93 results about "Energy constraint" patented technology

The invention provides a virtual power plant group resource scene adaptive scheduling method and system, and a storage medium, and the method comprises the steps: building a typical external feature model of a virtual power plant based on the resource characteristics and core parameters of different types of distributed resources; generating a feasible region of the single equipment based on power constraint, electric quantity constraint and climbing constraint of the single equipment in the virtual power plant, and aggregating the feasible region of the single equipment to form an aggregated feasible region of the virtual power plant; based on a typical external feature model of the virtual power plant and different service scene requirements, dynamically adjusting response capability index weights in different service scenes, and based on an aggregation feasible region of the virtual power plant, constructing a virtual power plant dynamic aggregation model adapted to multiple scenes; and solving the dynamic aggregation model of the virtual power plant by taking minimization of the power generation cost of the virtual power plant as a target to obtain an optimal scheduling scheme of the virtual power plant.

A distributed system and method for compressing and restoring data across edge computing devices and cloud infrastructure is disclosed. The system dynamically adjusts compression based on available computing resources, network conditions, and now energy constraints. Edge devices monitor power consumption and battery levels, optimizing compression parameters to extend battery life while maintaining data quality. A workload scheduler prioritizes tasks based on energy availability, offloading intensive processing to cloud infrastructure when necessary. The system utilizes an energy-aware coordination layer to balance workloads across multiple devices, ensuring efficient data flow and long-term operational stability. Homomorphic operations allow secure distributed processing on compressed data, while an adaptive neural upsampler enhances reconstructed outputs. By integrating energy optimization, the system improves performance and longevity of edge devices in power-limited environments.

The invention discloses a flexible wire harness assembly force control method based on reinforcement learning, and aims to solve the problems of inaccurate phase switching, impact and semi-locking caused by strong nonlinear resistance of a connector sealing ring and a buckle. According to the method, a phase gating threshold value and energy budget are generated through multi-modal sensing and phase probability estimation, reinforcement learning, a trajectory is generated under energy guidance through condition diffusion of phase gating, a passive consistent trajectory is obtained through energy projection and passive projection, variable impedance force control parameters are synthesized, and online energy shaping and closed-loop updating are implemented. The technical effects that the impact peak value and the semi-locking rate are reduced, instantaneous power and accumulated injection energy constraints are met, and the in-place locking success rate and the assembly stability are improved are achieved.

The invention discloses a force control joint dynamic error compensation method based on Kalman filtering, and the method comprises the steps: collecting and preprocessing multi-source signal data, and forming a force control joint input data set; constructing an energy hierarchical Kalman filtering model, executing energy constraint correction and outputting state estimation; calculating a prediction residual error, carrying out time-frequency analysis, adjusting a compensation gain, and generating residual error information; torque and angular velocity signals are extracted, a semantic state is recognized, and semantic gating parameters are output; fusing the state estimation, the residual information and the semantic parameters to generate a dynamic compensation instruction signal; energy layer and residual information changes are monitored, self-calibration is triggered, and a feedback closed loop is formed. According to the invention, by introducing energy hierarchical Kalman filtering, time-frequency modulation compensation and a semantic gating feedback mechanism, dynamic error self-adaptive accurate compensation of the force control joint in a complex multi-disturbance environment is realized.

The invention discloses a transformer area-oriented mobile energy storage scheduling method, which comprises the following steps of: acquiring historical and real-time measurement data of each node based on a three-phase line topological structure and line parameters of a transformer area; respectively establishing a quantile time sequence prediction model for the load and the photovoltaic output of each node in the prediction time period, and obtaining a quantile prediction set based on the load and the photovoltaic output; generating scenes / samples according to the quantile prediction set, and load and photovoltaic samples of each node at each moment contained in each scene; establishing a three-phase unbalanced power flow model satisfying a branch balance and power flow relationship, and establishing a mobile energy storage model based on node selection and energy constraint; for a three-phase unbalanced power flow model and a mobile energy storage model, joint optimization of a rolling time domain is established, and the optimization takes expected operation cost minimization as a target; and the optimized three-phase unbalanced power flow model and the mobile energy storage model are used for scheduling, so that the problems of safe operation and efficient absorption of the high-permeability photovoltaic transformer area are solved.

