465 results about "Genetic algorithm" patented technology

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

The invention discloses a power grid real-time optimization scheduling system and method based on digital twinning, and relates to the technical field of power grid scheduling. A sensor is deployed to collect environmental parameters of key nodes in real time, a dynamic environmental condition coefficient is constructed, a power grid state is analyzed in combination with frequency stability and a relative strength index, and a multi-model fusion prediction mechanism is established, so that space-time two-dimensional accurate prediction of load and power generation is realized. A multi-objective optimization model is adopted to take'maximization of new energy consumption + minimization of scheduling cost 'as a core objective, a genetic algorithm is introduced to solve an optimal scheduling scheme, and a feasible solution is screened in combination with forward simulation of a digital twin model. Through abnormal early warning triggering, environment correlation analysis and model iterative optimization, a scheduling strategy is dynamically adjusted, and the power supply efficiency and the emergency response capability in an extreme scene are improved. According to the method, multi-source heterogeneous data are effectively fused, and real-time sensing of a power grid operation state, collaborative optimization of multiple energy resources and adaptive iteration of a scheduling model are realized.

The invention discloses a BIM-based full-period management method and system for tunnel construction organization optimization, and belongs to the technical field of construction organization and cost management. The management method comprises the following steps: establishing a three-dimensional geometric model and a parameterized model of a tunnel based on a BIM technology; performing work decomposition on a construction task, and calculating a construction activity time parameter; calculating the cost of materials, machinery and manpower of each construction unit, and summarizing the total cost of the project; optimizing a construction period and resource allocation under a multi-constraint condition by adopting a genetic algorithm; predicting future process time consumption and construction progress based on an LSTM time sequence prediction model; and integrating dynamic data of the construction site, and updating the BIM model in real time. Through combination with dynamic data, efficient organization and scientific management of the whole tunnel construction process are realized, the construction efficiency can be improved, the engineering cost can be reduced, and the scientificity and rationality of project decision making are remarkably improved.

The invention discloses a building robot multi-machine collaborative operation optimizing and monitoring method and system based on digital twinning, and belongs to the technical field of building construction intellectualization. The method comprises the following steps: constructing a digital twinborn body comprising an environment perception model and a kinematics / dynamics model based on a building information model and robot physical parameters; performing multi-machine task allocation and path planning containing static / dynamic obstacle avoidance in the virtual environment; synchronizing physical environment data in real time through a multi-mode sensor; dynamically optimizing operation parameters by adopting a genetic algorithm or a particle swarm algorithm and realizing closed-loop control; and generating a safety early warning and emergency scheme based on machine learning. According to the method, Markov decision path planning of reinforcement learning and multi-agent game task allocation are creatively fused, laser radar-vision-inertial navigation multi-source data fusion is adopted, the technical problems that in a traditional method, digital twinning precision is insufficient, and dynamic cooperation efficiency is low are solved, and the method is suitable for large-scale popularization and application. And the construction efficiency, the safety and the man-machine interaction experience are remarkably improved.

The invention discloses an adaptive game-driven network defense method and system, and relates to the technical field of network security. The method comprises the following steps: generating a multi-modal bait according to network context information, and screening an optimal bait through credibility evaluation; constructing a time sequence feature tensor according to the network event sequence information of the bait, and obtaining predicted attack information by adopting a pre-trained attack prediction model; combining the network event sequence information of the bait and the predicted attack information to construct a defense income matrix, and carrying out iterative equilibrium solution to obtain an optimal strategy candidate pool; and taking the defense hybrid strategy of the optimal strategy candidate pool as an initial population, performing multi-objective optimization through a non-dominated sorting genetic algorithm to obtain a Pareto optimal strategy set, and performing screening to obtain an execution strategy set for dynamic defense decision making. According to the invention, the dynamic property, intelligence and self-adaptability of network defense are realized, and the ability of a network system to cope with complex attacks is effectively improved.

