1320 results about "Total energy" patented technology

This application discloses a method, system, and related equipment for intelligent energy management of new energy vehicles. Based on the vehicle's starting and ending points, at least one candidate energy-saving path is determined. The predicted energy consumption of the vehicle along at least one candidate energy-saving path is lower than that of other paths. The total energy consumption is predicted based on road condition information and energy consumption impact information for each path. In response to the selection of at least one candidate energy-saving path, a preset travel route is determined. The preset travel route includes multiple road segments, and the total energy consumption includes the energy consumption of each road segment. With the goal of minimizing fuel consumption along the preset travel route, the engine's operating state is controlled based on the initial state of charge (SOC) of the power battery in each road segment, the energy consumption of the road segment, and the vehicle's actual overall demand, ensuring the engine operates within its high-efficiency range. Using this application can reduce fuel consumption for users and improve the driving experience.

The invention provides an inter-fleet dynamic electric quantity balance and collaborative replenishment system, which relates to the technical field of shipping transportation, and comprises an intelligent scheduling module used for obtaining a data set containing electric power conditions, navigation parameters, sea condition influences and task progress requirements of each ship based on ship motion states, ship equipment is monitored in real time to obtain data and analyze environmental influence to obtain adjustment parameters, then the total energy consumption contribution of a single ship is calculated, the total loss of a fleet is quantified to fuse to obtain the initial amount of total energy consumption and correct to obtain the navigation efficiency, and then a power output adjustment strategy is formulated for a main propulsion ship according to the navigation efficiency and an electric power balance target. And finally, a collaborative supply path is planned by taking the low-power ship as a core and combining multi-party information. According to the invention, efficient coordination of deployment and supply of the fleet on power management is realized, the transportation efficiency is improved, the cost is reduced, and the navigation safety is enhanced.

The invention relates to a neuromuscular electrical stimulation multi-objective self-tuning optimization method, which comprises the following steps: dynamically establishing a framework for muscle response in a time window sliding modeling mode, and describing short-term excitability change and fatigue accumulation by the framework according to historical stimulation frequency density and total energy integral accumulation variable; introducing a short-time response curve recognition mechanism: after stimulation, according to micro changes of an electromyogram and a force output curve, judging whether a nerve and muscle coupling state is in a deviation trend or not; when the output trend is evolved towards the target improvement direction, current strategy fine tuning is kept; if the trend is stagnated or reversed, the weight combination or the step length is actively adjusted; constructing a multi-target co-integration matrix for analyzing whether the current response changes of different targets are in a consistent direction; when it is detected that two certain targets have an inverse correlation change, the system carries out conflict decomposition processing once; interference compromising is carried out on conflict indexes, and short-term power down-regulation is carried out; and forming a dynamic strategy framework.

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.

Provided in the embodiments of the present application are an energy storage apparatus, an energy storage system and a charging network. The energy storage apparatus comprises a container, a control module and a thermal management module, wherein the container comprises a container body and a plurality of battery cells, the plurality of battery cells are accommodated in the container body, and at least one of the dimensions of the container in the length direction, the width direction and the height direction is not equal to a corresponding dimension of a standard container; the control module is used for electrically connecting to the plurality of battery cells to perform electrical control over the battery cells; and the thermal management module is connected to the container, the thermal management module is used for managing the temperature of the battery cells, and at least part of the thermal management module is located at the top of the container. When the total energy of a container is fixed, the container can have a smaller volume, such that an energy storage apparatus occupies a smaller floor area, thereby increasing energy per unit floor area of the energy storage apparatus, and thus increasing the areal energy density of the energy storage apparatus.