The invention provides an energy distribution optimization method and device, electronic equipment and a storage medium, and can be applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring energy purchase cost data and energy supply attribute data of an energy supplier in a target scheduling period and energy demand attribute data and energy constraint attribute data associated with each energy load area; the energy purchase cost data, the energy supply attribute data, the energy demand attribute data and the energy constraint attribute data are processed based on an energy distribution prediction model, energy distribution information is obtained, and the energy distribution prediction model comprises a first-stage distribution sub-model constructed based on a multi-objective optimization function and a constraint condition set; and determining a target energy distribution scheme corresponding to the energy load area according to the energy distribution information. Cooperative optimization of key targets of cost, reliability and timeliness is realized in a target scheduling period, and the overall robustness and feasibility of an energy distribution scheme in a complex and uncertain environment are remarkably improved.

The invention discloses a performance simulation calculation method for an aviation hybrid electric power system. The method comprises the following steps: firstly, constructing a sub-component calculation model covering an aerodynamic thermal component, a power generation subsystem, a power conversion and transmission subsystem, a battery subsystem, a motor and an aerodynamic system; establishing a reverse logic calculation framework driven by a load side demand, and realizing hierarchical reverse parameter solution from a load end to a power source end; and establishing a power balance equation at a bus end, carrying out iterative solution by adopting a Newton-Rafson algorithm, and realizing dynamic distribution and coordination control of multi-source energy in combination with an energy constraint module. According to the method, through a reverse solution and bus end balance strategy, the simulation calculation efficiency and convergence of the complex aviation hybrid electric power system are remarkably improved, and meanwhile, the adaptability and expandability of the model to different topological architectures and task profiles are enhanced; and an efficient and high-credibility calculation tool is provided for design, performance evaluation and energy management strategy optimization of the hybrid electric propulsion system of the aircraft.

The application discloses a base station intelligent energy coordination method and system, and belongs to the technical field of energy management, and the technical solution points comprise the following steps: collecting a data set of each base station in a regional base station cluster; calculating energy constraint data based on energy data and environment data to obtain an energy scheduling sequence of each base station; obtaining a load prediction curve of each base station through a preset LSTM network model based on load data to determine a first base station; and obtaining a corresponding second base station and a third base station according to the load prediction curve of the first base station. The application obtains the load prediction curve of each base station through the preset LSTM network model and determines the first base station, obtains the second base station through the analytic hierarchy process, and obtains the third base station through multi-target reinforcement learning, so that the energy scheduling of the regional base station cluster is realized, the clean energy utilization efficiency and the energy scheduling precision are improved, and the stability of base station operation is improved.

A distributed system and method for compressing and restoring data across edge computing devices and cloud infrastructure is disclosed. The system dynamically adjusts compression based on available computing resources, network conditions, and now energy constraints. Edge devices monitor power consumption and battery levels, optimizing compression parameters to extend battery life while maintaining data quality. A workload scheduler prioritizes tasks based on energy availability, offloading intensive processing to cloud infrastructure when necessary. The system utilizes an energy-aware coordination layer to balance workloads across multiple devices, ensuring efficient data flow and long-term operational stability. Homomorphic operations allow secure distributed processing on compressed data, while an adaptive neural upsampler enhances reconstructed outputs. By integrating energy optimization, the system improves performance and longevity of edge devices in power-limited environments.

The invention discloses a shared energy storage fine scheduling method considering multi-scene uncertainty and hierarchical decision, and the method comprises the steps: building system power-energy constraint modeling based on a typical scene set; modeling is carried out based on a typical scene set and system power-energy constraint, electricity purchasing and selling, energy storage efficiency and life degradation cost are comprehensively counted in multiple scenes, a multi-target scheduling model is formed, and opportunity constraint is introduced to ensure supply and demand reliability; based on a multi-target scheduling model, constructing a hierarchical decision-making model which takes a shared energy storage operator as a main part and takes a plurality of micro-grids / communities as an auxiliary part, and establishing a coupling constraint and power balance relation of energy storage charging and discharging-energy state-capacity leasing price; an energy storage dynamic rental price, capacity distribution and a time-sharing charging and discharging plan are obtained by adopting distributed iteration solution, and income allocation can be carried out in combination with an improved Shapley mechanism. According to the invention, the optimal decision of consumption improvement, energy consumption cost reduction and multi-agent coordinated operation under the condition of high-proportion access of new energy can be realized.

