288 results about "Self-optimization" patented technology

The invention discloses a large-model-assisted self-lifting multi-modal industrial equipment knowledge graph construction method. The method comprises the following steps: collecting and preprocessing multi-modal data; obtaining structured knowledge data in a triple form based on a few-sample prompt; gradually self-optimizing the triple from three evaluation dimensions in combination with thinking chain skills to ensure normalization, integrity and reliability of knowledge expression; the problems of data redundancy and ambiguity in the knowledge graph are solved by adopting a triple-step entity alignment method and utilizing the self-lifting capability of a large model; based on a knowledge graph completion template and a retrieval enhancement mechanism, a relationship prediction and entity completion task is completed by applying a large model, so that the integrity and applicability of the knowledge graph are ensured. According to the method, a large amount of manually labeled data is not needed, the automation level and knowledge quality of knowledge graph construction are improved through the self-lifting thought of a large model and multi-time iterative optimization, and powerful support is provided for industrial equipment management, process optimization and intelligent diagnosis.

The invention discloses an exhibition hall intelligent control system and method based on the Internet of Things technology, and the method comprises the following steps: collecting environment data in an exhibition hall in real time through a self-adaptive multi-mode sensor network, and dynamically adjusting the sampling frequency of a sensor through a multi-dimensional data sudden change monitoring algorithm by the system, the central processing module is combined with historical and real-time data, time sequence multi-dimensional regression analysis is adopted, an environment change prediction model is generated, then equipment control parameters are generated, the system dynamically adjusts the operation states of temperature equipment, illumination equipment and the like in the exhibition hall according to behavior data of visitors and the environment parameters, the experience of the visitors is optimized, and the experience of the visitors is improved. The intelligent anomaly detection module monitors the operation state of equipment in real time and identifies and isolates fault equipment in time, and the system preferentially guarantees equipment operation and reduces energy consumption when visitors are dense through a long-term energy consumption optimization learning algorithm, so that energy-saving management and long-term self-optimization of the exhibition hall are realized.

The invention relates to the technical field of multimedia interaction, in particular to an intelligent interaction control method and system for digital exhibition hall multimedia equipment, and the method comprises the following steps: S1, carrying out the arrangement of a sensor, and carrying out the intelligent sensing and data collection; s2, generating an intelligent interaction strategy; s3, equipment cooperative control and execution; and S4, performing real-time feedback and system optimization. According to the method, user behaviors and environment data are collected in real time through the multi-modal sensor, user requirements are accurately speculated in combination with a deep learning algorithm, the optimal interaction strategy and task priority are generated, individuation and interaction efficiency of user experience are remarkably improved, multi-device cooperation is optimized through distributed task scheduling, and user experience is improved. According to the method, display content and environment parameters are dynamically adjusted, self-optimization of the system is realized by utilizing user feedback, real-time performance, smoothness and reliability of interactive experience of the exhibition hall are ensured, rapid fault response and accurate maintenance demand prediction are realized through anomaly detection and equipment log analysis, and the stability and sustainability of operation of the exhibition hall system are greatly improved.

The invention provides a training data synthesis method and device based on error extrapolation and inference chain analysis, a medium and a program product. The method comprises the steps of obtaining an initial sample set; performing multiple sampling reasoning on the problem of each task sample by using a small language model to generate a plurality of reasoning chains; calculating an overall error score of each reasoning chain based on a preset error evaluation rule, and determining a to-be-corrected reasoning chain; the inference chain to be corrected and the corresponding question are input into the large language model together, and a corrected answer is generated; forming a new task sample by the question and the corrected answer, and finely adjusting partial parameters of the small language model; repeatedly executing the process until the performance index change rate of the model on the task evaluation set is lower than a preset threshold value, and outputting a final task sample; and forming a training sample set by a plurality of final task samples, and performing all-parameter fine tuning on the small language model. According to the method, the training data self-optimization path is constructed by taking the model error as guidance, so that the semantic consistency and the data validity are improved.

