62 results about "Complex energy" patented technology

The invention discloses a cluster computing power energy efficiency perception scheduling and green computing system, which relates to the technical field of computers and comprises a multi-source energy efficiency perception and data acquisition module used for acquiring power consumption, utilization rate, temperature, cooling state, PUE index and environmental data of cluster nodes. According to the invention, through the multi-modal energy efficiency fusion sensing network and the multi-scale convolution and time sequence attention fusion network, multi-source heterogeneous energy efficiency data such as current, voltage, temperature, airflow and the like of a node level can be collected and fused in real time and with high precision, noise is effectively removed, abnormity self-correction is realized, the defect of energy efficiency sensing granularity in the prior art is made up, and the energy efficiency sensing precision is improved. And reliable input is provided for subsequent scheduling decisions. A cross-scale dynamic twinborn collaborative modeling mechanism is adopted, a physical information neural network and a computational fluid mechanics model are coupled, optimization is carried out through a generative adversarial network structure, and accurate prediction of a complex energy consumption evolution curve and a cooling flow field is achieved.

The invention discloses a hospital energy resource intelligent management method and related equipment. The method comprises the following steps: predicting the output of renewable energy and the energy demand of each department in a future time period by using an LSTM-Attention model; on the basis of a dynamic mixed AHP analytic hierarchy process, the department energy consumption priority is evaluated by comprehensively considering the emergency state, the number of patients, the equipment operation state, historical energy consumption data and multi-dimensional factors of special medical tasks; and formulating an energy scheduling strategy through a multi-objective optimization model by combining the real-time energy consumption demand and the renewable energy source yield of the hospital according to the department energy consumption priority evaluated by the dynamic hybrid AHP method. The problems that due to insufficient application of clean energy and energy storage technology in hospitals, hospital operation cost is high, carbon emission is increased, a traditional system is difficult to dynamically adapt to complex energy consumption requirements, and stable energy supply of key areas cannot be guaranteed can be solved.

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 power grid control, in particular to a wind-light-water storage cooperative system and method based on AI learning control. The method comprises the following steps: carrying out system layered architecture design, and deploying a multi-source data acquisition network; a lightweight AI model is constructed, and dynamic decision making is carried out according to the multi-dimensional features; hybrid energy storage economy is scheduled, and operation is optimized according to a multi-energy cooperation principle; analyzing the full life cycle economy, and making a decision according to a multi-objective optimization principle; by adopting the mode, data driving and rule fusion in a dynamic decision-making mechanism are realized, and dual optimization of the wind and light absorption rate and the economic benefit can be realized in a complex energy scene.

The application discloses a wearable all-weather energy collection system, and belongs to the field of energy collection, comprising a low-frequency vibration energy collection array, a photovoltaic cell and an energy management module, wherein the low-frequency vibration energy collection array comprises first, second, third and fourth low-frequency vibration energy collection units, the output ends of the first, second, third and fourth low-frequency vibration energy collection units are connected with an energy input interface of the energy management module, and the first, second, third and fourth low-frequency vibration energy collection units are respectively arranged on limbs of a human body, so as to convert low-frequency vibration energy generated by limb movement into alternating current energy; the output end of the photovoltaic cell is connected with a direct current input interface of the energy management module, and the photovoltaic cell is arranged at a position of the human body where light is the most, so as to collect solar energy in the environment and convert the solar energy into direct current energy; the energy management module efficiently distributes the alternating current energy and the direct current energy under complex energy input and storage conditions through a deep learning algorithm, and can provide electric energy for small electronic devices which are carried daily, without consuming grid energy.

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 invention relates to the technical field of energy planning and utilization, in particular to an integrated energy system capacity planning optimization method for a zero-carbon park, and the method comprises the steps: obtaining an output power curve and a consumption power curve, and dividing a plurality of dominant time periods on the output power curve and the consumption power curve respectively; a dominant characteristic coefficient of each dominant time period is calculated according to the performance parameter of each dominant time period, and a first profit and loss fluctuation time period and a second profit and loss fluctuation time period are screened according to analysis results of the output power curve and the consumption power curve under the first division comparison mechanism and the second division comparison mechanism; according to the method and the device, the energy storage end equipment is selected to determine the profit and loss fluctuation feature aggregation time periods, and the optimal calling mode of the energy storage end equipment is determined in each profit and loss fluctuation feature aggregation time period, so that the complex energy supply and demand relationship is captured according to a multi-matching mechanism, and energy storage optimal calling is performed in combination with physical constraints such as equipment capacity and geographical distribution. And the overall performance of the energy system is improved.

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 information, and discloses a regional building group source network load storage information security interaction technology, which comprises the following steps: S1, each energy participant collects exclusive energy data locally, and constructs a local database cluster for storage and security protection; s2, each energy participant generates a zero-knowledge proof based on the data in the local database cluster, and submits the zero-knowledge proof to an alliance block chain network; s3, the alliance block chain network performs consensus and verification on the received zero-knowledge proof; and S4, based on a verification result of the zero-knowledge proof, performing collaborative operation of the regional energy system. By introducing the zero-knowledge proof, each energy participant only needs to generate and submit a proof that the data of the energy participant accords with a specific rule, and each party is allowed to participate in a complex energy optimization and decision process on the premise of completely protecting own privacy.

