519 results about "Dynamical optimization" patented technology

This invention discloses a self-learning method and system for motor operation status and fault early warning, relating to the field of motor fault diagnosis technology. It includes the acquisition and standardized processing of multi-source operating data to generate a standardized feature set; a four-level self-learning model deployed at the edge and cloud levels, progressively completing sample mining, fault feature learning, early warning threshold optimization, and maintenance strategy generation, with bidirectional feedback iteration between each level; and master-slave collaboration between the edge master module and the cloud slave module to achieve real-time inference and accurate diagnosis, independent early warning at the edge in case of network anomalies, and synchronization of data and parameters after recovery. This self-learning method and system for motor operation status and fault early warning adopts a four-layer architecture: perception layer – edge master module – cloud slave module – application layer. Through the collaborative cooperation of real-time inference by the edge master module, accurate diagnosis by the cloud slave module, and dynamic optimization by the intelligent maintenance decision module, it achieves early warning of faults throughout the motor's entire lifecycle, prediction of remaining lifespan, and adaptive maintenance decision-making.

The invention relates a method for dynamically optimizing performance of a people conveyor system (100). The method comprising: obtaining (310) usage related data of a building (110); determining (320) a usage pattern of the building (110) and people conveyor parameters being optimal for the determined usage pattern based on the obtained usage related data by applying at least one artificial intelligence (AI) model (426); providing (330) the determined optimized people conveyor parameters to a user terminal device (210); and generating (340) an instruction to the people conveyor system (100) to update the people conveyor parameters of the people conveyor system (100) to dynamically optimize the performance of the people conveyor system (100), or in response to receiving (350) from the user terminal device (210) an approval response, generating (340) the instruction to the people conveyor system (100) to update the people conveyor parameters of the people conveyor system (100). The invention relates also to a computing system (220), a computer program (425), a computer-readable medium, and a performance optimization system (200) for dynamically optimizing performance of a people conveyor system (100).

This invention belongs to the field of production cost management and cost accounting, specifically involving a method, equipment, and medium for dynamic reconstruction of hierarchical codes in a cost calculation BOM. The method constructs a cost calculation BOM, assigns initial hierarchical codes, and detects cyclic nodes. Based on cyclic node information and calculation requirements, an adjustment strategy is determined, and a unified hierarchical reconstruction based on material codes or cost centers is performed. Hierarchical aggregation and alignment are performed on associated materials of joint by-products and object groups. The method achieves dynamic optimization of the BOM hierarchical codes through cyclic node awareness strategy selection, unified hierarchical reconstruction, and collaborative adjustment of special nodes. Reliability of cost convolution calculation is ensured through cyclic node detection and strategy binding. Hierarchical unification based on material codes or cost centers eliminates hierarchical redundancy for identical materials, improving the accuracy of cost aggregation. Collaborative adjustment of the hierarchical levels of joint by-products and associated materials of object groups ensures consistency between the cost convolution path and process configuration.

This invention discloses a ship dynamic positioning anti-interference control method considering vertical dynamic optimization, comprising: establishing a four-degree-of-freedom nonlinear dynamic model of the ship and obtaining the error vector by combining the desired pose; defining a virtual control law by backstepping and processing the velocity error using a first-order filter; approximating the uncertainties in the model based on an improved Actor-Critic reinforcement learning framework and designing an interference observer to estimate external disturbances; calculating the compensated desired acceleration by combining the approximation output of the uncertainties, the estimated value of the external disturbances, and the velocity error; obtaining the thruster pitch ratio based on the desired acceleration and generating the final control command through a thrust distribution model. This invention can effectively handle model uncertainties and external environmental disturbances in ship dynamic positioning, especially optimizing vertical dynamic performance and improving control accuracy, response speed, and system robustness.

