3167 results about "Distribution method" patented technology

The invention belongs to the technical field of electrical digital data processing and resource allocation, and provides an agent lightweight deployment method and a computing power elastic allocation method, lightweight deployment realizes one-time development of multi-platform deployment by constructing a plug-in container packaging tool and a multi-architecture compiling engine; static analysis is used for stripping redundant dependence of the model, and the model is dynamically cut in combination with edge resources; developing an intelligent resource description language, integrating a control group and a filtering technology to realize container-level resource monitoring, constructing a 12-dimensional dynamic state space fusing a node state, task characteristics and a network environment through computing power elastic distribution, and introducing a time delay, cost and reliability three-dimensional weighted reward model to quantify distribution earnings; an edge cloud cooperative training framework and an edge execution strategy are designed, experience is collected, a cloud end trains a Q network through federal learning, an optimization decision is played back in combination with priority experience, the agent deployment efficiency and the resource utilization rate are improved, and data privacy is guaranteed.

The invention discloses an employment talent supply and demand distribution method and system based on data analysis, and belongs to the technical field of data analysis, and the method comprises the steps: obtaining talent basic information and post basic information; cleaning and sorting the talent basic information and the post basic information, identifying and deleting repeated data, and processing missing values and abnormal values; key features are extracted from the talent basic information and the post basic information by using a deep learning technology, and the key features are weighted or screened; establishing a matching model between the talent basic information and the post basic information by using a machine learning algorithm; a matching result is analyzed, model optimization is carried out according to the result, parameters or feature weights are adjusted, and the matching accuracy and efficiency are improved; the generalization ability and accuracy of the matching model are evaluated through cross validation and confusion matrix methods; and continuously optimizing the matching model, and adjusting the matching model according to feedback and experience to improve the matching effect. The method has the effect of helping to reasonably configure human resources.

The invention relates to the technical field of power management, and provides a power distribution method based on BMS power control and related equipment thereof. The method comprises the following steps of: performing multi-mode synchronous acquisition and self-adaptive preprocessing on detected battery pack global state data to obtain a battery state tensor, and performing unsteady-state signal decomposition and time-space feature extraction on a received load power demand signal to obtain a power component matrix; performing multi-constraint dynamic planning on the battery state tensor and the power component matrix to obtain an initial power distribution strategy, performing dynamic correction in combination with a deep reinforcement learning model to obtain an adaptive distribution strategy, and performing multi-layer circuit equalization optimization on the adaptive distribution strategy to obtain a power regulation instruction set; and finally, performing multi-physics field coupling verification and fault-tolerant reconstruction on the power regulation instruction set to obtain an execution signal sequence. According to the invention, through data fusion, demand prediction, strategy optimization and deep learning, power distribution is accurately regulated and controlled according to the battery state and the load power demand.

The invention provides an information resource distribution method and system applied to a dynamic network environment, and the method comprises the steps: firstly obtaining a real-time state feature set of a target distribution network, determining a resource distribution constraint condition set in a current network environment based on the real-time state feature set, carrying out the priority analysis of a real-time resource request sequence of a target user group, and obtaining a real-time resource distribution constraint condition set; obtaining resource timeliness grade distribution and a request path optimization feature set, generating a dynamic distribution strategy parameter set matched with a resource distribution constraint condition, triggering a resource redirection operation, and enabling the resource request response rate of the target user group to be improved to a preset threshold interval; and the information resource distribution efficiency and stability in the dynamic network are improved.

The invention is applicable to the technical field of computing power distribution, and provides a power equipment edge gateway computing power dynamic distribution method, which comprises the following steps of: determining a system state representation vector based on multi-source data of power equipment; the system state representation vector comprises a real-time computing power occupancy rate, a task queue length, a network bandwidth utilization rate and an equipment energy consumption index; constructing target parameters and constraint conditions of the calculation power dynamic distribution model; taking the system state representation vector as input, solving the computing power dynamic allocation model through a reinforcement learning algorithm, and generating a task scheduling path and a resource allocation proportion; and optimizing a preset hierarchical buffer queue based on the task scheduling path and the resource allocation proportion, and generating a target dynamic allocation strategy. According to the method, the high-real-time task response speed and the resource utilization rate are improved, energy consumption is reduced, meanwhile, backlog tasks are efficiently processed in the idle period through an event-driven mechanism, and system load balance is guaranteed.