The invention belongs to the technical field of liquid cooling system control, and discloses a rotating speed prediction and control method for a charging gun liquid cooling system, and the method comprises the steps: obtaining the performance parameters of key parts, including a radiator, a water pump, a fan, a gun line and a liquid storage tank, in the liquid cooling system; inputting the performance parameters into the one-dimensional simulation model to obtain the highest temperature Tcoolant of the cooling liquid under different working conditions; on the basis of the Tcoolants under different working conditions, the relationship between the different working conditions and the Tcoolants is fitted, so that a rotating speed prediction model is obtained; the environment temperature and the gun line resistor heating power under the target working condition are input into the rotating speed prediction model, under the constraint met by Tcoolant, multiple sets of rotating speed combinations meeting the constraint are obtained through prediction, and the rotating speed combination enabling the total energy consumption of the liquid cooling system to be the lowest is selected from the multiple sets of rotating speed combinations to serve as the optimal rotating speed combination of the target working condition; and during charging, the fan rotating speed and the water pump rotating speed are regulated and controlled in real time based on the optimal rotating speed combination. The system has the advantages of advanced adjustment, quick response, energy conservation, high efficiency and the like.

The invention relates to a clustering and entropy-guided reentrant hybrid flow shop scheduling method. The method comprises the following steps: step 1, establishing a problem model; step 2, setting algorithm operation parameters; 3, adopting an initialization strategy to generate an exploration population and a development population; 4, judging whether a first-stage termination condition is met or not, if not, executing a first-stage evolutionary strategy and an updating strategy on the exploration population and the development population, and otherwise, executing the step 5; 5, constructing an elite population; 6, judging whether a second-stage termination condition is met or not, and if not, executing a second-stage evolutionary strategy on the elite population; otherwise, outputting a Pareto solution set; and 7, updating the elite population. According to the method, dynamic balance of global exploration and local development is realized, and the solution distribution can be improved while the solution set convergence is ensured, so that the completion time and the total energy consumption are reduced, the production cost is reduced, and the workshop scheduling efficiency is improved.

The invention discloses a nondestructive testing method for composite material defects, and relates to the technical field of nondestructive testing, and the method comprises the following steps: firstly, collecting a scanning waveform of a test piece A, determining a potential defect area based on an echo amplitude and a time difference, and outputting a sequence containing space coordinates, an original waveform and focusing parameters; an effective time window is intercepted after preprocessing, sub-bands are generated through wavelet packet decomposition, and total energy is calculated and normalized to obtain an energy vector; secondly, based on a known sample, evaluating the separability of sub-bands by using a Fisher criterion, sorting the sub-bands, determining an optimal energy dimension through cross validation, combining sub-band energy features with phase and time difference features into composite vectors, inputting the composite vectors into a dual-channel lightweight deep network, outputting defect categories and confidence coefficients, and mapping the defect categories and confidence coefficients to a C scanning frame image; and finally, backtracking the three-dimensional coordinates, calculating the defect volume and the residual wall thickness, and comparing with a material performance database to output a conclusion. The problem of misjudgment caused by echo waveform similarity is solved, and detection closed-loop optimization is achieved.

The invention discloses an electric vehicle charging power aggregation distributed regulation and control method, and relates to the technical field of AC charging pile charging power regulation and control, and the method specifically comprises the following steps: carrying out the cleaning and stabilization processing of power load and new energy data, employing an ARIMA model to predict the load, and employing an LSTM-Transform model to predict the new energy generation power; the upper layer aggregates the total state of the charging station, including the electricity price, the load, the total vehicle energy, the total docking power of the lower-layer single vehicle model and the battery demand; based on an MCTS enhanced DDPG algorithm, the total charging power is optimized through cost discount rewards and energy penalty; an improved ADMM algorithm distributes total power to each pile, and battery stress and heat loss are minimized; and comparing the actual battery energy with the virtual battery energy, and updating the data and re-deciding when the energy is inconsistent. According to the invention, three industry bottlenecks of poor real-time response of electric vehicle charging, slow large-scale scheduling and difficulty in consideration of economy and battery health are overcome, and power grid dynamic response acceleration, decision-making efficiency jump and comprehensive benefit breakthrough are realized.