This invention relates to the field of multi-processor multi-task joint scheduling technology, specifically disclosing a typed task joint scheduling system and method based on a heterogeneous multi-core architecture. It introduces a joint scheduling mechanism to divide and sort tasks according to task size and priority. Based on this, for the time-limited characteristics of real-time tasks, an inertial weight coefficient particle swarm optimization method is used to iteratively update the load balancing strategy, minimizing the maximum response time of real-time tasks while meeting their schedulability requirements. Simultaneously, for the low-priority characteristics of non-real-time tasks, the problem-solving is simplified using a Lagrange-based convex optimization approach, and an energy-constrained binary search algorithm is employed to effectively reduce the average response time of non-real-time tasks, achieving optimal load distribution for the system.

The invention discloses an unmanned aerial vehicle flight control method and system based on a Mangbar and a dual-channel attention mechanism under wind field disturbance, and particularly relates to the technical field of unmanned aerial vehicle autonomous navigation and intelligent control. The method comprises the following steps: constructing a wind field disturbance model based on computational fluid mechanics, and simulating an unsteady wind field environment among urban building groups; designing a deep reinforcement learning network fusing a Mama framework and a double-channel attention mechanism, wherein the deep reinforcement learning network is used for realizing dynamic modeling of long-sequence wind field features and attention weighting of key spatial-temporal features; a dynamic road sign point guiding mechanism and a multi-stage attenuation greedy strategy are put forward, the sparse reward problem is relieved, and the local path optimization capability is improved; and a multi-target composite reward function including energy constraint, safety distance and path efficiency is constructed, and global path planning of energy consumption perception is realized. The method can improve the flight stability, path smoothness and energy efficiency of the unmanned aerial vehicle in a strong wind disturbance environment, and is suitable for unmanned aerial vehicle autonomous navigation tasks in an urban complex environment.

The invention provides a wireless power supply network throughput optimization method and system. The method comprises the following steps: constructing a wireless power supply communication system model with energy constraint and time delay constraint; constructing a throughput maximization problem P0; s3, on the basis of the throughput maximization problem P0, constructing a charging energy maximization sub-problem P1 under the constraints of total energy and energy collection duration, and solving the sub-problem P1 under the conditions that the total available energy of the wireless power supply base station is less and the total available energy of the wireless power supply base station is more; based on the throughput maximization problem P0, constructing a throughput maximization sub-problem P2 with wireless communication duration as a variable under the condition that an energy collection user gives an energy collection strategy, and solving the throughput maximization sub-problem P2; based on the optimal wireless energy transmission strategy and the optimal wireless communication duration strategy characteristics, the throughput maximization problem P0 is converted into a single-variable optimization problem P3, and the single-variable optimization problem P3 is solved. According to the method, the actual throughput performance of the wireless energy supply communication network is improved, and the efficient communication requirement in a complex dynamic network environment is met.

This invention relates to the field of optical communication, providing a method, system, device, product, and medium for optimizing the energy efficiency of multi-access point optical communication. The method includes: calculating the link channel gain to obtain the composite channel gain; obtaining the received energy at the receiver; establishing a nonlinear received energy expression based on the received energy; constructing a battery dynamics model using the nonlinear received energy expression to obtain device energy constraints; establishing a arrival Markov matrix and a service Markov matrix based on the number of participating communication devices and their obstruction states; calculating the arrival martingale and service martingale; calculating the queue length change based on the arrival martingale and service martingale; establishing a delay default probability inequality through the queue length change to obtain an upper bound on the delay default probability; establishing communication optimization constraints; constructing a multivariate optimization equation; solving the multivariate optimization equation based on the communication optimization constraints to obtain communication optimization parameters; and optimizing the optical communication using these communication optimization parameters.

This application relates to a device capability description method, apparatus, computer device, and storage medium. The method includes: acquiring a capability description language document for a target device; the capability description language document includes device type information, operability status information, operation method information, physical constraint information, and feedback format information; the physical constraint information includes at least one or more of time constraints, energy constraints, safety constraints, lifetime constraints, and mutual exclusion constraints; upon receiving an intent instruction from an upper-layer application, parsing the intent instruction to extract one or more execution targets; and performing capability matching on the one or more execution targets according to the capability description language document to determine target operations from the target device that can achieve the execution targets. This method enables automatic capability adaptation between upper-layer applications and heterogeneous devices, reducing development costs and improving execution security.