The invention relates to the field of power equipment data processing, in particular to a multi-target dynamic optimization unit load intelligent distribution method, which adapts a micro-service cluster through a containerization protocol, adopts an OPC UA protocol and an MQTT protocol to respectively collect thermal power and new energy data at different frequencies, dynamically analyzes an IEC 61850 power grid instruction, converts and stores the IEC 61850 power grid instruction into a Kafka message queue, and sends the IEC 61850 power grid instruction to a network server. The problem of communication protocol heterogeneity is solved. Based on an IEEE 1588 protocol, sub-millisecond clock synchronization of edge nodes is realized, a cubic spline interpolation algorithm is adopted to align multi-frequency data, an interpolation window is dynamically adjusted in combination with a genetic algorithm, and a spatial-temporal characteristic matrix is generated and stored in a Redis database. And performing multi-objective optimization solution through an NSGA-II algorithm, and dynamically adjusting the weight of an objective function to adapt to the frequency modulation requirement of the power grid. A closed-loop feedback mechanism corrects model parameters through Kalman filtering, actual data are written back to a feature matrix, the robustness of the system is improved in combination with a hierarchical fault-tolerant strategy, the problem of space-time mismatch of data is effectively solved, and the economical efficiency and safety of load distribution are improved.

The invention discloses an efficient medicine logistics order scheduling and distribution method and system, and relates to the technical field of medicine logistics, and the method comprises the steps: constructing a multi-modal medicine feature extraction module, and outputting an order constraint matrix; establishing an adaptive space-time distribution network according to the order constraint matrix, and performing dynamic grid division on a distribution area to generate a multi-level distribution area model; constructing a double-layer heterogeneous graph neural network, and generating a distribution path planning candidate scheme set considering drug characteristics; defining a state space and an action space, constructing a dual-target reward function, and carrying out iterative training to obtain a distribution scheduling strategy model; and aggregating multi-region distribution empirical data by adopting a federated learning framework, and periodically updating the scheduling strategy model based on a differential privacy mechanism. According to the method, the multi-modal drug feature extraction module is constructed, and the image, text and storage condition information of the drug are fused, so that the drug storage requirement and the distribution limitation condition can be identified more accurately, and the identification accuracy of the distribution constraint is improved.

The invention relates to a low-carbon park comprehensive energy system multi-agent collaborative carbon responsibility allocation method, which comprises the steps of collecting multi-source data of a park, and performing multi-energy flow optimization scheduling by taking the minimum total operation cost of a comprehensive energy system as a target; constructing a dynamic carbon flow distribution matrix by utilizing a bidirectional carbon flow dynamic correction model considering carbon flow transfer caused by an energy storage charging and discharging process; a load carbon responsibility decoupling distribution model is adopted to decouple the total load into a plurality of sub-load categories, and different carbon responsibility distribution coefficients are given to each category of sub-load basis; the method comprises the following steps: performing multi-agent collaborative optimization by taking each benefit party in a park as a game participant, taking a Nash bargaining as a collaborative optimization framework, taking maximization of utility gains of all participants as an objective function, and taking a Shapley value as a carbon responsibility distribution basis, and obtaining a carbon responsibility distribution scheme of each agent in combination with a user carbon responsibility distribution coefficient. Compared with the prior art, the method ensures fairness, efficiency and operability of a carbon responsibility allocation scheme.

The invention discloses an intelligent energy consumption distribution method for a multi-source energy system. The method comprises the steps of energy supply data fusion, dual-channel joint prediction, energy response lag correction, multi-objective optimization and intelligent energy consumption distribution. The invention relates to the technical field of data processing of power management and resource scheduling, and the method comprises the steps: carrying out the unified collection and archiving of operation control data and environment information of power grid energy, distributed photovoltaic energy and an energy storage system, and constructing an energy supply fusion data set; respectively extracting energy availability and user load characteristics by adopting a double-domain attention decoupling fusion joint supply and demand prediction method, and realizing double-channel joint modeling; modeling, estimating and correcting response lags of different energy sources in combination with a dynamic response curve reconstruction method; and further constructing a layered target penalty function containing real-time response error, economic cost, carbon emission and response penalty terms, and embedding the layered target penalty function into a particle swarm optimization algorithm to obtain an energy distribution strategy under multi-target optimization.