The application discloses a steel bar production line intelligent scheduling system based on artificial intelligence. In the application, the dynamic adaptation problem of multi-specification steel bar mixed production is solved through a virtual-real fusion decision mechanism. In view of the demand of alternate rolling of screw thread steel and bright round steel, the system analyzes order parameters in real time, dynamically optimizes the matching logic of roll gap adjustment sequence and cold bed stepping rhythm, and ensures that the steel bars with different surface characteristics reduce transition waste during process switching. Through equipment health degree prediction and material mechanics simulation, the peak value of the main drive of the rolling mill is automatically reduced and the cooling time is prolonged when high-strength steel bars are rolled, so that the roll peeling or internal cracks of the steel bars caused by stress mutation are avoided. For small batch orders, the system intelligently compresses the shutdown window of die and groove changing, pre-adjusts the process parameters by using the idle gap of the equipment, and significantly reduces the production capacity breakpoint caused by specification switching.

This invention provides an intelligent manufacturing practice teaching system and method based on an AI large-scale model, relating to the fields of smart education and intelligent manufacturing training technology. The system includes a capability index generation module, an intelligent manufacturing comprehensive practice platform module, a practice teaching management module, and an intelligent central module. A structured capability index map is generated and dynamically updated through a first AI large-scale model. Intelligent task decomposition, standardized training module encapsulation, and automatic generation of training case processes are achieved through a second AI large-scale model. By constructing a three-layer feedback optimization closed loop, the system drives the automatic, collaborative, and continuous evolution of the capability map, training cases, and teaching strategies, thereby achieving dynamic optimization and precise adaptation of the intelligent manufacturing practice teaching system.

This invention provides a method and system for economical operation scheduling of water source pumping stations based on time-of-use pricing. By constructing a two-layer architecture model for water lifting capacity configuration and start-up combination decision-making, and using unit start-up and shutdown limits, canal system safety operation, and ten-day water lifting targets as constraints, an optimization model is built and solved to minimize pumping station operating energy consumption and electricity costs, accurately generating start-up combination schemes for each time period within a ten-day period. This invention overcomes the scheduling limitations of discrete flow regulation for constant-speed pump units, comprehensively considering engineering safety, water supply safety, and operational economy. It constructs a scheduling methodology adapted to the dual constraints of poor unit flexibility and weak canal system regulation and storage, achieving economical operation based on time-of-use pricing while ensuring the dynamic optimization performance of the model, improving the robustness and intelligence of scheduling decisions, and filling the research gap in the optimized scheduling of pure constant-speed pump water source pumping stations.

This invention discloses an anti-motion blur video stream edge detection system, belonging to the field of computer vision technology. Utilizing motion blur analysis and adaptive filtering techniques, this invention accurately identifies and corrects motion blur in dynamic scenes, avoiding the destruction of edge details during deblurring as is common in traditional methods. It can generate clear and complete edge images in complex environments. Through real-time processing requirement analysis and dynamic optimization mechanisms, the system automatically adjusts filtering parameters and feature extraction complexity, ensuring high detection accuracy while meeting real-time requirements. This significantly improves the overall performance and practicality of the system, enabling it to perform excellently in applications with high real-time requirements, such as intelligent monitoring and autonomous driving, ensuring efficient and stable operation in real-world scenarios.

The present application belongs to the technical field of industrial internet security, and relates to an industrial internet vulnerability library establishment method, which solves the problems of low construction quality, weak traceability, insufficient continuous evolution ability and difficulty in supporting deep security application of the existing vulnerability library. The present application obtains industrial internet multi-source vulnerability data and external feedback data, obtains extraction results and initial confidence based on multi-source vulnerability data by adopting rule extraction and remote supervision deep learning joint extraction, divides the certainty knowledge base and the to-be-reviewed queue according to the double threshold after the confidence is optimized by the graph convolution network, analyzes the external feedback data of the samples in the to-be-reviewed queue as the instant reward signal, optimizes the sampling strategy and updates the model through deep reinforcement learning, extracts triples from the certainty knowledge base to build a knowledge graph and generate a version hash chain regularly, and obtains a structured vulnerability knowledge base containing a hash chain and confidence evaluation. The present application realizes high-precision construction and dynamic optimization of the vulnerability library.

