907 results about "Power optimization" patented technology

The invention provides a demand side response-based distributed power supply optimal scheduling method and system, and relates to the technical field of power grids, and the method comprises the steps: building a distributed power supply prediction model based on a deep neural network, and predicting the photovoltaic power generation power, the energy storage charge state and the user load. Then, a robust optimization model is constructed, prediction errors and energy storage nonlinear characteristics are considered, an optimal time-phased load scheduling scheme and an energy storage charging and discharging strategy are generated, the scheme comprises an interruptible load execution time sequence, and the strategy comprises an energy storage charging and discharging power curve; and finally, establishing a hierarchical optimization execution system based on model predictive control, monitoring the state of the system in real time, and correcting a control instruction according to the deviation to realize optimal scheduling of the distributed power supply. The method can improve the consumption capability of the distributed power supply, reduce the power consumption cost, and enhance the stability of the power grid.

The invention discloses a non-energy-storage off-grid photovoltaic hydrogen production system and a control method. The control method comprises five steps of environmental data acquisition and preprocessing, photovoltaic cell output power estimation, electrolytic cell power management, converter control parameter optimization and system performance evaluation and adaptive adjustment. The method comprises the following steps: firstly, processing environmental data by adopting an isolation forest algorithm, Kriging interpolation and an empirical mode decomposition method, and extracting time sequence features; and then combining with the estimated photovoltaic output power. And then the genetic algorithm and dynamic programming are utilized to realize electrolytic cell power optimization distribution. And then converter control parameters are optimized through subspace identification, pole assignment and a hybrid optimization algorithm. And finally, system performance evaluation and adaptive adjustment are realized by adopting a sliding time window method, singular spectrum analysis and a fuzzy prediction control method. The technical problems of the energy-storage-free photovoltaic hydrogen production system in the aspects of environmental data processing, power estimation, energy management, control optimization and the like are solved, and the operation efficiency and reliability of the system are improved.

An object tracking device, such as may be attached to a trailer to track movements of the trailer, may have various communication capabilities, such as cellular / LTE, Wi-Fi and GPS, which each have varying power requirements. A power optimization routine determines which communications should be performed to conserve battery while also providing frequent location updates. The routine determines if another plugged in tracking device is within low power Bluetooth (BLE) range and, if so, communications location via that device. If not, a Wi-Fi scan is performed and a lookup table is consulted for the corresponding location of any identified Wi-Fi access points. If location data cannot be found for an identified Wi-Fi access point, GPS location of the device is determined and the lookup table is updated to include the GPS location of the Wi-Fi access point.

The invention relates to the technical field of low-power-consumption AI processors, and discloses a novel real-time data processing method and chip for calculating a low-power-consumption AI processor, and the method comprises the steps: carrying out the neural mimicry classification processing of a calculation task inputted by the low-power-consumption AI processor, and obtaining a calculation task feature matrix; performing multi-dimensional mapping operation on the calculation task feature matrix to obtain a heterogeneous calculation space vector; performing core resource allocation based on the heterogeneous calculation space vector to obtain a dual-mode processing unit configuration matrix; constructing a storage and calculation integrated data path based on the dual-mode processing unit configuration matrix to obtain a parallel calculation data flow diagram; executing dynamic voltage frequency mapping on the parallel computing data flow diagram to obtain a power consumption optimization control vector; and inputting the power consumption optimization control vector into the trained pulse neural network model to carry out computing power load balancing calculation and generate computing power load balancing parameters, and the method improves the real-time data processing capability of the low-power AI processor.