This invention relates to the field of UAV communication technology, and particularly to a method for UAV environmental monitoring and data processing supported by edge computing. The method includes: acquiring time-series data streams collected by multi-source environmental sensing devices, compressing and processing them based on a linear projection compression model to obtain compressed feature vectors; calculating communication costs based on current heterogeneous network conditions and energy constraints using a preset communication cost function; and executing different data processing strategies based on different communication costs. This invention achieves efficient edge compression in embedded resource-constrained scenarios; realizes resource-aware data upload control through the communication cost function; and reconstructs the Kalman filter matrix structure based on a Bayesian state fusion method using compressed feature space, effectively supporting local state estimation and uncertainty control, and triggering immediate warnings when environmental indicators exceed limits, effectively enhancing the system's risk perception and response capabilities.

The invention relates to the technical field of geologic model analysis, in particular to a coal bed gas fracturing three-dimensional well network seam network design method based on a geologic model, and the method comprises the steps: firstly constructing a three-dimensional discrete geologic model containing grid node mechanical parameters, and generating a three-dimensional dynamic induced stress potential energy tensor field; performing low-potential-energy gradient optimization calculation by using the field to obtain a shaft space topological coordinate avoiding a high-potential-energy area so as to reduce the well wall instability risk; meanwhile, fracture extension simulation is cut off through an equipotential surface threshold value in the field, and hydraulic fracture geometrical morphology parameters matched with formation energy constraint are accurately determined; and finally, integrating the parameters to generate an optimized three-dimensional well pattern seam network design scheme, and realizing efficient and safe fracturing design based on the rock mass accumulated deformation energy distribution characteristics.

The application discloses a kind of spatiotemporal modeling ground surface temperature downscaling method and system considering energy constraint, comprising: 1) acquisition and preprocessing of multi-source remote sensing and meteorological data;2) feature grouping and spatiotemporal feature tensor construction;3) time feature and spatial feature extraction;4) spatiotemporal feature modeling and high-resolution ground surface temperature estimation;5) loss construction and cross-scale consistency constraint;6) model iterative training and result output.The application breaks through the modeling limitation of time variation law and spatial detail description in the prior art, realizes the collaborative promotion of time continuity and spatial fine expression of ground surface temperature.On this basis, the overall temperature deviation and the problem of insufficient physical rationality commonly existing in the prior art are also effectively avoided, and the application is significantly superior to the prior art in terms of timing stability, spatial accuracy and physical reliability.

The invention discloses an energy storage power station capacity optimization method for customizing a package based on scene differentiation. The method comprises the following steps: firstly, extracting multi-scene lease demand features: through historical lease data acquisition and analysis, determining key features of different lease scenes, establishing a scene feature database, clustering different scenes, and designing packages adapted to different scenes; and constructing an energy storage battery life loss quantification model based on the state of charge and the deep charge-discharge coefficient. Establishing a capacity optimization model by taking maximization of the total benefit of the energy storage power station as a target and comprehensively considering an energy constraint, a power constraint, a physical limitation constraint and a package selection constraint; and finally optimizing the capacity of the energy storage power station through model solution. The model provided by the invention fully considers differentiated demands of different lease scenes, realizes flexible distribution of the energy storage capacity by dynamically adjusting the coefficient, can meet diversified lease demands, and improves the utilization rate of the energy storage power station.

The invention discloses an energy constraint tracking-oriented intelligent sensor network dynamic optimization deployment method and system, and relates to the technical field of sensor network dynamic optimization deployment. The method is technically characterized by comprising the following steps: acquiring a target initial position; performing real-time prediction on a target action track by using an unscented Kalman filtering algorithm; and according to the predicted target action trajectory, performing path planning on sensor nodes of a tracking target by using a reinforcement learning model based on a depth deterministic strategy gradient, namely, performing dynamic optimization deployment. According to the method, the optimal scheduling strategy is autonomously learned in the continuous high-dimensional space through the reinforcement learning model based on the depth deterministic strategy gradient, the track is predicted in combination with the unscented Kalman filtering algorithm, the nonlinear dynamic environment is effectively coped with, and dependence on complex manual rules is avoided.