This invention relates to the field of computer software technology and discloses a state-aware dynamic execution link generation and adjustment method. The method first receives a list of sub-intents decomposed from business intents, matches optimal execution resources for each sub-intent using cosine similarity calculation, and monitors the health status of resources in real time for hierarchical classification. Subsequently, based on sub-intent dependencies, matching resource status, and business rules, an improved greedy algorithm is used to generate an initial execution link in a hybrid serial-parallel manner. During execution, changes in resources, rules, and data are monitored in real time. Once a triggering condition is met, an impact range analysis algorithm is used to locate affected nodes, performing resource re-matching and link replanning only locally, and triggering a three-level service degradation strategy. This invention achieves dynamic link optimization and rapid fault tolerance, improving system execution efficiency and robustness.

The application relates to the technical field of hybrid solid state disks, and discloses a hybrid solid state disk dynamic optimization method and device based on a large language model, which systematically classifies the parameter space of a hybrid solid state disk, constructs a parameter sensitivity matrix based on the classified parameters, determines the influence difference parameters of the parameters on performance under different workloads, collects system state information of the hybrid solid state disk in real time, generates an optimized parameter configuration scheme based on a large language model according to the system state information, the influence difference parameters and historical configuration information. The application can realize real-time perception of work load changes and dynamic focusing on the most sensitive parameter combination under the current load, realize collaborative reasoning and adaptive adjustment among multiple parameters, significantly reduce the write amplification rate and operation delay, and solve the problem that the response efficiency, system stability and energy consumption performance of the hybrid SSD are poor under a dynamic work load in the prior art.

This invention discloses a power distribution IoT communication and equipment management system based on dynamic optimization algorithms. It adopts a four-layer architecture: a perception layer, an edge layer, a platform layer, and an application layer. The end devices in the perception layer are used to collect power distribution data and execute control commands. The edge devices in the edge layer, and their managed sub-devices, are used for protocol conversion, data preprocessing, and executing dynamic optimization algorithms. The platform layer is used for message parsing, algorithm optimization, device management, and data storage. The application layer provides a visual user interface. Through dynamic optimization algorithms and a full lifecycle device management scheme, the system significantly improves the reliability of power distribution IoT data transmission, optimizes network resource utilization, enhances equipment operating efficiency, ensures the real-time performance of critical services, and features strong compatibility, security, and concurrent processing capabilities.

This invention relates to the fields of computational mechanics and rock engineering, and particularly to a numerical simulation method for end-anchored rock mass engineering based on DDDA (Digital Directed Amplification and Analysis). The method includes: constructing a circular element discontinuous deformation analysis numerical model according to actual engineering conditions; performing discontinuous deformation analysis numerical calculations; analyzing the calculation results of the unanchored model to preliminarily determine the anchoring scheme; performing discontinuous deformation analysis numerical calculations again; determining whether the model contains unstable blocks, adjusting the anchoring scheme, and repeating the aforementioned steps until the model stabilizes, thus completing the design of the rock mass engineering anchoring scheme. This invention establishes a numerical simulation method capable of accurately simulating the interaction between anchor bolts (anchor cables) and rock mass, and integrating anchoring scheme design, stability assessment, and dynamic parameter optimization functions. It achieves accurate prediction of the deformation and failure behavior of anchored rock mass and optimizes anchoring design, featuring high simulation accuracy and strong practicality.

This invention provides a low-abnormality horizontal electroplating control system for diamond wire magnetic materials, belonging to the field of diamond wire electroplating technology. This invention uses a real-time pre-control compensation mechanism to capture and suppress early-stage anomalies, while dynamic optimization at the meso-level continuously adjusts process parameters to their optimal state. These two mechanisms work collaboratively within a macro-steady-state baseline framework, fundamentally changing the passive nature of traditional production models and enabling proactive management of production anomalies, significantly reducing the probability of defects such as agglomerates and incomplete plating. By establishing a learning loop that feeds micro-level intervention experience back to the meso-level optimization model, and by incorporating slow-time-varying factors such as plating solutions into the macro-baseline modeling, the system can continuously absorb knowledge from production practice, automatically adapting to long-term changes such as plating solution aging and raw material batch differences, reducing reliance on manual experience.

