669 results about "Collaborative filtering" patented technology

The invention discloses a multi-modal visual arrangement recommendation method, belongs to the technical field of artificial intelligence and data visualization crossing, and realizes visual arrangement recommendation based on multi-modal input analysis, a dynamic mixed recommendation model and an intelligent optimization algorithm. Comprising the following steps: multi-modal intention analysis: realizing intelligent analysis of multi-modal input through combined use of a base model and a fine tuning model, realizing high-precision intention classification in combination with a pre-training language model and a domain adaptation fine tuning technology, and triggering dynamic prompt word recommendation; performing intelligent layout generation: performing global optimization of component space allocation by adopting a genetic algorithm, performing business rule adaptation by combining a constraint solver, and modeling an interaction relationship between components by utilizing a graph neural network; and dynamic mixed recommendation: constructing a three-level recommendation architecture including collaborative filtering, content matching and reinforcement learning. According to the method, a closed-loop recommendation process of user intention-intelligent recommendation-feedback optimization is realized, and the intelligent level of visual arrangement and the user experience are remarkably improved.

The invention belongs to the technical field of e-commerce, and particularly relates to a big data-based recommendation algorithm design in an e-commerce platform, which comprises the following steps: completing data preprocessing based on data collection and preprocessing; constructing user portraits to generate personalized user portraits, and classifying the users by using a clustering algorithm to find group features of the users; analyzing the user similarity and the commodity similarity, and calculating the similarity between the users; carrying out user-based collaborative filtering and article-based collaborative filtering, and carrying out dimensionality reduction processing on user and commodity features by using an SVD or latent semantic model; modeling user behaviors by using a neural network, and performing data compression and feature extraction by using an auto-encoder; the user feedback data is collected for continuous optimization and iteration of the model, and online learning and A / B test are adopted to adjust the recommendation strategy, so that the method has the effects of realizing more accurate and personalized commodity recommendation, and improving the shopping experience of the user and the sales conversion rate of the platform.

The invention provides an intelligent recommendation method and system for an e-commerce platform, and the method comprises the steps: collecting user interaction behaviors and time-space context data in real time, and constructing a user behavior multi-modal feature matrix; extracting commodity multi-level features, and generating a commodity comprehensive feature matrix; identifying and predicting a user intention based on the user behavior feature matrix, and generating an intention distribution vector; a recommendation candidate set is obtained by combining the commodity feature matrix and utilizing a context awareness collaborative filtering enhancement technology; a multi-objective optimization function is constructed, and after the user intention vector is input, a personalized recommendation sequence is generated in combination with an optimization result and the candidate set; and user feedback is monitored in real time, online learning and reinforcement learning algorithms are adopted, and a recommendation strategy is continuously optimized based on user instant feedback and long-term satisfaction. According to the scheme, the recommendation accuracy, the diversity of recommendation results and the user experience can be improved.

The invention discloses an AI-based cultural tourism content personalized recommendation system, which relates to the technical field of tourism planning and comprises a user information acquisition module, a cultural tourism content database information storage module, a recommendation engine module, an emotion analysis module, a real-time recommendation updating module and a multi-channel display module. According to the method, basic information, tourism preference, historical behaviors, geographic positions and social media data of users are collected, personalized portraits are generated and accurately recommended, sentiment analysis, real-time positions and dynamic behaviors are combined, the recommended content is continuously optimized by the system to adapt to the changing requirements of the users, and the user experience is improved. Through multi-channel display, personalized labels and clustering analysis, the system provides customized tourism suggestions, supports collaborative filtering and content recommendation strategies, real-time feedback and a self-optimization mechanism to improve the precision, adapts to intelligent equipment, recommends tourism contents according to environmental changes, and improves the user experience.

Access to secured items in a computing system is requested instead of being persistent. Access requests may be granted on a just-in-time basis. Anomalous access requests are detected using machine learning models based on historic patterns. Models utilizing conditional probability or collaborative filtering also facilitate the creation of human-understandable explanations of threat assessments. Individual machine learning models are based on historic data of users, peers, cohorts, services, or resources. Models may be weighted, and then aggregated in a subsystem to produce an access request risk score. Scoring principles and conditions utilized in the scoring subsystem may include probabilities, distribution entropies, and data item counts. A feedback loop allows incremental refinement of the subsystem. Anomalous requests that would be automatically approved under a policy may instead face human review, and low threat requests that would have been delayed by human review may instead be approved automatically.

