1829 results about "Linear programming" patented technology

The invention discloses an urban greening system based on big data and an optimization method thereof, relates to the technical field of artificial intelligence, and solves the problems of difficulty in heterogeneous data integration, insufficient real-time analysis capability of dynamic data and low management efficiency in an existing urban greening system. Multi-source heterogeneous data are collected through an API interface and Internet of Things communication, and are cleaned, aligned, converted and fused through a distributed real-time data stream processing platform; image recognition and a machine learning algorithm are used for monitoring diseases and insect pests in real time, and a growth prediction model is trained in combination with an LSTM network, so that the quick response capability of the system to environmental changes is enhanced; according to the method, target weights are dynamically adjusted and optimized through the deep Q network, a multi-target optimization model is constructed in combination with mixed integer linear programming, context awareness and a genetic algorithm, intelligent optimization of irrigation, fertilization, pest control and greening layout is achieved, and the management efficiency and the resource utilization rate of an urban greening system are remarkably improved.

The invention relates to the technical field of automatic logistics, in particular to an intelligent warehouse cargo scheduling device, which comprises a dynamic priority adjustment module, a self-adaptive classified storage module, a cargo flow optimization module, a resource allocation module, a fault analysis and maintenance module, a behavior monitoring module, an energy management module and a cost control module. According to the method, the cargo scheduling priority is optimized through a support vector machine algorithm and a decision tree method, the warehouse space utilization rate is improved by utilizing a convolutional neural network and a genetic algorithm, the cargo flow is optimized by adopting linear programming and a network flow analysis algorithm, and the cargo flow trend is estimated through an autoregressive integral moving average model and a recurrent neural network. The system utilizes a long short-term memory network to identify equipment faults and maintain, reduces the risk of accidental shutdown, improves the operation safety through a computer vision technology and a behavior identification algorithm, and utilizes an energy consumption mode analysis and cost-benefit analysis method to reduce the operation cost.

The invention relates to the technical field of municipal road construction, and discloses a municipal road construction section scheduling optimization method and system based on artificial intelligence, and the method comprises the steps: collecting the multi-source dynamic data of a construction region through Internet of Things equipment and a satellite remote sensing technology, and constructing a time sequence database; a multi-objective optimization model is established, and an initial scheduling scheme is generated by adopting a non-dominated sorting genetic algorithm with the goal of minimizing construction period, resource waste and traffic jam influence. Dynamically adjusting the priority of the construction section by using a reinforcement learning algorithm, and updating a construction task sequence; a graph neural network is utilized to detect space-time conflicts of construction sections, and conflict-free scheduling constraint conditions are generated; and optimizing resource allocation based on mixed integer linear programming, simulating a construction process in combination with a digital twinborn technology, correcting a construction progress deviation by using a Kalman filtering algorithm, and outputting a final scheduling instruction. According to the invention, optimization of municipal road construction section scheduling is realized, the construction period is effectively shortened, resource waste is reduced, and traffic jam is relieved.

The invention discloses a multi-agent-based virtual power plant intelligent control method and system, and the method comprises the steps: dividing a virtual power plant into a plurality of sub-virtual power plants, deploying an agent in each sub-virtual power plant, collecting a local resource state through each agent, and predicting a load demand and the output of a distributed power supply, a hierarchical control unit is adopted to carry out collaborative optimization among the sub-virtual power plants according to a prediction result, and an upper-layer optimization control module constructs a linear programming model according to the prediction result and solves the linear programming model to obtain an initial scheduling scheme; and the lower-layer reinforcement learning control module performs local adjustment on the preliminary scheduling scheme according to a multi-agent depth deterministic strategy gradient algorithm to obtain a decision scheme. Based on a distributed control strategy of a multi-agent architecture, the fault-tolerant capability and reliability of the system are improved, a hierarchical control architecture is adopted, global optimization and local adjustment are organically combined, and efficient coordination and real-time adjustment capability of global resources are realized.