The invention discloses a power distribution network protection resource dynamic allocation method, system and device and a storage medium, and relates to the field of power system network security protection, and the method comprises the steps: collecting the operation data of a power distribution network in real time, constructing a multi-dimensional fusion data set, and predicting a potential attack path through an attack path prediction model in combination with a historical attack mode library; performing risk assessment on the predicted potential attack path, and calculating the protection resource demand quantity of each region of the power distribution network in combination with the topological structure of the power distribution network and the importance of key nodes; according to a risk assessment result, dynamic allocation and real-time scheduling of protection resources are realized in combination with a resource constraint condition; the method can grasp the operation of the power distribution network in real time, accurately predict the potential attack path, accurately evaluate the risk and calculate the protection resource demand. Dynamic allocation and real-time scheduling of protection resources are realized, the resources are reasonably utilized, the cost is reduced, the protection effect is improved, and safe and stable operation of the power distribution network under complex network attacks is ensured.

The invention relates to the technical field of intelligent power system optimization scheduling, and discloses a power load optimization distribution method and system based on an intelligent algorithm, and the method comprises the steps: collecting historical load data of a power system, and carrying out the preprocessing; the fractal dimension of the load time sequence is calculated, a load change critical point is identified, and the size of a time window for load prediction is dynamically adjusted. And inputting the dynamically adjusted time window data into an LSTM model for load prediction. And according to a load prediction result, carrying out intelligent optimization distribution on the power load by adopting an optimization algorithm. According to the method, the optimal distribution of the power load not only has high precision, but also can flexibly cope with various sudden fluctuations and complex constraint conditions in a power system. Through the steps, the operation efficiency, the stability and the economical efficiency of the electric power system are improved, meanwhile, the uncertainty and the risk in load distribution of the electric power system are reduced, and a new thought and technical support are provided for a future intelligent power grid and an electric power dispatching system.

The invention discloses a task allocation method, device and equipment, a storage medium and a computer program product, and relates to the technical field of computers.The method comprises the steps that reasoning task information in a first preset time period and computing power information of all computing cards in a heterogeneous computing system are obtained; predicting a predicted number of the reasoning requests received in a second preset time period based on the reasoning task information; and according to the reasoning task information, the computing power information of each computing card and the predicted number, allocating a pre-filling task or a decoding task to each computing card, and determining the number of tasks allocated to each computing card, so that the time for the heterogeneous computing system to respond to the predicted number of reasoning requests is shortest. According to the method, the optimal matching of the heterogeneous resources and the reasoning tasks is realized, the utilization rate of the heterogeneous resources is improved, and the execution efficiency of the reasoning tasks is improved.

The invention discloses a multi-objective optimization-based enterprise resource coordination and distribution method, which comprises the following steps of: S1, acquiring multi-dimensional description information of enterprise task requirements, and constructing a requirement diagram; s2, based on the demand graph structure, performing clustering division on task nodes by adopting a self-organizing clustering algorithm, generating task clusters, generating a candidate resource demand set, and constructing a resource node set; s3, constructing a resource scheduling candidate topological graph; s4, defining a scheduling adaptability function containing a multi-target index, wherein the multi-target index comprises cost, period, resource utilization rate and resource scheduling distance; s5, extracting an optimal scheduling sub-graph from the candidate scheduling topology by adopting an improved heuristic aggregation algorithm; and S6, outputting a corresponding resource allocation scheme to complete resource allocation. According to the method, task structure modeling and multi-target scheduling optimization are fused, the precision and adaptability of resource allocation are improved, and the method is suitable for an enterprise resource management system in a complex scheduling scene.

The invention relates to the technical field of energy storage systems, and provides a self-adaptive power distribution method and system for multiple energy storage units of an island microgrid. According to the invention, a discrete consistency algorithm is adopted to realize coordination control of multiple units, each unit only exchanges information with adjacent units, and power distribution targets of all the units are converged to a consistent value through iterative computation, so that the communication bottleneck and the single-point fault risk of centralized control are avoided; according to the invention, a complete fault-tolerant processing mechanism is designed, the operation state of each energy storage unit is monitored in real time, topology updating and power redistribution are automatically carried out when a unit fault is detected, and the self-healing operation of the system is realized. Experiments show that compared with a traditional method, the method has remarkable advantages in key performance indexes such as capacity balance among energy storage units, system operation efficiency, communication overhead and fault recovery time, and the operation efficiency and reliability of the island microgrid system are remarkably improved.