The invention discloses an AI-based network security event tracing method, system and device and a medium, and the method specifically comprises the steps: constructing a network entity association graph based on a multi-modal data set, mining the implicit association between entities through a graph convolutional network, recognizing an APT attack chain, and obtaining graph feature data; based on the multi-modal data set, an LSTM-Transform hybrid model is adopted to analyze time sequence characteristics of network traffic, slow penetration and low-frequency detection behaviors are detected, and time sequence characteristic data are obtained; based on the graph feature data and the time sequence feature data, high-value features are screened through a genetic algorithm, and cross-modal combination features are generated by using a depth auto-encoder; based on cross-modal combination features, a network environment digital twin is constructed, an attack diffusion path is simulated, and a service influence range is quantified. According to the method, accurate tracing of the network security event is realized, and the detection and tracking capabilities of complex network attacks and the intelligent level of a response strategy are comprehensively improved.

A wellbore temperature optimization and predication method integrating numerical models and machine learning includes the following steps: establishing a wellbore-formation transient heat transfer model, obtaining an initial data set composed of relevant parameters, normalizing the initial data set, training a wellbore temperature prediction model by using a random forest algorithm, then optimizing the hyperparameters of the random forest algorithm by using a genetic algorithm, performing global optimization by using an annealing algorithm to obtain the optimized wellbore temperature and related parameters, calculating the wellbore temperature by substituting the optimized parameters into the wellbore-formation transient heat transfer model, and performing comparative verification on the optimized wellbore temperature and the calculated wellbore temperature.

The invention relates to the technical field of structural design, and discloses a topology and size joint optimization design method and system for a hierarchical composite structure, and the method comprises the following steps: dividing a structural domain into a plurality of hierarchical sub-domains, defining the size constraint range of each sub-domain, and distributing a multi-phase material for each sub-domain; grid discretization processing is carried out on the structural domain, a topological design variable is given to each discrete unit, acoustic unit modeling is carried out on the sound field domain, sound pressure boundary conditions are defined, and coupling boundary conditions are applied to the interface of the sound field domain and the structural domain; solving an acoustic-structure coupling equation based on a finite element method, calculating an objective function and constraint conditions, analyzing the sensitivity of the objective function to topological variables by adopting an adjoint method, and iteratively updating the topological design variables through an optimization algorithm until a convergence criterion is met; and on the basis of a topological optimization result, constructing a neural network agent model of an input layer, a hidden layer and an output layer, and carrying out global optimization on sub-domain size parameters by adopting a genetic algorithm so as to minimize a target function.

The invention provides an HC-MOGA-based agent task collaborative scheduling method in an industrial internet, and aims to solve the key problems of low scheduling efficiency, poor system robustness and the like caused by heterogeneous types of equipment (subsequent modeling is an agent), complex task dependence and non-uniform resource distribution in an industrial site. According to the method, a heterogeneous agent scheduling model with task identification, resource matching and dependent modeling capabilities is constructed for a multi-stage operation task cooperatively completed by a data acquisition agent and an operation type agent in an industrial internet environment. An HC-MOGA (heterogeneous constraint multi-objective genetic algorithm) is provided, multi-dimensional chromosome coding, a layered mapping mechanism, a dual-stage variation strategy and a dynamic constraint repair mechanism are introduced, and a dual-objective optimization model fusing work completion income and comprehensive resource consumption and potential loss is established. The method is suitable for industrial internet typical application scenes such as intelligent manufacturing, automatic production lines and multi-agent task collaborative management.

The invention relates to the technical field of engineering cost dynamic measurement and calculation, in particular to an intelligent engineering cost dynamic measurement and calculation and cost control method and system. Comprehensive influence factors of nodes can be extracted from a complex construction task incidence relation through a graph convolutional network; comprising resource intensity, progress sequence and potential resource conflict problems, so that accurate identification of key construction paths is improved, the real-time feedback capability of project progress and cost is enhanced, dependence of a traditional budget model on static factors is effectively avoided, project decision is optimized according to real-time changes, and the construction efficiency is improved. The efficient distribution of budget and resources is ensured, through the application of the non-dominated sorting genetic algorithm, and according to the residual budget, the construction period and the resource value, comprehensive evaluation and node screening optimization are carried out, so that project management can flexibly adjust the budget and the construction period based on the actual situation, the situation of unreasonable fund scheduling or resource waste is avoided, and the project management efficiency is improved. And the accuracy and flexibility of engineering project management are improved.