The invention discloses an e-commerce personalized recommendation method based on cross-domain collaborative filtering, and relates to the technical field of e-commerce, and the method comprises the steps: collecting and preprocessing data, including user behavior data and commodity attribute data, of different fields of an e-commerce platform; materializing the collected data in different fields, and integrating the data into a structured e-commerce knowledge graph; and constructing a user-based cross-domain collaborative filtering model based on the behavior data of the user in the multiple fields, and predicting commodities in which the user is interested. By integrating data in different fields and constructing user portraits and commodity portraits, the problem of inaccurate recommendation caused by data heterogeneity in a traditional recommendation system is effectively solved, interests and preferences of users in different fields can be more accurately captured through cross-domain data association analysis, and the recommendation efficiency is improved. Therefore, personalized recommendation better meeting the requirements of the user is provided for the user, the recommendation accuracy and satisfaction degree are remarkably improved, and the user stickiness and the platform competitiveness are enhanced.

The invention relates to a method for intelligently pushing commodity display according to user behavior habits, and belongs to the technical field of artificial intelligence and electronic commerce recommendation systems. Aiming at the problems of low recommendation accuracy and insufficient real-time performance caused by dynamic change of user behaviors in the existing commodity pushing technology, the method comprises the following steps of: acquiring multi-dimensional data such as user browsing tracks, click preferences, purchase records and page staying duration, and constructing a dynamic user portrait in combination with a time sequence analysis and clustering algorithm; fusing real-time behavior feedback by adopting an improved collaborative filtering algorithm, mining potential association between behavior characteristics and commodity attributes through a deep learning model, and establishing an adaptive weight adjustment mechanism; and finally, a personalized commodity sorting strategy is generated based on the current scene and behavior trend prediction of the user, and dynamic optimization of the pushed content is realized. The method can be applied to an e-commerce platform, an advertisement putting system and a mobile application, the recommendation accuracy, the user conversion efficiency and the platform sales volume are remarkably improved, and meanwhile computing resource consumption is reduced.

The invention relates to a personalized learning resource recommendation method based on a subject knowledge graph and a graph neural network, and belongs to the field of education content recommendation. The method comprises the following steps: extracting key entities in a textbook and a relationship between the key entities, and constructing a subject knowledge graph; establishing a preliminary learning record table according to the behavior data of the student in the learning process; through knowledge tracking model integration, predicting mastery degree distribution of students for each knowledge point in the learning record sheet, and constructing student portraits; embedding the knowledge points in the subject knowledge graph and the association relationship thereof into the student portrait, and updating the knowledge embedding representation in the student portrait through a graph neural network; and constructing a personalized recommendation model, and pushing learning resources conforming to the current learning state of the student by adopting a mixed recommendation method of collaborative filtering and content recommendation. According to the method, personalized demands of students can be accurately captured in multiple dimensions, and the accuracy, generalization and interpretability of recommended contents are effectively improved.

The invention discloses a homogeneous real estate space intelligent search method and system based on space partition, and relates to the technical field of space data processing and intelligent search. According to the homogeneous real estate space intelligent search method and system based on space partition, the data of the target real estate area is obtained and preprocessed, so that the accuracy and integrity of the data are ensured; dividing the pre-processed area into a plurality of space subareas through a geographic information system, extracting homogeneous features through a clustering algorithm, and analyzing a peripheral matching adaptation degree index of each subarea in combination with geographic position preference of a user to form a homogeneous house resource set; and searching and matching the homogeneous house resource set through a collaborative filtering algorithm, generating a user recommended house resource list, calculating a matching degree index of house resources in each partition, setting a dynamic feedback mechanism, and sorting the recommended house resource list in real time, so as to improve the user satisfaction. Personalized housing resource recommendation is provided for the user, and the convenience and accuracy of real estate transaction are improved.

The invention relates to the technical field of education management, in particular to a practical training teaching and evaluation management system, and aims to ensure that each task strictly corresponds to a teaching target by acquiring practical training task parameters and establishing a mapping relationship between practical training tasks and a teaching outline through a knowledge graph, and to realize practical training teaching and evaluation management by utilizing integration and mapping of multi-source data. A data basis is provided for subsequent analysis; a collaborative filtering algorithm is utilized to match student ability and training task requirements, and a matching degree score is output, so that the rationality of training task allocation is improved; the training process of the students is monitored and evaluated in real time through a multi-modal data intelligent analysis technology, and timely feedback is provided for teachers and the students; the skill tree is dynamically constructed and updated through a multi-modal evaluation result, a personalized practical training task recommendation sequence is generated, and the structure and node attributes of the skill tree are adjusted in time according to the actual learning condition and ability development of students, so that the skill tree has better pertinence and adaptability, and the learning efficiency is improved.