The invention discloses a multi-modal large-model multi-agent collaborative scheduling and distribution method, and relates to the technical field of agent deploying.The method comprises the steps that a complex task input by a user is received, the complex task is disassembled into atomic-scale subtasks, and a task decomposition graph is generated; establishing an intelligent agent capability evaluation model, and obtaining a comprehensive capability score of each intelligent agent according to the capability behavior of each intelligent agent in the candidate intelligent agent set; the intelligent agent set generation module is used for dynamically matching and distributing roles through a decision function according to subtask types and intelligent agent comprehensive capability scores, and generating a candidate intelligent agent set; and according to the task decomposition graph and the comprehensive capability score of each agent, constructing a task-agent ant colony allocation algorithm, and solving an allocation scheme. The problems that an existing multi-agent system lacks a dynamic role adjustment mechanism, flexible cooperation cannot be achieved according to task progress and agent states, and the efficient and flexible task processing requirement of a multi-mode large model application development platform is difficult to meet are solved.

The invention provides a distributed reasoning task allocation method for an edge computing large model, which belongs to the technical field of mobile edge computing and comprises the following steps of: considering a large language model distributed deployment scene, constructing a workflow for cooperatively finishing a reasoning task by a plurality of edge servers, and adopting accurate secondary reconstruction by considering the generation type characteristic of the reasoning task; the uncertainty of reasoning task resource occupancy is solved, mathematical model expression is constructed for the edge reasoning task allocation problem in the scene, the aim is to maximize the service provider income, the constructed mathematical model is reconstructed into a combination selection problem, an approximate solution with theoretical guarantee is given by adopting a dual method, and the problem of edge reasoning task allocation is solved. And the reasoning task distribution decision of the service provider is optimized, so that the income of the service provider is maximized.

The invention provides a charging station rectifier cabinet power distribution method and system, and the method comprises the steps: building a first pre-distribution power path between each charging gun in a plurality of charging guns in charging and a direct connection power module, and obtaining a first pre-distribution power path set; determining a second pre-distribution power path set of the charging gun in each charging process to obtain a plurality of second pre-distribution power path sets; processing the first pre-allocated power path set and the plurality of second pre-allocated power path sets according to a preset power module scheduling priority ranking strategy to obtain a third pre-allocated power path set meeting a preset power sum constraint condition; and according to the third pre-distributed power path set, updating the electric connection relationship between the plurality of charging guns in charging and the plurality of power supply modules. The method can effectively solve the problem of path blockage when the multiple charging piles share the rectifier cabinet, and facilitates the improvement of the power utilization rate.

The invention provides an air hospital intelligent logistics distribution method based on deep reinforcement learning, and the method comprises the steps: receiving all target tasks, determining the priorities of the tasks through a hierarchical task decision model, sorting all the tasks according to the priorities, and entering a scheduling stage; constructing a task and aircraft resource matching model to perform single-task and single-aircraft matching between tasks and aircrafts, performing multi-aircraft cooperative scheduling task allocation in combination with the cooperation cost of the aircrafts, and performing optimization adjustment on the priority based on real-time monitoring; generating a flight instruction based on the optimized and adjusted priority; an optimized flight instruction is generated by adopting a fuzzy logic control method, and dynamic path adjustment is performed on the total flight path to ensure that the aircraft always keeps the optimal flight path in the task execution process; feedback of the flight instruction for executing optimization is collected, and dynamic optimization is conducted on the flight task.

The invention provides a charging pile power distribution method and system based on dynamic load balancing, and the method comprises the following steps: obtaining the local state information of each charging pile, the state information at least comprises the charging queuing length, the current vehicle charge state, the current load power, the battery health state, the predicted residence time, the electricity price information and the power grid load state; based on the historical charging behavior, the electricity price fluctuation and the traffic flow data, the vehicle access frequency, the electricity price trend and the power demand of each charging pile in the future time period are predicted through a time sequence neural network; a prediction model is constructed; according to the method, forward-looking power planning is realized by entering a multi-dimensional prediction mechanism; the power distribution efficiency is improved by adopting a local collaborative game; the self-adaption and generalization ability is realized through reinforcement learning, and the robustness of a scheduling strategy in multiple scenes is enhanced; through a multi-target scheduling index, the electricity price, the battery life, the waiting time and the power grid load are optimized at the same time.

The invention provides a credible industrial agent task distribution method and system, and relates to the technical field of data processing, and the method comprises the steps: receiving a registration request sent by an industrial agent developer, and storing the metadata information of an industrial agent into a distributed account book based on the registration request; and based on the metadata information, receiving a task distribution request sent by a task requester, and matching candidate industrial agents and unique identifiers meeting function requirements in a distributed account book. Credible agents are screened through distributed account book evidence storage core information and dynamic trust evaluation, the credibility level is iteratively updated after execution, and credibility traceability of the whole task distribution process and dynamic optimization of the credibility of the agents are achieved.