The invention discloses an oil reservoir carbon dioxide geological sequestration process parameter multi-objective optimization method, and relates to the technical field of oil exploitation, and the method comprises the steps: employing an oil reservoir carbon dioxide geological sequestration effect prediction data set to train a variational automatic coding-deep belief neural network model, a reservoir carbon dioxide geological sequestration effect prediction agent model is obtained; constructing an oil reservoir carbon dioxide geological sequestration effect optimization model according to the safety risk coefficient, the effective sequestration coefficient and the benefit optimization coefficient; the reservoir carbon dioxide geological sequestration effect prediction agent model is combined, a non-dominated sorting genetic algorithm is utilized, and a reservoir carbon dioxide geological sequestration process parameter multi-objective optimization mathematical model is solved. The constructed reservoir carbon dioxide geological sequestration process parameter multi-objective optimization mathematical model aims at sequestration safety, potential and economy; and the optimization precision and efficiency of the oil reservoir carbon dioxide geological sequestration process parameters are improved.

The invention relates to a Markov game-based satellite cluster observation resource allocation method and system. Through a two-stage decomposition strategy, a multi-target balance problem of a task integrity rate, a resource utilization rate and a cost-efficiency ratio is effectively solved. In the first stage, a genetic algorithm (GA) is adopted to complete satellite-task-time window three-dimensional matching, and optimal distribution of limited visible windows is achieved. And in the second stage, a distributed decision-making mechanism is implemented based on an improved Improved-MADDPG framework, and after an intelligent agent adopts a random game strategy to execute exploration in a distributed manner, optimal dynamic configuration of observation resources is achieved through global information sharing and local strategy iteration, and key calculation indexes such as an average reward value and the like are remarkably improved.

The invention discloses an intelligent electric meter box operation and maintenance management method and system, and relates to the field of electric power operation and maintenance management. The intelligent electric meter box operation and maintenance management method comprises the steps of multi-source data acquisition and preprocessing, equipment health state dynamic evaluation, fault root cause positioning and prediction, maintenance task dynamic scheduling, adaptive maintenance execution and closed-loop optimization, feedback of a maintenance result to a health evaluation model, updating of a dynamic weight coefficient, and monthly generation of an equipment health white book. And continuously optimizing the operation and maintenance strategy. According to the intelligent electric meter box operation and maintenance management method, the fault prediction model is established based on the LSTM neural network, the typical fault mode is matched in combination with the knowledge graph, the fault prediction accuracy is improved, and non-planned power failure is reduced. The improved genetic algorithm improves the utilization rate of maintenance resources and shortens the emergency response time. Predictive maintenance replaces regular maintenance, invalid inspection is reduced, and the spare part inventory turnover rate is increased. Human errors are reduced through AR assistance; and the maintenance operation accuracy is improved.

The invention discloses a complex geological weak surrounding rock tunnel micro-disturbance green blasting construction method which comprises the following steps: acquiring multi-source data through a sensor and geological survey equipment, and extracting a standardized feature set; and constructing a blasting parameter and surrounding rock response prediction model by using a random forest algorithm, inputting explosive load and hole net parameters, and outputting predicted surrounding rock deformation and vibration values. And if the vibration value exceeds the environment-friendly threshold value, adjusting the parameter by adopting a gradient descent algorithm until the requirement is met. And in combination with real-time monitoring data, the surrounding rock response is dynamically updated by using a Kalman filtering algorithm. If the overexcavation probability exceeds the threshold value, the parameters are further optimized through a genetic algorithm. And finally, optimizing resource allocation through a linear programming algorithm, and determining a final blasting scheme. Intelligent regulation and control of blasting parameters of the weak surrounding rock tunnel are achieved, vibration and deformation are effectively controlled, the overexcavation risk is reduced, the construction efficiency and quality are improved, and a new technical scheme is provided for similar projects.