The invention discloses a computing power analysis method and system for a shared server and a storage medium. The computing power analysis method comprises the steps that S1, a shared server running state data set is obtained through a monitoring module; s2, preprocessing the operation state data set of the shared server; s3, the dynamic analysis model evaluates the current state and the change trend of the multi-dimensional calculation power index at the same time; s4, applying the dynamic analysis model of the computing power of the shared server to the real-time operation state data set of the shared server to generate an evaluation result of the computing power state; s5, optimizing server resource scheduling through a crown porcupine optimization algorithm; s6, applying the computing power optimization strategy of the shared server to a shared server environment; and S7, generating a prediction result by using the historical operation state data and the calculation power state evaluation result. According to the method, the trend prediction model is constructed through the crown porcupine optimization algorithm, so that the change direction and strength of the future computing power demand can be sensed in advance, and a prospective reference is provided for resource scheduling.

The invention provides a multi-mechanical-arm cooperative control method which comprises the following steps: modeling initial states and virtual constraints of mechanical arms, constructing an overall description of a multi-mechanical-arm cooperative task, decomposing the task into sub-tasks of the mechanical arms based on the description, and optimizing load distribution; the initial state is represented by a quintuple and comprises the position, posture, linear velocity, angular velocity and load information of the tail end of the mechanical arm; the virtual constraints comprise relative positions, relative postures, synchronous speeds, load balancing and power optimization constraints and are used for limiting kinematics and dynamics behaviors of the multiple mechanical arms; on the basis of the initial state and the virtual constraint, load distribution is optimized by utilizing a genetic algorithm, and a smooth trajectory meeting the collaborative task requirement is generated by adopting a quintic polynomial interpolation method; and the pose deviation of the multiple mechanical arms is corrected based on a virtual spring damper by combining trajectory tracking control and collaborative error compensation. According to the method, the movement efficiency and the task completion quality of the multi-mechanical-arm cooperative task are improved.

The invention relates to the technical field of power distribution, and discloses a power distribution method and system for a liquid cooling string PCS energy storage converter, and the method comprises the steps: constructing a multi-dimensional state matrix of each energy storage unit in the liquid cooling string PCS energy storage converter; establishing a thermal coupling model among the energy storage units according to the temperature parameters in the multi-dimensional state matrix, and constructing a temperature constraint projection mapping function; constructing a power optimization objective function considering the thermal coupling effect; calculating temperature change caused by disturbance through a thermal coupling model to obtain a temperature sensitive gradient vector; the power distribution weight of each energy storage unit is dynamically adjusted, and when the iteration change value of the power optimization objective function is smaller than a preset threshold value and the thermal coupling model is stable, a target power distribution strategy is output, collaborative optimization of power distribution and temperature control is achieved, the problem of thermal interference between the energy storage units is effectively solved, and the reliability of power distribution and temperature control is improved. And the overall operation efficiency of the liquid cooling string PCS energy storage converter is obviously improved.

A GPU cluster computing power optimization architecture oriented to large model training is characterized in that a computing power resource management module is used for monitoring and managing the computing power resource state of each GPU in a GPU cluster in real time, a task scheduling module is used for allocating GPU resources according to the requirements of training tasks, and a computing power optimization module is used for dynamically optimizing the computing power of the GPU cluster in the task execution process. And the result feedback module is used for collecting and analyzing the optimized computing power use condition so as to adjust a subsequent task scheduling strategy. According to the method, the computing power resource state of the GPU cluster is monitored and managed in real time, the GPU resources are dynamically allocated according to the demand of the training task, and the computing power of the GPU cluster is dynamically optimized in the task execution process, so that the computing power utilization efficiency of the GPU cluster is effectively improved, and the training cost is reduced.

The invention relates to the technical field of power distribution station control, in particular to a reactive power optimization control system of a power distribution station. Comprising a power distribution station data acquisition module, a reactive prediction module, an optimization decision module, a distributed cooperative control module and a monitoring interaction module. The power distribution station data acquisition module acquires parameters such as voltage and current and guarantees data integrity; the reactive power prediction module performs modeling based on a graph neural network and generates a prediction report; the optimization decision module adopts a differential evolution algorithm to generate an optimal compensation scheme; the distributed cooperative control module realizes cooperation between devices through fractional calculus; and the monitoring interaction module realizes equipment diagnosis, aging prediction and dynamic feedback. Through an advanced algorithm and a cooperative control technology, high efficiency, reliability and intelligence of reactive power optimization control of the power distribution station are realized, and the operation efficiency and reliability of the power distribution station are improved.

