213 results about "Global energy" patented technology

The invention discloses a mining equipment energy consumption prediction method, and relates to the technical field of mining equipment energy consumption prediction.The mining equipment energy consumption prediction method comprises the steps that through multi-dimensional data collection, operation, process and environment parameters are collected through a sensor cluster; noise reduction is carried out through a generative adversarial network in combination with empirical mode decomposition, and data is restored through a space-time interpolation network; identifying working conditions and extracting features by means of a hidden Markov model in combination with an attention mechanism; a cross-device transfer learning framework is constructed, and a cloud training general model is combined with local data fine tuning; the edge end deploys a lightweight model for real-time prediction, and the cloud end generates a global energy-saving strategy; through digital twinborn visualization, a model and a strategy are automatically corrected based on SHAP value analysis. The equipment idling rate is reduced; the unit energy consumption of the crushing link is reduced; the abnormal response time is shortened; and the prediction precision and the system adaptability are remarkably improved.

The invention discloses a ship power system optimization control method and system based on a simulated annealing algorithm, and relates to the technical field of ship power control, and the method comprises the steps: obtaining operation parameters and historical energy consumption data in real time, and constructing a multi-objective optimization function of dynamic weight distribution; generating an initial temperature parameter and a solution set in combination with the navigation state and the environment data; a neighborhood search strategy disturbance solution set is improved, a new solution is evaluated by using a dynamic acceptance probability function, temperature parameters are adaptively adjusted for iterative optimization, and an optimal control parameter combination is output; and an adjustment instruction set is generated after multi-dimensional efficiency verification, and a propulsion device, a generator set and an energy storage module are cooperatively controlled, so that global energy consumption optimization is realized. According to the method, through data driving and intelligent algorithm fusion, energy efficiency and environmental adaptability are improved, and stable operation under complex working conditions is guaranteed. According to the ship power system optimization control method and system based on the simulated annealing algorithm, the energy efficiency level and the environmental adaptability of the ship power system are improved.

The invention discloses a mining area methane emission inversion method based on satellite observation and a Gaussian plume model. The method comprises the following steps: S1, acquiring a satellite inversion XCH4 data set, meteorological data, emission list data and global energy monitoring data; s2, determining a methane emission source and a research area in a geographic map by using the emission list data and the global energy monitoring data; s3, obtaining methane background concentration and XCH4 enhancement in the geographic map by using an atmospheric background concentration calculation method; s4, combining XCH4 enhanced spatial distribution and meteorological data in a geographic map to identify a methane enhanced plume of a methane emission source in the research area; and S5, performing Gaussian plume flow field simulation inversion of the methane emission source through the Gaussian plume model, and outputting the methane emission rate of the methane emission source at the current time through inversion of the Gaussian plume model. According to the method, the methane emission and the time sequence data of the methane emission can be obtained through accurate inversion, the frequency of obtaining the methane emission is improved, and the methane emission can be obtained according to time scale accumulation.

The invention relates to the technical field of automobile power system control, in particular to an automobile start-stop system management method based on intelligent control, which comprises the following steps: S1, a multi-mode environment sensing system; s2, a deep reinforcement learning decision framework; s3, hierarchical cooperative control execution; s4, a real-time health assessment system; s5, a cloud collaborative optimization mechanism; through intelligent start-stop strategy optimization and energy recovery management, under the urban congestion working condition, compared with a traditional start-stop system, the method can further reduce oil consumption by 15-20%; and under the comprehensive working condition, the oil consumption can be reduced by 8-12%. The fuel consumption cost of the vehicle is reduced, the development trend of global energy conservation and emission reduction is also met, and important economic and environmental significance is achieved.