The application relates to a smart city-based urban planning decision feasibility analysis system, aiming to solve the problems of experience dominance, static solidification, appeal imbalance and technology isolation in traditional urban planning. The system includes four modules: data collection, algorithm processing, decision output and dynamic optimization. The data collection module dynamically collects urban basic space, dynamic monitoring, industry and livelihood data and completes preprocessing; the algorithm processing module realizes spatial structure quantization, resource allocation optimization, planning risk assessment and dynamic feasibility prediction; the decision output module generates a planning scheme containing land use layout, energy configuration and public service facility distribution parameters; the dynamic optimization module updates and threshold triggering mechanism, iteratively adjusts the planning scheme. The system is suitable for new area planning and stock area renovation scenarios, realizes the transformation of planning decision, and improves the scientificity and landing effect of planning decision.

The present application relates to a power distribution network grounding fault accurate positioning method based on fusion of multi-source data analysis, aiming at the problems of insufficient accuracy of causal relationship modeling and poor transparency of result reliability in existing methods, by fusing power distribution network topology, equipment parameters and historical fault data, combining with relay protection action logic, applying causal discovery and time series analysis, a weighted directed causal graph with causal strength interval is constructed. Further, based on the minimum strong causal path set, simulation intervention is implemented to form structured counterfactual experimental data, realize intermediate node role identification and causal reasoning link construction. Finally, through the reliability grading and feedback correction mechanism, a fault positioning report with logical interpretability and dynamic optimization ability is output. The scheme improves the accuracy, interpretability and self-adaptive evolution ability of power distribution network fault diagnosis.

The application discloses a kind of space equipment oriented to whole life cycle of number-real fusion test process model construction method and system, belong to aerospace digitization and computer science technology field.The method includes: the unified test process framework and state monitoring model of whole life cycle such as covering conceptual demonstration, design, test, operation and maintenance are constructed;Establish the data interaction process model of multiple number-real fusion modes, improve compatibility and scalability;Consistency maintaining mechanism of three layers of data, model and execution is constructed, to ensure the stability and synchronization of multiple system parallel test;Finally, through modular integration and management, form reusable, scalable test process system.The application solves the problem that traditional test process relies on manual experience, is difficult to reuse and dynamic optimization, significantly improves the consistency, coverage, efficiency and traceability of test.

This invention relates to the field of natural language processing, specifically to a method for training and optimizing an intelligent translation model based on transfer learning. The method involves acquiring a pre-trained translation model; obtaining the target text to be translated and inputting it into the translation model; analyzing the domain features of the target text using a domain classifier and calculating activation weights based on these features; activating the target adapter among multiple adapters according to the activation weights to adjust the multilingual backbone model; and then translating the target text using the adjusted multilingual backbone model to obtain the target translated text. By acquiring a pre-trained translation model, analyzing the domain features of the target text to calculate activation weights, activating the target adapters to adjust the model, and performing translation, the method achieves dynamic optimization of the model adjustment, thereby solving the problems of domain misjudgment and adapter selection bias in existing technologies and improving translation accuracy.

The application relates to the field of construction scheduling control, in particular to a water conservancy engineering construction mechanical group collaborative operation intelligent scheduling system, which comprises the following steps: a geological mechanical coupling twin platform constructs a bidirectional coupling closed loop of a geological field and construction mechanical behavior according to real-time working condition data of a region to be constructed, obtains a dynamic geological attribute field quantity, and fits a geological disturbance sensitive coefficient of the region to be constructed; a cloud edge end collaborative network collects real-time working condition data through an intelligent agent node; a cloud computing center solves a global construction task scheduling scheme by adopting a multi-objective optimization algorithm; an edge computing node adopts a multi-agent deep reinforcement learning algorithm to perform real-time rolling optimization on the collaborative operation of construction machines in a local jurisdiction region according to a screened optimal local construction task distribution scheme, generates a local collaborative scheduling instruction, and schedules each construction machine. The scheduling system can realize real-time sensing of geological changes, reduce the probability of engineering accidents, and dynamically optimize the collaborative operation of the mechanical group.

