49results about How to "Realize intelligent optimization" patented technology

The invention provides a power plant intelligent early warning method and system based on big data, and relates to the technical field of power systems, and the method comprises the steps: generating a high-dimensional feature map, generating a multi-modal feature tensor through adaptive chaotic mapping, constructing a feature relation network, generating a time sequence multi-dimensional feature vector, and reconstructing a multi-dimensional state vector based on topological entropy. In combination with a topological data analysis method, carrying out high-dimensional topological space mapping on the multi-dimensional state vector, identifying an operation mode of the power plant equipment, calculating a fuzzy region and a mode abrupt change point of the operation state of the equipment, and obtaining a dynamic evaluation result of the health state of the equipment; a multi-level recurrent neural network is combined, a prediction result is obtained through multi-scale time sequence prediction, uncertainty corresponding to the prediction result is quantitatively solved based on a quantum probability graph model, quantum probability distribution of an early warning signal is generated, a real-time operation strategy of equipment is adjusted, and an intelligent early warning signal of the power plant equipment is generated.

The invention provides an ocean wave disaster defensive area demarcation optimization method based on ship AIS data, and belongs to the technical field of natural disaster prevention and control, and the ocean wave disaster defensive area demarcation optimization method based on ship AIS data comprises the steps: dividing a target sea area into regular grids, and building coordinate mapping; cleaning the data, removing abnormal values and segmenting the data; a distributed track segment pre-clustering method is adopted, and clustering is carried out through dimension reduction, feature extraction and an Euclidean distance algorithm; establishing a multi-dimensional feature tensor according to ship length classification; constructing a composite sea wave disaster danger index model, and comprehensively evaluating the risk; carrying out statistics on ship track space-time characteristics in each grid, and constructing a passing frequency tensor; fusing the trajectory frequency and the disaster risk model, and constructing a dynamic risk assessment hypergraph; based on multi-criterion threshold clustering, defensive areas are identified, boundaries are optimized, and key defensive areas are delimited; according to the invention, sea area risk conditions can be provided for marine mobile disaster-bearing bodies such as sailing ships and the like.

The invention relates to the technical field of automatic loading and logistics control, in particular to an automatic powder loading device based on visual identification, which comprises a visual identification module, a flexible grabbing module, a transfer module and a stacking module, the form, internal distribution and material characteristic data of the powder bag are obtained through a multi-mode sensor, a grabbing path is generated, and grabbing operation is completed through a flexible manipulator; the transfer module dynamically adjusts the transportation state of the powder bag in combination with path optimization and posture monitoring; the stacking module monitors stress of a stacking layer in real time on the basis of the stacking thermal distribution diagram, adjustment suggestion data are generated, stacking position and angle optimization is completed through a self-correction unit, and the stability of a stacking structure is ensured; the powder loading efficiency, the stacking precision and the long-term stability are remarkably improved, and the method is suitable for efficient logistics management of complex loading scenes.

The invention discloses a laser cutting parameter adjusting system based on machine learning, and relates to the technical field of laser cutting. According to the method, multiple laser cutting parameters in the laser cutting process are collected, a short-term state index and a long-term state index are calculated based on the cutting parameters, and a laser cutting instant index and a laser cutting long-term index are generated respectively; based on the laser cutting instant index and the laser cutting long-term index, optimized laser cutting parameters are generated, and working parameters of the laser cutting machine are adjusted in real time; the adjustment result and the related data are sent back to the data storage module for updating the historical database; a real-time distribution curve is generated and compared with a target distribution curve, deviation is calculated, and a parameter optimization target curve is generated; and according to the optimized sub-targets, laser cutting parameters are adjusted in real time, and the stability and high efficiency of the laser cutting process are ensured. According to the system, accurate control over the laser cutting path is achieved through multi-layer and non-uniform control unit division and time-space correlation model construction.

