972 results about "Dynamic energy" patented technology

The invention provides a server energy management method and device based on a BMC (Baseboard Management Controller). The method comprises the following steps: collecting hardware state data of a server in real time through the BMC; preprocessing the hardware state data, removing abnormal values and supplementing missing data to determine standardized hardware data; inputting the standardized hardware data into a load prediction model to determine a load prediction result; generating an energy efficiency evaluation report according to the standardized hardware data and the load prediction result; determining a dynamic energy adjustment strategy based on the energy efficiency evaluation report; and executing the dynamic energy adjustment strategy, and dynamically adjusting hardware operation parameters of the server so as to realize optimization of energy consumption. According to the method, the hardware control capability and the intelligent analysis algorithm of the BMC can be deeply fused, refined, automatic and intelligent management of server energy is realized, the energy utilization efficiency of a single server is remarkably improved, and conversion from extensive power supply to refined energy consumption management is realized.

The invention discloses a dynamic energy-saving control method and system for a cold storage based on deep learning and multi-source data fusion, and relates to the technical field of cold storage energy-saving control, and the method comprises the steps: constructing a thermal field sensing network through a fixed sparse sensor array and a mobile thermal field scanning robot, and collecting temperature, humidity and thermal imaging data; reconstructing a three-dimensional thermal pixel field through registration, interpolation and other processing, and performing space-time alignment on the three-dimensional thermal pixel field, equipment parameters and inventory thermal physical properties; constructing digital twinborn deduction thermal field evolution based on a physical information neural network, and recognizing a high-temperature hot spot and a low-temperature safe area in combination with a space-time diagram attention network; the method comprises the following steps: by taking power consumption cost minimization and over-temperature risk controllability as targets, solving an optimal control strategy containing tuyere parameters and compressor frequency by using a multi-agent depth deterministic strategy gradient algorithm, driving equipment to execute targeted cold supply, and enabling a system to comprise a thermal field sensing layer, a data fusion layer, a digital twinning and prediction layer, a decision optimization layer and an execution layer. And accurate temperature control and energy conservation are realized.

The invention discloses a leveling machine leveling precision self-calibration control system based on real-time displacement detection, and relates to the technical field of calibration data processing, and the system comprises a data collection and preprocessing module which is used for periodically collecting leveling monitoring data and preprocessing the collected data; the attitude reconstruction gap correction module is used for generating a theoretical gap of each node and correcting the theoretical gap; the deviation energy triggering judgment module is used for constructing a leveling deviation matrix, evaluating the dynamic energy level of the leveling deviation and judging whether a self-calibration process is triggered or not; and the fulcrum compensation calibration execution module is used for evaluating the adjustment force requirement of the leveling calibration action, generating a target height correction value, driving four fulcrums to synchronously adjust the attitude and completing the leveling attitude self-calibration closed loop after triggering the self-calibration process. The problems that in the existing thick plate leveling process, the posture of an upper roller is misaligned and cannot be quantitatively recognized in real time, leveling gap deviation is hidden and accumulated in multiple areas, and self-calibration triggering is not timely are solved.

The invention relates to the technical field of ship energy consumption control, and particularly discloses an FSRU ship energy consumption and carbon emission intelligent management and control system based on route big data, and the system comprises a route big data collection unit which is used for obtaining the multi-source data of an FSRU ship in the full-route-segment navigation process, and transmitting the multi-source data to an FSRU ship. The multi-source data at least comprises ship operation data, route environment data, carbon capture data and waste heat data; wherein the FSRU ship is a floating type storage regasification device ship; and the data preprocessing unit is connected with the airline big data acquisition unit and is used for cleaning the multi-source data and outputting preprocessed data. According to the method, the staged cooperative control model is established, the unique working condition requirements of the FSRU ship in different operation stages such as berthing, loading and unloading, offshore cruising and open sea navigation can be accurately identified and adapted, the limitation of a traditional single control strategy in variable scenes is overcome, and refinement and dynamics of energy distribution and emission control are achieved.