The invention relates to a method and system for predicting the service life of a filling body based on energy constraint fusion deep learning, and belongs to the technical field of health monitoring and service life prediction of a filling body structure, and the method comprises the steps: collecting a transient elastic wave signal of a filling body sample; performing multi-stage data cleaning processing on the transient elastic wave signal; on the basis of the cleaned data, utilizing a physical damage evolution model based on accumulated energy normalization to calculate damage stage characteristics; the multi-scale feature construction module is used for constructing multi-scale features based on the cleaned data; the original waveform sequence features, the multi-scale features and the damage stage features in the sliding window are input into a Transform deep neural network model based on multi-source information fusion, model training is carried out, and a prediction model is obtained; and utilizing the prediction model to predict the residual life of the filling body. The method can accurately predict the residual service life of the filling body structure in real time, and has important theoretical significance and practical application value.

The invention belongs to the field of structure optimization design, and particularly discloses a topological optimization method and system for accurately and efficiently regulating and controlling the deformation characteristic of a structure, and the method comprises the steps: carrying out the finite element mesh generation of a to-be-optimized structure with the spring deformation characteristic, and defining a design variable corresponding to a mesh; determining the total strain energy constraint of the structure according to the stiffness coefficient and the maximum deformation of the spring, and establishing a first topological optimization model by taking the minimization of the total volume of the structure as a target; performing topological optimization based on the first topological optimization model to obtain a structural configuration and a total volume thereof; setting an upper limit of a structure total volume constraint according to the total volume, constructing the structure total volume constraint, and meanwhile, continuously using the structure total strain energy constraint of the first topological optimization model to establish a second topological optimization model by taking a p norm of minimizing an external force local deformation error as a target; and carrying out topological optimization based on the second topological optimization model to obtain a final structure configuration. The system is high in universality, diversified in function, accurate and efficient.

The invention discloses an execution uncertainty-oriented air edge computing resource allocation method, device and equipment and a storage medium, and relates to the technical field of air edge computing, and the method comprises the steps: obtaining user number information, edge server task information, bit length information, time delay constraint information and energy constraint information; determining a target system resource allocation probability based on the user number information, the edge server task information, the bit length information, the time delay constraint information and the energy constraint information; and controlling a system to carry out resource allocation based on the target system resource allocation probability, and completing execution uncertainty-oriented air edge computing resource allocation. According to the method and the device, the uncertainty factors in the dynamic environment are quantified into probability constraints, calculation is performed through the optimal resource allocation strategies corresponding to different scenes, and the resource allocation probability of the target system is obtained, so that the system performs resource allocation in a self-adaptive manner, and the utilization efficiency of the system resources is maximized.

This invention relates to an intraday MPC scheduling optimization method for combined cooling, heating, and power (CCHP) microgrid systems. The purpose of this invention is to overcome the shortcomings of existing technologies and propose an intraday MPC scheduling optimization method for CCHP microgrid systems. The technical solution of this invention provides an intraday MPC scheduling optimization method for CCHP microgrid systems, comprising: S1, constructing transfer function models for each device in the microgrid system; S2, constructing a custom constraint set based on the energy constraints of each device; the energy constraints include capacity; S3, using the sum of the standard cost function and the custom cost function as the final optimization objective function; S4, setting appropriate time intervals, prediction time domains, and control time domains, and employing a sequential quadratic programming optimization algorithm to achieve MPC optimized scheduling calculations. This invention is applicable to the field of energy dispatching technology.

This invention proposes a method and system for optimizing the throughput of wireless power supply networks, comprising: constructing a wireless power supply communication system model with energy and delay constraints; constructing a throughput maximization problem P0; S3, based on the throughput maximization problem P0, constructing a charging energy maximization sub-problem P1 under constraints of total energy and energy collection duration, and solving it under two cases: low and high total available energy of the wireless power supply base station; based on the throughput maximization problem P0, constructing and solving a throughput maximization sub-problem P2 for energy collection users with a given energy collection strategy and wireless communication duration as the variable; and transforming the throughput maximization problem P0 into a single-variable optimization problem P3 based on the characteristics of the optimal wireless energy transmission strategy and the optimal wireless communication duration strategy, and solving it. This invention improves the actual throughput performance of wireless power supply communication networks and meets the high-efficiency communication requirements in complex dynamic network environments.

