage module. According to the method, through accurate mapping between industrial post requirements and student abilities, point-by-point comparison between a course structure and ability requirements is combined, an ability vacancy interval in a course system is identified on the basis of dynamic data analysis, resource scheduling is optimized on the basis of a multi-dimensional data distribution relationship, and the resource scheduling efficiency is improved. Efficient matching of curriculum arrangement, practical training resources and site use multiple elements in different time intervals is promoted, real-time linkage analysis between teacher and student abilities and post requirements brings a continuous feedback mechanism for teaching planning, and the resource utilization level and the ability growth structure are synchronously optimized; and the dynamic adaptation relationship between industrial requirements and professional teaching is continuously strengthened.

The invention provides an intelligent course arrangement method based on multi-constraint hierarchical optimization and dynamic backtracking, and is applied to the technical field of data processing. According to the method, basic data of setups, classes, courses and the like are firstly obtained, and a multi-dimensional constraint model is established through preprocessing and is quantified into a time slot state value; based on a two-level layering strategy and dynamic teacher priority scheduling, independent classes are processed firstly, then sub-classes are processed, conflicts are cleared, and a stage course arrangement result is generated. In combination with the weekly class hours of the courses, time is distributed through an intelligent hall connection and degradation strategy, conflicts are detected in real time, and a backtracking mechanism is triggered. And verifying the conflict-free persistent data, and generating an initial course arrangement scheme. And finally, according to indexes such as course arrangement success rate and the like and application feedback, a constraint model, a scheduling algorithm and a class connection strategy are optimized, a multi-dimensional coupled intelligent course arrangement optimization scheme is formed, and the course arrangement problem under complex constraints is efficiently solved.

Described are platforms, systems, media, and methods for providing an accreditation management system (AMS) to validate educational resources by performing content validation operations comprising: applying a cryptographic hash function to educational resources to generate a content validation hash; receiving a data stream from a computing device of a student user engaged with the educational resources; applying the cryptographic hash function to each educational resource to generate a content consumption hash; and comparing the content validation hash to the content consumption hash.

The invention belongs to the technical field of education informatization and industrial data fusion, and discloses a system and a method for constructing production and education fusion data based on a knowledge graph. The problems that traditional production and education fusion data is dispersed and heterogeneous and lacks an effective integration and analysis means, a traditional analysis method depends on a static model to cause poor dynamic adaptability and hysteresis, and the production and education fusion degree is difficult to quantitatively evaluate to cause the lack of decision basis are solved. And a course optimization scheme lacks data support, so that the implementation effect is poor. According to the method, the standardized data stream of industry and education multi-source data is constructed, the ontology model of the production and education fusion core elements is established, the production and education fusion knowledge graph is established, the relation weight is dynamically evolved, and the production and education fusion degree index is quantitatively calculated in combination with the semantic similarity and the industry weight factor; and a course optimization scheme is generated based on a knowledge graph topological structure and reinforcement learning simulation, so that intelligent matching, dynamic optimization and decision support functions of a production-teaching fusion relationship are realized.