The invention discloses a multi-target AGV task allocation optimization method, and the method comprises the steps: obtaining the position information, load information, task information and related constraint conditions of an AGV according to a manufacturing workshop environment and order production demands; performing initialization coding, and generating a task relation matrix, a task data matrix and an AGV state matrix by adopting integer coding for representing a task allocation scheme; constructing a multi-target AGV task allocation optimization model by taking the total transportation distance, the total energy consumption and the load time deviation as optimization targets; solving the multi-objective optimization model by adopting an improved hunting optimization algorithm to obtain an optimal task allocation optimization scheme; according to the method provided by the invention, the problems of load unbalance and the like in AGV task allocation can be effectively solved, the balance and efficiency of task allocation are improved, the task completion time is shortened, the energy consumption of the system is reduced, and the overall operation efficiency of the multi-AGV system is improved.

The invention relates to the technical field of refrigeration house energy-saving control and digital twinning, in particular to a refrigeration house fan energy-saving operation method and system, and the method achieves the whole-field temperature deduction of a sensor-free area by constructing a CFD reference model and simulating and predicting the three-dimensional transient airflow and temperature distribution in a refrigeration house. Model parameters are calibrated through measured data, and the order of the model is reduced by adopting intrinsic orthogonal decomposition and Galerkin projection, so that the calculation amount is remarkably reduced, and online optimization is supported. And in combination with a data assimilation algorithm, the prediction result of the reduced-order model is continuously corrected, and the adaptability to environment change and system aging is improved. The optimization control stage takes minimization of the total energy consumption of a fan as a target, solves an optimal fan control sequence under the hard constraint that the whole-field temperature does not exceed the cargo safety upper limit and does not exceed the cargo safety lower limit, and adopts a rolling time domain mode for execution to realize collaborative optimization of safety and energy conservation.

A riding lawn mower, including: a frame; a seat; a walking assembly and a walking motor; a cutting assembly and a driving motor configured to drive the cutting assembly; a first energy storage device configured to supply power to at least one of the walking motor or the driving motor, the first energy storage device detachably mounted to the frame and enabled to supply power to a handheld power tool when detached from the riding lawn mower; and a second energy storage device configured to supply power to at least one of the walking motor or the driving motor; wherein a ratio of a total energy of the second energy storage device to a total energy of the first energy storage device is greater than or equal to 2 and less than or equal to 20.

The invention discloses a range extender power generation system and a control method, and relates to the technical field of power systems. The system comprises an energy storage module, a driving module, a range extender module and a control module, the control module combines model predictive control and reinforcement learning and optimizes energy distribution, so that the system can automatically select an optimal strategy according to different driving working conditions, the energy utilization efficiency is further improved, the total energy consumption is reduced, and the driving efficiency is improved. The efficient cooperative work of the internal combustion engine, the generator and the driving motor is realized; self-adaptive gear shifting control is carried out based on real-time vehicle working conditions, path prediction information and motor efficiency, and it is ensured that a driving motor always works in an efficient area. Besides, the system also has fault diagnosis and fault-tolerant control functions, can detect the performance deviation of the engine and the generator in real time, automatically enters a fault-tolerant mode when a fault occurs, and ensures that the vehicle can safely run to a preset place even under the fault condition by cutting off or adjusting the output of the engine.

The invention provides an electromagnetic thermal multi-physical field simulation method based on an FDTD / FVTD algorithm, and belongs to the technical field of electromagnetic simulation. Performing time domain finite difference discretization on the established spatial discrete model by adopting a Yee cellular structure, and calculating electromagnetic field distribution according to a Maxwell rotation equation; constructing an adaptive steady-state judgment criterion based on the instantaneous total energy change rate, and updating electromagnetic field distribution of the spatial discrete model by adopting an FDTD explicit method; calculating the electromagnetic loss power density of the spatial discrete model, and transmitting the electromagnetic loss power density into a thermal field for calculation; based on an FVTD method, thermal field distribution of the discrete model is calculated through an explicit or implicit format, and thermal field distribution information is obtained. The FDTD method is based on explicit time domain iteration, transient behaviors such as electromagnetic wave propagation and scattering can be visually captured, and the FDTD method has broadband response capability and complex medium adaptability and is especially suitable for the high-frequency electromagnetic problem. The FVTD method strictly ensures energy conservation, directly treats boundary heat flux and is suitable for non-uniform materials and complex heat transfer boundary conditions.

