34 results about "Complex energy" patented technology

The invention provides a resource scheduling method and device for a virtual power plant, equipment and a storage medium, and the method comprises the steps: collecting power grid data of a source network load storage scene, and carrying out the preprocessing of the power grid data; performing operation prediction based on the preprocessed power grid data through a generative AI large model to obtain a prediction result of a source grid load storage scene in a future preset time period; generating a plurality of candidate scheduling strategies based on the prediction result through a multi-objective optimization model; evaluating the plurality of candidate scheduling strategies to obtain corresponding evaluation results; and determining a target scheduling strategy based on the evaluation result, and executing a resource scheduling task based on the target scheduling strategy. According to the resource scheduling scheme of the virtual power plant provided by the invention, the energy utilization efficiency can be improved, the power resource distribution can be optimized, and meanwhile, the stability and economy of a power system are ensured, so that the resource scheduling scheme adapts to a complex energy system, deals with the uncertainty of a market environment and meets the requirement of high real-time performance.

The invention relates to the technical field of industrial data asset pricing, in particular to an industrial data asset intelligent pricing system and method based on big data analysis, and the system comprises a fluctuation recognition module which aligns a signal to recognize a fluctuation generation mapping table, and a feature value mapping module which associates production data to establish a value curve; the value weight balance module fuses income and expenditure correction deviation to generate a balance table, the industry value migration module carries out benchmarking on industry energy efficiency calibration to generate a connection table, and the asset pricing generation module analyzes cost conversion to construct a price map, and generates an industrial asset pricing mapping set. According to the method, a continuous energy efficiency track is constructed by aligning multi-source energy consumption flow, quantitative mapping of output efficiency and fluctuation is determined, dynamic pairing of income information and cost input is realized, a value weight is recombined to establish a cross-industry feature distribution system, measurement response accuracy under a complex energy efficiency condition is enhanced, and an energy efficiency income input continuous reflection mechanism is constructed. And coordination and stability of data-driven industrial value conduction are promoted.

The application discloses a green clean energy comprehensive energy-saving management platform based on an Internet of Things intelligent terminal and particularly relates to the technical field of intelligent monitoring and optimal control, and is used for solving the problem that the prior art has no solution or outputs a high-risk operation strategy due to neglecting the coupling relationship between subsystems when dealing with multiple security constraints; through the Internet of Things terminal, operation parameters and security constraint boundaries are acquired, when multiple constraints are close to being reached, the key coupling path of the connected constraint is identified; then, the change of the conflict correlation strength between the constraints under different relaxation schemes is simulated, a target scheme that can reduce the network structure tightness to the maximum extent is selected by constructing and analyzing the conflict relationship network, a group of coordinated constraint boundaries is generated; finally, the global energy efficiency optimization calculation is performed and the equipment set value is output based on the boundaries, so that the coordinated control of the safe and efficient operation of the complex energy system is realized.

The invention relates to the technical field of energy management and energy storage, in particular to a water energy storage dynamic energy analysis decision system based on application scene requirements, which comprises a water-based energy storage device, a data acquisition unit, a central processing unit and a system control unit. A dynamic energy demand prediction model is constructed, an energy storage and release instruction set is generated, accurate energy scheduling is realized, the system supports multi-energy cooperative control and remote manual intervention, and the energy utilization efficiency and the operation flexibility are remarkably improved. According to the invention, complex energy consumption requirements can be effectively met, the operation cost is reduced, and an efficient and economical energy management solution is provided for scenes such as building heating and refrigeration and industrial steam supply.

This invention discloses an intelligent optimization method for energy management based on a swarm algorithm, comprising the following steps: S1, collecting multi-source time-series data and adjustable equipment parameters from the energy management system to generate a prediction sequence, constituting a scheduling space; S2, calculating a risk index sequence to form a risk map; S3, constructing a mapping structure between scheduling schemes and gap fields; S4, constructing a coupled coding structure between population individuals and gap fields to form an initial population cluster; S5, forming an updated population cluster using an improved HHO algorithm; S6, performing selection, replication, and elimination operations to form a new generation population cluster; S7, performing convergence determination, updating the risk map and prediction sequence, and completing the optimization closed loop. This invention enables risk perception, dynamic adjustment, and global optimization of complex energy systems across multiple time scales, improving the economy, energy efficiency, and operational safety of the energy management process.