The invention relates to the technical field of power distribution, in particular to a dynamic power distribution method and system for an automobile charging pile, and the method comprises the steps: extracting the historical load data of the charging pile through a sensor, converting the data into a time series data set in a unified range, and carrying out the window sliding fitting through employing a convolutional neural network based on the time series data set; the future load demand of each charging pile is predicted, meanwhile, a graph structure network of a power transmission relation is constructed, node power distribution information is fused, a charging pile cooperation relation is established, the relation between the power demand of a single charging pile and the power grid bearing capacity is evaluated by adopting a genetic algorithm, power output is adjusted, the output ratio is calculated, and a reasonable power distribution scheme is formed; and finally, monitoring a power grid load and a charging pile operation state in real time, constructing a graph structure of a charging pile cooperation network based on a geographic position, changing independent management of the isolated charging piles into networked cooperation management, and enhancing reasonable resource allocation among the charging piles.

The invention discloses a multi-user-oriented smart home resource conflict negotiation and allocation method, which comprises the following steps of: detecting equipment use conflicts caused by at least two users by constructing a structural causal model for representing a causal relationship of a plurality of variables in a smart home environment, generating a candidate decision strategy set comprising resource isolation and alternative compensation, and allocating the candidate decision strategy set to the smart home environment; carrying out anti-fact inference by utilizing a causal model, quantifying the causal effect of each strategy on the user state, and selecting an optimal strategy for execution based on a minimum negative effect and a Pareto optimal principle; besides, the method ensures that the system dynamically adapts to user habit changes through online monitoring of prediction errors and correction of the causal model, and compared with the prior art, the method improves the decision accuracy through causal reasoning, realizes fair and personalized user resource allocation, and improves the user experience and long-term effectiveness of the smart home system.

The invention discloses a virtual power plant building key load priority distribution method and system, and relates to the technical field of load dispatching management. The virtual power plant building key load priority distribution method comprises the following steps: S1, acquiring load switching scheduling data, and preprocessing the load switching scheduling data; s2, identifying an executable switching window between the main power supply and the standby power supply, quantifying a switching impact index of a load, and screening a candidate load set; s3, evaluating the switching priority of the load and generating a sorting list, and sequentially selecting according to the upper limit of the residual impact capacity of the bus to form a current period execution plan; and S4, executing a scheduling instruction, recording operation parameters before and after switching in real time, constructing a disturbance evaluation input set, evaluating a load switching disturbance degree, judging a switching state based on an evaluation result, and triggering a feedback mechanism to adjust a subsequent sorting weight and an impact tolerance. The problem of bus impact caused by concurrent access of multiple loads in the main and standby power supply switching process is solved.

The invention provides an intelligent cooling capacity distribution method and system for a data center refrigeration system, and relates to the technical field of data center refrigeration. Through the thermal model based on data driving, airflow distribution and temperature field change are accurately simulated, thermal load prediction errors are effectively reduced, and high-precision data support is provided for cold distribution, so that the energy utilization efficiency and the refrigeration efficiency are improved, and energy efficiency improvement and reliable operation of a data center refrigeration system are achieved. And meanwhile, the interaction among heat transfer, power consumption and a control strategy is comprehensively considered, the overall efficiency loss caused by local optimization is avoided, and the comprehensive energy efficiency of the refrigerating system is improved. Besides, the cooling capacity distribution optimization model is constructed based on the power-cooling capacity characteristic curve, so that the optimal balance between the cooling power and the cooling index is realized, and the PUE is effectively reduced.

The invention discloses a memory allocation method and device, electronic equipment and a storage medium, and relates to the technical field of computers.The method comprises the steps that when a memory application of a requester is received, the memory size requested by the memory application is determined; if the memory size is smaller than or equal to the first threshold value, responding to the memory application based on the memory page which is not completely allocated; wherein the size of the memory page is larger than or equal to a preset value, and the first threshold value is smaller than the size of the memory page; if the memory size is greater than the first threshold and less than or equal to a second threshold, responding to the memory application based on an idle memory page in a preset memory page set; wherein the second threshold value is larger than the size of the memory page; and if the memory size is greater than the second threshold value, responding to the memory application based on the unreleased idle memory page of the cache and / or the idle memory page in the operating system. The memory allocation efficiency is improved.