The invention discloses an AUV (Autonomous Underwater Vehicle) path planning method based on a multi-strategy collaborative enhanced artificial mouse algorithm, and belongs to the technical field of AUV path planning. In order to solve the problems of slow convergence speed and low convergence precision of an existing path planning algorithm in the field of AUV path planning, the method comprises the following steps: carrying out path normalization by adopting an artificial travel mouse algorithm to obtain an optimal path, taking a connecting line of a starting point and an end point as a center line during initialization, adding random disturbance to a center line interpolation point to generate a path point, and carrying out path planning on the path point; generating an initial population; the path with the smaller fitness is selected as a superior path, and global exploration, hole digging or foraging / pursuer avoiding is decided according to the value of the self-adaptive nonlinear attenuation dynamic energy factor; evaluating the fitness of the individual, and if the fitness of the iteration is more excellent than the optimal fitness of the individual, updating the optimal fitness of the individual; if the fitness is more excellent than the global optimum fitness, updating the global optimum; and finally obtaining a final path based on the global optimal solution.

The invention relates to the technical field of flywheel and battery hybrid energy storage control, and discloses a quick power response and energy management method of a flywheel and electrochemical hybrid energy storage system. The method comprises the following steps of: acquiring flywheel rotating speed, kinetic energy allowance, battery end voltage and internal resistance in real time to form a state vector, performing consistency correction on an initial power decomposition instruction according to the state vector, and generating a final power instruction matched with unit real-time capability. Meanwhile, by monitoring the change trend of the flywheel kinetic energy and the battery charge state, a power decomposition coefficient is dynamically adjusted according to a preset coordination rule, and the power decomposition coefficient is fed back to an initial decomposition link to form closed-loop control. According to the method, accurate matching of the power instruction and the dynamic characteristics of the energy storage unit is realized, and the safety and accuracy of system response are improved; and through closed-loop trend adjustment, the long-acting adjustment capability and operation stability of the system are maintained.

The invention discloses a self-adaptive photovoltaic energy storage comprehensive optimization method and system, and belongs to the technical field of photovoltaic energy storage optimization control. According to the method, a system operation model is established based on historical and real-time operation data of a photovoltaic unit, an energy storage unit and a load side, a hybrid robust-stochastic optimization framework is introduced, a collaborative optimization model with energy balance and system stability as targets is constructed, and an initial control parameter set is solved; in the system operation process, a power fluctuation mode and energy storage response characteristics are identified through a multi-layer agent algorithm, and a dynamic energy storage control instruction is generated and corrected; and according to the cross-cycle change trend of the operation performance index, adaptively adjusting the weight and step length of the control parameter, and realizing strategy evolution and dynamic closed-loop optimization. According to the invention, cooperative operation of photovoltaic and energy storage can be realized under complex operation conditions, and the energy utilization rate and the operation stability are improved.

The invention provides a tunnel water pump dynamic energy-saving adjusting method and system, and relates to the technical field of tunnel water pump energy-saving control, and the method comprises the steps: collecting multi-dimensional working condition data in a tunnel drainage system through a collection unit; based on the multi-dimensional working condition data, combining the water level change rate, the rainfall capacity and the total drainage demand, classifying tunnel drainage working conditions to obtain a working condition classification result; according to the working condition classification result, the water pump rotating speed is adjusted through a fusion control algorithm, and multi-pump cooperative operation is completed based on water pump efficiency sorting and load distribution logic; a fault judgment threshold value is set based on rated parameters of water pumps, historical operation data and real-time working condition data, potential faults are warned in advance by analyzing the parameter change trend, and therefore the working condition of the water pumps can be accurately judged through multi-dimensional working condition data collection, intelligent algorithm dynamic adjustment, multi-pump collaborative optimization and accurate fault warning. And on-demand energy supply of the tunnel water pump is achieved, energy consumption is reduced, operation is stable, and maintenance is efficient.