The invention belongs to the technical field of three-dimensional point cloud processing, and particularly relates to a non-repetitive laser radar point cloud fractional order space-time registration method and device. The objective of the invention is to solve the problems of poor robustness and insufficient convergence of non-repeated scanning point cloud registration. The method comprises the following steps: acquiring a source point cloud and a target point cloud, and constructing a weighted graph structure on the target point cloud; defining a residual vector field based on the current pose, and constructing a Riesz fractional order space regularization item to apply non-local smooth constraint; constructing a unified energy function containing the regularization item; a Grunwald-Letnikov fractional order dynamics solver is adopted to iteratively optimize the pose, and the long memory effect of historical increment is utilized to calculate the effective update quantity; and outputting the optimal pose after convergence. Through space-time fractional order mechanism coupling, sampling missing and noise can be effectively suppressed, convergence stability under complex energy landscapes is improved, parameters can be adjusted to adapt to various scenes, the theory is firm and easy to integrate, the registration success rate exceeds 90%, and a powerful tool is provided for non-repetitive laser radar point cloud processing.

The invention provides a sunlight direct pumping disc laser, which belongs to the field of lasers and comprises a sunlight collection system, an energy output end of the sunlight collection system is connected with an optical fiber coupling head, and the optical fiber coupling head is connected with a disc laser through an optical fiber; or, the energy output end of the sunlight collection system is provided with an optical lens group, and the light source output end of the optical lens group is provided with a disc laser. Sunlight can be directly used as a pumping source, and stable and efficient laser output can be generated without an external power supply or complex energy conversion equipment.

The invention relates to the technical field of intelligent control of an energy system, and discloses an AI energy dynamic optimization control system and method based on an intelligent agent. The method comprises the following steps: extracting energy network equipment attributes and topology, and constructing a multi-dimensional data cube; and real-time sensing data and a flexible constraint rule are synchronously injected. And driving the intelligent agent to carry out incremental semantic annotation on the data stream, generating an energy event with a space-time mark, and compiling the energy event into a source load feature portrait. And according to the portrait, activating a matched regulation and control mode sub-graph in the strategy knowledge graph, generating a dynamic control parameter, and loading the dynamic control parameter to an actuator. According to the method, real-time deep semantic understanding of the operation state of the energy system and dynamic self-adaptive accurate regulation and control based on historical experience are realized, and the intelligent level and the operation efficiency of complex energy network control are improved.

The invention discloses an improved power distribution method for stabilizing wind power fluctuation through hydrogen composite energy storage, and the method comprises the steps: obtaining wind power output power data, decomposing power through employing an algorithm combining adaptive moving average and amplitude limiting weighted filtering, and obtaining grid-connected reference power and composite energy storage execution reference power; dividing a composite energy storage execution reference power frequency band through wavelet packet decomposition based on the response characteristic difference between the hydrogen energy storage and the super capacitor, realizing primary power distribution and outputting initial output reference power; then introducing a reference line variable, and calculating the minimum difference value of the accumulated charge and discharge quantities to compensate the power loss and balance the charge and discharge quantities; and constructing a power collaboration mechanism and a collaboration rule by combining the operation characteristics of the electrolytic cell and the state of the energy storage equipment, secondarily correcting the initial output reference power by using a double-end Logistic function correction coefficient, constraining the output in a safety interval, and outputting the corrected output power, thereby realizing the wind power fluctuation stabilization and the efficient collaboration operation of the energy storage system.

The invention discloses an RNA sequence prediction protein structure system and method based on quantum computing and electronic equipment, and the system comprises a user interaction terminal layer which is used for obtaining a target RNA sequence input by a user; the classical computing layer is connected with the user interaction terminal layer and the quantum computing cloud platform layer, and is used for processing and analyzing the target RNA sequence, distributing a computing task according to an analysis result, and generating a predicted protein structure according to an obtained computing result; the quantum computing cloud platform layer is used for computing the quantum computing tasks allocated by the classic computing layer; the calculation result comprises a classical calculation result and / or a quantum calculation result. The system disclosed by the invention can more effectively pass through an energy barrier in a complex energy landscape and find a globally optimal solution with a higher probability. Therefore, the problem that a prediction structure is inaccurate due to the fact that a traditional classical algorithm often falls into a local minimum value when searching for the lowest energy conformation of the protein is solved.