The invention relates to the technical field of data processing, in particular to a digital twinning-based comprehensive energy efficiency analysis and judgment system for a high-speed rail station building, and the system comprises a multi-dimensional perception acquisition module which outputs an environment tensor, an equipment vector and a people flow matrix which are synchronous in time; the dynamic coupling modeling module generates an equipment coupling characteristic impedance coefficient matrix; the optimization algorithm knowledge base module dynamically activates a multi-target genetic algorithm, a particle swarm optimization algorithm and a model prediction control or reinforcement learning algorithm according to the passenger flow growth rate and temperature rise rate characteristic indexes, and outputs an optimization parameter set to the four-dimensional parallel simulation module; the simulation module generates an equipment state parameter combination and an energy efficiency index; the energy efficiency collaborative evaluation module generates a global energy efficiency evaluation report through Pareto frontier analysis and Markov decision; the energy efficiency report generation module outputs a visual chart and equipment maintenance suggestions, and the energy efficiency and state double-closed-loop module triggers model correction based on pipe network pressure difference, temperature uniformity and energy efficiency ratio deviation.

The invention discloses a 5G base station micro-photovoltaic multi-source data dynamic charging method and system, and belongs to the technical field of 5G base station charging. Multi-source data (including a base station operation state, weather forecast and a regional power consumption task) are integrated, an LSTM and XGBoost fused fault prediction model, time convolutional network power consumption demand prediction and a multi-target optimization model (optimizing power grid power purchase cost, energy storage loss and photovoltaic utilization rate) are adopted, a charging strategy is dynamically generated in combination with a cloud, and the power consumption of the power grid is optimized. And the strategy is adjusted in real time based on the fault prediction result. According to the method, the optimized strategy is executed by remotely controlling the micro-photovoltaic equipment, global energy scheduling driven by multi-dimensional data is realized, the photovoltaic utilization rate is remarkably improved, meanwhile, through a fault feedforward mechanism and dynamic weight adjustment, the operation cost is reduced on the basis of guaranteeing the power supply stability, and the power supply efficiency is improved. The problems of data island, response lag, fault vulnerability and the like in a traditional scheme are solved.

The invention relates to the technical field of sewage treatment process control, in particular to a sewage treatment process control system and device based on a multi-scale time sequence diagram neural network, and the system comprises an energy consumption digital twin modeling module which constructs a whole-process equipment energy consumption and process state twin mapping model; the energy consumption trend prediction module captures three-level energy consumption association by using a multi-scale time sequence diagram neural network based on the model data to obtain a prediction result; the multi-objective optimization module constructs an optimization function containing total energy consumption and the like according to the total energy consumption and the like to obtain a balance strategy; the process self-adaptive regulation and control module adjusts process parameters and data updating network according to the inlet water quality and the optimization target; and the energy scheduling optimization module constructs a model containing peak and valley electricity price perception and outputs a scheduling result. By designing a multi-objective optimization framework and combining water quality adaptive adjustment process parameters and a peak-valley electricity price sensing mechanism, global energy consumption collaborative optimization of the sewage treatment plant can be realized, the energy utilization efficiency can be improved, the operation cost can be reduced, carbon emission can be reduced, and efficient green treatment can be realized.

The invention discloses a micro-grid energy coordination control method considering communication topology dynamic change, which belongs to the technical field of electric energy control and management, and comprises the following steps: monitoring a communication connection state between control nodes, and generating a topology change event; evaluating energy supply capability and communication reliability and adjusting influence in energy coordination to generate adaptive weights; in combination with real-time sensor information and historical operation data, short-term energy prediction data is generated, in combination with adaptive weight, a global energy distribution consensus is achieved, and then an energy distribution decision is generated; in the execution process, the topology change event and the deviation between the short-term energy prediction data and the real-time energy data are monitored, and if a deviation threshold value is exceeded, an event signal is triggered to drive the control strategy to be updated. According to the method, the adaptive weight is generated by monitoring the communication topology and the energy state, and distributed negotiation and event triggering updating are performed in combination with short-term prediction, so that the energy coordination robustness and the operation stability of the micro-grid in a dynamic environment are improved.