The invention discloses a cooking robot menu management method and system based on big data and a medium, and relates to the technical field of cooking robot menu management. The cooking robot menu management method comprises the following steps: a data acquisition step: collecting various menu data through a web crawler technology; a data preprocessing step: cleaning the collected menu data; a data analysis and classification step: analyzing the preprocessed menu data by using a data mining algorithm; a menu recommendation and customization step: recommending a proper menu to the user by using a collaborative filtering algorithm or a content-based recommendation algorithm according to historical ordering records, health requirements and personal taste preference information of the user; and an intelligent cooking control step: the cooking robot receives the customized cooking instruction. According to the invention, the modules cooperatively and efficiently operate, links of data acquisition, processing, analysis, recommendation, cooking control and the like are closely linked, the system response is rapid, and the user operation experience and cooking efficiency are greatly improved.

The invention discloses a credit marketing intelligent recommendation system. According to the system, for the existing credit marketing problem, a customer portrait is constructed through multi-channel data acquisition, cleaning and preprocessing, and personalized credit product recommendation is carried out based on content and collaborative filtering. And training a recommendation model by using a neural network model and a reinforcement learning algorithm, updating recommendation in real time or at regular intervals according to real-time conditions and feedback of customers, establishing a system evaluation effect including recommendation accuracy, customer satisfaction and business indexes, and optimizing model parameters, improving the algorithm and updating data according to evaluation. The method can improve the customer satisfaction and the business conversion rate, reduces the risk, adapts to the market change, and effectively solves the defects of the existing credit marketing channel and strategy.

The embodiment of the invention provides a local life service integration method and device and terminal equipment. The method comprises the following steps: integrating a plurality of heterogeneous local life service providers into a gas station service platform to obtain a local life service platform, and collecting real-time service data in real time; non-oil sales fluctuation of the gas station is predicted based on the real-time service data, and the order quantity and the delivery cycle of upstream and downstream suppliers are dynamically adjusted according to a prediction result; constructing a multi-dimensional data analysis model, predicting user traffic and various service demands in different time periods every day, and configuring a corresponding local life service time window based on a prediction result; dynamically updating callable local life service resources and dynamically deploying gas station human resources based on the order quantity, the delivery cycle and the local life service resource configuration; and generating a personalized service list of the user by using a collaborative filtering algorithm and a reinforcement learning algorithm, interacting with the user in an interaction interface, and providing corresponding local life service resources for the user.

The invention discloses an intelligent financial question and answer system based on a large language model, and relates to the technical field of financial question and answer, the system solves the problems of financial term ambiguity and context ambiguity through cooperative work of a plurality of modules, and the system comprises the following modules: a context recognition module, a query module and a query module, a natural language processing algorithm is combined with a context analysis technology in the financial field to automatically extract context clues in user questions; and a financial term knowledge base module. According to the method, context recognition and a financial term knowledge base are combined, and the accuracy of understanding the term ambiguity and the complex context is improved through the graph neural network. And the intelligent recommendation module dynamically provides personalized financial suggestions by using collaborative filtering and content recommendation algorithms in combination with user history and market data, and performs real-time adjustment. The risk assessment and prompt module assesses risks through a Bayesian network and a logistic regression model, and the adaptive learning module continuously optimizes risk prediction and helps users to avoid financial risks.

The invention provides a multi-modal-based advertisement recommendation method and a related device, and systematically solves the technical bottleneck of a traditional recommendation technology in a complex scene through multi-modal feature alignment, dynamic weight optimization and causal effect decoupling. The method comprises the following steps: firstly, respectively extracting modal features of a video, a text and a user behavior sequence, and constructing a cross-modal contrast loss function to strengthen multi-modal semantic alignment; secondly, dynamically updating each modal weight through back propagation, and realizing advertisement recall and sorting in combination with cross-modal similarity calculation; an anti-fact causal reasoning module is further introduced, interference of environment mixed variables on the recommendation effect is eliminated through tendency score estimation and anti-fact result prediction, and the real causal effect of advertisement exposure is accurately quantified. According to the scheme, representation learning, dynamic decision and causal inference are deeply fused, a generalized and anti-noise technical framework is provided for short video advertisement recommendation, and the performance boundary is remarkably superior to that of traditional collaborative filtering, matrix decomposition and other methods.