The invention discloses a talent matching method and system based on artificial intelligence, and aims to improve human resource configuration efficiency and decision intelligence. The method comprises the following steps: collecting talent and demand data in multiple channels, especially unstructured communication data including interview records and work communication records; processing data through technologies such as multi-mode resume analysis, extracting features and fusing the features; an enterprise talent knowledge base which is used for continuous learning and dynamic maintenance based on a system operation result and multi-source feedback and integrates structured and unstructured data is constructed; based on the natural language query of the user, providing intelligent question answering and decision support by using a retrieval enhancement generation model connected with the specific enterprise talent knowledge base; an advanced deep learning algorithm is adopted, a knowledge base is combined to carry out man-post matching and generate recommendation of context perception, and self-optimization of a matching strategy is realized through mechanisms such as reinforcement learning and the like. According to the invention, accurate and dynamic talent matching and intelligent decision making can be realized.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle task automatic planning and situation display system based on artificial intelligence. Comprising a task demand identification and priority dynamic adjustment module, a multi-task path optimization module, a multi-dimensional situation awareness and data fusion module, a three-dimensional situation display module, a task dynamic adjustment and feedback control module, a task data storage and historical analysis module and an emergency task response and environment interference adaptation module. By collecting task requirements and environment parameters in real time, the system can dynamically generate a task priority queue and optimize path planning based on priorities, it is ensured that the unmanned aerial vehicle has efficient response ability when executing tasks such as reconnaissance, monitoring, searching and rescue, the system combines a multi-source data fusion technology, the three-dimensional situation of the task environment is displayed in real time, and the system has good application prospects. Visual support is provided for decision making; the self-optimization capability and the adaptability to emergency situations lay a foundation for long-term intelligent development of unmanned aerial vehicle tasks.

The invention discloses a BIM-based traffic engineering construction progress and resource management optimization system, and the system comprises a three-dimensional cognitive interaction body which adjusts the construction progress and resource configuration in real time, and eliminates the physical difference between a virtual model and an actual environment; the self-adaptive calculation engine is used for realizing butt joint between the virtual model and an actual construction site by utilizing real-time sensing data and a cognitive correction mechanism; the progress prediction simulation module is used for realizing dynamic prediction and optimization of the construction progress through deep learning and a self-adaptive feedback mechanism; the virtual-reality synchronous perception grid is constructed based on the obtained progress dynamic prediction and optimization result, and a virtual model and a construction site are synchronized and dynamically corrected; and the self-optimization module is used for realizing self-adaptive optimization and long-period evolution through a genetic algorithm and multi-objective optimization by utilizing a self-optimization and ecological optimization mechanism. According to the method, the problem of virtual reality distortion is solved, and the difference between virtuality and reality in the construction process is accurately corrected.

The invention discloses a precast beam field production scheduling analysis management method and system based on a large model, and the method comprises the steps: combing precast beam production technological processes, constructing a beam field Internet of Things sensing network, and collecting production information in real time; defining a general engineering data format, establishing a unified standardized beam field production knowledge base, and performing multi-modal data fusion on production elements; analyzing feasibility constraint rule conditions, describing a precast beam production scheduling problem, and establishing a beam field self-organizing scheduling decision mechanism; defining production state characteristics, establishing a beam field scheduling decision process model, and training a large scheduling model based on a PPO algorithm; and developing a beam field scheduling analysis management platform, automatically generating a production scheduling scheme in combination with a real-time production state, and realizing dynamic management of beam field production scheduling. The problems that in the prior art, the resource utilization efficiency is low, scheduling decision making depends on artificial experience, and continuous learning and self-optimization capacity is lacked are solved, and the intelligent level and the management effect of the precast beam field are 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.