The invention provides an electronic component intelligent production control method and system, electronic equipment and a storage medium, and relates to the technical field of electronic component intelligent manufacturing, and the method comprises the steps: synchronously collecting second-level power consumption data, temperature field distribution information and environment temperature and humidity data of a chip mounter, reflow soldering equipment and optical detection equipment; and fusing to generate multi-source time sequence stream data. And generating thermal imaging data based on temperature field distribution, identifying an abnormal power consumption region, extracting cross-equipment cycle correlation characteristics through expansion convolution operation in combination with multi-source time sequence flow data, and predicting future energy efficiency state change points. And decomposing the global energy efficiency target into chip mounter start-stop time sequence constraint, reflow soldering power parameter constraint and optical detection sampling frequency constraint by using a time sequence target cascading algorithm to realize multi-device coordinated regulation and control. According to the invention, the energy efficiency management level and the product yield stability of the electronic component production line are improved.

The invention relates to the technical field of three-dimensional point cloud segmentation and terrain feature extraction, and discloses a step terrain segmentation method for enhancing L0 gradient minimization, which comprises the following steps of: calculating point cloud features based on iterative principal component analysis: constructing a dynamic weight distribution mechanism by introducing a robust M estimator; direction self-adaptive neighborhood optimization is carried out on the slope and panel transition area of the original point cloud, the continuity of the normal vector of the slope area is enhanced, and the geometric difference of the transition area is highlighted; multi-scale super voxel segmentation: adopting a normalized spatial metric segmentation method, generating super voxel units by using a distance, normal vector and perpendicularity weighted normalization mechanism, performing planar and non-planar classification on super voxels in combination with a multi-scale strategy, and performing recursive segmentation on non-planar super voxels at a smaller resolution; performing curved surface segmentation based on L0 gradient minimization and global energy optimization; according to the method, the definition of the segmentation boundary and the completeness of slope structure extraction can be remarkably improved, and the slope and the disc table can be accurately distinguished by using the vertical characteristics.

The invention relates to the technical field of hybrid power buses, and discloses a global energy-based management system and control method for a hybrid power bus, and the system comprises a cloud global energy management system and a vehicle execution control system, and the cloud global energy management system and the vehicle execution control system carry out bidirectional communication through a communication protocol. The cloud global energy management system comprises a multi-source data acquisition and fusion module, a global SOC track optimization module and an energy distribution strategy generation module; the vehicle execution control system comprises a sensing layer, a decision-making layer and an execution layer. The SOC track and power distribution are globally optimized through the cloud, so that the time proportion of the engine working in the efficient interval is increased by 15%-20%, and the comprehensive fuel saving rate can reach 8%-12%.

The invention discloses a regenerative braking energy recovery method combining dynamic planning and model predictive control, which relates to the technical field of vehicle energy conservation, and comprises the following steps: establishing a dynamic model of a vehicle braking process, performing global energy recovery optimization based on dynamic planning, generating a global optimal reference trajectory, and adopting model predictive control rolling optimization. The reference trajectory is tracked to maximize energy recovery efficiency. According to the method, global optimum is guaranteed through dynamic planning, and meanwhile, the energy recovery efficiency of the regenerative braking process is improved by utilizing model predictive control.