The invention relates to the field of business intelligence and artificial intelligence, and discloses an AI analysis technology-based store site selection accurate data comprehensive measurement and calculation system, which comprises a data acquisition module, a data preprocessing module, an AI analysis and optimization module, a financial prediction and risk assessment module and a report generation module. The data acquisition module acquires geographic information, human traffic, rent cost, competitor information and the like of candidate positions from a multi-dimensional data source; the data preprocessing module preprocesses the data; the AI analysis and optimization module generates a recommendation scheme of a candidate position by using multi-objective optimization, linear programming and a machine learning algorithm; the financial prediction and risk assessment module carries out financial income prediction and risk analysis on each position; and the report generation module displays the analysis result through a visual tool and generates a decision support report. According to the method, the optimal store site selection scheme can be dynamically and accurately recommended, and merchants are helped to make scientific decisions and adapt to market changes.

The invention belongs to the technical field of resource scheduling, and particularly discloses a resource scheduling method and system based on reinforcement learning, and the method comprises the steps: describing the dependency and conflict relation between tasks through constructing a task causal relation graph which can be dynamically updated; constructing a state space and an action space based on a current task state and a causal relationship, training an intelligent agent by adopting a reinforcement learning model in combination with a multi-target reward function, and generating a scheduling and resource allocation strategy; linear programming and heuristic joint resource allocation are carried out under strategy guidance, and task priorities and causal relationships are dynamically adjusted in combination with task states; by collecting execution data and analyzing strategy deviation, a model structure and parameters are further adjusted, and a complete closed-loop optimization process is formed. According to the method, task priority dynamic adjustment, resource allocation strategy self-adaptive updating and scheduling process closed-loop optimization are realized, and the method is suitable for complex project management scenes under multi-task and multi-resource constraints.

The invention relates to the field of nutrition and health management, and discloses a nutrition evaluation and prediction method and system based on big data, and the method comprises the following steps: firstly collecting the physiological data, diet record and activity data of a user, and carrying out the preprocessing of the data, and guaranteeing the data quality; dimensionality reduction is carried out through feature extraction and principal component analysis, and key features are extracted; the method comprises the following steps: evaluating daily nutritional requirements of a user by using a multivariable linear regression model, a Bayesian network and causal inference, predicting a nutritional requirement change trend of the user in a future period of time based on a long-short-term memory network, and optimizing daily diet selection of the user by using a Markov decision process; and finally, generating a personalized nutrition scheme by adopting linear programming and a genetic algorithm, and ensuring that the nutritional requirements and health targets of the user are met. According to the method, personalized and scientific diet suggestions can be provided, and the quality and effectiveness of individual nutrition management are improved.

The invention discloses a disaster emergency rescue command system and method, and relates to the technical field of rescue command systems. Comprising an information collection module which collects information, including disaster situations, personnel distribution and environmental conditions, of a disaster site through sensors and communication equipment; the collected information is input into the system, so that accurate disaster assessment can be conveniently obtained; the decision support module determines optimal allocation of rescue resources by using a linear programming model according to disaster assessment provided by the information collection module, and plans a rescue path by using a graph theory algorithm; and the command control module is used for converting the rescue decision of the decision support module into a specific rescue instruction. According to the system, the disaster assessment time can be shortened through real-time monitoring and rapid data processing, and the rescue decision-making speed is increased; optimal configuration of resources is realized through an algorithm, resource waste is avoided, and resource availability at a critical moment is ensured.

The invention relates to the technical field of power-carbon collaborative comprehensive energy systems, in particular to a virtual power plant robust collaborative optimization method considering uncertainty demand response. According to the method, a multi-dimensional feature fusion demand response model is established, and the characteristics of four dimensions of time delay, adjusting speed, tracking power and response energy and the influence of the characteristics on demand response precision are identified and analyzed. And fitting a normal distribution function through historical data, estimating a confidence interval of uncertainty features, constructing a multi-dimensional coupling deviation model, and accurately depicting a demand response dynamic contour. A two-stage optimization strategy is adopted, the first stage is an instruction decomposition model, the second stage is an instruction tracking model, and the operation cost and the total penalty cost are minimized through a mixed integer linear programming and energy storage system deviation compensation strategy. According to the method, the uncertainty in demand response can be effectively managed, and the flexibility and economy of the virtual power plant are improved.

The invention relates to the technical field of supply chain management, and discloses an intelligent supply chain optimization management and control method. The method comprises the following steps: acquiring full-link real-time operation data of a supply chain through a distributed data acquisition module; extracting topological feature data by using a graph neural network; inputting the multi-target collaborative optimization model, and generating supply chain strategy optimization parameters by adopting a mixed integer linear programming framework and a dynamic constraint relaxation mechanism; constructing an adaptive resource scheduling model, realizing node dynamic scheduling by using an improved particle swarm algorithm, and outputting optimal configuration data; and establishing a layered supply chain management and control model which comprises a strategic planning layer, a dynamic coordination layer and an execution control layer, and realizing full-link intelligent optimization management and control. The invention also relates to risk event processing, strategic planning layer resource network planning and the like. The system can comprehensively collect data, optimize resource configuration, improve response speed, reduce cost, effectively control risks and improve the overall competitiveness of a supply chain.