The invention relates to the technical field of deep learning, and discloses a cultural and creative product iterative design method and system based on user emotion feedback. The method comprises the following steps: performing multivariate feature fusion on a first user evaluation text to obtain a fine-grained emotion dictionary in the field of cultural and creative products; extracting enhanced word representation and constructing a user emotion feedback information extraction model; processing the second user evaluation text and calculating to-be-optimized design parameters; performing grid division to generate a diversified optimization design scheme set; performing score prediction through the initial design scheme evaluation model to obtain a candidate design scheme set; and iteratively optimizing the candidate design scheme set, updating the initial design scheme evaluation model, and outputting a target design scheme evaluation model and a target creative product design scheme when a termination condition is satisfied. And the schemes can be effectively evaluated and screened.

The invention relates to the technical field of hydroelectric generators, in particular to a three-dimensional flow field load reconstruction method based on reinforcement learning of a gated circulation unit, which comprises the following steps of: establishing a Python-based finite element secondary development platform, and realizing finite element simulation and obtaining a finite element simulation result; fusing a plurality of simulation characteristic indexes extracted from the finite element simulation result by using a multi-scale model fusion algorithm to obtain a finite element simulation result evaluation index, and optimizing simulation parameters; constructing an initial three-dimensional flow field load reconstruction model based on reinforcement learning of a gated circulation unit, and training the initial three-dimensional flow field load reconstruction model by using the optimized simulation parameters; and based on the trained three-dimensional flow field load reconstruction model, obtaining a reconstructed three-dimensional flow field load. By means of the reconstruction method, automatic simulation of finite element software and full-automatic optimization iteration of a parameter decision and reinforcement learning reconstruction network in the three-dimensional flow field load reconstruction process of the hydroelectric generating set component can be achieved.

The invention provides a photovoltaic direct current fault arc signal noise reduction method and device, and relates to the technical field of photovoltaic direct current fault arc diagnosis. The method comprises the following steps: decomposing a photovoltaic DC fault arc signal based on an IMGO-ICEEMDAN decomposition method to obtain a photovoltaic DC fault arc intrinsic mode component; calculating a multivariate intrinsic mode index, and dividing the photovoltaic DC fault arc intrinsic mode component into a high-frequency noise component, a low-frequency signal component and a noisy signal component according to the multivariate intrinsic mode index; performing noise reduction on the noisy signal component through the improved wavelet threshold to obtain an improved wavelet threshold noise reduction signal; and reconstructing the low-frequency signal component and the improved wavelet threshold noise reduction signal to obtain a noise reduction signal. According to the noise reduction method, the noise reduction effect of the fault arc signal is effectively improved while the key characteristics of the photovoltaic direct current fault arc signal are reserved, and the fault arc recognition accuracy can be remarkably improved.

The invention relates to the technical field of coal wastewater treatment, in particular to an intelligent monitoring method for HPAM microbial degradation for coal slime water treatment. The method comprises the steps that index data in coal slime water are collected and preprocessed in real time, the preprocessed index data are intelligently processed and analyzed through a hybrid dynamics model and an adaptive optimization algorithm, and an analysis result is obtained; according to an analysis result, automatically adjusting reaction conditions and operation parameters through a multi-dimensional time sequence feedback and nonlinear adaptive learning algorithm; meanwhile, detecting the degradation process to obtain detection data; and performing anomaly analysis based on the detection data to obtain an anomaly analysis result, and performing anomaly diagnosis and alarm according to the anomaly analysis result. The problems that in the coal slime water treatment process, the analysis accuracy of the microbiological degradation process is low, and the self-adaptability is poor are solved.

The invention discloses a fully weathered granite stratum grouting parameter intelligent optimization method and system, and relates to the technical field of rock mass grouting. The invention discloses an intelligent optimization system for grouting parameters of a fully weathered granite stratum. The intelligent optimization system comprises an initial grouting parameter determination module and an intelligent grouting parameter optimization module, through cooperative work of the grouting reinforcement parameter pre-output model and the grouting reinforcement reaction effect prediction model, intelligent optimization of fully-weathered granite stratum grouting operation is achieved, the reaction influence features in the grouting process are accurately calculated through the steps of data preprocessing, parameter prediction, feature extraction, classified analysis and the like, and the grouting reinforcement effect prediction method is high in practicability. Grouting parameters are adjusted in real time, so that the grouting efficiency and the reinforcing effect are improved, and the safety and reliability of a project are ensured; and meanwhile, the time period of the grouting feedback parameter set X is determined through historical data optimization, and the accuracy and response speed of grouting operation are further improved.