Access to secured items in a computing system is requested instead of being persistent. Access requests may be granted on a just-in-time basis. Anomalous access requests are detected using machine learning models based on historic patterns. Models utilizing conditional probability or collaborative filtering also facilitate the creation of human-understandable explanations of threat assessments. Individual machine learning models are based on historic data of users, peers, cohorts, services, or resources. Models may be weighted, and then aggregated in a subsystem to produce an access request risk score. Scoring principles and conditions utilized in the scoring subsystem may include probabilities, distribution entropies, and data item counts. A feedback loop allows incremental refinement of the subsystem. Anomalous requests that would be automatically approved under a policy may instead face human review, and low threat requests that would have been delayed by human review may instead be approved automatically.

The invention belongs to the technical field of catering informatization service, and provides an automatic matching catering menu pushing system, which comprises a user data acquisition module, a catering menu pushing module and a catering menu pushing module, the menu data management module stores and manages menu classification, labels and states, and obtains content feature data; the user portrait construction module generates a multi-dimensional portrait according to the multi-aspect information of the user; the menu matching module combines collaborative filtering and a content recommendation algorithm, and generates a recommendation menu based on the user portrait; the intelligent pushing module pushes dishes of nearby merchants according to the portraits, and the pushing effect is evaluated through the click rate and the conversion rate; according to the method, personalized and accurate catering menu recommendation is realized, and real-time updating and optimization are carried out in combination with geographic positions and time period factors, so that personalized pushing which better fits user preferences and dining environments is provided.

The invention relates to the field of image cultural relic disease recognition and evaluation, in particular to a cultural relic disease recognition and evaluation system and method based on image analysis, and the system comprises a multispectral image collection module, an image processing module, a disease recognition module, a repair evaluation module, a decision optimization module and a disease database. The multispectral image acquisition module optimizes a light source through a reflective area detection algorithm to obtain a multispectral image; the image processing module uses a multi-band collaborative filtering algorithm to eliminate noise and extract fusion features, and divides disease areas; the disease identification module determines disease positions through a connected region marking algorithm, and performs disease classification by means of a neural symbol model; the repair evaluation module outputs a quantitative score; the decision optimization module weighs the repair effect, the cost and the time by introducing a multi-objective optimization model of a genetic algorithm, and outputs an optimal scheme; according to the invention, the disease identification precision and the automation and scientificity of repair evaluation are improved.

The invention discloses a drug risk monitoring method based on multi-source data fusion, and relates to the technical field of data analysis. Through multi-source data fusion and processing, the problems of data isomerism and information islands are solved, a comprehensive and high-quality data basis is provided for drug risk monitoring, a weighted collaborative filtering algorithm and a Bayesian network are adopted, potential risk signals are accurately mined, real-time dynamic monitoring and early warning of drug risks are achieved, and the drug risk monitoring and early warning efficiency is improved. The timeliness and the reliability of monitoring are obviously improved; meanwhile, the real-time stream processing framework is used for carrying out incremental updating on newly-added data, dynamically adjusting a risk assessment result, and carrying out automatic early warning when a risk value exceeds a standard, so that the contradiction between a complex process and a rapid early warning demand is effectively solved, the efficiency and accuracy of drug safety supervision are improved, and powerful support is provided for guaranteeing the public drug safety.

The invention discloses a knowledge graph recommendation method based on generative denoising and multistage contrast learning, and belongs to the technical field of recommendation system and knowledge graph combination, and the method comprises the steps: firstly constructing a user-project interaction graph, and supplementing project semantic information in combination with an external knowledge base to generate a knowledge graph; thirdly, obtaining decoupled embedded representations, and enhancing the learning of the representations by adopting LightGCN; noise is added to the original knowledge graph, and mask processing is carried out to optimize the anti-noise capability of the model; then extracting a user multi-preference representation from each sub-view and optimizing a final user representation through a contrast learning task; embedding and mapping items under the knowledge graph and the collaborative filtering view into a unified space to realize semantic alignment; and finally, designing a multi-task joint loss function to fuse the knowledge graph and collaborative filtering information so as to improve the personalized recommendation performance. According to the method, the accuracy, the robustness and the personalized service capability of the recommendation system are effectively improved.