The invention provides a vaccine cold storage AGV operation navigation method and a cold storage AGV, and the operation navigation method comprises the following steps: S1, dividing cold storage regions by taking temperature collection points as boundaries, and determining the temperature distribution in each region; s2, obtaining AGV position data and temperature data of a current position in real time; and S3, determining a starting point and a target point, establishing a multi-objective function with the minimum temperature variation, the minimum path length and the minimum energy consumption, and solving the multi-objective function to obtain a dynamic planning optimal path. According to the invention, path planning is carried out through temperature distribution in each area, temperature shock areas are avoided, temperature fluctuation of vaccines in the transportation process is reduced, and the damage rate of goods is reduced; the adaptability of the distance, the temperature and the energy consumption is considered in the path planning of the multi-objective function, so that the operation efficiency is improved; the dynamic energy consumption distribution strategy reduces the energy loss in a low-temperature environment, prolongs the endurance time of the AGV, and reduces the charging frequency of the AGV entering and exiting the refrigeration house, thereby reducing the disturbance to the temperature field in the refrigeration house.

The invention discloses a stern shaft sealing device for a ship, and relates to the technical field of ship equipment. The device comprises a tail shaft barrel, a tail shaft, a sediment intercepting assembly, a first sealing unit and a pipeline system. And a sealing structure is arranged in a gap between the stern shaft and the stern shaft barrel. The first sealing unit comprises four sealing leather cups which are arranged in the axial direction and divide the interval into a first installation cavity, a gas cavity, a second installation cavity and a cooling liquid cavity, and the pipeline system is communicated with the gas cavity and the cooling liquid cavity so as to maintain pressure. The core is a buffer structure in the first mounting cavity and the second mounting cavity, and the buffer structure comprises a sealing ferrule, an adjusting assembly and an abutting assembly. When the seawater pressure suddenly increases, the buffer structure converts impact kinetic energy into elastic potential energy and gas compression potential energy through elastic deformation, pressure conduction and the like, pressure buffering is achieved, meanwhile, the sealing performance is dynamically enhanced, seawater backflow is prevented, the ship navigation safety is guaranteed, and the reliability and stability of the sealing device are improved.

The invention discloses an intelligent regulation and control method and system for the environmental comfort degree of a central air conditioner terminal, and relates to the technical field of air conditioner regulation and control. The intelligent regulation and control method comprises the following steps that a pressure fluctuation track generated by aerodynamic disturbance is collected in the operation process of a draught fan, and the pressure fluctuation track is converted into energy spectrum distribution in a time sequence; establishing dynamic energy representation of the fan frequency; based on dynamic energy representation of fan frequency, a sound vibration field is projected in a spatial range, excitation response of inherent frequency is synchronously recorded through a room structure sensing array, and instantaneous energy coupling fingerprints of a room structure are formed. Through dynamic energy characterization and structural response coupling analysis, real-time identification and quantitative control of the standing wave noise risk are realized, a phase drift mechanism is introduced at a risk critical point, a fan disturbance phase is actively adjusted, standing wave superposition is effectively blocked, and acoustic safety and operation stability of a tail end environment are improved.

The invention discloses a new energy automobile endurance mileage increasing system and method based on multi-mode energy collaborative management. The system comprises a multi-mode energy input module which is used for converting solar energy into direct current electric energy through a photovoltaic array and integrating power battery energy and hub motor kinetic energy recovery energy; the predictive management module is used for acquiring front road condition information through a V2X technology and predicting an energy consumption curve based on a deep learning model; the dynamic weight distribution module is used for calculating photovoltaic, battery and kinetic energy recovery weight coefficients through a dynamic weight distribution algorithm according to the real-time vehicle environment information and the prediction result; and the energy distribution control module is used for carrying out weighted distribution on the multi-modal energy based on the weight coefficient and outputting total available energy and a control signal so as to drive electric equipment or charge a battery. According to the invention, the problem of dynamic energy distribution during multi-energy coupling is solved, and the purposes of improving the endurance of urban roads, improving the endurance of expressways, reducing the increment cost of the system and being compatible with a 400V / 800V platform are achieved.