The invention discloses an electric power inspection method and system based on big data and a random tree algorithm, and relates to the field of electric power system management and maintenance. Comprising the following steps: collecting key information, historical information and personnel basic data information of power equipment in real time, and processing the obtained key information; based on the processed key data, a plurality of factors are comprehensively considered, and an inspection line model is constructed; solving the inspection line model by using a fast search random tree algorithm; and outputting the optimized power inspection path, and carrying out data storage. According to the method, the information subsets of the electric power equipment and the inspection maintenance personnel object provided by the big data platform are introduced, multiple feasible paths subjected to coarse screening are further screened, the optimal electric power inspection path is generated through iteration for a certain number of times, the line scheme is output and stored, and intelligent optimization of the electric power inspection path is achieved.

The invention discloses an intelligent lighting control method and system, electronic equipment and a storage medium, and relates to the technical field of lighting control. The method comprises the following steps: acquiring personnel information in a working area and environment illumination information of an environment where the working area is located; determining a visual fatigue index of the employees in the working area based on the screen use duration and the blinking frequency in the personnel information, and determining a brightness difference according to the standard illumination brightness and the environment brightness in the environment illumination information; if the visual fatigue index is greater than or equal to a fatigue index threshold value, combining the visual fatigue index and the brightness difference to calculate target illumination brightness of the working area; and according to the target illumination brightness, determining a target capacitance parameter of the illumination lamp in the working area, and based on the target capacitance parameter, adjusting the capacitance of the main circuit to control the illumination lamp to illuminate. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of intelligent lighting control is achieved.

The invention discloses a video data transmission method, device and system. The method comprises the steps that quality data of video data to be transmitted and network environment data are acquired, and the network environment data are used for representing the delay degree of a network environment where the video data are located in the video data transmission process; determining a target coding parameter of the video data based on the quality data and the network environment data; according to the target coding parameter, coding the video data to obtain coded video data; and determining a target transmission rate of the coded video data based on the target coding parameter and the coded video data, and transmitting the coded video data according to the target transmission rate. The technical problem that video data cannot be effectively transmitted is solved.

The invention discloses an automatic dip-coating and drying method for a sand core, and relates to the technical field of sand core casting. The method is applied to a core making system comprising a mechanical arm, a sand core is clamped through the mechanical arm and moved to a coating pool, and the mechanical arm is controlled to move so that the sand core can reach the preset dip-coating depth; lifting the mechanical arm to a preset height, and driving the mechanical arm to swing based on a preset swing strategy so as to prevent the coating on the surface of the sand core from continuously dripping; the swung sand core is moved to a drying furnace, and drying adsorption of coating on the surface of the sand core is achieved based on a preset drying strategy; and the deformation amount of the dried sand core is detected and compared with a preset deformation amount threshold value, and whether the sand core is qualified or not is judged according to the comparison result. Accurate control and optimization of the sand core manufacturing process are achieved, and the manufacturing quality and production efficiency of the sand core are remarkably improved.

The invention provides an intelligent course arrangement method fusing learning characteristics of students and teaching nodes of teachers, and the method comprises the steps: obtaining original data used by daily equipment of the students, the use data comprises the use duration, the use frequency and the use time period, and obtaining a standardized equipment use data set through data preprocessing and abnormal value and noise data removal; the method comprises the following steps: acquiring use data of learnable electronic equipment, including login frequency, learning duration and interaction condition, cleaning and converting the data, and constructing a tag vector of the participation degree of a digital course; and establishing a tracking feedback mechanism of the electronic equipment use habit and the digital course participation degree of the student, regularly collecting equipment use data and course participation data of the student, updating and optimizing the electronic equipment use habit and digital course participation degree association model, and dynamically adjusting the course proportion.