This application discloses a method and system for determining pollution causes based on the correlation between atmospheric diffusion conditions and emission contributions, relating to the field of atmospheric pollution cause identification. The method includes: aligning meteorological, emission, and receptor observations to a unified spatiotemporal reference to obtain multi-source baseline data; calculating a diffusion stability index characterizing vertical mixing and horizontal transport using meteorological monitoring, generating time-varying diffusion condition weights; generating a time-response diffusion kernel from the emission source to the receptor point using a diffusion model under unit emission conditions, and constructing diffusion-contribution correlation information with observed concentrations to characterize the temporal coupling strength; establishing a diffusion-contribution dynamic optimization model, using diffusion condition weights to constrain the coupling relationship, and iteratively solving for time-varying contribution coefficients based on the convergence target of simulated and observed concentrations, outputting the dynamic contribution of each source and the pollution cause. Thus, time-varying diffusion conditions are explicitly introduced into the source contribution inversion framework, achieving stable separation and reliable attribution of source contributions.

This application relates to the field of civil aviation optimization technology, and in particular to a dynamic fuel consumption optimization system for civil aircraft based on real-time route data. The system includes modules for data storage, input, multi-source information fusion and feature extraction, intelligent evaluation, adaptive decision support, and output response. The system extracts multi-dimensional features by fusing real-time route and QAR data, constructs a correlation dynamic evaluation model using gradient boosting decision trees, simultaneously calculates flight performance index, fuel efficiency index, and correlation score, and generates a structured report based on a decision rule base, including cost index adjustment suggestions, altitude change schemes, and expected fuel savings predictions. This application enables dynamic and quantitative evaluation of the correlation between route environment and fuel consumption, overcomes the lag of traditional planning, and achieves precise and personalized flight strategy optimization, resulting in significant benefits in reducing costs, improving on-time performance, and reducing emissions.

This invention relates to the field of intelligent testing technology for power equipment, specifically an AI-based performance testing device for power safety tools. The device includes: collecting performance state sequences of the tools, identifying inflection points in operation response time to divide monitoring intervals, and automatically identifying specific testing environments requiring focused analysis by comparing the distribution of abnormal events. For each key environment, the device calculates the dynamic similarity of the tool's physical parameter sequences and generates a sensitivity vector by combining it with historical data, then corrects this using statistical features to obtain a comprehensive evaluation vector. A hierarchical cascaded network is constructed based on the comprehensive evaluation vectors of all tools, and appropriate performance testing programs are dynamically allocated based on the network structure. This invention achieves intelligent identification of testing environments and dynamic optimization allocation of testing programs, improving the targeting of testing and resource utilization efficiency.

The application relates to the technical field of data processing, and discloses a steel structure plant profile blanking optimization method and system based on Tekla. The method comprises the following steps: extracting component parameters based on Tekla, comparing with a standard database to distinguish rolled pieces and welded pieces; disassembling the welded pieces and correcting a list according to plate thickness; counting inventory to generate an available resource list; adopting a multi-level matching strategy to determine the demand for the rolled pieces; adopting plate cutting optimization to generate thin plate and thick plate cutting plans for the welded pieces, and processing variable cross-section webs through pair-by-pair complementary splicing, so as to determine the demand for coiled materials and steel plates; and constructing a dynamic optimization model integrating inventory and fund constraints, so as to output an optimal scheme under the fund limit constraint. The application improves the comprehensive utilization rate of materials, preferentially guarantees the supply of main components, reduces procurement and inventory costs, and provides scientific decision support for steel structure plant blanking.

The application discloses a kind of three-dimensional geological modeling dynamic updating methods based on tunnel face information, comprising: S1, obtains borehole data, the borehole data is preprocessed, the data after pre-processing is mapped to three-dimensional space and is visualized and presented, preliminary data analysis is carried out, and initial three-dimensional geological model is generated using improved coupled Markov chain model;S2, real-time acquisition tunnel face image, using improved Swin Transformer model extracts stratum feature, and feature is converted into structured face feature data;S3, fusion borehole data and construction stage's face feature data, and the data after fusion is input initial three-dimensional geological model, dynamic updating model, and finally output optimized model.The application uses the above method, realizes the dynamic optimization of three-dimensional geological model, improves the accuracy of geological prediction in the process of tunnel construction, and provides reliable support for real-time decision-making of tunnel construction.