The invention discloses an unmanned aerial vehicle adaptive energy efficiency prediction and optimization management method and system and a medium, and the method comprises the steps: constructing a physical-data dual-drive AI prediction model according to a fusion electrochemical model or an empirical formula as a physical constraint before a task starts; bMS real-time data, historical flight logs and charging and discharging records serve as input of an AI prediction model, the current battery state is obtained according to the input, and an initial flight path with the optimal global energy consumption is initially predicted based on the current battery state and the environment situation; in the flight process, the current battery state and environment situation are continuously updated, the intelligent task and flight control module continuously monitors multiple trigger states, and when one trigger state meets a preset dynamic adjustment trigger condition, a dynamic adjustment decision is executed, and an optimization instruction is generated and issued to a flight controller for execution; according to the management method, through global and closed-loop energy efficiency optimization, unnecessary energy consumption is effectively reduced, and the cruising ability is remarkably improved.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle energy consumption optimization path planning method considering a wind speed gradient, and the method comprises the following steps: S1, building a three-dimensional environment model; s2, establishing an unmanned aerial vehicle energy consumption model; s3, searching a path with optimal energy consumption in the three-dimensional grid map in a mode of iteratively expanding a current node from a starting point; s4, when the current node is expanded, calculating a local energy consumption gradient formed when the current node moves towards a plurality of preset candidate directions based on the energy consumption model; and S5, according to the calculated local energy consumption gradient, screening from the plurality of candidate directions to generate a self-adaptive neighborhood set for the expansion. According to the method, the wind speed gradient energy consumption model related to the height is established, and the self-adaptive neighborhood search is carried out in combination with the local energy consumption gradient, so that a flight path with better global energy consumption in a complex wind field can be planned, and the actual energy-saving effect of the path is improved.

The invention discloses a distributed cooperative control method and system for cluster temperature controllers, and relates to the technical field of temperature controller control. Comprising the following steps: establishing a temperature control consensus: establishing a temperature controller alliance chain, and adopting a practical Byzantine fault-tolerant algorithm to achieve a temperature control target consensus; sharing a prediction model: based on a federated learning architecture, each node locally trains a temperature prediction model and uploads a model parameter gradient to a cloud server for aggregation updating; global energy consumption optimization: adopting a multi-agent depth deterministic strategy gradient algorithm to carry out minimization optimization on the global energy consumption; and dynamic topology recombination: supporting plug and play of the equipment and automatically recombining the cluster topology according to the state of the equipment. According to the method, the information island problem is solved, sharing and privacy protection of the temperature prediction model are realized, global energy consumption is optimized, cluster temperature control consistency can be improved, energy utilization efficiency can be optimized, user comfort experience can be improved, hundred-level equipment expansion is supported, and complex scene requirements are met.

The invention relates to the technical field of heat pump drying control, and discloses a dynamic identification control method of a closed heat pump drying system based on waste heat recovery. The method comprises the following steps: establishing a multi-level parameter monitoring network by arranging a sensor array, and continuously collecting system operation parameters to form time sequence data; constructing a waste heat distribution characteristic spectrum, and mapping the collected parameters to a thermodynamic state space to identify a coupling relationship among the parameters; analyzing a heat transfer path to determine a key control variable, and establishing a dynamic weight adjustment mechanism to generate a parameter priority sequence; and a multi-target coordination control strategy is made based on the sequence and the real-time operation state, and an actuator control instruction is output. According to the method, through global energy state recognition and dynamic priority adjustment, adaptive optimization operation of the system under different working conditions is realized, and the energy utilization efficiency and the control quality are improved.

The invention discloses an equipment control method for optimizing the energy consumption of a servo module, and the method comprises the steps: obtaining multi-dimensional real-time state parameters of the servo module, carrying out the loss characteristic decomposition, and obtaining a loss characteristic parameter set; performing dynamic allocation and directional tuning on a power channel of the servo module based on the loss characteristic parameter set to obtain an energy consumption regulation and control instruction set; brake energy characteristics of the servo module are obtained, electromechanical energy recovery is carried out in combination with the loss characteristic parameter set, and energy consumption recovery control parameters are obtained; and performing closed-loop optimization control based on the energy consumption regulation and control instruction set and the energy consumption recovery control parameters to obtain a global energy consumption optimization parameter set. According to the method, global energy efficiency optimization of the full work cycle of the servo module can be achieved, and the defect of an existing control method in the aspect of overall optimization capability is overcome.