The invention relates to the technical field of energy storage scheduling, and discloses a distributed energy storage scheduling prediction method and system driven by intelligent environment monitoring data. The method comprises the following steps: acquiring environmental parameters and processing to obtain an environmental data set; decomposing the environment data in a multi-level manner to obtain feature vectors and uncertainty indexes; inputting a hybrid deep network to obtain demand prediction distribution; constructing a robust optimization model to obtain a scheduling instruction sequence; performing decomposition execution through a layered architecture to obtain a running track; and carrying out deep reinforcement learning iterative optimization to obtain an energy storage scheduling strategy. According to the energy storage scheduling prediction method, the communication delay influence is overcome, the self-optimization capability is achieved, and efficient cooperative operation of the distributed energy storage system based on environmental data driving is achieved.

The invention discloses an electric energy meter verification optimization scheduling system based on a multi-agent large model algorithm. The system comprises a data acquisition module, a multi-agent optimization scheduling module, a scheduling plan generation module and a user interaction module. The data acquisition module is used for data acquisition and monitoring, data preprocessing and cleaning and data storage and management; the multi-agent optimization scheduling module is used for an agent cooperation mechanism, agent adaptive learning and agent management; the scheduling plan generation module is used for prediction, analysis and integration, scheduling scheme generation and self-optimization and learning; and the user interaction module is used for interface design and implementation, user-defined functions and user feedback and improvement. According to the invention, the efficiency, the accuracy and the automation level of the electric energy meter verification process are improved through technical innovation and intelligent means.

The invention relates to the technical field of artificial intelligence, and discloses a professional knowledge question and answer intelligent customer service system based on a self-optimization mechanism. The system comprises a user intention analysis module, a knowledge processing module, a self-optimization learning module and an interactive presentation module. A semantic understanding unit of the user intention analysis module generates a user intention signal; after the knowledge processing module receives the signal, a knowledge retrieval unit outputs related knowledge fragments and confidence, an answer generation unit forms candidate answers, and a quality evaluation unit determines an optimal answer according to the confidence; in the self-optimization learning module, a feedback analysis unit adjusts answer generation parameters according to user interaction data, a strategy adjustment unit optimizes a retrieval strategy in combination with an optimal answer and a historical dialogue, and a knowledge updating unit updates a knowledge base depending on an external knowledge source; and a multi-round dialogue management unit of the interactive presentation module adjusts a dialogue process, and a visual presentation unit outputs a natural language text and collects user feedback to a feedback analysis unit.

The invention provides a generative search data processing method and device, equipment and a storage medium. The invention relates to the technical field of computers, in particular to the technical field of search technologies and large models, and can be used for application scenes such as generative search, intelligent questions and answers, intelligent recommendation and the like. The method comprises the following steps: collecting feedback data of a target object to a search answer which is firstly displayed on a search page; analyzing the feedback data to identify key factors influencing the satisfaction degree of the search answers; mining a first sample set and a second sample set from the feedback data in combination with the key factors; based on the first sample set and / or the second sample set, optimizing the first large language model to obtain a second large language model; and deploying the second large language model into the search system to replace the first large language model. According to the scheme disclosed by the invention, the high-confidence sample can be automatically mined, the self-optimization loop of the generative large language model is realized, and the quality of the search answer is improved.

The invention relates to the technical field of new energy consumption, and discloses a new energy consumption calculation method considering a hydrogen production process, and the method comprises the steps: building a hydrogen production coupling model which comprises a new energy power generation power prediction sub-model and a hydrogen production efficiency dynamic sub-model, and obtaining a new energy power generation prediction value and a hydrogen production real-time efficiency coefficient; designing a multi-energy collaborative optimization framework, and generating a scheduling scheme based on a dynamic game theory algorithm; executing a dynamic correction strategy, and adjusting the scheme according to real-time conditions; and establishing a data feedback mechanism, and collecting data to update model parameters. According to the method, the accuracy of new energy consumption measurement and calculation is improved, multi-energy collaborative scheduling is optimized, the dynamic adaptability of the system is enhanced, model self-optimization is realized, the cost can be effectively reduced, wind curtailment and light curtailment are reduced, and remarkable economic benefits and environmental benefits are achieved.