The invention provides an arch bridge cable force adjusting method and device, a medium and a program product, and relates to the technical field of arch bridge cable force adjusting.The method comprises the steps that by establishing an arch bridge parameterization finite element model, specific unit force is applied to all suspenders to construct a first influence matrix; singular value decomposition is carried out on the first influence matrix, the first k suspenders with the total energy contribution rate exceeding a first preset value are extracted, and the sensitivity of the suspenders to the global suspender cable force is calculated; calculating an importance index in combination with sensitivity and suspender cable force deviation, and screening the first n important key suspenders to construct a second influence matrix; an adjusted vector is obtained based on the difference value of the second influence matrix and the target influence matrix, and the cable force adjustment amount of the key suspender is solved; the key suspender cable force is sequentially adjusted according to the importance sequence. According to the method, the key suspender is accurately identified through matrix decomposition and sensitivity analysis, the adjustment amount is quantified, interaction is considered, full suspender adjustment is avoided, the construction cost is remarkably reduced, and the cable force adjustment efficiency and accuracy are improved.

The invention provides an anti-surge control method. The anti-surge control method comprises the following steps: acquiring historical data of a surge point; inputting the historical data of the surge points into the to-be-trained model to obtain an anti-surge model; based on the anti-surge model, determining an anti-surge line of the fan; the anti-surge line is generated by translating the abscissa of the surge line of the fan in the anti-surge coordinate system rightwards by a first preset unit and translating the ordinate downwards by the first preset unit; the real-time fan inlet flow and the real-time fan total energy head of the fan are obtained, and the real-time working condition point of the fan is determined; whether the real-time working condition point is located below an anti-surge line or not is judged, and if not, the opening degree of an anti-surge valve is adjusted to 100%; if yes, the anti-surge opening degree and the anti-surge valve opening time are determined based on the real-time working condition point, the anti-surge valve is opened according to the anti-surge valve opening time, the anti-surge valve is controlled to operate at the anti-surge opening degree, and the problem that the position area where the working condition point of the draught fan is located cannot be accurately and automatically adjusted at present is solved.

The invention discloses a fresh corn storage and transportation fresh-keeping control method based on big data analysis, and the method comprises the following steps: S1, collecting transportation environment parameters and fresh corn quality parameters, and forming a multi-dimensional time sequence perception data set; s2, transmitting to a cloud end in real time to generate a real-time data stream; s3, generating prediction data by adopting a frequency enhancement decomposition Transform algorithm; s4, establishing an optimization model according to the quality loss degree and the total energy consumption of the fresh corn, and solving by adopting a Mount optimization algorithm to obtain an optimal control parameter; s5, the optimal parameters are sent in real time to automatically adjust the transportation environment; s6, continuously collecting actual data, comparing the actual data with predicted data, calculating a deviation value, and automatically triggering joint self-correction when the deviation value exceeds a threshold value; and S7, performing loop execution to form a real-time feedback closed loop. Dynamic self-adaptive fresh-keeping control in the transportation process is achieved, and the quality stability of the fresh corn is improved.

The invention relates to an economic evaluation method and system for in-situ conversion of medium and low-cooked shale, and belongs to the technical field of shale oil development. Comprising the following steps: 1) establishing a medium-low mature shale in-situ conversion geological model; 2) constructing a three-dimensional heat conduction equation, setting a definite solution condition, and solving the three-dimensional heat conduction equation in combination with the thermophysical parameters to obtain a temperature field on each time-space node in the medium-low mature shale in-situ conversion geological model; the method comprises the steps of (1) obtaining a dynamic hydrocarbon generation process, (2) obtaining a hydrocarbon generation kinetic parameter, (3) obtaining a total energy output, (5) obtaining a total energy input in a heating process, and (6) calculating an energy consumption ratio (ER), and carrying out economic feasibility evaluation of a shale in-situ conversion technology. The method can calculate a dynamic hydrocarbon generation process at any time and position in a shale bed in real time, and obviously improves the prediction precision of the hydrocarbon generation amount.