This disclosure relates to the field of virtual currency regulatory technology, and discloses a method and apparatus for identifying virtual currency address associations based on energy penetration. The method includes: acquiring proxy resource transaction data and main currency transfer transaction data in a virtual currency network that supports a proxy resource mechanism; extracting transaction pairs that meet preset self-charging characteristics to establish a mapping relationship between energy pool addresses and receiving addresses; determining the corresponding target energy pool address in the proxy resource transaction data; determining the receiving address associated with the target energy pool address; screening main currency transfer transactions, and determining the sender address of the screened main currency transfer transactions as the actual payment address. This disclosure, by constructing a self-charging feature mapping mechanism across datasets of proxy resource transaction data and main currency transfer transaction data, achieves effective penetration of the physical isolation between resource flow and fund flow in a virtual currency network, thereby improving the efficiency and accuracy of locating hidden actual controllers in complex energy leasing scenarios.

The application discloses a kind of multi-energy complementary power generation system optimization scheduling method based on carbon emission minimization, to solve the problems such as insufficient carbon emission reduction pertinence, multi-objective imbalance and multi-constraint coupling conflict of existing scheduling scheme.The method realizes optimization through six-step core process: clear system boundary and build "source-net-load" whole chain carbon flow transfer function, establish dynamic weight driven carbon emission-economic-stability multidimensional objective function, quantize each sub-objective function and multidimensional constraint condition, design carbon flow dominant double-layer nested iteration algorithm and realize scheduling result real-time update output through programmed platform.The application integrates carbon flow dynamic tracking, adaptive weight adjustment and constraint priority mechanism, maximally reduces whole cycle carbon emission under the premise of guaranteeing system safety and stability operation, considers economy and operability, provides technical support for multi-energy complementary power generation system low-carbon and efficient operation, and is suitable for complex energy system scheduling scene containing renewable energy, fossil energy, energy storage and coupling unit.

The present application relates to the technical field of energy planning utilization, and particularly relates to a comprehensive energy system capacity planning optimization method for a zero-carbon park, the present application obtains an output power curve and a consumption power curve, divides a plurality of explicit time periods on the output power curve and the consumption power curve respectively, calculates explicit characteristic coefficients of the explicit time periods according to performance parameters of the explicit time periods, screens first profit and loss fluctuation time periods and second profit and loss fluctuation time periods according to analysis results of the output power curve and the consumption power curve under a first division ratio matching mechanism and a second division ratio matching mechanism, determines profit and loss fluctuation characteristic aggregation time periods, and determines an optimized calling mode of an energy storage end device in each profit and loss fluctuation characteristic aggregation time period, thereby, a complex energy supply and demand relationship is captured according to a multi-matching mechanism, and energy storage optimization calling is combined with physical constraints such as device capacity and geographical distribution, so that the overall performance of the energy system is improved.

This invention relates to a mapping method between multiple energy modes of an oven and cooking parameters. Addressing the problems of complex energy input characteristics, limited model generalization ability, and difficulty in achieving fine-grained adaptive control in traditional cooking processes, this invention proposes to acquire multi-dimensional temporal physical characteristics of multiple energy inputs, normalize them to generate uniquely identifiable energy fingerprints, and combine this with recipe information input into a lightweight master track model to output matching cooking parameters in real time. The system collects actual operating data and performs alignment and deviation identification with model predictions, triggering local model fine-tuning and energy fingerprint-incremental patch mapping updates to achieve adaptive correction of the model to new operating conditions. For multi-energy mixed scenarios, a composite fingerprint is generated through power weighting, and combined with a multi-source model to achieve thermal hysteresis compensation and power conversion jitter suppression. This scheme improves the dynamic adaptability of cooking parameters and energy utilization efficiency, while also possessing high precision and scalability.

This invention discloses a method, system, device, and storage medium for suppressing energy level leakage, relating to the field of quantum information processing. The method applies parametric modulation to qubits to establish and activate effective exchange coupling between qubits under dispersive coupling conditions, thereby performing a two-qubit entanglement gate operation. During the gate operation, a computational subspace control model and an energy level leakage model are constructed by performing representational and unitary transformations on the system's Hamiltonian. Based on these models, the entanglement gate control pulse is optimized. The energy level correction introduced by the parametric driving is dynamically compensated through modulation of the effective Rabi frequency of time-shaping and time-dependent detuning parameters, thus suppressing energy level leakage from the computational subspace to higher energy levels. Compared with existing technologies, this invention improves the controllability and fidelity of entanglement gate operations under complex energy level structures and is applicable to quantum operations in modular quantum systems.