The application relates to the technical field of data analysis, and discloses a karst fissure identification-based open-pit mine blasting parameter optimization method and system. The method comprises the following steps: obtaining karst fissure distribution characteristic data through multi-source detection, establishing a three-dimensional fissure network topology structure, analyzing static breaking agent inflation stress propagation rules, performing multi-objective optimization solving based on stress propagation direction and intensity distribution to obtain an initial blasting parameter combination, and obtaining dynamic adjustment blasting parameters through real-time tracking and regulation according to fissure dynamic evolution in the blasting process. The application solves the problem that static blasting parameters cannot be real-time regulated according to the dynamic evolution state of the fissure network in the blasting process, resulting in deviation between the blasting effect and the expected target.

The invention discloses a biosafety laboratory energy efficiency optimization method and system based on analog simulation, and the method comprises the steps: collecting simulation data and related data of a simulation biosafety laboratory, and carrying out the preprocessing of the simulation data and the related data; performing comparison clustering on the simulation data according to an energy efficiency threshold value to obtain grouped data, and performing energy efficiency evaluation on the grouped data to obtain an energy efficiency ratio; performing comparative analysis on the grouped data according to the energy efficiency ratio to obtain an optimization rate, and performing association influence analysis on the related data to obtain a variable coefficient; and constructing a biosafety laboratory energy efficiency optimization model according to the optimization rate and the variable coefficient, optimizing the biosafety laboratory energy efficiency optimization model, inputting to-be-optimized data into the biosafety laboratory energy efficiency optimization model, and outputting an optimization result. According to the method, the precision of biosafety laboratory energy efficiency optimization based on analog simulation is improved, meanwhile, the method has good interpretability, and the method can be directly applied to an energy efficiency optimization system.

This application discloses a method, apparatus, and computer program product for processing outbound call services. Relating to the field of artificial intelligence, the method includes: executing an outbound call task to a user; processing the target service according to a preset service processing procedure; collecting the user's voice signal during the target service processing; extracting voice features and voice content from the voice signal; determining the current service processing node corresponding to the voice content in the preset service processing procedure; determining the service path map corresponding to the preset service processing procedure; determining a path deviation index value based on the current service processing node and the service path map; inputting the voice features into a target model to obtain the user's behavior recognition result; determining a risk index value for the user interrupting the target service based on the behavior recognition result and the path deviation index value; and adjusting the preset service processing procedure according to the risk index value. This application solves the problem of poor accuracy in recognizing user intent in outbound call services in related technologies.

The invention discloses a communication performance optimization method, electronic equipment and a computer readable storage medium, and belongs to the technical field of communication. The communication performance optimization method is applied to a dual-computer communication system in which a master processor and a slave processor are in communication connection based on a preset communication protocol. The communication performance optimization method comprises the following steps: dynamically obtaining to-do tasks in a task list; determining a task category and a cache space category of the to-be-handled task according to the urgency degree and the complexity of the to-be-handled task; determining an execution position and a task priority of the to-be-handled task according to the task category; and dynamically adjusting data in the cache space according to the cache space type. The working performance of the wearable device can be improved through the dual-machine system.

The invention relates to the technical field of renewable energy station intelligent control, in particular to a modular intelligent control method and system for an assembly type heat pump energy station, and the method comprises the steps: obtaining a monitoring signal of a unit self-control module through the modular intelligent control system for the assembly type heat pump energy station; performing unified processing according to the monitoring signals of the unit self-control modules to obtain standardized installation data of the corresponding unit self-control modules; analyzing and processing according to the standardized installation data of the unit self-control module to obtain a corresponding tail end load demand result; performing communication control connection of a unit self-control module based on a central intelligent control module according to the end load demand result to obtain a modular intelligent control result of the assembly type heat pump energy station; compared with the prior art, the on-site installation and debugging time is obviously shortened, the coordination among the modules is improved, the overall optimization is realized, the energy supply strategy is dynamically adjusted according to the monitored actual load condition, the real intelligent optimization is realized, and the energy is saved.