Methods and apparatuses for energy policy reinforcement during energy conservation tasks. A method for managing service quality of one or more applications includes receiving application energy tolerance information for an application and receiving energy consumption information associated with the application. The application energy tolerance information includes at least one tolerance value indicating an amount of service quality degradation permitted for the application under one or more energy constraints and at least one service quality level at which the tolerance value is to be applied. The method further includes determining that at least one of the one or more energy constraints is satisfied based on the energy consumption information and the application energy tolerance information, determining, based on the application energy tolerance information, degraded quality of service (QoS) configurations for the application at the at least one service quality level, and enforcing the degraded QoS configurations for the application.

The present application relates to a kind of distributed flexible resource aggregation scheduling method, equipment and medium, comprising: according to the steady-state operating characteristics of the basic operating data of each flexible resource and the probability distribution of uncertainty parameter, construct the robust operating external characteristic model of each flexible resource;Using the multiple standard robust component models corresponding to each flexible resource, the robust feasible region of each flexible resource under power constraint, energy constraint and uncertainty influence is decomposed into multiple standard robust feasible region clusters;After the multiple standard robust feasible region clusters of each flexible resource are aggregated respectively, upload to cloud end;Cloud end generates scheduling instruction based on aggregation result and issues to aggregator, and aggregator distributes scheduling instruction according to the feasible region of each flexible resource under power constraint and energy constraint based on scheduling instruction.Compared with prior art, the present application realizes the efficient robust response of multiple heterogeneous resources in power system scheduling.

The invention discloses a multi-robot task balanced distribution method under energy constraint based on segmented anchoring, which comprises the following steps of: firstly, distinguishing robot separation by marking path information with 0, 1 and-1, returning when the load is full or the electric quantity is insufficient, and generating an initial solution by hybrid coding of task number and completion proportion recorded by split information, and performing initial repair; in iteration, conventional population evolution is combined with probability to screen non-dominated solutions, and a charging interruption path is optimized through a sequence anchoring and balancing mechanism of segmented optimization, iterative anchoring and residual workload balancing; and finally, finishing time is finely adjusted by a re-balancing mechanism based on proportional splitting, and a non-dominated solution set is output. According to the method, the opportunity of converting passive charging constraint into active optimization is achieved, and the complex problem of multi-robot task allocation under energy limitation is systematically solved.

This invention belongs to the field of structural optimization design, and specifically discloses a topology optimization method and system for accurately and efficiently controlling structural deformation characteristics. The method includes: performing finite element mesh generation on the structure to be optimized, which exhibits spring deformation characteristics, and defining design variables corresponding to the mesh; determining the total strain energy constraint of the structure based on the spring constant and maximum deformation, and establishing a first topology optimization model with the goal of minimizing the total structural volume; performing topology optimization based on the first topology optimization model to obtain the structural configuration and its total volume; setting an upper limit for the total structural volume constraint based on this total volume to construct the total structural volume constraint, while simultaneously using the total strain energy constraint of the first topology optimization model, and establishing a second topology optimization model with the goal of minimizing the p-norm of the local deformation error under external force; and performing topology optimization based on the second topology optimization model to obtain the final structural configuration. This invention is highly versatile, functionally diverse, and precise and efficient.

This invention relates to an intelligent scheduling method and system for new energy logistics fleets that coordinates dynamic task allocation and energy replenishment. The invention constructs a multi-dimensional disturbance vector by collecting parameters such as the time window offset, spatial dispersion, load mutation rate, and emergency level transition of new tasks. Within a sliding time window, Shannon entropy is used to quantitatively characterize the uncertainty of the task flow, distinguishing between high-disturbance and steady-state regions. The invention adaptively adjusts the energy constraint set based on the task uncertainty state, dynamically relaxing the lower energy limit in high-entropy regions and compensating for the impact of battery aging and degradation in low-entropy regions. It achieves globally optimal vehicle-task-energy ternary matching under the constraints of path continuity and task uniqueness using mixed-integer linear programming, and realizes online adaptive correction of the entropy judgment threshold based on actual execution feedback. This invention improves the responsiveness and energy adaptability to complex dynamic scenarios, effectively reduces the impact of uncertainty, and optimizes operational efficiency.