The invention relates to the technical field of energy consumption optimization, and provides a heater energy consumption optimization method and system based on gas-steam combined cycle. The method comprises the steps that gas turbine exhaust, waste heat boiler steam and heater operation parameters are collected in real time, and a dynamic energy consumption data set is constructed; training a load prediction model based on the historical load curve and the meteorological data, and outputting future load distribution; performing multi-working-condition energy consumption simulation according to the data set and the predicted load, and generating an energy consumption characteristic topological graph and a thermal efficiency fluctuation graph; and based on the atlas, optimizing heater control parameters by taking energy consumption minimization as a target, generating an optimal control set, and regulating and controlling the running state of the heater. The technical problems that in an existing gas-steam combined cycle system, heater energy consumption control is not accurate, and efficiency fluctuation is large are solved, and the technical effects that through combination of real-time data collection and a prediction model, heater energy efficiency optimization and control parameter dynamic adjustment are achieved, and therefore energy consumption is reduced to the maximum extent, and heat efficiency is improved are achieved.

The invention relates to the technical field of energy internet and intelligent micro-grids, and discloses a dynamic energy-carbon coupling modeling and collaborative optimization method for a wind and light storage and charging system, which comprises the following steps of: a, acquiring operation data of the wind and light storage and charging system; b, calculating to obtain the equipment-level dynamic real-time carbon potential of the output port of the energy conversion equipment; step c, updating the virtual total carbon load by using the state data of the energy storage unit and the real-time carbon potential; step d, constructing and solving a rolling horizon optimization problem to obtain an optimal control strategy covering the prediction horizon; and step e, issuing and executing a control instruction, and repeating the step a to the step e. According to the method, the physical working condition and the real efficiency of the equipment are dynamically mapped, the carbon emission intensity and the equipment operation state are associated in real time, and then static emission factors are replaced, so that the influence of the equipment on the carbon flow is accurately and dynamically reflected, and the precision and the real-time performance of microscopic carbon accounting are improved.

The invention relates to the technical field of micro-grid control, in particular to a photovoltaic and energy storage-based micro-grid multi-energy complementary cooperative control method. The invention provides a photovoltaic and energy storage integrated micro-grid multi-energy complementary cooperative control method. The method comprises the following steps of system architecture construction, real-time feedback of an adjustment mechanism and scene adaptive optimization. The system architecture comprises a four-layer cooperative control architecture including a data acquisition layer, a data processing layer, a cooperative control layer and an execution layer, and photovoltaic data includes photovoltaic array output power, irradiance, assembly temperature and inverter state; the energy storage data comprises SOC (state of charge), SOH (state of health), charging and discharging power, monomer voltage and temperature of the energy storage battery; the method has the beneficial effects that 1, the photovoltaic consumption rate is improved: through accurate prediction and dynamic energy storage scheduling, the photovoltaic consumption rate is improved by 15%-25%, and the light abandoning loss is remarkably reduced; in an intelligent zero-carbon micro-grid demonstration area project, after the method is applied, the average monthly light abandoning rate is reduced from 8% to 2% or below.

The invention discloses a heating monitoring method for a stainless steel seamless tube in a heat treatment furnace, which belongs to the technical field of metal heat treatment and specifically comprises the following steps: acquiring an electromagnetic induction signal of a steel tube in rotation advancing in real time and binding spatio-temporal information; kinematics inversion is carried out on the spatio-temporal information based on the movement speed of the steel pipe, and an electromagnetic parameter evolution track of each physical point on the surface is reconstructed; continuously deviated abnormal points are identified based on the evolution trajectory and clustered to extract electromagnetic characteristics of the heating abnormal area, temperature deviation is obtained through a preset electromagnetic-temperature mapping model, and a compensation instruction is generated; and predicting space-time coordinates of the abnormal area reaching the next heating area, and driving the corresponding independent heating unit to execute dynamic energy compensation in the matched time window. Real-time, global and accurate sensing and closed-loop regulation and control of the heating uniformity of the moving workpiece are achieved, the uniformity and stability of heat treatment quality are remarkably improved, and energy consumption is reduced.