The invention provides a sewage treatment multi-objective optimization method and device and a storage medium, and relates to the field of sewage data analysis and processing.The method comprises the steps that firstly, multiple types of sewage data are collected in a time sequence mode at a specified sewage data station and preprocessed to form a data set, then a sewage prediction model is constructed based on a deep learning model, and a sewage prediction result is obtained; and inputting the data set for training, and predicting the energy consumption of the sewage treatment equipment and the effluent pollutant concentration. Then, a multi-objective optimization function is constructed, so that the total energy consumption is minimized, and the effluent pollutant concentration reaches the standard; and finally, solving the function by using a multi-objective particle swarm optimization algorithm to obtain an optimal solution set, and screening out an optimal operation parameter from the optimal solution set. According to the method, two important targets of energy consumption minimization and effluent pollutant concentration standard reaching can be considered at the same time, the relation between energy consumption and water quality can be balanced, the optimal operation parameters are screened out from the optimal solution set, and the overall performance of sewage treatment equipment is improved.

The invention discloses a building group energy-saving management and control method and system based on big data analysis, and relates to the field of big data analysis, and the method comprises the steps: carrying out the standardization processing of the real-time visitor flow data of a commercial district, and the subentry energy consumption data, time information and environmental parameters of the commercial district and a residential district, and constructing a current scene feature vector; screening matched similar historical scene samples, and generating a commercial district people flow prediction curve in a future target time period through weighted fitting calculation; screening matched historical regulation and control records, and performing weighted calculation to obtain a mean value as a historical reference regulation and control parameter; generating a candidate parameter combination according to a preset step length, calling the corresponding total energy consumption and the indoor temperature of the residential area, and selecting an energy-saving regulation and control instruction; and adjusting operation parameters of the commercial district energy system according to the energy-saving regulation and control instruction. According to the building group energy-saving management and control method and system based on big data analysis, the problem that the use comfort of a residential area is poor due to the fact that the energy consumption fluctuation of a commercial area is too large is solved.

The invention discloses an enterprise electric power energy monitoring analysis method and system based on the industrial internet, relates to the technical field of energy monitoring, and provides reliable data for a system by collecting operation electric power data and production management system data through a sensor, preprocessing the data and storing the data in a historical data set Dhis. And constructing an energy consumption prediction model through the historical data set Dhis to provide data support for a response strategy. And constructing a reward and punishment function based on the energy consumption prediction model, comparing the reward and punishment function with a reward and punishment threshold Rth, generating a power supply mode decision Mpow, and intelligently adjusting the power supply mode. And outputting the optimized production schedule Sopt by taking the total energy consumption cost TEC and the schedule stability SSP as optimization targets, so as to improve the production continuity. Model parameters are dynamically corrected through comparison of an optimization effect evaluation value Oscore and a threshold value Oth, it is ensured that the system continuously approaches the actual working condition in long-term operation, and strategy failure caused by equipment aging or process change is avoided.

The invention discloses a multi-rotating-speed flexible job shop scheduling method applied to double-resource constraint, and the method comprises the steps: taking the maximum completion time and minimum total energy consumption of a minimum machine as target functions, and constructing a flexible job shop scheduling model considering the rotating speed energy consumption of the machine and the production demands of a fine process; a machine speed gear constraint, a fine process constraint, a process sequence constraint, a completion time constraint, a machine processing constraint and a worker operation constraint are established as constraint conditions of the model; the flexible job shop scheduling problem is solved by adopting an improved artificial bee colony algorithm, bee colony search guided by excellent genes is adopted in bee learning operation in the improved artificial bee colony algorithm, and nectar source optimization is carried out based on the searched excellent genes; the following bee operation adopts a neighborhood structure which considers machine speed change and balances the working time of workers to carry out dynamic neighborhood search so as to optimize a nectar source. The effectiveness of the improved strategy is verified through experiments, and the superiority is verified through comparison of different algorithms on expansion standard examples.