The invention relates to the technical field of power grid optimization, in particular to a regional power grid reactive power optimization control method and system, and the method comprises the steps: building a dynamic response matrix according to key characteristic parameters of reactive power compensation equipment in a regional power grid; dynamically tracking the reactive power flow of the power grid according to the dynamic response matrix and the regional power grid line power flow operation state data to obtain a reactive power demand change trend; determining a target output adjustment value of each reactive compensation device according to the reactive demand change trend; generating an output adjustment control instruction according to the target output adjustment value; and adjusting the reactive power output of each reactive power compensation device according to the output adjustment control instruction, and dynamically adjusting the output limit of the reactive power compensation device according to the device operation state and the reactive power data of the reactive power compensation device in the reactive power output adjustment process of the reactive power compensation device. By dynamically adjusting the output limit of the reactive power compensation equipment, accurate control and optimal adjustment of the output of the reactive power compensation equipment are realized, and the reactive power compensation efficiency of a power grid is improved.

The invention relates to the technical field of intelligent control, in particular to an intelligent power optimization scheduling method in a new energy grid-connected scene, which comprises the following steps of: acquiring a current load, a rated load, an adjustment rate and a temperature rise value through a data interface, and normalizing to generate a characteristic parameter matrix; the product of the adjusting rate and the temperature rise value is used for calculating the correlation degree with the aging factor to obtain an elastic weight, extracting the voltage stability and the weight, inputting the voltage stability and the weight into a Dijkstra algorithm to screen a path meeting the safety margin, monitoring the slope of an inertia change curve, adjusting a compensation coefficient through fuzzy PID control, and feeding back the compensation coefficient to correlation degree calculation. According to the method, multi-source parameters are fused through a rule tree structure, the load regulation rate and the aging factor weight are dynamically distributed, bus voltage stability and elastic weight path optimization are combined, a synchronous inertia margin safety correction system is constructed, the system response precision, the state cooperation efficiency and robustness are improved, local optimum is avoided, and the system reliability is improved. And the standby capacity configuration and the frequency advance correction capability are optimized.

The invention discloses a laser power optimization method based on a deep reinforcement learning strategy, belongs to the technical field of simulation and artificial intelligence, and is suitable for a laser powder bed melting (LPBF) process. The method comprises the steps that firstly, an LPBF simulation model is built, a molten pool three-dimensional temperature field distribution model is built through a moving heat source, and the forming process under a laser scanning path is simulated in combination with a part STL file; a state space and a reward function are constructed by extracting state features of a molten pool in real time, and a laser power adjustment strategy is optimized by using a deep reinforcement learning DQN network model. An optimal laser adjustment action is generated by adopting an epsilon-greedy algorithm, and intelligent agent interaction data is stored and updated based on an experience pool, so that network model parameters are optimized, and the stability and uniformity of the depth of a molten pool are ensured. According to the method, internal defects are effectively reduced, the quality and intelligent control level of the LPBF process are improved, and a new thought is provided for efficient and accurate process parameter optimization.

The invention discloses a multi-modal perception and optimization method and system for a low-power-consumption AR device, and the method comprises the steps: collecting multi-modal data, task demands, resource state data and environment data for the AR device; lightweight processing is carried out on the multi-modal neural network model through model pruning, parameter quantification and distillation technologies; inputting the collected data into a lightweight multi-modal neural network model for dynamic reasoning to obtain a multi-modal recognition result; comprising the steps of executing modal adaptive weight acquisition based on task requirements and environment data; executing energy consumption constraint scheduling according to the equipment resource state data, dynamically selecting a reasoning path strategy, and obtaining corresponding modal feature output; multi-modal feature fusion is carried out, task reasoning is completed, and a multi-modal recognition result is obtained; early-leaving control is executed based on a middle-layer confidence coefficient threshold value in the reasoning process; and interactively outputting a real-time multi-mode identification result. According to the invention, energy efficiency and precision balance and multi-mode fusion low-power-consumption optimization can be realized.