The invention relates to the technical field of material processing control, and discloses a method for efficiently cutting a vacuum brazing soldering lug. The method comprises the following steps: firstly, receiving material parameters and target geometrical shape data of a to-be-cut soldering lug, and generating and optimizing an initial cutting track based on a preset algorithm; in combination with real-time temperature field monitoring data, thermal stress distribution is calculated through a finite element analysis method, and the cutting speed and laser power parameters are dynamically adjusted; and then the cutting precision and the equipment energy consumption are synchronously optimized through a multi-objective optimization algorithm, and final cutting parameters are output to a vacuum brazing control system. In addition, signals are collected in real time during cutting to recognize abnormity, and algorithm weights are calibrated by comparing geometric parameters after cutting. The cutting precision and efficiency can be effectively improved, equipment energy consumption is reduced, cutting abnormity is treated in time, self-optimization of the cutting process is achieved, and the method is suitable for the production field with the high requirement for vacuum brazing piece cutting.

The invention provides an intelligent question and answer method based on a preset multi-dimensional knowledge base and a large language model, and the method comprises the steps: receiving a natural language query, converting the natural language query into a query semantic vector, and recognizing a corresponding intention type and a first confidence coefficient; if the first confidence coefficient is higher than an intention threshold value and the highest similarity between the query semantic vector and a standard question semantic vector in a preset multi-dimensional knowledge base is higher than a matching threshold value, returning a standard answer; otherwise, retrieving a generated context based on the query semantic vector, fusing the user query and the generated context, inputting the fused user query and generated context into a large language model to generate a preliminary answer, checking fact consistency of the preliminary answer and a standard answer in the generated context to generate a credibility score, and obtaining a target answer when the credibility score exceeds a credibility threshold; and dynamically optimizing the system based on feedback data of the user to the target answer. The intelligent customer service system with controllability, flexibility and self-optimization capability is realized, and the semantic understanding depth and response accuracy of the system are improved.

The invention relates to the technical field of intelligent traffic monitoring, in particular to a toll station pedestrian and non-motor vehicle intrusion intelligent early warning system and method, a terminal layer comprises a sensing terminal composed of a camera, a hard disk video recorder and the like, and an early warning terminal composed of a directional sound post and the like; an edge computing unit is deployed on the edge layer, targets are detected in real time, tracks are tracked and classified, and cloud rechecking is triggered by low-confidence targets; the cloud layer utilizes a visual language large model to recheck a target, pre-mark data, generate an electronic fence and store data; the system adopts a dynamic grading early warning module, three-level early warning is triggered according to a target track, a position and staying time, and automatic degradation is carried out along with the presence of a worker; in addition, through a closed-loop optimization mechanism, a positive / false alarm feedback iteration visual small model is collected. According to the invention, cloud side-end cooperation is realized, multiple models and algorithms are fused, high-precision detection, real-time response, dynamic self-adaption and self-optimization capabilities are realized, the intrusion risk can be effectively prevented, and the safety management level of the toll station is improved.