The invention discloses an unmanned aerial vehicle-unmanned agricultural machine collaborative operation staged scheduling method under energy consumption constraint, belongs to the technical field of intelligent scheduling, and innovatively designs a three-stage dynamic scheduling mechanism based on operation urgency, and realizes stage adaptive switching through real-time energy consumption monitoring. And an unmanned aerial vehicle-agricultural machinery energy consumption prediction model considering flight conditions and operation parameters is established, and accurate prediction of the residual endurance time is realized by using an LSTM network. An improved NSGA-II multi-objective optimization algorithm is provided, and under a three-objective Pareto optimization framework of total energy consumption minimization, task completion rate maximization and time cost minimization, an energy consumption perception auction mechanism is combined to realize intelligent distribution of emergency tasks to high-electric-quantity equipment. A distributed real-time scheduling system based on edge computing is developed, an edge server is deployed to run a lightweight algorithm, and a real-time scheduling algorithm and a dynamic task conflict resolution mechanism are combined, so that the task redistribution delay is controlled within 500ms, and the conflict resolution success rate is increased to 92.3%.

The invention relates to an unmanned aerial vehicle cluster target monitoring path planning method based on space-time constraint multi-agent reinforcement learning, and belongs to the field of unmanned aerial vehicle path planning. Comprising the following steps: acquiring an unmanned aerial vehicle cluster and environment characteristic information, establishing time and space constraint conditions, and constructing a constraint optimization problem taking minimization of total energy consumption as a target; based on the obtained feature information, mapping a constraint optimization problem into an Actor network and a Critic network corresponding to each unmanned aerial vehicle and a Critic network corresponding to an unmanned aerial vehicle cluster; carrying out reverse training on the Actor network and the Critic network based on a simulation result of the unmanned aerial vehicle cluster; and inputting the feature information at the current moment into the Actor network corresponding to each unmanned aerial vehicle for path planning. According to the method, the situation that actions of unmanned aerial vehicles at the current moment meet time / space constraint conditions is evaluated based on time / space feature information, energy consumption of an unmanned aerial vehicle cluster at the current moment is evaluated based on global state information, and therefore it is ensured that optimal path planning meeting various constraint conditions and achieving a target is found.

The invention discloses an energy consumption prediction and optimization system and method based on artificial intelligence, and relates to the technical field of energy consumption prediction and optimization, and the method comprises the following steps: calculating the energy consumption intensity indexes of all devices in different time periods, and forming a time segment set of historical energy consumption abnormity of the devices; generating a candidate scheduling strategy, for any strategy, obtaining an equipment calling sequence and energy consumption switching time between equipment, and when the scheduling time of the equipment falls into a historical energy consumption abnormal paragraph set, calculating the actual operation energy consumption of the equipment in an abnormal time period; traversing the equipment calling time in the strategy to calculate the actual total energy consumption and the number of times exceeding the predicted energy consumption, calculating the energy efficiency matching value of each candidate strategy, and generating a strategy recommendation sequence; and selecting the strategy with the highest energy efficiency matching value from the strategy recommendation sequence as a preferred execution scheme, collecting real-time energy consumption data after execution, calculating an energy consumption deviation rate, comparing the energy consumption deviation rate with a threshold value, and judging the effectiveness of the strategy, so that energy consumption prediction and optimization are realized.

The invention provides a cloud laboratory device resource scheduling method, system and device based on cloud edge collaboration and a storage medium, and relates to the technical field of cloud edge collaboration.The method comprises the steps that device real-time energy consumption data, task queue depth data, historical fault rate data and device operation frequency data of a plurality of cloud laboratory terminals are collected; based on the equipment real-time energy consumption data, the task queue depth data and the historical fault rate data, constructing a multi-dimensional resource portrait of each cloud laboratory terminal; generating an equipment health degree index based on the multi-dimensional resource portrait and the equipment operation frequency data; generating a target task allocation scheme by taking the minimum total energy consumption estimated value as a target through a near-end strategy optimization method; and according to the target task allocation scheme, preferentially allocating the time delay sensitive tasks in the parallel task queue to cloud laboratory terminals of which the equipment health degree indexes are greater than or equal to a preset health threshold value, so as to realize cloud-side collaborative equipment resource scheduling. The resource utilization rate and the task response speed of the cloud laboratory equipment are improved.