The invention discloses a dual-scale resource optimization method for a multi-unmanned aerial vehicle auxiliary edge computing system. The method comprises the following steps: establishing a dynamic mobile edge computing system model comprising multiple unmanned aerial vehicles and ground mobile users; establishing a joint optimization problem of task unloading, computing resource allocation and unmanned aerial vehicle trajectory planning by taking minimization of total energy consumption of an unmanned aerial vehicle system as an optimization target; based on a designed double-time-scale layered optimization framework, aiming at a joint optimization problem, on a small time scale, an improved clustering algorithm and a closed solution method are adopted, and a real-time optimal task unloading decision and a computing resource allocation strategy are efficiently obtained; and on a large time scale, a near-end strategy optimization algorithm in deep reinforcement learning is used to carry out autonomous learning optimization, and an optimized unmanned aerial vehicle flight path is obtained. The invention provides a resource optimization method which can give consideration to dynamic adaptability, multi-time scale collaboration, global energy efficiency optimality and low calculation complexity.

The invention relates to the technical field of transformer substation operation environment regulation and control, and discloses a transformer substation operation environment low-carbon energy-saving intelligent regulation and control method, system and device and a medium. The method comprises the steps that transformer substation operation environment parameters and device state signals are collected, and a first environment device coupling dynamic representation model is constructed; and in combination with the time sequence prediction and condensation risk assessment sub-model, the nonlinear response relationship of various factors to the heat and humidity distribution and condensation risk of the cabinet is described, and a foundation is laid for accurate regulation and control. And in collaborative optimization, the second multi-target collaborative optimization regulation and control model comprises an upper-layer global energy consumption optimization and lower-layer local environment precise control target function, so that the overall energy consumption is reduced. And low-carbon operation targets and equipment safety constraints are considered, so that low-carbon energy conservation is realized during safe operation of the equipment. The solving strategy is a first improved solving strategy based on model predictive control and particle swarm optimization improvement, the efficiency and precision of processing the complex multi-target collaborative optimization regulation and control problem are improved, and the optimal regulation and control scheme is found quickly and accurately.

The invention provides a GRPO-RL driven data center multi-area cooling system group control method, which comprises the following steps of S1, implementing environment arrangement at inlets of machine room air conditioning units, a hot channel and a cold channel in each area of a controlled data center, and collecting multi-dimensional operation data required by air conditioning system group control and reinforcement learning; s2, data center cold source system strategy optimization modeling is carried out, and the control problem of each air conditioning system of a data center is approximately modeled as a Markov decision process; and S3, performing reinforcement learning control strategy online learning in combination with a group relative strategy optimization algorithm to obtain optimal energy-saving and environment control effects. According to the invention, the generalization ability and stability of the control strategy in the multi-region data center control can be improved, the cooperative control of the global energy conservation of the large data center and the comfort of each region can be realized, and the memory and computing resource occupation in the RL training process can be obviously reduced through the optimization of the multi-region sharing strategy.

The invention discloses an intelligent energy management control method, device and equipment for an electric tractor and a medium thereof, and relates to the technical field of electric drive control in agricultural machinery and vehicle engineering. Planning a global energy demand based on a predicted load; dynamically optimizing control and distribution in real time; executing and coordinating control; by constructing a dynamic closed-loop control framework integrating feedforward prediction and real-time feedback, energy management of the electric tractor can be passively responded in real time, and through multi-source information fusion and intelligent prediction, the prediction result is creatively used as feedforward input in a real-time control layer and is combined with current running state feedback, so that the real-time control of the electric tractor is realized. The control system can smooth instantaneous power impact caused by soil mutation and topographic relief, and the service life of a core power component is remarkably prolonged; in addition, through the combination of pre-matching and dynamic fine adjustment, the operation endurance mileage of the whole vehicle and the economy of the whole life cycle are improved.