The invention relates to the technical field of recommendation algorithms, provides a graph collaborative filtering course recommendation method based on interactive attention and comparative learning, and aims to solve the problem that a traditional recommendation system is insufficient in modeling ability in a sparse interaction scene. The method comprises the following steps: firstly, constructing a user-course bipartite graph, and utilizing a dynamic attention mechanism guided by an interactive opposite-end node: carrying out vector dot product through original embedding of the opposite-end node (for example, course embedding is used during user aggregation) and current embedding of a neighbor node, and generating an attention coefficient in combination with temperature parameter normalization; and multi-level structure information aggregation is realized. Afterwards, local context features are fused through a multilayer graph convolutional network, random noise disturbance is introduced to generate a multi-view comparison sample, the consistency of positive samples is maximized in combination with an InfoNCE loss function, and the robustness of the model to noise and sparse data is enhanced; and finally, optimizing user-course embedding in combination with Bayesian personalized sorting loss and comparison loss, and generating a personalized recommendation list. According to the method, the key interaction relationship is screened through guided attention, the representation discrimination is improved in combination with comparative learning, and the recommendation precision in cold start and data sparse scenes can be improved.

The invention relates to a new media AI marketing content creation method and device, equipment and a medium. The method comprises the steps of obtaining user demand configuration, and obtaining a creation intention vector through natural language analysis; based on the platform characteristic knowledge base, extracting a corresponding structure specification and a propagation mechanism according to the target platform, and encoding to obtain a platform characteristic vector; obtaining historical content interaction data corresponding to the audience group, and generating a user-content interaction vector by adopting collaborative filtering and a label similarity algorithm in combination with the content keyword; and calling an artificial intelligence large model, and performing content generation according to the creation intention vector, the platform feature vector and the user-content interaction vector to obtain content creation data. By adopting the method, the goal of automatic, high-quality and personalized new media marketing content creation in a multi-platform environment can be realized by means of natural language analysis, knowledge structure extraction, large model generation constraint and the like.

A system for personalized product recommendations in an e-commerce platform, comprising: a user data collection module configured to collect browsing history, purchase records, search queries, and demographic information; a data preprocessing module configured to apply cleaning, feature extraction, and natural language processing (NLP) techniques to structure raw data; a product information management module configured to extract, classify, and update product attributes using NLP and image recognition; a recommendation engine that uses machine learning models, including collaborative filtering, content-based filtering, and deep learning techniques, configured to generate personalized product recommendations; a contextual filtering module configured to dynamically refine recommendations based on real-time factors such as user location, device type, and market trends; a feedback learning module configured to continuously update recommendation models using user ratings, reviews, and engagement metrics; and an API integration module configured to enable seamless connectivity with third-party e-commerce applications, inventory management systems, and analytics tools.

The invention discloses a recommendation system and method for keeping semantic integrity based on a large language model. According to the method, user-article interaction data and text information are fused, a prompt template of a user and an article is constructed, a configuration file with rich semantics is generated by utilizing a large language model, and initial embedded representation is extracted. Then, two-stage dimensionality reduction transformation is carried out through principal component analysis and a multi-layer perceptron, semantic information is reserved, and low-dimensional embedding is generated; on the basis of the embedding, cosine similarity is calculated, a user-user and article-article similar graph is constructed, and final embedding representation is generated through graph convolutional network coding. Meanwhile, collaborative filtering is combined to capture an interaction relationship and optimize a joint learning target, including recommendation loss, cross-modal comparison loss and regularization terms, so as to align semantics and collaborative filtering embedding, and finally generate a high-precision personalized recommendation result. The method effectively improves the semantic comprehension ability and recommendation accuracy of a recommendation system, and is suitable for various recommendation scenes.

The invention provides an intelligent message pushing method and system, and the method comprises the steps: collecting the social behavior data and personal attribute data of a user in a social network; dividing the users into a plurality of groups by using the social behavior data and the personal attribute data based on an ant colony algorithm; analyzing a social relation and an interaction mode among users in the group; collecting message resources in the social network, and classifying and labeling messages; performing collaborative filtering processing on the to-be-pushed message, determining a target user group, and generating a message recommendation list; and pushing the recommended message to users in the target user group according to a preset pushing strategy. The user social behavior data and the personal attribute data are converted into the ant feature vectors based on the ant colony algorithm, similar feature vector ants are gathered through pheromone updating and path selection mechanisms, different user groups are formed, the user group division accuracy is improved, and user requirements are more accurately grasped.