The invention relates to the technical field of atmospheric dust pollution prevention and environmental protection, and discloses a multi-objective optimization layout method for dust monitoring points of a bulk cargo port, which comprises the following steps: processing historical meteorological data and port geographic information, and calculating the comprehensive evaluation concentration of each candidate grid point in combination with a pollutant diffusion model; constructing a multi-target nonlinear mixed integer programming model taking maximization of environment sensitive point coverage and minimization of concentration interpolation deviation as target functions, wherein the model comprehensively considers multiple reality constraints; solving the model by adopting an improved genetic algorithm to obtain an optimal solution set capable of forming tradeoff among different targets; and carrying out quantitative evaluation on the solution set through preset monitoring area coverage rate and monitoring accuracy indexes, and determining an optimal monitoring point position layout scheme. According to the invention, a scientific and quantitative optimized layout scheme can be provided for bulk cargo port dust monitoring, and the reliability and spatial representativeness of a monitoring network are improved.

The invention discloses a machine learning-based membrane pollution pretreatment optimization control method and system, and the method comprises the steps: constructing a high-dimensional feature data set for Fenton pretreatment prevention and control of membrane pollution, and carrying out the comprehensive pretreatment of data; training and optimizing the candidate machine learning model based on the decision tree; evaluating the trained machine learning model, and selecting the model with the most generalization ability as a Fenton pretreatment control membrane pollution prediction model; based on the prediction model, reversely searching the optimal reagent dosage under a preset environment variable by adopting a genetic algorithm; and carrying out membrane pollution Fenton pretreatment under the corresponding environment variable condition by utilizing the optimal reagent adding amount. According to the technical scheme, the reagent adding amount of Fenton pretreatment with the optimal membrane pollution retarding capacity of different pollutants under different membrane filtration conditions can be optimized and directionally controlled, and the Fenton pretreatment device can be suitable for different types of pollutants and membrane assemblies and has the characteristics of low cost, high efficiency, accuracy, reliability and the like.

The invention relates to a data-driven deformation nickel-based superalloy multi-objective performance reverse design optimization method, which belongs to the technical field of metal material design and development, and comprises the following steps: calculating a stable high-strength deformation nickel-based superalloy system based on a first principle, and on this basis, calculating a deformation nickel-based superalloy system; constructing a deformed nickel-based superalloy component design space based on the knowledge of the nickel-based superalloy field; based on a deformed nickel-based superalloy component design space, the deformed nickel-based superalloy component design space is reduced by adopting an empirical formula in combination with thermodynamic high-throughput calculation to obtain the reduced deformed nickel-based superalloy component design space, and based on the reduced deformed nickel-based superalloy component design space, based on machine learning and a genetic algorithm, the deformation nickel-based superalloy component design space is obtained. And a deformed nickel-based high-temperature alloy reverse design model is established, and the deformed nickel-based high-temperature alloy with the target performance is screened. Compared with the prior art, the oriented development method for the high-strength and high-toughness nickel-based high-temperature alloy is achieved by fusing cross-scale calculation, domain knowledge constraint and machine learning reverse design.

The invention relates to the technical field of intelligent risk analysis, in particular to an intelligent risk analysis system of a financial system. The system comprises a data flow analysis module, a relational graph construction module, an abnormal mode detection module, a security boundary monitoring module, a staged strategy planning module, a scene simulation evaluation module, a decision support module and a comprehensive risk management module. A graph database and a graph convolutional network are utilized to deeply analyze a financial entity relationship, reveal a risk association network, improve abnormal behavior recognition through a K-mean value and an isolated forest algorithm, enhance security boundary monitoring through a random forest algorithm, enable risk threshold adjustment to be more dynamic, combine a decision tree with a genetic algorithm, optimize a risk management strategy, and improve risk management efficiency. The system dynamics and proxy model technology in scene simulation evaluation strengthens risk prediction and increases foresight, principal component analysis and risk matrix evaluation are applied in a comprehensive risk management module, a quantitative analysis tool is provided, and more systematic and comprehensive risk management is realized.