The invention discloses an oil depot full-process comprehensive management monitoring method and system, particularly relates to the field of oil depot full-process comprehensive management monitoring, and aims to solve the problems that data correlation analysis among different links is insufficient, and potential systematic risks are difficult to comprehensively identify and predict. A transfer entropy method is introduced into upstream real-time data and historical association mapping, information transmission strength between upstream and downstream key parameters is quantitatively analyzed, and an effective causal link is established; in combination with dynamic time warping and synchronous entropy correlation degree quantity, trace deviation characteristics are matched with downstream key parameter cluster information, and a deviation propagation path is accurately drawn; strictly judging a non-stationary conduction trend from the aspects of time and structure by using a multi-scale complexity increment index and cluster structure change measurement; on the basis, a mixed integer nonlinear programming optimization model is adopted, a downstream link pre-intervention strategy is quantitatively formulated, and efficient solving of parameter adjustment and early warning signal level decision is achieved.

The invention discloses a renewable energy power generation power prediction and power dispatching method and system, and the method comprises the steps: collecting the historical power generation data and real-time meteorological data of renewable energy power generation, carrying out the linear interpolation of the historical power generation data and the real-time meteorological data, and carrying out the missing value filling and box plot anomaly detection, obtaining a normalized training data set; constructing a hybrid prediction model by using the normalized training data set and adopting a neural symbol acceleration technology with time logic constraints, extracting medium and long term space time features, and generating a renewable energy power generation power prediction result; and according to the renewable energy power generation power prediction result and the system constraint condition, adopting a linear one-dimensional projection constrained distribution robust control method to formulate a scheduling strategy, and utilizing the scheduling strategy to solve an optimal scheduling scheme through mixed integer linear programming. According to the method, the renewable energy power generation power prediction precision and the power dispatching robustness are remarkably improved.

The invention provides a medical wholesale and retail comprehensive service method and system based on the Internet, and relates to the technical field of medicines.The medical wholesale and retail comprehensive service method comprises the steps that a hospital HIS system is docked in real time through an API, and electronic prescription data are collected; the collected electronic prescription data is connected with a medicine wholesale enterprise ERP system, the inventory state, batch number and validity period information of each medicine are obtained, and inventory information is obtained. According to the invention, through real-time connection of the hospital HIS system, the drug wholesale enterprise ERP system and the retail terminal POS system, inventory information can be obtained and dynamic management can be carried out. The real-time data updating not only improves the inventory turnover rate, but also can automatically warn the medicines close to the expiration date, and reduces the loss of the expired medicines. The LSTM neural network is adopted to predict the drug demand, and the replenishment suggestion is generated in combination with the linear programming model, so that the replenishment process can be optimized while the operation cost is minimized, and sufficient and efficient inventory is ensured.

The invention provides a hybrid parallel and dynamic scheduling method of a hybrid expert model based on 3D near-memory processing. The method comprises the following steps: establishing a joint performance analysis model; performing off-line linear programming optimization expert distribution; performing Bayesian optimization on physical node mapping; performing online reasoning; carrying out online dynamic priority detection; and an expert pre-broadcast and communication-friendly lexical element distribution strategy with optimal efficiency is provided. According to the method, node balancing optimization is realized through offline linear programming, and the problem of load imbalance of 3D NMP calculation is remarkably improved; in combination with a Bayesian optimization mapping strategy of link balance, the communication speed-up ratio is increased, and NoC link congestion is reduced; a dynamic scheduling strategy adapts to dynamic changes of expert activation in real-time reasoning through calculation load prediction and a pre-broadcast mechanism. Through cooperation of the offline automatic hybrid parallel mapping algorithm and the online dynamic scheduling strategy, the calculation load and the communication overhead are effectively balanced, and the reasoning efficiency of the hybrid expert model MoE on the 3D near-memory processing architecture is remarkably improved.