The invention provides a wind turbine generator performance anomaly detection method and system, and the method comprises the steps: collecting an operation state data set of a wind turbine generator in a continuous operation period, constructing a dynamic energy topological graph which reflects the energy conversion and transmission process of the wind turbine generator based on the operation state data set, and mapping the operation state data set to the dynamic energy topological graph, analyzing and extracting an energy flow feature vector representing the transfer characteristic of energy between the components through a topological structure; calling a pre-constructed historical health state energy flow model to perform dynamic matching processing on the energy flow feature vector, and calculating an energy flow deviation degree between a current energy flow mode and a historical health mode; and according to the energy flow deviation degree and the wind speed environment parameters collected in real time, optimizing an anomaly judgment threshold value, and outputting a wind turbine generator performance anomaly detection result based on the optimized anomaly judgment threshold value. According to the method, the performance anomaly detection of the wind turbine generator can be more accurately and reliably realized.

The invention provides a multi-axis cooperative shearing motion trajectory planning method and system. The method comprises the following steps: reconstructing a space-time elastic coefficient of a slave axis synchronization interval; outputting a dynamically compressed or extended synchronous window, and establishing a speed-interval nonlinear mapping rule; the kinetic energy change gradient is converted into a slave axis motion boundary constraint condition, a time-varying acceleration decision curve is generated, and motion energy distribution is made to be matched with load kinetic energy characteristics; the mechanical-control coupling parameter offset is reversely decoupled through difference feedback of the real-time motion characteristics and the virtual trajectory; and autonomously reconstructing a track to generate kernel parameters, and forming a self-digestion mechanism of a dynamic error factor. The system comprises a synchronous window output module, a constraint condition establishment module and an offset decoupling module. Under the variable acceleration working condition, the slave shaft position synchronization error is controlled, the speed synchronization precision is improved, and meanwhile the system resonance risk index is reduced; the technical bottleneck that the dynamic response and the steady-state precision cannot be obtained at the same time in the traditional follow-up shearing control is broken through.

The application provides a double-motor driving and rigidity actively adjustable series elastic actuator and a control method thereof, which utilizes elastic elements to convert kinetic energy and potential energy, reduces system energy consumption, improves system energy utilization efficiency, improves dynamic performance of the actuator by introducing the elastic elements, strengthens the anti-impact capability of the system, and improves the safety of human-machine interaction; rigidity is controlled by changing the effective lever arm length between the end of the actuator and the elastic element, the deformation of the elastic element under the load of the actuator is measured, the transmission ratio of the system is calculated, the output torque is obtained, and the output force control of the actuator is further realized. The application utilizes double-motor driving and double-differential transmission mechanism to realize active adjustment of the output rigidity of the actuator system, and has a more extensive use scene and application range.

The invention discloses a dynamic energy-saving cluster resource collaborative allocation method. The method comprises the following steps: acquiring environment sensing data: acquiring environment data and node state data in real time through an unmanned aerial vehicle sensor; predicting a task demand: predicting a future task demand change condition by adopting a deep learning method according to historical data and environment change; optimizing the resource utilization rate: generating a resource allocation strategy, and dynamically adjusting calculation and communication resources of each node; making an energy-saving scheduling decision: generating an energy-saving scheduling strategy by using deep reinforcement learning, wherein the scheduling strategy considers two targets of task completion rate and energy consumption at the same time; feedback is collected, and self-adaptive adjustment is carried out: according to a task execution result and a node state feedback condition, an allocation strategy and energy-saving parameters are dynamically adjusted. By monitoring, predicting and optimizing resource allocation in real time and dynamically responding to environment changes, the task efficiency is improved, meanwhile, energy consumption is reduced, and the node operation time and service life are prolonged.

The invention relates to the technical field of property intelligent management, and provides a collaborative optimization intelligent lighting system dynamic energy scheduling method, which comprises the following steps: constructing an energy concentrator network of an intelligent lighting system, abstracting lighting equipment of a plurality of projects into an energy concentrator model, wherein each energy concentrator describes a conversion relation between electric energy and luminous flux through a coupling matrix; a multi-objective optimization function with maximization of the electric energy utilization rate as the core is established, the multi-objective optimization function integrates comfort, equipment and power grid constraints, and dynamic configuration is carried out based on operation parameters of the energy hub network; solving the multi-objective optimization function by adopting a two-stage optimization process, generating a reference operation plan and performing rolling correction; and issuing a dynamic dimming control signal to each lighting terminal through an energy concentrator network based on the optimized scheduling instruction obtained by solving so as to cooperatively schedule the electric energy of the intelligent lighting system. Therefore, the cross-region and multi-project intelligent lighting system is optimized in a collaborative manner, and the energy utilization rate is improved.