The invention discloses a digital twinborn monitoring method for a self-evolution concrete-filled steel tube arch bridge, and the method comprises the steps: collecting multi-source monitoring data through deploying a multi-mode sensor at a key component of a bridge, and constructing an initial structure topological graph; and dynamic evolution and anomaly detection of the structure topology are realized by using a graph neural network and a Transform structure. Intelligent recognition and fault diagnosis of a structure state are realized by constructing a multi-physical field five-dimensional tensor and a health knowledge graph. And finally, generating and optimizing a structure alternative scheme by adopting a graph generative adversarial network and a genetic algorithm, verifying the performance of the scheme through finite element simulation, and realizing intelligence of bridge health monitoring and maintenance decision making. According to the method, real-time sensing, dynamic modeling and intelligent diagnosis of the structural state of the concrete-filled steel tube arch bridge are achieved by fusing multi-modal sensor data, multi-physical field coupling analysis, graph neural network modeling and an intelligent reasoning mechanism, the real-time performance and accuracy of monitoring are improved, and a scientific basis is provided for structural optimization design of the bridge.

The invention discloses an internet of things-based amusement equipment light dynamic control method and system, and the method comprises the steps: collecting the acceleration, angular velocity and coordinate data of equipment, carrying out the wavelet denoising and Z-score standardization processing, and constructing a dynamic topology model based on a graph attention network and a graph convolution network. A spectral clustering algorithm is utilized to carry out space partitioning on equipment nodes to generate a topological sub-graph, and an LSTM network is combined to predict an equipment motion track and generate a continuous track sequence. Track correlation features are extracted through a graph attention network, a genetic algorithm is fused to optimize and generate a light conversion sequence, dynamic time warping alignment timestamps are synchronously adopted, and phase synchronization of the light sequence and the motion track is ensured. And finally, brightness, color and flicker frequency are adjusted in real time based on a PID control algorithm, and a closed-loop feedback mechanism is formed. According to the method, high-precision dynamic matching of light and equipment movement is realized, and immersive experience and visual interactivity are improved.

The invention relates to a tunnel construction progress model generation method based on artificial intelligence. The method comprises the following steps: acquiring multi-source heterogeneous data such as tunnel field equipment operation and geological change, and performing multi-modal fusion to form a unified data set; based on this, a particle filter algorithm is used to correct equipment positioning deviation, and a real-time position optimization result is generated. Comparing the time and space with the progress plan, and identifying and quantifying deviation points through a critical path method and a deviation analysis algorithm to form deviation data. And constructing a construction progress model by combining construction basic parameters and adopting a genetic algorithm, generating an adjustment scheme by taking a construction period and resources as targets, and obtaining an optimal scheme through compliance screening and fitness calculation iterative optimization. By the adoption of the method, efficient integration of heterogeneous data can be achieved, the equipment positioning precision is improved to the centimeter level, multi-dimensional quantification of progress deviation is achieved, progress deviation recognition and resource allocation efficiency is improved, and a technical scheme is provided for tunnel intelligent construction.

The invention discloses a cross-regional computing power resource collaborative allocation method based on computing power centers, and relates to the technical field of computing power resource scheduling and optimizing.The cross-regional computing power resource collaborative allocation method comprises the steps that real-time load data, historical task execution data and network delay data of all computing power centers are collected, and a regional load feature database is constructed; predicting the load demand of each computing power center in a future time window by using a long short-term memory network model to generate a load prediction value; calculating a resource gap coefficient and a resource margin coefficient of each region according to the load prediction value and the current resource capacity, identifying a resource insufficient region and a resource surplus region, and generating a resource collaborative matching matrix between the regions; and optimizing and solving the cross-regional task allocation scheme by adopting an improved genetic algorithm to generate an optimal resource allocation strategy, and allocating the to-be-processed task to a corresponding computing power center according to the optimal resource allocation strategy. According to the method, the cooperative utilization rate and the distribution efficiency of the computing power resources in the cross-regional scene are effectively improved.