The disclosure relates to the technical field of power system management, and in particular to a large-scale electric vehicle cluster coordinated charging optimization method based on greedy repair genetic algorithm. the method includes the following steps: Si, establishing a task model: establishing a task model of electric vehicle cluster coordinated charging in large charging stations; S2, constructing constraint conditions: constructing constraint conditions for the coordinated charging optimization problem; S3, constructing a constrained optimization problem: designing an objective function with the minimum charging cost, performing linear mathematical transformation on non-linear constraint conditions, and constructing a 0 / 1 integer linear programming problem; and S4, solving the constrained optimization problem with a custom genetic algorithm based method: carrying out genetic coding on a decision model of the charging station, and specifically designing a genetic algorithm with greedy repair operators to solve the constructed constrained optimization problem.

The invention provides a virtual power plant energy storage system co-scheduling and control method and system, and relates to the technical field of power management, and the method comprises the steps: obtaining power grid scheduling data, calculating the charge and discharge income, collecting the real-time parameters of a battery, analyzing the performance attenuation law through deep reinforcement learning, determining the initial working parameters, predicting the system state based on a rolling time domain, and obtaining the real-time parameters of the battery. An optimal scheduling scheme is calculated through mixed integer linear programming and model prediction control in combination with a power distribution strategy and is corrected in real time, operation is executed according to a time-phased scheduling instruction and is monitored in real time, and an emergency strategy is started when necessary, so that the economic benefit and the safety of an energy storage system are improved, and the service life of a battery is prolonged.

The invention discloses a park integrated energy scheduling method and system based on a master-slave game model, and relates to the technical field of energy scheduling, and the method comprises the steps: collecting basic operation parameter data in a park integrated energy system; constructing an upper-layer game model taking an operator as a dominant, setting constraints, and making an electric heating and cooling price strategy and a unit output plan by taking operator profit maximization as a target; constructing a lower-layer game model, and making a load energy consumption strategy by taking user energy consumption cost minimization as a target; and converting the upper-layer game model and the lower-layer game model into a mixed integer linear programming problem, and obtaining an electric heating cold price scheduling scheme and an output scheduling scheme of each unit by adopting an optimization solution algorithm. The building thermal inertia and demand response mechanism is introduced, the KKT condition, the linear relaxation technology and the strong dual theory are used for converting the complex problem into the MILP model easy to solve, optimization of operator and user benefits is achieved, and the calculation speed and the solving efficiency are remarkably improved.

The invention relates to the technical field of resource allocation, in particular to a data preset learning-based dynamic material allocation system for multiple stations, which comprises a topology modeling module, a demand clustering module, a strategy generation module and a decision optimization module. According to the method, laser ranging and dynamic time warping are combined, the work station layout is converted into a moving time consumption parameter, a space correlation coefficient is adjusted according to a difference value proportion, a topological relation matrix is updated in real time, DTW quantifies the mode similarity of a demand interval standard deviation and a usage variable coefficient, a multi-dimensional demand feature vector is constructed, and the prediction and actual consumption matching degree is improved; the method comprises the steps of establishing a response capability curve based on OEE data, dynamically associating residual capacity with demand characteristics through linear programming, realizing strategy pre-generation and capacity early warning, constructing a cooperation unit through a Hungary algorithm, matching material and equipment parameters through cosine similarity, triggering strategy reconstruction, synchronously updating topology, forming a closed-loop optimization system and enhancing the cross-station dynamic response capability.

The invention discloses a double-layer and double-stage heterogeneous unmanned aerial vehicle task allocation and flight path planning method, and relates to the technical field of unmanned aerial vehicles. The method comprises a dual-stage task allocation method and a dual-stage path planning method. The beneficial effects of the invention are that the dual-stage task allocation method and the dual-stage path planning method are provided for improving the efficiency of the multi-unmanned aerial vehicle cooperative execution of the search rescue task; according to the dual-stage task allocation method, a joint optimization framework combining mixed integer linear programming and an improved ant colony algorithm is provided, so that the task load balance and the total flight distance can be optimized, and a better task allocation effect is realized; the dual-stage path planning method provides a dual-stage path planning scheme in which global task sequence optimization and local obstacle avoidance planning are coordinated, and a better path planning effect is obtained by combining the global and local dual-stage optimization scheme.