An AI and machine learning-based system for automating employee management and work information processing within an organizational structure, the system comprising: a central processing module configured to aggregate and pre-process data from multiple sources, including attendance records, performance logs, task management systems, and communication channels, with the central processing module normalizing and filtering the data to ensure consistency and accuracy in real-time analysis; a machine learning-based analysis unit operatively connected to the central processing module, the analysis unit comprising a natural language processing (NLP) sub-module, a sentiment analysis sub-module, and a pattern recognition sub-module, and configured to extract, analyze, and interpret both structured and unstructured data for insights into employee behavior and performance assessment, and further configured to adapt and refine models based on continuous data inputs from the central processing module; a predictive task scheduling component comprising a reinforcement learning-based model that leverages employee skill profiles, historical task completion rates, and workload patterns to dynamically distribute tasks, with the predictive task scheduling component further configured to self-optimise based on real-time feedback regarding task completion efficiency, priority changes, and schedule adjustments within the organization; a performance tracking unit configured to receive inputs from the central processing module and the machine learning-based analytics unit, wherein the performance tracking unit continuously monitors employee performance, challenges, and areas for improvement and stores these insights in individualized, encrypted employee profiles that can be accessed in real time, thus supporting data-driven performance reviews and improvement plans; an input / output interface for user interaction, the interface providing managers with interactive access to review employee metrics, task assignments, and performance feedback, and enabling employees to securely view individual performance metrics, feedback, and task details, the interface further being configured with user-level access controls based on historiographical organizational roles; a secure data storage component configured to securely store employee data, task logs, and performance metrics, where the data storage component supports both local and cloud-based storage solutions, uses encryption protocols for secure data retention, and provides access control mechanisms for authorized retrieval of stored data; a communication interface module operatively connected to the central processing module and configured with multi-protocol communication capabilities, including Wi-Fi, Ethernet, and Bluetooth, wherein the communication interface module facilitates real-time data synchronization between remote and local devices and is integrated with an AI-based anomaly detection system that flags irregularities or potential security threats in data transmissions; and an adaptive learning module operatively connected to the machine learning-based analytics unit and configured to continuously retrain machine learning models based on real-time data inputs, where the adaptive learning module uses reinforcement learning and unsupervised learning techniques to refine task recommendations, adjust performance metrics, and optimize task assignment rules based on the evolving needs of the organization.

The invention provides a flow anomaly analysis and detection method, system and device based on an industrial control network, and a medium, and the method comprises the steps: carrying out the collection and analysis, data preprocessing, data calculation and analysis, and persistent storage of the flow data of a process business network of an industrial control system; a network flow behavior baseline model is automatically generated according to a process service communication condition, a flow threshold sensitivity dynamic adjustment mechanism can be utilized, learning and self-optimization can be performed from new flow data, and the network flow behavior baseline model becomes more intelligent and accurate as time goes on, and can better adapt to continuous evolution of network threats; the network traffic behavior baseline model is used for carrying out real-time network traffic dynamic detection, the traffic abnormal condition of each device in the network is found in time, an alarm is given, and the problems that in the prior art, a statistical analysis means is not established for a specific process service communication link, threshold setting is lack of accuracy, and threshold adjustment is difficult are solved.

The invention discloses a multi-device cooperative line loss anomaly cooperative detection method and system, and belongs to the technical field of electric power, and the method comprises the steps: collecting original time series data of multiple dimensions, carrying out the preprocessing, generating a dynamic space-time diagram based on the time series data to reflect the cooperative work characteristics of devices, extracting the cooperative feature vectors of the devices, and carrying out the fault detection. And meanwhile, self-optimization is carried out, a system strategy is adjusted, and a result is finally output to the equipment. According to the scheme, the problem that an existing method cannot adapt to dynamic changes of power equipment in real time is solved, and the adaptive capacity of the system to the complexity of a power network is enhanced through self-adaptive optimization of an intelligent algorithm.

The invention relates to the technical field of cloud computing, and discloses an elastic line scheduling method based on cloud computing, comprising the following steps: S1, establishing a virtual network architecture in a cloud computing environment; comprising the steps of virtualizing network resources, defining network topology and creating network virtual machine instances (VMs) and service instances; s2, real-time flow monitoring and demand prediction; comprising a flow monitoring system, a flow prediction model and prediction result feedback. S3, intelligent elastic resource scheduling; comprising a bandwidth allocation strategy, multi-path selection, a path switching mechanism and dynamic routing adjustment. S4, ensuring fairness and priority management; comprising priority management and fair resource allocation; s5, carrying out system self-optimization and learning; comprising continuous monitoring and feedback, a self-learning mechanism and performance evaluation and adjustment. According to the invention, through the elastic scheduling method, only a larger bandwidth needs to be used in the peak period actually, so that the waste of bandwidth resources and the cost expenditure are greatly reduced.