The invention relates to the technical field of water conservancy projects, and discloses a water conservancy project water quality detection method and system based on artificial intelligence, and the method comprises the steps: collecting first multi-source data for a water conservancy project, carrying out the alignment of water quality parameter data, remote sensing image data and meteorological and hydrological data in the first multi-source data based on a timestamp, and obtaining second multi-source data; performing data preprocessing and feature extraction processing on the second multi-source data to obtain target feature data; designing a multi-task space-time diagram convolutional network, performing parameter optimization by adopting a genetic algorithm, and constructing a water quality detection model; inputting the target characteristic data into a water quality detection model, and outputting a water quality detection result through the water quality detection model; when the water quality detection result determines that the water quality is abnormal, generating a pollution diffusion simulation map based on a space-time diagram structure and a water quality diffusion rule, and providing emergency decision suggestions according to a water conservancy project scheduling scheme; the accuracy and functional integrity of water quality detection are improved.

The invention discloses an oil reservoir injection-production optimization method based on an improved multi-objective particle swarm algorithm, and belongs to the technical field of oil and gas field development. According to the method, an attention mechanism, a Transform and a long-short-term memory network are introduced based on a relational graph convolutional network, geological features and production system information are combined, inter-well water content prediction is taken as a target, an HSTMF model is constructed, an injection-production well production system does not need to be adjusted, the calculated amount is greatly reduced, and the prediction precision is remarkably superior to that of a traditional method; in the aspect of optimization algorithms, a Cauchy disturbance variation mechanism is introduced into a genetic algorithm and combined with a particle swarm optimization algorithm to form a new optimization strategy, a real-time closed-loop collaborative oil reservoir management framework is constructed in combination with the moisture content prediction capability of an HSTMF model, the net present value and the total oil production are effectively increased, the total moisture content is reduced, and good practical application value is shown.

The invention discloses a multi-robot path planning method based on multi-heterogeneous population coevolution and parallel search. The method comprises the following steps: firstly, modeling an environment by adopting a two-dimensional grid method, and generating a multi-robot initial path of a total population by utilizing a risk perception A * algorithm; then, dividing the total population into three sub-populations and corresponding external cooperative populations through a hierarchical roulette selection strategy; the three types of heterogeneous algorithms are executed in parallel, namely the collaborative genetic algorithm, the collaborative particle swarm algorithm and the collaborative ant colony algorithm are executed in parallel and synchronously, and a global solution set is generated in combination with a double-solution pool fusion mechanism. And then, a priority-based conflict resolution strategy is adopted, and time-space conflicts are solved by adopting time migration, path detour and local path planning strategies. And finally, path smoothing is realized through a cubic B-spline curve based on path reconstruction. According to the method, heterogeneous population coevolution is combined with parallel calculation, so that the efficiency, safety and cooperation capability of multi-robot path planning are remarkably improved.

The invention discloses a wind power plant unit load reduction optimization method and system based on cabin type laser radar real-time wind measurement, and relates to the technical field of wind power generation. Constructing a load estimation model; performing real-time detection on a blade root bending moment predicted value sequence and a tower bottom vibration acceleration predicted value sequence, constructing a multi-objective optimization function including blade root bending moment, tower bottom vibration acceleration and power generation power, and setting a dynamic weight coefficient of the multi-objective optimization function according to three-dimensional wind field prediction sequence data; obtaining an optimal control variable through a multi-objective genetic algorithm based on the multi-objective optimization function; and according to the optimal control variable and vertical wind shearing in the three-dimensional area, differential adjustment is conducted on the pitch angle of each blade, and a personalized variable pitch angle sequence of each blade is obtained. According to the method, by establishing the optimization objective function fusing the load and the power, multi-variable collaborative optimization is achieved, and the safety and the economical efficiency of wind power plant unit operation are remarkably improved.