The invention provides a wind power plant AGC optimization control method and system based on model prediction control, and relates to the technical field of model control, and the method comprises the steps: building an adaptive prediction model, carrying out the online identification of parameters through a recursive least square method, constructing an active power optimization objective function, solving a control instruction sequence, designing a feedback compensator, and correcting a control instruction. And finally, issuing to each fan to execute and realize closed-loop control. According to the method, the AGC control precision of the wind power plant can be improved, the power fluctuation is reduced, and the power grid friendliness and the adaptability of an AGC control system are enhanced.

The invention relates to a new energy charging pile dynamic scheduling method, system and device and a medium, and the method comprises the steps: collecting the real-time load data of a power grid, user charging request information, battery health state data and historical charging data, and carrying out the classification and aggregation of charging behavior characteristics, and generating behavior grouping labels; demand prediction and power safety boundary calculation are carried out based on a preset power prediction model in combination with the battery health state, and a power prediction result is formed; detecting power grid load conflicts and optimizing power distribution to generate a preliminary scheduling scheme; performing multi-target power optimization adjustment on the conflict points to generate a charging power scheme, and issuing and executing the charging power scheme; and finally, dynamically updating prediction model parameters according to charging pile feedback data. According to the method, the problems of neglect of battery health change, insufficient charging behavior distinguishing, response lag, weak collaborative optimization capability and the like in the prior art are solved, and collaborative optimization of power grid stability, equipment safety and charging efficiency is realized.

The invention discloses a transmission driving control method of a bimodal integrated engine. The transmission driving control method comprises the following steps: S1, state sensing; s2, mode decision making; S3, dynamic adjustment; s4, mode switching; S5, power optimization distribution; s6, adaptive learning is carried out; and S7, fault-tolerant guarantee. The invention further discloses a bimodal integrated engine transmission system which comprises the following modules: a power input module; a transmission adjusting module; a state sensing module; an intelligent control module; and a fault-tolerant guarantee module. Parameters are dynamically adjusted through fuzzy PID control according to speed deviation, meanwhile, real-time correction is conducted in combination with a multi-mode intelligent algorithm, speed fluctuation is precisely controlled within + / -3 km / h, flight stability is guaranteed, a model prediction control and multi-mode intelligent control cooperation mechanism is adopted, an optimal power output sequence is calculated in advance, coordinated distribution is completed, and the optimal power output sequence is controlled to be within + / -3 km / h. The dynamic response speed is increased by 40%, and the high-dynamic task requirement is met.

The invention relates to the technical field of computing power optimization, and provides a computer and cloud computing power optimization method based on multi-task collaboration. The method comprises the following steps: receiving Q to-be-executed tasks of a local cloud collaborative network; performing priority constraint on the Q to-be-executed tasks according to the multivariate priority evaluation factors, and establishing a task chain; collecting callable computing resource information of a local cloud collaborative network, and establishing a resource space; based on the resource space, performing task chain resource allocation by utilizing a computing resource scheduling optimization algorithm to obtain a scheduling strategy chain; performing cloud computing power balance optimization according to the scheduling strategy chain to obtain an optimized chain; and executing the task chain according to the optimization chain based on the cloud collaborative network. The technical problems of unbalanced scheduling and low resource utilization rate between the cloud and the local computing resources are solved, and the technical effects of improving the cooperative work efficiency and the resource utilization rate of the cloud and the local computing resources through priority constraint and computing resource scheduling optimization are achieved.