The invention discloses a thermal power generating unit control parameter adjusting method and device based on a large model and digital twinning. The method comprises the steps that variable selection and data preprocessing oriented to multi-task learning are carried out; based on results of variable selection and data preprocessing, establishing a data-driven thermal power generating unit control parameter optimization adjustment large model; and carrying out real-time feedback and self-adaptive adjustment of a fusion digital twinborn technology based on a data-driven thermal power generating unit control parameter optimization adjustment large model. The device comprises a data preprocessing unit, a model establishing unit and a feedback adjusting unit which are connected in sequence. According to the invention, the continuous sensing, analysis and evaluation of the operation state of the unit and the intelligent sensing and self-optimization of the control parameters are realized, and a technical basis is provided for a few-person-on-duty power plant or an unattended-on-duty power plant in the future.

The invention provides an enterprise project and personnel management method and system based on a knowledge base and an AI model and a medium, and belongs to the technical field of enterprise management.The method comprises the steps that implementation experience of historical projects of an enterprise is collected and analyzed based on a knowledge graph, and a project knowledge base is constructed; enterprise employees are evaluated, classification is carried out in combination with evaluation dimensions, and labels are set; and collecting real-time operation data of the project, and recommending and adjusting personnel allocation for the project in combination with employee labels and project attributes. According to the invention, full-life-cycle digital management of the project is realized, and a traditional management mode is broken; the project management efficiency and the resource utilization rate are improved through AI-driven dynamic decision and resource allocation; a knowledge base capable of being self-optimized is constructed, and technology and talent support is provided for enterprise development.

The invention discloses an APT anomaly detection method fusing a dual-channel GNN thought, and belongs to the technical field of network security and machine learning. The method comprises the following steps: 1, data processing: converting an acquired benign system log into an origin graph; 2, feature extraction and fusion: extracting data features through a behavior sensing channel and a static sensing channel to obtain comprehensive feature representation of each node; and 3, anomaly detection: converting the fused features into anomaly scores by using MLP, and performing final classification decision. According to the method, static characteristics and dynamic behavior characteristics can be analyzed at the same time, and the benign behavior mode of the user can be more comprehensively understood; the graph neural network can capture complex interaction and communication modes among entities, and is crucial for identifying key behaviors and modes in a data set; the design allows real-time analysis of system behaviors and quick response to potential APT attacks; meanwhile, self-optimization and updating can be carried out according to new detection feedback, and the robustness and adaptability of detection are improved.

The invention discloses an open type CIM space-time deep learning application platform based on value sharing, which is suitable for multiple fields of smart cities, such as city planning and traffic management. A dynamic learning and self-optimization platform is innovatively constructed by integrating multi-source heterogeneous data and utilizing a deep learning model and a block chain technology. The smart contract in the platform clearly divides ownership and earnings of data, models and algorithms, and ensures that all parties obtain fair earnings according to contributions thereof. According to the method, technologies of data acquisition and preprocessing, spatio-temporal data fusion, dynamic model training and the like are adopted, further through an open base and a value sharing mechanism, the limitation of an existing CIM system in the aspects of data fusion capability and collaborative innovation is solved, and the operation efficiency and decision support capability of a smart city are remarkably improved.