The invention discloses a team diesel locomotive fuel consumption real-time monitoring method based on multi-factor coupling, and belongs to the technical field of locomotive fuel consumption real-time monitoring. The team diesel locomotive fuel consumption real-time monitoring method comprises the steps that according to locomotive global sensing requirements, original data are collected through a multi-modal sensing network, space-time alignment and anomaly detection are carried out through an edge calculation unit, and a locomotive fuel consumption real-time monitoring result is obtained; according to the method, the asymmetric game model is constructed, the strategy interference intensity between the locomotives is brought into a revenue function, the asymmetric game model quantifies the strategy interference of the locomotive i to the locomotive j, dynamic game solving of the multi-locomotive collaborative strategy is achieved, the game revenue function is further combined with multi-objective optimization through Pareto optimal search, and the multi-objective optimization of the multi-locomotive collaborative strategy is achieved. The optimal solution is screened through weighting and strategy comprehensive scores, the one-sidedness of single-target optimization is avoided, the stability of locomotive operation and individual fairness are considered while the lowest global energy consumption is guaranteed, meanwhile, the dynamic updating mechanism of the game model adapts to real-time working condition changes, and the system flexibility is improved.

The invention belongs to the field of metallurgical industry automation, and relates to an iron and steel joint enterprise energy management system scheduling method based on Lyapunov optimization and deep reinforcement learning. In order to solve the problems that an existing system depends on static rules or experience, is difficult to adapt to dynamic changes, is low in energy scheduling efficiency and the like, a multi-dimensional state space covering energy storage, a virtual queue and environment parameters is constructed, and a scheduling action space including capacity adjustment, multi-energy medium charging and discharging control and the like is defined; a Lyapunov drift function is introduced as a stability constraint, a reinforcement learning agent of an Actor-Critic framework is constructed, and global energy collaborative optimization is realized in combination with an energy sub-optimization problem solving and parallel sampling mechanism. The method can give consideration to scheduling economy and system stability, has dynamic adaptive ability, ensures project feasibility of the scheme, can improve energy utilization efficiency, reduces cost and carbon emission, and is suitable for iron and steel enterprises of different scales.

The invention relates to an intelligent construction system and method for an energy consumption model based on artificial intelligence, in particular to the field of artificial intelligence. Expert operation experience is extracted from historical smelting data to form a safety benchmark, a dynamic virtual environment capable of simulating complex working conditions is constructed, and on the basis, a multi-agent cooperative training mechanism is utilized to construct an energy consumption model. According to the method, a matching strategy among control units is explored and optimized in a virtual environment to seek an optimal solution of global energy consumption and cost, finally, a maturely trained control strategy is applied to an actual smelting process, and through online monitoring and an active learning mechanism, the method continuously adapts to changes of a production environment and absorbs new excellent operation practices, so that the actual smelting process is optimized. Therefore, continuous, safe and intelligent control and reduction of energy consumption in the titanium alloy smelting process are achieved.

A relay for a beam of wireless power and a satellite with the relay are disclosed. The relay includes: an array of coaxial waveguide elements, each element including: an input polarizing section, an output polarizing section, and a phase shifting section located between said input and output polarizing sections, wherein said input polarizing section, said output polarizing section, and said phase shifting section are controllably rotatable around a longitudinal axis of said coaxial waveguide; and a processor to control rotation of said input polarizing section, said output polarizing section, and said phase shifting section.

The invention relates to the technical field of power supply, in particular to a temperature difference self-powered system and a control method thereof, and the method comprises the steps: collecting the real-time operation data of the system, carrying out the feature extraction, obtaining a feature tensor, inputting the feature tensor into a trained classification model, recognizing the current energy state, and carrying out the dynamic adjustment of the energy operation strategy of the system. The control method of the temperature difference self-energized system is based on a technical path of'perception-analysis-decision-optimization ', fully considers cooperation of multi-dimensional parameters such as thermal management and energy storage state of the system in an extreme environment, and can accurately judge the energy state stage of the system; and corresponding energy operation strategies are adaptively executed according to the energy state stages, so that autonomous diagnosis and optimization of global energy are realized, the energy utilization efficiency is remarkably improved, and the problems that an existing temperature difference self-energy-supply system lacks environment self-adaption capability, the operation strategies cannot be automatically adjusted according to dynamic changes of an extreme environment, and the energy utilization efficiency is low are solved. And the energy utilization efficiency is low.