The invention relates to the technical field of power grid energy storage demand abrupt change early warning, in particular to a power grid energy storage demand abrupt change early warning method based on non-Hermitian system topology phase change. According to the method, on the basis of non-Hermite system topology phase change, power grid energy storage demand sudden change early warning is achieved through dynamic energy quantum domain division, non-Hermite Hamiltonian quantity modeling and complex energy spectrum analysis. Specifically, energy quantum domains are divided in combination with new energy fluctuation, load characteristics and topological density, and a plurality of non-Hermite Hamiltonian quantities are constructed to represent energy flow and fluctuation loss; energy storage nodes and sub-domains are bound by using a Thiessen polygon algorithm, and eigenvalue track change recognition abnormity is monitored in real time; the risk is quantified through the phase winding degree, and a high-risk area is positioned; and inverting eigenvalue mapping physical space demand increment, and triggering a hierarchical response strategy. According to the method, topology analysis and dynamic modeling are fused, the energy storage configuration precision and the early warning reliability are improved, and the stable operation requirement of the power grid under new energy fluctuation is met.

The invention relates to a method for cleaning a packed tower, a cleaning system and application of the cleaning system, and discloses the method for cleaning the packed tower, and a plurality of distribution devices for dispersing a cleaning agent are arranged on the inner peripheral walls of a tower body and a tower kettle of the packed tower; the method comprises the following steps: step 1, feeding a cleaning agent through a distribution device, dispersing the cleaning agent in a tower body and a tower kettle of the packed tower to clean the packed tower, and feeding gas through a feeding hole of the tower kettle; 2, stopping feeding the cleaning agent, adjusting the gas speed and temperature of the feeding gas at the feeding hole of the tower kettle to purge and take away the liquid, and ending the cleaning process; and step 3, adjusting the gas speed and temperature of the feeding gas at the feeding port of the tower kettle to blow and dry the cleaned packed tower, and ending the drying process. According to the method, original components can be removed without complex and energy-consuming tower washing operation, the cleaning efficiency and the cleaning effect are improved, and rectification of other systems is facilitated.

This invention discloses a city-level integrated smart energy control and energy-saving collaborative optimization method. This method constructs a hierarchical collaborative structure of terminal layer, edge layer, and cloud center layer. Based on the unified energy path theory, it decomposes and abstracts the complex energy management tasks generated by city-level multi-energy devices, forming a task set mapped from various energy types. The joint cost is normalized to construct a city-level task feature space. The corresponding joint cost is calculated among local terminals, edge nodes, and the cloud center. A dynamic hybrid offloading strategy that minimizes hierarchical joint cost is adopted, selecting the computation layer with the lowest overall cost for execution to achieve city-level collaborative optimization. This invention compares and evaluates the energy consumption, average latency, and maximum latency of the baseline local computation scheme and the hierarchical collaborative offloading scheme, verifying that the proposed method can significantly reduce the total system energy consumption and improve task latency performance without relying on deep reinforcement learning.

The invention discloses an energy management intelligent optimization method based on a population algorithm, and the method comprises the following steps: S1, collecting multi-source time sequence data and adjustable equipment parameters of an energy management system, generating a prediction sequence, and forming a scheduling space; s2, calculating a risk index sequence to form a risk map; s3, constructing a mapping structure between the scheduling scheme and the gap field; s4, constructing a coupling coding structure between the population individuals and the gap field to form an initial population set; s5, forming an updated population through an improved HHO algorithm; s6, executing a selection operation, a copying operation and an elimination operation to form a new generation of population; and S7, executing convergence determination, updating the risk map and the prediction sequence, and completing optimization of a closed loop. Risk perception, dynamic adjustment and global optimization of the complex energy system under multiple time scales are achieved, and the economical efficiency, the energy efficiency level and the operation safety of the energy management process are improved.