The invention discloses a production plan generation method, device and system, a medium and a product. The method comprises the following steps: acquiring power consumption capacity data corresponding to at least one production workshop in a production department; wherein the power consumption capacity data comprises workshop unit power consumption and initial workshop capacity corresponding to initial scheduling time; determining an initial energy demand quantity of the production department according to the unit generating capacity of the generator set and the at least one power utilization capacity data; according to the initial energy demand quantity and the energy productivity data, determining the predicted power generation time of the generator set; and if the predicted power generation time exceeds the initial scheduling time, generating a production plan according to the predicted power generation time and the at least one initial workshop capacity, thereby solving the problem of poor accuracy of the production plan formulated according to experience, achieving the purpose of intelligent optimization of the production plan, and reducing the power consumption cost of the production task.

The invention provides an e-commerce commodity category adaptive recognition and semantic evaluation method, which comprises the following steps of: extracting path hops and associated edge semantic type labels between target commodities from a commodity relation graph, and extracting all shortest path sets through graph traversal to obtain a hop distance between the commodities and an edge type combination; performing multi-layer information propagation on the hop count distance between the commodities by adopting a multi-layer graph convolutional neural network through the information transmission loss value, and determining a commodity semantic embedding vector after propagation of each layer; according to the propagated commodity semantic embedding vector of each layer, the vector distance between commodity semantic embedding is extracted, the similarity is evaluated, and the optimized information propagation aggregation layer number is obtained based on the similarity convergence trend; and evaluating the coincidence degree of the commodity category inference result and the actual commodity category attribution, and identifying the category inference deviation to obtain the correction direction of the self-adaptive identification of the e-commerce commodity category.

The embodiment of the invention provides a link generation method, a reinforcement learning method, a data processing method, equipment and a storage medium. The method comprises the steps of obtaining a first task demand of a first data processing task; according to a first task demand, matching a plurality of required first operators from the plurality of alternative operators, and generating a first to-be-optimized data processing link based on the plurality of first operators; according to the first to-be-optimized data processing link, the performance estimation of the first to-be-optimized data processing link and the current operation states of the plurality of alternative operators, optimizing the first to-be-optimized data processing link by using a trained reinforcement learning model to obtain a first target data processing link; wherein the first target data processing link is used for processing the first data processing task.

The application discloses a multi-position camera control method and system based on cooperative scheduling optimization, and the method comprises the following steps: acquiring real-time images and device positions of multiple photographic devices in a shooting area; according to the real-time images, identifying shooting object data corresponding to the shooting area based on an object recognition algorithm; determining a scheduling path corresponding to each photographic device according to the shooting object data and the device positions; and determining a cooperative scheduling scheme corresponding to at least two photographic devices based on a dynamic programming algorithm according to the scheduling paths corresponding to all the photographic devices. It can be seen that the application can realize intelligent optimization of a multi-device cooperative shooting path, improve shooting coverage efficiency and cooperativeness, and reduce the risk of shooting quality decline caused by path conflicts or insufficient coverage.

The present invention relates to the field of intelligent diagnosis technology, and discloses an intelligent diagnosis and maintenance method, device, equipment and storage medium for an incinerator, wherein the method comprises: collecting original data sets of multiple target parts of the incinerator and preprocessing them to obtain a preprocessed data set; performing multi-scale feature extraction to obtain initial multi-dimensional features; performing local maximum average difference calculation to obtain aligned multi-dimensional features; inputting the aligned multi-dimensional features into a double Q network, learning the optimal incineration parameter combination, and obtaining a target operating parameter combination; using a two-dimensional coupled complex chaotic mapping to perform iterative prediction to obtain a dynamic behavior prediction result of the incinerator; performing remaining service life prediction and global dynamic adaptive maintenance analysis on the multiple target parts to obtain a target maintenance plan, wherein the method realizes flexible scheduling of maintenance activities, minimizes the impact of maintenance on production, and improves the overall operating efficiency of the incinerator.

The invention provides a cross-border Internet of Things equipment fault diagnosis and prediction system based on artificial intelligence, and the system comprises a data collection module which is used for collecting the operation data of cross-border Internet of Things equipment, and a data preprocessing module carries out the cleaning, standardization and feature extraction of the collected data, so as to facilitate the subsequent analysis and prediction. According to the method, machine learning and deep learning are utilized to more accurately identify the fault type of the equipment and predict the fault occurrence probability in the future; the efficiency of fault diagnosis and prediction is improved, data acquisition, preprocessing, diagnosis and prediction tasks are automatically completed, manual intervention is reduced, and the error rate is reduced; according to fault diagnosis and prediction results, operation parameters of the equipment are intelligently optimized, the fault occurrence probability is reduced, the service life of the equipment is prolonged, and the operation efficiency of the equipment is improved; fault diagnosis and prediction results and equipment optimization suggestions are displayed through a user interface, and a user is helped to better understand and master the running state of equipment.