The invention belongs to the technical field of battery energy storage systems. The invention provides a power distribution method and system for a battery energy storage system, a medium and computer equipment, when the target power of the energy storage system is not zero and the target power is greater than or equal to the sum of the maximum charging and discharging power of all energy storage units, all the energy storage units operate at the maximum charging and discharging power; when the target power of the energy storage system is not zero and the target power is smaller than the sum of the maximum charging and discharging power of all the energy storage units, determining the number of required energy storage units according to the target power and the maximum charging and discharging power of each energy storage unit, and selecting the energy storage units to be used according to an energy storage unit priority rule; performing global optimization on the target power distribution to obtain an optimal or approximately optimal power distribution value of each energy storage unit to be used; according to the invention, the SOC balance degree of the battery energy storage system is improved, and the risk of over-charging and over-discharging is reduced.

The invention relates to the technical field of storage automation, and particularly discloses an electric power storage dynamic storage location distribution method and system fusing multi-dimensional factors, and the method comprises the following steps: constructing an electric power storage digital twin environment based on edge calculation, collecting multi-source heterogeneous data in real time through an OPC-UA protocol, and building a three-dimensional data association model; and based on the three-dimensional data association model, extracting a 28-dimensional state vector, and constructing a reinforcement learning state space. According to the power storage dynamic storage location allocation method and system fusing the multi-dimensional factors, a real-time mapping storage virtual environment is constructed through edge calculation and a digital twinning technology, and intelligent upgrading of power storage management is realized in combination with reinforcement learning and a cross-system linkage rule; according to the scheme, storage location distribution can be dynamically optimized according to multi-dimensional parameters such as the remaining service life and the maintenance cycle of the equipment, the storage space utilization rate and the equipment scheduling efficiency are remarkably improved, and the intelligent and automatic level of electric power storage is comprehensively improved.

The invention discloses a cross-regional computing power resource collaborative allocation method based on computing power centers, and relates to the technical field of computing power resource scheduling and optimizing.The cross-regional computing power resource collaborative allocation method comprises the steps that real-time load data, historical task execution data and network delay data of all computing power centers are collected, and a regional load feature database is constructed; predicting the load demand of each computing power center in a future time window by using a long short-term memory network model to generate a load prediction value; calculating a resource gap coefficient and a resource margin coefficient of each region according to the load prediction value and the current resource capacity, identifying a resource insufficient region and a resource surplus region, and generating a resource collaborative matching matrix between the regions; and optimizing and solving the cross-regional task allocation scheme by adopting an improved genetic algorithm to generate an optimal resource allocation strategy, and allocating the to-be-processed task to a corresponding computing power center according to the optimal resource allocation strategy. According to the method, the cooperative utilization rate and the distribution efficiency of the computing power resources in the cross-regional scene are effectively improved.

A charging pile group dynamic power distribution method and system based on load prediction relates to the field of electric digital data processing, and the method comprises the following steps: obtaining charging data of charging piles in real time, and when a new vehicle requests to be charged, calculating the difference value between the required power and the current available power to obtain a power gap; and if the power gap exists, calculating a power amount which can be reduced by each charging vehicle according to the charging curve parameters and a preset rule. And calculating the charging time extension amount of each vehicle after power reduction, and calculating the pile position occupation time sensitivity in combination with the original predicted completion time. And sorting the sensitivities from small to large to form a power recovery candidate queue. And selecting proper vehicles from the candidate queue to form a recovery scheme, calculating the total pile position occupation time of the system corresponding to the scheme, and selecting the scheme with the minimum total time as the optimal scheme. And reducing the charging power of the selected vehicle according to the optimal scheme, and distributing the recovered power to the new vehicle. By implementing the method, the pile position utilization rate can be increased.

The invention belongs to the technical field of computer storage, and particularly relates to a solid state disk data management and efficient storage distribution method, which comprises a data characteristic sensing and classifying unit, a data characteristic sensing and classifying unit, a data processing unit, a data storage unit and a data distribution unit, wherein the data characteristic sensing and classifying unit is configured for analyzing write-in request data flow entering a solid state disk in real time or periodically, extracting various characteristic parameters associated with data blocks and storing the data blocks; classifying the data on the basis of the characteristic parameters, and distributing one or more data classification labels for each data block to be written or stored; and the self-adaptive storage allocation strategy unit is configured to be used for dynamically selecting an optimal physical block for data writing according to the data classification label and in combination with the physical block state information of the current flash memory medium. According to the invention, the solid state disk can better adapt to various dynamic and variable workloads, and especially shows more excellent continuous write-in performance, lower delay jitter and obviously prolonged service life when processing a large amount of random write-in, small block write-in and write amplification sensitive applications.