The invention provides a power distribution network scheduling strategy generation optimization method and system, and the method comprises the steps: obtaining historical operation state data of all equipment in a power distribution network, and carrying out the preprocessing of the historical operation state data, and obtaining a target data set; establishing a deep learning network for predicting the performance of the power distribution network, inputting the target data set into the deep learning network, and predicting the performance of the power distribution network through the deep learning network; according to the predicted performance of the power distribution network and the current operation state of each device in the power distribution network, determining a scheduling strategy of the power devices in the power distribution network by adopting a genetic algorithm; establishing a simulation model of the scheduling strategy, wherein the simulation model is a mathematical model for describing the actual structure of the power distribution network; and inputting the scheduling strategy into a simulation model for a simulation experiment, and correcting the scheduling strategy according to a simulation result. Based on the method, the invention further provides a generation optimization system of the power distribution network dispatching strategy. The scheduling strategy can be continuously optimized and improved, and the overall performance of the system is improved.

The invention discloses a carbon emission prediction, regulation and control system and method based on multi-scale dynamic optimization. The system comprises a data acquisition layer, a five-dimensional coupling model layer and an optimization regulation and control layer. The data acquisition layer acquires multi-source heterogeneous data in real time by deploying an Internet of Things sensor network; a five-dimensional dynamic coupling model is arranged in the five-dimensional coupling model layer and comprises a multi-scale carbon emission kinetic equation, an industrial association network model, a space-time coupling prediction model and a dynamic optimization control model; an improved particle swarm optimization algorithm and a non-dominated sorting genetic algorithm are integrated in the optimization regulation and control layer, and parameter dynamic identification and multi-target optimization solution in the five-dimensional dynamic coupling model are realized based on multi-source heterogeneous data acquired by the data acquisition layer. The method can be applied to a regional carbon emission management platform, can be embedded into a high-energy-consumption enterprise production system, optimizes energy distribution and technical investment in real time, can also be used for carbon trading market auxiliary decision making, and provides data support for enterprise carbon quota distribution and emission reduction strategy making.

The invention discloses an inference task collaborative scheduling system and method oriented to multiple terminals of a swan gap. The inference task collaborative scheduling system comprises a communication module Broker, a system monitoring module SystemProfilter, a scheduling module Scheduler and an inference task execution module Worker. According to the method, a structured resource state vector is constructed based on an NDK native interface so as to comprehensively represent the real-time availability of equipment; a lightweight MQTT protocol is adopted to support low-overhead and high-robustness cross-terminal state synchronization and task distribution; a comprehensive load scoring mechanism fusing task priorities and dynamic weights is designed, a multi-objective optimization problem based on a non-dominated sorting genetic algorithm (NSGA-II) is introduced, task delay is minimized in a combined mode, terminal loads are balanced, and energy consumption is controlled; meanwhile, the execution time delay is efficiently estimated in combination with a proxy model based on linear regression, and the scheduling overhead caused by real reasoning and calling is avoided; the whole architecture is deeply adaptive to a swan-mong system, and is compatible with an Android platform through modular packaging and unified communication interface design, and efficient, self-adaptive and cross-platform collaborative scheduling oriented to a swan-mong multi-terminal reasoning task is realized for the first time.

The invention discloses an intelligent agent system and a control method thereof, and relates to the technical field of natural language processing. The system comprises an execution layer, a planning layer and an auditing layer, the planning layer identifies and extracts innovation task meta-information, constructs a task tree to complete operations such as hierarchical clustering, and determines a task execution path; the execution layer calculates the semantic similarity between the content of the knowledge graph and the user innovation question, determines the optimal question and answer, extracts heuristic information, and decomposes the heuristic information into sub-questions to construct a dependency graph; generating an answer set based on the graph solving sub-problems, and integrating answers by using a genetic algorithm to obtain an overall solution; and the auditing layer performs multi-dimensional scoring on the scheme, and determines a structured scheme report and optimization suggestions for users to use according to a scoring result. The method has the capabilities of structured reasoning, problem recursive decomposition and scheme closed-loop optimization, can generate a multi-dimensional evaluation result, and efficiently realizes systematic modeling of a clear path for analogy heuristic information support problem solution.