The invention discloses an energy storage equipment power balance dynamic load automatic regulation and control method and system, and the method comprises the steps: building a charging power optimization model based on an electricity price policy divided in peak and valley time periods, combining historical electricity demand data, and obtaining the optimal charging power in each time period; the method comprises the following steps: constructing a discharge power dynamic adjustment model through operation state parameters acquired by a real-time environment parameter acquisition device in combination with real-time electricity demand data, and acquiring optimal discharge power in each time period; parameters of the two models are optimized, and the load state based on the optimal charging and discharging power is balanced; based on the historical regulation and control data, the overload condition under the abnormal factors is predicted; and on the basis of a power imbalance condition, preferentially ensuring power supply of a key load according to a power distribution priority and executing energy storage equipment state self-checking. The method has the advantages that the charging power and the discharging power are optimized in combination with the electricity price and the real-time requirement, so that the load state is balanced, the energy cost is reduced, and the equipment overload risk is avoided.

The invention provides a circulation power optimization method and system for a three-phase dual-active bridge, and relates to the field of power electronics, the method comprises the following steps: determining an optimal backflow modulation strategy set corresponding to a hybrid modulation group, the hybrid modulation group comprising triangular wave modulation, trapezoidal modulation, synchronous modulation and extended phase shift modulation, the optimal backflow modulation strategy set corresponding to the hybrid modulation group comprises modulation modes and degree-of-freedom relationships corresponding to different transmission power per unit value ranges in different voltage conversion ratio ranges; determining a degree of freedom and power relationship set corresponding to the hybrid modulation group, wherein the relationship set comprises a relationship between the degree of freedom and power of different transmission power per unit values corresponding to each mode; the phase shift angle, the primary side duty ratio and the secondary side duty ratio are determined by combining the real-time voltage conversion ratio and the real-time virtual power per unit value, operation of the three-phase dual-active bridge is controlled, and the method has the advantages of reducing complexity and improving portability and efficiency.

The invention belongs to the technical field of charging pile thermoelectric control, and particularly relates to a high-power liquid cooling charging pile charging power optimization scheduling method and system. The method comprises the following steps: collecting operation state parameters of a plurality of liquid cooling charging piles; identifying a target pile body in a charging state or a standby state; calculating a theoretical energy supply upper limit based on an electrothermal model; combining the vehicle charging demand, the priority, the battery state and the power grid load to construct a power scheduling model and execute distribution; power adjustment and redistribution are carried out when cooling abnormity or connection faults are detected; and finally, a scheduling control instruction is generated and executed in real time. The system comprises an information acquisition module, a state identification module, a model calculation module, a power distribution module, a dynamic adjustment module and an instruction issuing module. According to the invention, efficient, safe and dynamic power scheduling control of the high-power liquid cooling charging pile in a multi-device parallel connection scene is realized.

The invention relates to the technical field of maximum power point tracking, in particular to a solar photovoltaic panel generation power optimization method and system, and the method comprises the following steps: obtaining a photovoltaic panel branch power sequence, constructing a direction change matrix, recognizing a same-direction change section, extracting a difference value, generating a trend labeling table, and comparing power change to screen adjustment nodes. And sending an adjustment instruction to record the difference value, and generating an optimization response table. According to the invention, through regular acquisition of voltage and current of a photovoltaic branch, construction of a power change trend matrix and extraction of power change directions of adjacent moments, trend identification and change modeling are realized, segments with persistent and significant amplitude changes are screened, accidental disturbance is eliminated, and adjustment and judgment accuracy is improved. And comparing the trend segment with a power tracking record, selecting a node with a promotion potential to send an adjustment instruction, recording response feedback, and introducing a quantization threshold and a feedback mechanism to improve the maximum power point identification precision and the power generation efficiency of the system under environmental fluctuation.