Dynamic energy transfer (DET) systems are costly and time-consuming to install. Accordingly, an installation system is disclosed that comprises stowage for rail segments and poles, two strategically positioned cranes, and / or strategically positioned platforms, to enable less expensive and faster installation of the various components of a DET system. Installation, utilizing the installation system, can significantly decrease the cost of installation, while increasing the rate of installation and maintaining safety.

The invention relates to the technical field of non-electrical variable control or regulation systems, and discloses an exhaust gas aftertreatment flow channel back pressure dynamic regulation method, which comprises the following steps: acquiring a supply source phase reference signal and establishing cyclic mapping; a fluid channel pressure signal pulse range change rate is extracted to determine a feed-forward factor; determining a pressure signal trough interval as an action window; determining a reference amplitude based on the feedback deviation and using a feed-forward factor for compensation to determine a target adjustment instruction; the adjusting mechanism is driven to move in the action window, so that the response peak value is locked in the trough interval, the adjusting action is locked in the low-energy phase, kinetic energy intervention of fluid pulses on the adjusting mechanism is avoided, and mechanical abrasion is reduced; pulse characteristic feed-forward compensation is utilized to enable an adjusting instruction to sense the trend of a flow field, the response speed is increased while a signal is smoothed, and pressure balance under the transient working condition is maintained.

The application discloses a mobile redundant manipulator real-time grabbing and trajectory tracking method and system based on optimization theory. In the method, the mobile redundant manipulator platform carries out real-time distance measurement on a target object based on a camera, and when the distance is less than a set threshold, a grabbing operation is performed. The application constructs a mobile redundant manipulator platform dynamics model based on robot forward kinematics, establishes a mobile redundant manipulator platform optimization model based on kinetic energy loss, designs a corresponding objective function, and sets constraint conditions of the corresponding optimization model in combination with physical limit conditions of the mobile redundant manipulator platform. By sampling time, inputting a current state of the mobile redundant manipulator platform, solving joint angular velocity and mobile platform speed at a next time, real-time trajectory tracking of the mobile redundant manipulator platform is realized. The application greatly increases the working range of the manipulator, the model is simple and easy to solve, accurate trajectory tracking can be realized, and the application prospect is wide.

The invention discloses an intelligent control system of an air compressor, and relates to the field of intelligent control of industrial equipment. The data acquisition and processing module is used for acquiring multi-source operation parameters of the air compressor system and constructing a dynamic energy flow topological graph based on the multi-source operation parameters and a pipe network physical connection relation; the system energy state evaluation module is used for calculating the system energy flow topological entropy by analyzing the dispersion degree of the state of a pipe network valve and the uniformity of branch flow distribution according to the dynamic energy flow topological graph; the multi-target collaborative optimization decision module is used for solving and generating a collaborative target setting set by taking minimization of the total power consumption of the system and the topological entropy of the energy flow of the system as a collaborative optimization target; and the cooling system cooperative control module is used for carrying out feed-forward compensation by combining the system energy flow topological entropy according to the cooperative target setting set and the real-time temperature data, and generating an adjusting instruction of the cooling system. By defining and optimizing the topological entropy of the energy flow of the system and cooperatively regulating and controlling the air compressor system, the energy efficiency and the stability are synchronously improved.

The invention relates to the technical field of industrial intelligent manufacturing, and discloses a digital factory-oriented dynamic energy price response type production regulation and control system, which comprises a data acquisition module, a data processing module, a model establishment module, a scale regulation and control module, an instruction verification module, a visualization module and a closed-loop optimization module, the dynamic energy price is introduced into production scheduling, so that the comprehensive energy cost is reduced; a multi-agent reinforcement learning model is adopted for real-time response; an optimization strategy is decomposed into long-term, short-term and real-time instructions, macroscopic planning is carried out by using a cloud, and accurate control is executed by relying on edges; three defensive lines of safety, feasibility and stability are set for all control instructions, so that the harm of wrong instructions is effectively prevented; a decision view and a manual intervention interface are provided to ensure the reliability and credibility of the decision; the successful scheduling case is stored in a strategy library, online fine adjustment and offline training are carried out on the core model, and the continuous evolution ability is achieved.