The invention relates to the technical field of communication, and discloses a three-dimensional radio environment map construction method and system, a terminal and a storage medium, and the core is to construct a'base station unmanned aerial vehicle 'bidirectional interaction closed-loop optimization framework to realize efficient construction of a high-precision map. Self-adaptively fusing sparse radio measurement data and environmental building structure features; introducing a confidence evaluation mechanism based on adversarial learning and position weighting, and generating a pixel-by-pixel confidence map; an intelligent planning method based on a trajectory diffusion model is designed, local perception constraint and long-term information gain are cooperated with a classifier-free guide mechanism, and an optimal trajectory considering both safety and sampling efficiency is generated; and a continuously self-optimized closed-loop system is formed through newly acquired data of the unmanned aerial vehicle and periodical updating of the model. According to the invention, a high-reliability technical basis is provided for applications such as urban air communication and spectrum resource management.

The invention discloses a garment supply chain risk analysis method and system based on artificial intelligence, belongs to the technical field of garment supply chain analysis, and effectively enhances crisis pre-judgment and response capability of a supply chain through a dual mechanism of simulation deduction and dynamic optimization. A built-in game learning framework of the system can autonomously construct a multi-factor coupled complex interruption scene, for example, an extreme condition that a port is closed in a typhoon season, cross-border tax policy mutation and alternative raw material transportation route congestion occur at the same time is simulated. The deduction not only reveals the problem of excessive dependence of a single node, which is difficult to discover by traditional auditing, but also can verify the practical feasibility of a standby scheme, and helps an enterprise to establish a multi-layer defense system. The continuous self-optimization characteristic of the system enables the system to be prominent in response to novel challenges, and when the international logistics network is suddenly adjusted, a supplier recombination scheme considering both cost and time efficiency can be quickly generated, and the potential chain breakage risk is resolved in the germination stage.

Disclosed in the present invention are a lifting synchronous control system and control method for a high-rise steel structure corridor. The control system comprises a data acquisition module comprising a sensor network, an execution module, and a data-processing and decision-making module comprising a central control unit. The execution module comprises a lifting drive provided with a receiver. The sensor network comprises a coordinate sensor, an inclination angle sensor, a temperature and humidity sensor, and a wind speed sensor. The sensor network continuously acquires coordinate information, inclination angle information, and environment information and sends same to the central control unit. By means of a machine learning or deep learning algorithm, the central control unit performs self-optimization and regulates the lifting drive in real time on the basis of the continuously collected environment data and structural data. In the present invention, by combining dynamic regulation of an intelligent algorithm with the application of an Internet of Things technology, all components in the control system are modularly designed, thereby obviously mitigating the defects of conventional synchronous control systems, and significantly improving the stability of the control system and the capability of resisting the influence of external factors.

The invention discloses a defect detection system and method for a glass bottle, and belongs to the field of industrial visual defect detection and analysis. The system comprises a GBDM module, the GBDM module is a defect detection module, and the GBDM module comprises a front attention enhancement structure, a rear attention enhancement structure and a defect detection structure, a multi-branch feature extraction structure is used for generating four feature branch diagrams; and the feature fusion structure is used for fusing the four feature branch diagrams. The GBDM module is embedded in the backbone network of the YOLOv8 network model, and multi-branch structures such as wavelet transform, Gabor direction filtering and specular reflection suppression are utilized, so that the feature extraction capability of multiple types of complex defects such as cracks, smudginess and damage on the surface of the glass bottle is enhanced, the generalization capability of the model in a small sample scene is remarkably improved, and the accuracy of the model is improved. The real-time requirement of an industrial scene is met, continuous self-optimization is achieved, and the method has high robustness, high accuracy and high application value.

The invention provides an intelligent operation and maintenance method and system for a data medium table based on a multi-modal large model, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-modal data to construct a knowledge graph, expanding the knowledge graph based on node features and connection weights, matching similar scenes to recognize historical decision information, and generating and evaluating a decision scheme set. An optimal scheme is selected, a new decision scheme is generated through reinforcement learning optimization, and finally operation and maintenance operation is executed and a result is recorded. According to the method, intelligent decision-making and self-optimization of operation and maintenance of the data medium station are realized, and the operation and maintenance efficiency and accuracy are improved.