The invention provides a power supply optimization model and method for a converter station. The method comprises the steps that firstly, a converter station energy consumption characteristic analysis and electric quantity prediction model is constructed, and the converter station energy consumption characteristic analysis and electric quantity prediction model comprises user converter station energy consumption characteristic analysis based on wavelet transformation and an optimization fast density peak value clustering algorithm and a converter station electric quantity prediction model based on an XGBoost algorithm; then, constructing a wind power and photovoltaic novel power supply output prediction model, and adopting a wavelet decomposition-bidirectional long-short term memory network based on an Attention mechanism as a prediction model; and finally, in combination with the electric quantity prediction model and the novel power supply output prediction model, constructing a power supply combination optimization design model for the converter station. Through the model, the power optimization capability and the use efficiency of the converter station can be improved, electric energy resources are managed and reasonably utilized, and the economical efficiency and the safety of a power system are improved. The power supply optimization model and method for the converter station are innovative and have practical value.

The invention discloses a power distribution micro-grid double-layer optimization scheduling method, which is suitable for the technical field of power optimization scheduling, and comprises the following steps: S1, constructing an upper-layer power distribution network optimization scheduling model by taking the minimum network loss in an active power distribution network system as an objective function, and setting a corresponding upper-layer optimization constraint condition; s2, taking the minimum grid-connected operation cost in the micro-grid system as an objective function to construct a lower-layer micro-grid grid-connected optimization scheduling model, and setting corresponding lower-layer optimization constraint conditions; and S3, solving the grid-connected optimal scheduling model of the lower-layer micro-grid by adopting a second-order cone technology to obtain a tie line coordination parameter, determining a micro-grid access point type according to the tie line coordination parameter, and solving the optimal scheduling model of the upper-layer power distribution network by calling a Cplex solver and combining with a parameter corresponding to the micro-grid access point type to obtain a scheduling strategy. According to the invention, efficient collaborative optimization scheduling of the distribution microgrid system is realized, and the economical efficiency, robustness and flexibility of the system are improved.

The invention discloses a power distribution area reactive compensation control method based on an energy storage PCS converter, and the method comprises the following steps: 1, data collection and processing: collecting the power parameters of a power distribution area in real time through employing the technology of Internet of Things, such as voltage, current, active power, reactive power, and frequency; the data preprocessing module is used for cleaning, filtering and normalizing the collected data to ensure the accuracy and consistency of the data; 2, constructing a multi-objective optimization model; designing an objective function; and constructing a multi-objective optimization model comprising reactive power compensation, active power optimization, voltage stability and energy efficiency improvement. According to the invention, the performance and reliability of the reactive compensation control method for the power distribution area based on the energy storage PCS converter are effectively improved, and more efficient and more stable power grid operation is realized; the system can quickly isolate a fault unit after detecting a fault, realizes cooperative work among multiple PCS converters through distributed optimization, and ensures stable operation of the system.

The invention provides a power grid system control method and device, computer equipment and a storage medium in the field of power grid system control, and the method comprises the steps: firstly, determining a dynamic inertia coefficient through integrating node frequency deviation, an energy storage charge state and voltage deviation, calculating inertia compensation power through employing the frequency deviation, precisely capturing the dynamic characteristics of a system, and carrying out the calculation of inertia compensation power; and constructing a target function based on the equivalent impedance matrix and the power deviation, solving a target control sequence under constraint conditions, realizing system power optimization distribution, and further adjusting node power output from multiple aspects by determining composite inertia, synthesizing inertia power, regulating and controlling a power vector and an inertia matrix. And finally, based on energy function construction and partial derivative calculation, generating a control instruction of each node. The steps can effectively deal with system power disturbance, rapidly compensate insufficient inertia, optimize power distribution, maintain system frequency stability, improve safety, reliability and stability of operation of a power system, and enhance adaptability of the system to complex working conditions.

The invention relates to the technical field of electric power dispatching, in particular to an electric power optimization dispatching method and system based on reinforcement learning, and the method comprises the steps: obtaining the operation data, load prediction data and renewable energy output data of an electric power system; based on the operation data, the load prediction data and the renewable energy output data, constructing a power system scheduling optimization model; generating an optimization scheduling strategy based on the power system scheduling optimization model by using a deep reinforcement learning algorithm; calculating the scheduling cost, the environmental influence and the system reliability index of the power system according to the optimal scheduling strategy; and outputting the optimal scheduling strategy and the corresponding scheduling cost, environmental influence and system reliability indexes, so that the operation cost of the power system can be reduced, the utilization rate of renewable energy sources can be improved, and safe and stable operation of a power grid is ensured.