The invention discloses a multi-AGV path planning method based on cooperation of a Petri network and mixed integer linear programming, and the method comprises the steps: introducing the Petri network for modeling, and carrying out the modeling of corresponding places, tokens and transitions for three resources, namely AGVs, tasks and orbits; an MILP model is constructed; the Petri network triggers a path planning request change in an AGV resource module, and converts resource conflicts, path feasibility, cargo width change and task time limit in the Petri network into decision variables and logic constraints in MILP; each AGV calls an MILP model according to the current position and the starting points and the ending points of all the distributed tasks, and the analyzed optimal path and moment data are written into an AGV token in an AGV resource module; petri scheduling execution processes path conflicts among multiple AGVs, and a solving result of the MILP model is put into a Petri network for operation verification. Extensible, dynamic and intelligent multi-AGV path scheduling can be realized.

The invention belongs to the technical field of power system planning and power market mechanisms, and relates to a network construction type energy storage auxiliary service market method combining electric energy and capacity markets. Comprising the steps of establishing a transfer factor matrix PTDF, establishing a network construction type energy storage upper-layer investment model, establishing a lower-layer joint clearing model, establishing a lower-layer capacity market clearing model, converting a double-layer model into a single-layer model based on a duality theory, processing a nonlinear term and converting the nonlinear term into a mixed integer linear programming problem, and solving based on a gurobi commercial solver. Carrying out economic evaluation on the network construction type energy storage investment planning; according to the invention, by considering that the network-building type energy storage participates in the electric energy, frequency modulation auxiliary service and capacity market at the same time, various values of the network-building type energy storage as flexible and power grid supporting resources are further mined, and the problem that the investment cost of the network-building type energy storage is difficult to recover is effectively solved.

This invention advances power grid operational planning by introducing a mid-term scheduling framework for integrated hydro-wind-solar systems that accounts for heterogeneous daily load profiles across multiple receiving-end power grids. The proposed approach utilizes an adaptive variable-step search algorithm to segment loads into peak, flat, and valley intervals. By synthesizing five key metrics, including mean daily load, daily load factor, peak-valley differential ratio, load rates during peak / valley periods, and timing of peak / valley occurrences, the method accurately captures region-specific load patterns and peak-shaving demands. This enables a refined reconstruction of load profiles of receiving-end power grids. A nested multi-temporal scheduling model that couples medium- and short-term horizons to simultaneously maximize total energy production and minimize transmission imbalances among power grids. The model is addressed by using the mixed-integer linear programming (MILP) to obtain medium- and short-term generation schedules and power transmission schedules.

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention relates to an electricity-carbon cooperative scheduling optimization method and device for a low-carbon park integrated energy system, and the method comprises the steps: carrying out the cooperative prediction of a multi-state parameter through employing a panoramic situation deduction model, and generating a panoramic dynamic situation scene set; establishing an electricity-carbon cooperative scheduling model considering a carbon transaction mechanism, and deeply embedding the real-time carbon cost into a target function to carry out Pareto optimization of economic cost and carbon emission cost; an electricity-carbon cooperative scheduling model is converted into a standard mixed integer linear programming model, a situation deduction-day-ahead optimization-rolling correction hierarchical calculation framework is adopted to decompose a cooperative scheduling optimization problem to different time scales for decision making, and a global optimization plan is made on the day-ahead layer based on a panoramic dynamic situation. Deviation is corrected on line through rolling optimization in the intraday layer; and the integrated energy system executes the corrected scheduling plan. Compared with the prior art, the method has the advantages that the consumption rate of renewable energy sources can be remarkably increased and carbon emission can be effectively reduced while the operation economy of the system is ensured.

The invention discloses a multi-stage unit commitment method for a power system containing high-proportion new energy, and the method comprises the steps: constructing a unit commitment model covering a day-ahead stage and a real-time scheduling stage, describing the uncertainty of a new energy output prediction error through employing a fuzzy set based on Wasserstein measurement, introducing a hybrid approximation rule, and carrying out the prediction of a new energy output prediction error. The start-stop state of the quick start-stop unit is expressed as a linear function of a new energy output prediction error in a previous stage, the model is converted into a mixed integer linear programming problem easy to solve by using a duality theory, and a final unit combination scheme is obtained in combination with a rolling optimization framework and real-time new energy output prediction information; the method is simple and easy to implement, the system cost can be effectively reduced, the new energy consumption level is improved, the reliability and flexibility of the power system are enhanced, and technical support is provided for safe and economical operation of the high-proportion new energy power system.