The invention relates to the technical field of mechanical equipment fault diagnosis, and discloses a coal mill intelligent operation and fault early warning method based on multi-source information fusion. The method comprises the following steps: S1, acquiring multi-source sensor time sequence data of a plurality of key parts of the coal mill, and performing standardization processing to obtain multi-path standardized time sequence data; s2, based on multi-channel standardized time sequence data; s3, based on an energy topology network; s4, providing a health reference manifold, and executing dual-scale health assessment; s5, when the fault early warning signal is triggered; a dynamic energy topology network is constructed, and a causal relationship between energy nodes is quantified by using transfer entropy, so that systematic description of a multi-physical field coupling state in the coal mill is realized. Compared with a scheme depending on single sensor signal analysis or simple correlation analysis in the prior art, the defects that deep causal linkage between components cannot be revealed, and a fault root transmission path is difficult to capture are overcome.

The invention relates to the technical field of energy consumption intelligent distribution and control, in particular to a multi-load energy consumption intelligent distribution and control method and system based on a distribution box, and the method comprises the steps: obtaining initial operation parameter information of multi-load equipment connected with the distribution box, extracting energy consumption fluctuation characteristics and load response characteristics, and generating an energy consumption load incidence matrix; optimizing the energy consumption distribution proportion of the multi-load equipment based on the energy consumption sensitive index to obtain an energy consumption distribution control configuration table, constructing an energy consumption intelligent distribution simulation model based on the energy consumption distribution control configuration table, and constructing an energy consumption parameter correction model based on the design power supply parameters of the distribution box and the rated parameters of the multi-load equipment, and generating a dynamic energy consumption distribution adjustment scheme based on the energy consumption parameter correction coefficient and the energy consumption distribution control configuration table, and executing energy consumption intelligent distribution and control operation of the multi-load equipment based on the dynamic energy consumption distribution adjustment scheme. According to the invention, energy consumption distribution can be dynamically adjusted according to actual conditions through an energy consumption distribution adjustment scheme, and resource waste is avoided.

The invention discloses an underwater robot multi-source energy system and an energy management method, and relates to the technical field of underwater robot energy, and the system comprises a lithium ion battery which is directly connected in parallel with a DC bus; the super capacitor is connected to the direct current bus through the bidirectional DC / DC converter; the flexible power generation module is used for capturing water flow disturbance and mechanical vibration energy in the environment where the underwater robot is located; the propelling kinetic energy recycling unit has a driving mode and a power generation mode and is used for recycling kinetic energy in the propelling and braking processes of the robot; the battery management system is used for monitoring the voltage, the current, the temperature and the SOC state of each energy unit in real time and executing an energy distribution strategy based on multi-source information fusion; wherein the battery management system communicates with the bidirectional DC / DC converter through a CAN bus, and cooperatively controls an energy flow path. The method has the advantages that the energy utilization efficiency is improved, the dynamic response capability is enhanced, and the cruising ability is prolonged.

The invention discloses an intelligent scheduling method and system for a new energy load at a vertical take-off and landing point, and belongs to the technical field of new energy scheduling, and the method specifically comprises the steps: obtaining multi-main-body energy demand data through a power supply circuit device, and carrying out the distributed storage and desensitization processing; combining the processed multi-subject energy demand data with the real-time state information of the battery of the electric energy storage system, and calculating the dynamic energy distribution weight of each partner by adopting an improved Shapley value algorithm; on the basis of the weight and the real-time task dynamic state of each partner, a new energy scheduling rule is constructed, a control instruction is generated according to the new energy scheduling rule, new energy resources are scheduled and allocated, and a result is fed back to the electric energy storage system and each partner; according to the invention, the intelligent level, fairness, system energy efficiency and adaptive ability of new energy scheduling in a multi-partner scene are effectively improved.