This invention provides a backdoor defense method for federated learning based on gradient consistency and dual unsupervised clustering, comprising: obtaining gradient updates submitted by multiple clients participating in federated learning in the current training round; for each client, predicting the gradient update of the current round based on historical global model parameters and the client's historical gradient updates; calculating the difference between the actual gradient update and the predicted gradient update of each client to generate a malicious score for the client; adaptively determining the optimal number of clusters based on the malicious scores of all clients using the Gap statistic; performing dual unsupervised clustering detection with a coarse-to-fine approach based on the optimal number of clusters: first, removing clients with high malicious scores using K-means clustering, and then removing residual malicious clients from the remaining clients using DBSCAN clustering, thus selecting benign clients; and performing federated learning global model aggregation based on the gradient updates of the selected benign clients.

The present invention discloses a floating platform mooring system design method, including: establishing an initial mooring model through OrcaFlex; defining optimization variables, objective functions and constraints, setting each variable to change within a reasonable range; establishing an initial sample set through an adaptive Latin hypercube sampling method, and then calling OrcaFlex for time domain analysis; constructing an initial proxy model using the optimization variable values ​​and the objective function calculation results; calculating the expected improvement, determining the sampling points, iteratively updating, and determining and outputting a solution set that meets the requirements. The present invention uses an improved algorithm to optimize the constructed proxy model, thereby improving the optimization efficiency and reducing the computing power requirements for the machine; realizing the intelligent optimization of the mooring system design scheme, and obtaining the optimal design scheme within the range by setting the initial parameter range, so that the mooring design is simple and fast, and the economic benefits of the mooring system are greatly improved under the premise of meeting the requirements of the specification.

The invention discloses a user interface optimization system and method based on user behavior analysis driving, and the system comprises a data collection module which is used for collecting behavior data on a user interface in real time; the data analysis and preprocessing module is used for cleaning and normalizing the collected data; the user behavior analysis module is used for performing behavior analysis on the preprocessed data; and the optimization strategy generation and application module is used for generating an optimization strategy according to the behavior analysis result and dynamically adjusting a user interface at the front end. Intelligent optimization of the user interface is achieved through real-time data collection, behavior analysis, automatic optimization strategy generation and dynamic adjustment, and therefore it is ensured that the user always obtains the optimal interface experience in the interaction process.

The invention discloses a special paper-based new material energy consumption monitoring and intelligent management platform, belongs to the technical field of energy-saving intelligent management, and can solve the technical problem that the energy consumption of a special paper-based new material cannot be accurately monitored and intelligently managed in a traditional energy consumption monitoring system. The method adopted by the management platform comprises the steps of obtaining energy consumption and load conditions in a historical period, and forming an energy consumption and load data set; establishing a grey prediction model; adjusting the grey prediction model by using a particle swarm optimization algorithm; obtaining a prediction data set through the material energy consumption and the load data set by using a grey prediction model; and adjusting an execution strategy of the management platform according to the prediction data set.

This invention discloses a multi-model fusion method, apparatus, device, storage medium, and program product. It acquires current environmental state data and prediction results from multiple deep reinforcement learning models; calculates initial weights for each deep reinforcement learning model based on reinforcement learning update rules, using the current environmental state data, historical performance metrics of each deep reinforcement learning model, and the prediction results; calculates a deterministic metric for each deep reinforcement learning model based on deep evidence theory, using the prediction results; calculates final weights for each deep reinforcement learning model based on the initial weights and the deterministic metric; and weights and fuses the prediction results of all deep reinforcement learning models according to the final weights to obtain the optimal decision action. Using this invention, intelligent optimization through multi-model fusion can be achieved, providing users with reliable decision support and improving the overall performance and adaptability of the system.