The invention discloses a building integrated energy system optimization scheduling method giving consideration to the comfort level of a light storage soft environment, and relates to the field of building energy system optimization scheduling, and the method comprises the steps: building a photovoltaic output uncertainty model based on Beta distribution according to typical daily solar radiation intensity data; secondly, constructing an energy storage model; thirdly, a comfort level quantitative model considering temperature regulation and control of the indoor air conditioner and the electric water heater is constructed, and the influence on the environmental comfort level is reflected laterally; and finally, according to the various formed models, constructing a building integrated energy system optimization scheduling model considering the light storage soft comfort degree, and performing solving based on a mixed integer linear programming method. The environment comfort level of the building integrated energy system can be quantified, the flexibility of the building integrated energy system is improved by introducing photovoltaic and energy storage equipment, flexible scheduling of load resources is facilitated, peak load shifting is better achieved, the operation cost of the building integrated energy system is reduced, and the operation efficiency of the building integrated energy system is improved.

The invention relates to the technical field of network resource scheduling, and provides a heterogeneous network intelligent slice resource scheduling method and device, electronic equipment and a medium. The method comprises the following steps: performing three-dimensional joint modeling on heterogeneous network physical resources to obtain a dynamic resource state tensor, and performing a dynamic resource allocation decision by combining a deep reinforcement learning model with a service demand matrix to obtain an initial slice resource allocation matrix; and performing mixed integer linear programming through a preemption decision model in combination with the URLLC service request queue to obtain a resource allocation sequence, and performing occupancy rate updating on the dynamic resource state tensor according to the resource allocation sequence to obtain ground network resource data, and carrying out resource fusion in combination with the satellite beam capacity data to obtain a space-ground integrated resource pool tensor, thereby carrying out traffic prediction in combination with historical traffic data through a long-short memory network to obtain a bandwidth pre-adjustment strategy. Through organic integration of technologies such as mixed integer linear programming and traffic prediction, the overall network resource utilization rate is improved.

The invention discloses a wearable thermal power plant personnel route planning device based on UWB, and relates to the technical field of route planning, an inspection task is generated based on an inspection plan, and the Euclidean distance between an inspector and an inspection task point is calculated through a linear programming method by combining real-time position data of the inspector and adopting the minimum distance optimization principle, so that the route planning efficiency is improved. Distributing an inspection path; equipment state scores, historical fault scores and safety risk scores are introduced, a multi-factor weighted optimization target is constructed, high-fault-frequency and high-risk equipment is subjected to priority inspection, safety accidents possibly caused by equipment abnormity are reduced, meanwhile, the advancing route of inspectors is optimized, and the inspection coverage rate is increased; the UWB electronic fence technology is adopted to monitor an inspector in real time, the Euclidean distance is adopted to calculate the distance between the inspector and a high-risk area, through a two-stage early warning mechanism, visual, vibration or voice reminding is provided when the inspector approaches or enters the dangerous area, and a safety early warning signal is synchronously sent to a background.

The invention provides a multi-data center computing power-electric power cooperative scheduling and demand response declaration capacity optimization method, which comprises the following steps of: firstly, acquiring multi-dimensional historical time sequence data of a data center; a machine learning algorithm is used to predict the workload of each data center in each time period of a future single day in a future scheduling period and the power market price of the place where each data center is located, and then a space-time coupling-oriented multi-data center computing power-power cooperation model considering uncertainty is constructed; the collaborative model comprises a joint optimization framework of computing power scheduling and power scheduling, and takes actual profit maximization as a target, then the established collaborative model is converted into a mixed integer linear programming problem and solved, and the optimal declaration capacity of each data center participating in demand response is obtained; and each data center carries out scheduling according to the task load of space migration and time migration, the charging and discharging power of an energy storage system and the power generation amount of renewable energy sources, so that overall optimization of demand response declaration capacity of the multiple data centers is realized.

The invention discloses a task migration method and device and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a calculation graph corresponding to a calculation task of a source device; through a linear programming model, target equipment is allocated to each computing node in the computing graph, and the target equipment is used for executing each computing node; and optimizing the operation of each computing node according to the device resource vector of the target device and the computing demand of each computing node. According to the method, the calculation graph is reconstructed, so that the task can be efficiently executed on the target equipment after being migrated, and the execution efficiency of the task is improved.