The invention relates to the technical field of energy transfer data analysis, and discloses a gate-water flow coupled vibration space-time energy transfer visual analysis system and method, and the system comprises an energy fingerprint extraction system, an energy potential gradient network construction system and a dynamic energy potential topology mapping system. The method comprises the steps that a space-time continuous energy density distribution field is generated through a space-frequency field reconstruction technology, non-uniform sampling data is converted into a scalar field with energy fingerprint characteristics, energy fingerprint codes are aggregated into energy infinitesimal elements with directivity, and a space-time energy infinitesimal element array is formed; generating an energy potential gradient network based on the dynamic coupling relationship, and identifying a main energy path and a branch energy path to form a hierarchical energy potential gradient network structure; and mapping the energy potential gradient network to an affine space-time coordinate system, and generating a dynamic energy potential topological curved surface according to path energy flux intensity and directivity. According to the invention, multi-parameter synchronous observation in a complex energy transfer process is realized.

The invention relates to the technical field of energy scheduling, and discloses an energy scheduling method and device based on digital twinning, and the method comprises the steps: obtaining energy facility position data and meteorological condition data; arranging and constructing according to the two types of data to obtain an energy facility spatial distribution model; according to the model, visual rendering and meteorological data coding are combined to obtain an energy facility spatial distribution map; and analyzing a coupling relationship according to the distribution map, adjusting path parameters and the like, and obtaining a fused cross-regional scheduling scheme. According to the method, energy scheduling full-process optimization can be realized, energy supply continuity and cross-regional cooperation efficiency are improved, and support is provided for fine management of a complex energy system.

The invention relates to the field of charging, and provides a vehicle charging method for a charging and discharging device and the charging and discharging device in order to solve the problem that complex energy flows among a power grid, an energy storage system, a charging vehicle and a discharging vehicle in a charging and discharging station cannot be coordinated in the prior art. The method comprises the following steps: acquiring a discharge request and discharge capability information sent by a first vehicle through a first charge and discharge pile; acquiring a charging request and charging demand information sent by a second vehicle through a second charging and discharging pile; performing electric energy scheduling based on the discharging capability information and the charging demand information; the charging source priority of the second vehicle is the first vehicle, the energy storage device and the power grid in sequence.

The invention discloses a multi-energy cooperative scheduling method and system for an integrated energy system based on data driving, and relates to the technical field of energy big data, and the method comprises the steps: in the integrated energy system, determining multi-energy cooperative scheduling features according to the operation parameters, historical scheduling data and strategy factor data of each energy unit; carrying out priority measurement through the characteristics, and constructing a power generation side first-class scheduling association graph and a power utilization side second-class scheduling association graph; and finally, determining an optimal scheduling scheme by combining an energy scheduling strategy library and utilizing a Transform-based multi-energy collaborative scheduling model. The technical problem that a scheduling scheme is difficult to adapt to a complex energy unit scene due to the fact that traditional integrated energy system scheduling cannot fully integrate multi-class energy related data and lacks effective carding of power generation and power utilization side collaborative logic is solved, the accuracy and high efficiency of multi-energy collaborative scheduling of the integrated energy system are achieved, and the scheduling efficiency of the integrated energy system is improved. And the technical effects of supply and demand balance and scheduling optimization target of the system are guaranteed.

This invention relates to a multi-scenario, multi-energy dynamic optimization and control method for complex energy systems, comprising the following steps: S1, acquiring production plans and production data; S2, preprocessing the acquired production plans and production data; S3, inputting the preprocessed data into a production scenario identification model to determine the corresponding production scenario; S4, matching the determined production scenario with the corresponding multi-energy coupled optimization scheduling model; S5, formulating and implementing a scheduling scheme based on the corresponding multi-energy coupled optimization scheduling model. The control method provided by this invention not only improves the economic efficiency of enterprises, but its operational logic also better meets the actual production needs under multiple scenarios.

The invention discloses an energy simulation adaptive analysis method, an analysis system, a medium and a product, and relates to the field of energy analysis. By implementing the application, the preset energy model general library comprises comprehensive modules from energy production, conversion, consumption to balance, and the analysis system can automatically screen the related analog variable set and analog variable calculation rule set from the preset energy model general library according to the specific energy analysis target; and a targeted energy target analysis model is constructed. The adaptive method can flexibly meet the analysis requirements of different regions, especially regions with complex energy systems, such as Ordos, and avoids the problem that a traditional standardized analysis framework is difficult to completely describe region features. Meanwhile, through quantitative calculation of the energy target analysis model, the analysis system can obtain a more accurate and reliable analysis result, and scientific decision support is provided for clean and efficient development of a regional energy system.