The application provides a media information delivery strategy optimization method and system based on reinforcement learning, relates to the technical field of information delivery, and comprises the following steps: analyzing a user behavior sequence, detecting a mutation point, and constructing a hidden Markov model to quantify a behavior mode drift; generating candidate actions for a current environment, using a model to simulate and predict a state transition path and a drift amplitude, and screening out to-be-verified actions meeting conditions; setting an execution observation window to monitor a real-time data distribution offset, deciding to execute or withdraw an action, and finally updating a model and related threshold parameters according to an execution reward. The application realizes dynamic optimization and risk control of a delivery strategy, and improves delivery effect and stability.

This invention discloses an automated optimization method for operational processes that integrates data analysis and a rule engine, applied to the solder paste printing process in a high-precision electronic component surface mount production line. The method includes: real-time acquisition of squeegee pressure, squeegee travel speed, demolding speed, and corresponding average solder paste volume, average solder paste height, and defect rate, while simultaneously acquiring ambient temperature and humidity values; constructing a response surface experimental matrix using a sliding window period to obtain sample data; fitting and solving a multivariate quadratic response surface model to obtain the optimal combination of process parameters that optimizes quality indicators; injecting the optimal parameters as dynamic optimization rules into the rule engine and assigning them the highest execution priority, returning the parameters after matching with environmental parameters; and the programmable logic controller (PLC) of the solder paste printing equipment receiving and executing the parameters to form an automated closed-loop control.

This application relates to a method, system, device, and medium for personalized vocational skills learning path planning based on artificial intelligence. The method includes: collecting a user's structured event dataset; constructing a dynamic causal skill graph with skill points as nodes and causal effect strength as directed edges using two-stage linear regression; extracting the user's dynamic behavior sequence and inputting it into a pre-trained model to obtain a skill mastery vector; searching for a personalized causal learning path with the current mastered skill points as the starting point, the career goal as the ending point, and maximizing the cumulative causal effect as the objective; performing counterfactual reasoning on the learning path based on the structured causal model to predict the counterfactual mastery, learning time, and path under different intervention conditions; and obtaining the optimal learning path adjustment scheme by weighted fusion of the counterfactual results. This method enables personalized learning path planning and dynamic optimization with causal logic.

The application discloses a kind of outbound line decision-making method and system based on incoming call display perception, the method includes: determining outbound task and obtaining the number attribute of outbound line, the number attribute and outbound task are modeled to obtain number feature vector and outbound service feature vector;Based on the willingness prediction network of pre-set, according to number feature vector and outbound service feature vector, the willingness score sequence corresponding to outbound line is obtained by prediction;For any outbound line, according to the constraint condition of willingness score and line cost, the target outbound line executed by outbound task is determined by calculating through the pre-set resource allocation algorithm;The feedback sample of target outbound line corresponding outbound task is obtained, and the willingness prediction network and resource allocation algorithm are continuously updated by hybrid learning mechanism.It can be seen that the application can realize the intelligent selection and dynamic optimization of outbound line, so as to effectively improve the reach rate of intelligent outbound system.

This invention relates to the field of aluminum plate processing control technology, and particularly to an aluminum plate cutting path planning method, system, and storage medium. The method includes: acquiring an image of the aluminum plate surface, extracting texture direction vectors, and constructing a heat conduction simulation model based on anisotropic parameters introduced according to the texture direction, generating a heat distribution matrix; calculating the temperature gradient and internal stress distribution of the material based on the heat distribution matrix, identifying high-risk deformation areas, and mapping these areas to the cutting path; adaptively correcting the path by analyzing the angle between the path and the texture direction; obtaining the deviation distribution through processing simulation, constructing a compensation matrix and integrating it into CNC system parameters to achieve dynamic optimization of the cutting path; and adjusting the compensation matrix based on real-time monitoring of thermal deformation during processing. This invention solves the problem of uncontrollable thermal deformation caused by neglecting the material texture direction in existing technologies, and achieves precise optimization of the cutting path and improved processing accuracy.