The invention belongs to the field of hydroelectric power generation facilities, and discloses elastic hinged series hydrofoil power generation equipment and method.The elastic hinged series hydrofoil power generation equipment comprises an elastic hinged mechanism and two oscillating hydrofoil power generation devices, and each oscillating hydrofoil power generation device comprises a supporting frame, a power generator, a sliding rail, a sliding block, a hydrofoil and a bidirectional damping mechanism; one end of the hydrofoil is used for stretching into water flow, a connecting shaft is installed at the other end of the hydrofoil, the connecting shaft is rotatably installed on the sliding block and connected with the generator, and the bidirectional damping mechanism is fixedly connected with the sliding block. The two hydrofoils are connected through an elastic hinging mechanism, the elastic hinging mechanism comprises a first extension spring and two hinging pieces, and the first extension spring is hinged to the hydrofoils. The two hydrofoils are elastically connected through the elastic hinge mechanism, the hydrofoils can better adapt to the change of water flow and the coupling effect between the hydrofoils, energy in the water flow is more fully utilized, the effective working flow speed range of power generation equipment is widened, and the total energy harvesting amount of a unit water area is increased.

The invention discloses a task scheduling and energy consumption optimization system and method based on distributed satellite calculation, and the method comprises the following steps: obtaining the floating point calculation amount and data size of each subtask, obtaining the calculation power and link rate of each satellite, and calculating the estimated execution time of the subtasks on the satellites according to an execution time formula; further obtaining the calculation power, the communication power and the corresponding duration of each satellite, and estimating the total energy consumption through an energy consumption formula; and optimizing a task allocation scheme based on the energy consumption evaluation, thereby reducing the overall energy consumption of the satellite cluster.

The invention relates to the technical field of intelligent manufacturing, and particularly discloses a space-time constraint modeling-based multi-AGV-mechanical arm collaborative carrying system, which comprises a space-time constraint modeling module, a task decomposition and priority evaluation module, a collaborative path planning module, a dynamic collision prediction and adjustment module and an energy consumption and efficiency optimization module, according to the method, the total energy consumption, the total duration and the congestion index are subjected to normalization weighting, the unified fitness function is constructed, and the genetic algorithm and the particle swarm optimization are adopted for joint optimization, so that energy consumption minimization and operation period minimization can be taken into consideration at the same time, and a high-congestion section is actively avoided on path selection. According to the multi-target fusion, side effects caused by single index optimization are avoided, the sustainable operation capacity of the system is improved, the actual time length fed back after task execution, energy consumption and collision near-loss information are improved, model parameters are updated through a reinforcement learning or online incremental learning module, and self-evolution of a strategy is achieved.

The invention discloses a control method of a cooperative energy feedback suspension system, and belongs to the technical field of vehicle suspension systems, and the control method comprises the following steps: S01, information acquisition; s02, road condition judgment; s03, performing disconnection control on the stabilizer bar; s04, connecting and controlling the stabilizer bar; s05, energy recovery and output control; s06, carrying out smoothness control; and S07, performing dynamic adjustment. The method has the beneficial effects that by intelligently judging the vehicle driving state and the road condition, connection and disconnection of the active stabilizer bar and energy recovery and output strategies of the shock absorber and the active stabilizer bar are flexibly controlled according to different working conditions. Under various complex working conditions such as turning, hollowing and bumping, the system can efficiently recover energy, the application range of energy recovery is remarkably expanded, the total energy recovery amount is increased, then the endurance mileage of a battery is prolonged, and the problems that in the prior art, the energy recovery efficiency is low, and the application range is limited are solved.