The invention relates to the technical field of product recommendation, in particular to an intelligent product recommendation method based on a collaborative filtering algorithm. According to the invention, the popular style of each product is obtained according to the product sales volume change trend of each product at different transaction moments; obtaining the conformity of each user in combination with the transaction time characteristics of the corresponding historically purchased products; for any user as a target user, according to the similarity between each emerging product and different historically purchased products, obtaining the product similarity between the emerging products and the historically purchased products, and screening emerging recommended products; according to the transaction times and transaction time distribution of different historically purchased products, obtaining transaction performance of the user corresponding to each historically purchased product; the interest tendency of the target user corresponding to the historical purchased product is obtained; and obtaining a recommendation index of the target user corresponding to the emerging recommended product. According to the method, the accurate recommendation index of each emerging product to the user is obtained, and the product recommendation accuracy is improved.

The invention relates to the technical field of intelligent cook machine recommendation, and discloses an intelligent cook machine recipe recommendation and operation control method, which comprises a user input module used for receiving user instructions including food material types, cooking targets, diet restrictions and equipment models; the recognition module is used for collecting food material weight and state data; the data processing module adopts a multi-modal fusion algorithm; the recipe recommendation module is based on an improved collaborative filtering algorithm and a knowledge graph; and the output module is used for controlling the instruction generation unit to output a sectional operation instruction set according to the selected recipe. By integrating the image recognition unit and the weight sensor, the type, weight and state data of food materials placed in the chef machine by a user can be collected in real time, and correction processing is carried out in combination with the environment temperature and humidity sensor; therefore, the existing food material inventory of the user can be accurately considered during recipe recommendation, and the problem that the existing food materials of the user cannot be fully utilized during recipe recommendation by an existing system is solved.

The invention provides an anesthesia virtual simulation training system fusing knowledge, skills and a thinking closed loop. The anesthesia virtual simulation training system comprises a medical knowledge base module, a clinical thinking module, a skill training module, an examination question brushing module, a knowledge graph module and an intelligent platform bottom layer framework. The intelligent platform underlying architecture comprises a data middle platform, an AI engine and a 3D engine, collects student behavior data of each module, constructs a dynamic student ability portrait through a gradient boosting tree algorithm and a collaborative filtering recommendation model, analyzes knowledge blind areas and skill shortages, plans a personalized learning path and pushes targeted training content, and provides a personalized learning result. A closed-loop process of evaluation, learning, practice and re-evaluation is formed; and deep fusion of theoretical knowledge, clinical thinking and skill operation is realized through a cross-module collaboration mechanism. The problems that traditional anesthesia teaching is high in practical operation risk, scattered in resource and insufficient in individuation are solved, and the clinical comprehensive ability and teaching quality of anesthetists are effectively improved.

The invention relates to the technical field of performance appraisal management, in particular to a mine workshop performance appraisal management method, which comprises data acquisition and preprocessing, dynamic performance evaluation, task difficulty adjustment, an incentive mechanism and performance feedback and optimization. Optimizing factor weights and predicting employee performance based on a multi-factor weighting model in combination with a neural network and reinforcement learning, and calculating performance scores in real time; the task difficulty is quantified by applying an analytic hierarchy process and fuzzy comprehensive evaluation, and real-time adaptive optimization is analyzed in combination with employee ability; a market supply and demand and collaborative filtering algorithm is adopted to adjust an integral value and a customization scheme, and an award standard is analyzed and optimized according to cost effectiveness and performance sensitivity; a report is generated by means of a visual library, suggestions are generated by means of text mining, and through prediction and causal analysis early warning, a management strategy adjustment scheme is recommended according to reinforcement learning, and feedback optimization is performed; according to the invention, the performance of the mine workshop can be comprehensively, accurately and dynamically assessed and managed, the operation efficiency and the enthusiasm of employees are improved, and sustainable development of enterprises is promoted.

The present invention is applicable to the field of work order management, and discloses an intelligent work order management method, system, equipment and medium. The method first classifies work orders according to the differences in work order types to obtain work orders of different categories, and vectorizes the characteristics of each type of work order from two dimensions: work order processing complexity and required resources, to construct a work order feature vector; based on the work order feature vector, a work order processing strategy selection model is established; in the process of work order processing, resource consumption data of each link is collected to construct a feature matrix of work order resource consumption and value contribution; a work order resource allocation recommendation model is established using a collaborative filtering algorithm; after the work order processing is completed, a work order cost accounting model is established based on the actual resource consumption and work order characteristics; in addition, in the entire process of work order processing, data of each link is continuously collected, and each model is updated and optimized in real time to achieve a dynamic balance between processing efficiency and cost sharing.