The invention discloses a global energy efficiency optimization design method for a medium-temperature cold water central air-conditioning system, which can effectively reduce the modeling workload of a large building on a tail end by considering the form of tail end equipment and the sensible heat load and latent heat load borne by each tail end equipment, and meanwhile, ensures that the tail end operation meets the room heat comfort requirement. The genetic algorithm is adopted to optimize key equipment configuration parameters and system operation parameters of the air conditioning system, the influence of annual load fluctuation of the air conditioning system on system operation energy consumption is considered, and an optimal design scheme with the optimal annual comprehensive energy efficiency is obtained in the system design stage. And the overall operation energy efficiency of the air conditioner is higher while the tail end heat comfort is met, and the system is suitable for a medium-temperature cold water central air-conditioning system.

Along with global energy transformation, a grid-connected micro-grid serves as a key carrier for distributed energy access, and operation faces complex working conditions. A system is often interfered by colored noise caused by an intermittent power supply and an electromagnetic environment, and meanwhile, information loss is caused by communication aging, acquisition faults and the like, so that challenges are brought to modeling and parameter identification. According to the method, a grid-connected micro-grid Volterra-Wiener model of a static nonlinear block based on a Volterra sequence is taken as an object, and a two-stage expansion-minimization iterative estimation algorithm based on filtering is provided. Firstly, data filtering is carried out on collected input and output data to suppress colored noise, then a hierarchical principle is utilized to decompose a filtered system into two subsystems, and efficient and high-precision identification of model parameters under missing output data is realized through a proposed algorithm. The invention aims to improve the parameter identification precision of the micro-grid system with noise and incomplete data.

This application relates to the field of energy dispatching, and in particular to a method and system for distributed energy storage dispatching based on multi-energy collaboration. The method includes: acquiring a set of factory order information; based on this, extracting and fusing multi-dimensional production value features to generate a dynamic production value assessment information set; based on this, combined with preset energy cost constraints, calculating dynamic electricity value thresholds and making global energy dispatching strategy decisions to generate an energy dispatching strategy information set; acquiring a set of energy supply influencing factors; based on the energy dispatching strategy information set and the energy supply influencing factors set, performing multi-timescale rolling optimization and global preset energy allocation to generate an energy allocation execution instruction set; based on this, issuing and executing dispatching instructions and dynamic production compensation for production lines and energy storage layers, generating and outputting a multi-energy collaborative dispatching report. This application can improve the economic efficiency of factories during energy dispatching.

This invention relates to a multi-constraint trajectory planning method for bouncing movement on the surface of complex small celestial bodies, belonging to the field of deep space exploration. The implementation method is as follows: Establishing a closed-form relationship between bouncing movement distance, velocity, takeoff angle, and energy consumption, and a two-layer expansion radius obstacle representation model; establishing escape velocity constraints, no-fall constraints, fly-through feasibility constraints, and convex obstacle fly-through constraints; using the two-layer expansion radius obstacle representation model and multiple constraints, establishing a fly-through feasibility determination mechanism for obstacles; using Poisson-disk sampling to establish the bouncing reachability domain and an analytical solution for the energy-optimal trajectory satisfying multiple constraints; establishing a reachability domain filtering method based on the bouncing reachability domain and the analytical solution for the energy-optimal trajectory; using energy consumption as the weight, employing the A* algorithm to search for a global energy-optimal bouncing path point sequence and the corresponding velocity and takeoff angle of each path point, generating a global energy-optimal trajectory, thus realizing multi-constraint trajectory planning for bouncing movement on the surface of complex small celestial bodies.