This invention discloses a method and application for preparing compound energy feed based on synergistic saccharification, relating to the field of animal feed technology. This method and application includes graded and directional pretreatment of raw materials, synergistic gradient saccharification and co-fermentation by enzymes and bacteria, and spray drying and pulverizing of the fermented materials to obtain a powdered compound energy feed product. It solves the core contradiction between "rapid fermentation leading to acidosis" and "insufficient energy due to incomplete conversion" of non-grain raw materials, and can stably maintain rumen pH within the optimal range, reducing the incidence of subclinical rumen acidosis; it also achieves synergistic effects from multiple raw materials. Slightly acid-heat modified cassava starch provides a dual-gradient starch substrate, molasses-honey composite high-sugar solution provides fermentation substrate, natural enzymes, and antibacterial substances, and composite fruit peel and pomace provide slow-release fiber substrate and functional active ingredients. After graded pretreatment, the substrates are utilized in a stepwise manner during the three-step fermentation process.

The application discloses a numerical prediction method for operation characteristics of a one-way turbine wave energy power generation system, and belongs to the field of wave energy development and utilization. The numerical prediction method for operation characteristics of the one-way turbine wave energy power generation system comprises the following steps: establishing a three-dimensional geometric model of an air chamber integrated with a one-way valve and a one-way air turbine and a numerical wave pool; setting a calculation domain and dividing the grid of the three-dimensional geometric model and the numerical wave pool; setting boundary conditions; setting a turbulence model, a wave making type, a pressure-velocity coupling solution algorithm and air density; setting a momentum source term of the one-way valve area; setting the one-way air turbine area as a rigid body rotating around a fixed shaft and setting corresponding physical parameter monitoring; and solving energy conversion efficiency and operation stability indexes of the system. The method completely reproduces a complex energy chain transmission process from wave energy to mechanical energy, and ensures physical authenticity of the prediction result.

The problem addressed by the invention described in claim 1 is that of reducing the energy consumption of electrical systems to the minimum required value depending on the operating conditions, without having to make changes to the existing system structure. The method and the device for reducing the energy consumption of existing electrically operated systems containing consumers whose power feed from a network (#1A) is controlled in each case via control modules are characterized in that the power is supplied to the control modules of the existing system via a control device according to the invention having a power supply (#1B) that is independent of the network (#1A) and in that all relevant operating parameters of the consumers of the existing system are updated in the control modules of the existing system and are maintained in the event of the consumers of the existing system losing power. The device according to the invention is thus used to achieve considerable economic advantages by virtue of reducing the energy consumption of existing electrical systems with minimal changes to the existing systems.

The application provides a micro-grid regulation method based on data feature analysis, a program product and equipment, and relates to the field of power supply and distribution systems. The micro-grid regulation method comprises: obtaining original time sequence data of electric energy of a micro-grid; using a discrete wavelet transform algorithm to decompose the original time sequence data of electric energy, to obtain low-frequency components of the original time sequence data of electric energy; performing clustering processing on the low-frequency components, to determine a data analysis model corresponding to a type to which the low-frequency components belong, as a target analysis model; using the target analysis model to predict the original time sequence data, to obtain electric energy prediction data of the micro-grid; generating a regulation scheme of the micro-grid according to the electric energy prediction data, and controlling the micro-grid to execute the regulation scheme. The scheme of the application, the analysis model conforms to the overall operation law of the micro-grid, the prediction accuracy is high, the electric energy utilization efficiency, the economy and the safety of the micro-grid can be improved, and the micro-grid can adapt to a complex energy environment.

The invention discloses a battery energy storage system dynamic reconstruction switching method based on multistage monitoring. The method belongs to the technical field of battery energy storage system intelligent monitoring and management. According to the method, a device-level, module-level and system-level multi-level sensing monitoring network is constructed, local acquisition, preprocessing and preliminary anomaly screening of data are realized through distributed intelligent nodes, and high-sensitivity detection and rapid positioning of faults are realized by adopting multi-layer Bayesian data fusion and an improved graph structure attention network. Based on a dynamic decision algorithm of an evolutionary game, the health degree, the energy state and the system safety margin of an energy storage unit are comprehensively considered, an optimal reconstruction switching strategy is generated and executed, and automatic isolation of a fault unit and access of a standby module are realized. The method is suitable for a modularly designed complex energy storage system, and has dynamic self-healing and efficient operation capabilities under various abnormal and sudden working conditions.