The invention discloses a heterogeneous intelligent computing power optimization management scheduling system for accelerating a large model reasoning task, and relates to the technical field of computing power optimization management scheduling. The video memory fragmentation problem in a long sequence scene is converted into a controllable block migration task, and the performance bottleneck of a traditional video memory exchange mechanism is broken through; based on an operator-level scheduling strategy of a hardware capability fingerprint database, position coding and other compute-intensive tasks are accurately matched with vector instruction set hardware, and resource mismatch loss caused by black-box scheduling is eliminated; an expert selection process is reconstructed by an integer routing and counting sorting algorithm, near-lossless reasoning is realized at a limited node of an instruction set, and the potential value of an old computing power pool is activated.

The invention discloses a flexible interconnected power distribution network active-reactive optimization method considering electric energy quality requirements, and belongs to the technical field of power distribution network optimization operation. The invention aims to provide the active-reactive optimization method for the flexible interconnected power distribution network considering the electric energy quality demand, which simultaneously optimizes the voltage deviation, feeder balance and network loss of the power distribution network through various control means such as soft switching power regulation participation response. The method comprises the following steps: proposing a flexible interconnection system power quality demand subjective and objective weighting evaluation method through calculation of a flexible interconnection system day-ahead index time sequence value, and proposing a flexible interconnection power distribution network active-reactive power optimization model considering a node comprehensive weight through coordinated operation of a traditional network side resource and an intelligent soft switch. According to the invention, the active-reactive optimization method of the flexible interconnection power distribution network considering the power quality demand is analyzed, the effective cooperation of the flexible interconnection device and the controllable network side resource regulation and control is explored, and the influence on the voltage deviation, feeder balance and network loss of the power distribution network is optimized by multiple control means at the same time; and reference is expected to be provided for guiding optimal scheduling of the flexible interconnected power distribution network.

The invention discloses an Internet of Vehicles vehicle information interaction system based on a YTS engine, and relates to the technical field of vehicle information interaction, a data acquisition module fuses multi-source sensing data, and an AI computing power optimization module adopts a dynamic computing power scheduling strategy; the 3D modeling and physical simulation module ensures the real-time performance of modeling based on an AI-driven 3D environment reconstruction and physical simulation technology; the cloud cooperative computing module processes multi-vehicle data in a distributed manner by using a cloud computing architecture of a YTS engine, optimizes a local model and improves the accuracy of an automatic driving decision; and the intelligent interaction module adaptively adjusts a modal man-machine interaction mode according to the dynamic change of the automatic driving decision, so that the driving experience is improved. The technical bottlenecks of information interaction delay, insufficient 3D modeling precision, poor decision stability and the like of the existing vehicle networking system in a high-speed dynamic environment are broken through; efficient energy consumption management, intelligent cloud optimization and accurate interaction are realized, and the safety and reliability of automatic driving are improved.

The invention relates to the technical field of power dispatching, in particular to a power optimization dispatching method and system for a virtual power plant. The method comprises the following steps: acquiring electrical load data of a user in each day by using a virtual power plant; determining the fluctuation trend of the electrical load data at each moment in each day; determining the similarity between the electrical load data of each day and the electrical load data of other days; determining a target sequence and regularity of each day according to the similarity; predicting the average load of the day to be predicted; and according to the average load, regulating and controlling start and stop of a generator set, and completing power optimization scheduling of the virtual power plant. By considering the fluctuation trend of the power consumption load data of the user in each day and combining similarity and regularity analysis, the power consumption load of the target day can be predicted more accurately, so that the load prediction is closer to the actual power consumption behavior, and errors caused by the fact that fluctuation factors are not fully considered in a traditional prediction method are reduced.