The invention discloses a building group energy-saving system based on electrical load prediction. The building group energy-saving system comprises a data acquisition module, a data processing module, a central control module and a power supply module, the data acquisition module is used for detecting the electrical load of each building; the data processing module comprises a BiLSTM-XGBoost prediction model, a target function and a solving algorithm, and the prediction model predicts the electrical load in a period of time in the future based on the historical electrical load data of each building of the data acquisition module; the objective function is based on an output result of the prediction model and takes the minimum system operation power and carbon emission as an objective; the algorithm is used to solve the objective function. The prediction result is more accurate, and a reliable basis is provided for optimized operation of the energy-saving system; and the optimal power output scheme of each device is determined by utilizing the improved eagle algorithm optimization solution, so that the efficient and low-carbon operation of the building group energy system is realized, and the complex requirements of the building group energy system on the energy optimization scheduling are better met.

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.

The invention discloses a preparation method of a colorful and white-light long-afterglow luminescent material and full-color display application thereof.The preparation method of the long-afterglow luminescent material comprises the steps that a luminous body solution is dropwise added to a polymer matrix, hot pressing is conducted at the temperature of 190-210 DEG C for at least 5 minutes, cooling is conducted to the room temperature, and the long-afterglow luminescent material is obtained, the luminous body solution comprises luminous small molecules and a solvent, the luminous small molecules are one or a mixture of more of sarranine, coumarin and acridone, the polymer matrix comprises a polymer, and the polymer is a styrene-acrylonitrile copolymer. The white light afterglow luminescent material with chromaticity coordinates closer to standard white light can be obtained only by taking sarranine, coumarin and acridone as luminescent micromolecules without complicated energy transfer while colorful afterglow luminescence is induced.

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 discloses a modularized composite energy field scribing and coupling cutting device. The device comprises a detachable cutter module, an ultrasonic vibration transduction module, a laser auxiliary module, a specially-made fixing base of the device and a detachable laser cutting head module. The cutter module and the laser cutting head module are connected at the same position and can be replaced by being connected with the vibration transduction module, the ultrasonic vibration transduction device works by being connected with the base, and the laser auxiliary module is fixed to the rear end of the base to work. Various cutting modes of laser heating-ultrasonic vibration-composite auxiliary cutting, laser heating-auxiliary cutting, ultrasonic vibration-auxiliary cutting, ultrasonic vibration auxiliary-laser cutting, laser cutting and the like can be achieved.

The invention discloses a building energy consumption prediction method and system based on a hybrid deep learning model, belongs to the technical field of smart buildings and building energy consumption prediction, and is used for solving the technical problems of low prediction precision, poor adaptability to complex energy consumption modes and insufficient real-time performance in the existing building energy consumption prediction technology. The method comprises the steps of collecting building energy consumption data in real time through Internet of Things equipment, and constructing a multi-dimensional original data set; performing preprocessing and feature reconstruction on the multi-dimensional original data set to obtain an input feature matrix; constructing a mixed deep learning model by using a long short-term memory network LSTM and a gating cycle unit GRU, and training the mixed deep learning model to obtain a building energy consumption prediction model; and inputting the input feature matrix into a trained building energy consumption prediction model to obtain an energy consumption prediction value of a future preset duration. By means of the method, accurate and real-time prediction of building energy consumption is achieved, and the method can adapt to prediction in a complex energy consumption mode.

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 discloses a multi-station tool rotary type curve cutting feeding device and a curve cutting feeding method thereof, and relates to cutting equipment, the multi-station tool rotary type curve cutting feeding device comprises a base platform, an upper end table top, a lower end table top, an X-shaped lifting feeding mechanism, a connecting rod type feeding driving mechanism and a belt wheel transmission system. The base platform is a supporting body, and the upper end table top is provided with a cutter, connected with a belt wheel transmission system and driven by the belt wheel transmission system to rotate. The upper end of the X-type mechanism is connected with the upper end table top, the lower end of the X-type mechanism is connected with the movable table top, the movable table top is connected with the connecting rod type mechanism and driven by the feeding driving motor, and all the components are linked through a hinge shaft and a connecting base. During work, the belt wheel transmission system drives the cutter to rotate, the feeding motor drives the table top at the lower end of the X-shaped mechanism to ascend and descend through the connecting rod mechanism, the cutter is driven to conduct radial feeding, and cutting is completed cooperatively. The motor and mechanical transmission are combined, complex energy is not needed, and stability and high efficiency are achieved; multiple stations are arranged in parallel, and batch production is facilitated; the cutter is detachable and adapts to various workpieces, the mechanism is adjustable, and different motors are combined to meet requirements; the structure is modularized, and maintenance is easy.

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 present application relates to a kind of energy spectrum recovery method and system based on high-order repair equation, comprising the following steps: the original energy spectrum obtained at each measuring point is preprocessed, obtains the m energy section information corresponding to the original energy spectrum at each measuring point;With the m energy section information corresponding to the original energy spectrum at each measuring point as initial condition, the energy spectrum distribution information under the preset range scene is recovered using the preset high-order repair equation and numerical solution mode.The present application can recover the energy spectrum distribution in a large range of space, and has good recovery effect for complex energy spectrum data with large noise and non-uniformity, solves the problem of weak adaptability of linear interpolation, and can be widely applied in nuclear application technology field.

The invention provides an evaluation report generation method and system based on parameter management and model combination, and the method comprises the following steps: obtaining input data which comprises energy parameter data and model data; preset standard structure data tables are filled with the energy parameter data, a target database filled with data is obtained, and the number of the standard structure data tables is at least four; different modules in the model data are freely combined, and then energy parameters in the target database are called to obtain evaluated models of different energy combinations; and performing logic verification on the parameter dependency relationship in the evaluated model, and calculating and outputting economic evaluation results of different energy combinations after the verification succeeds. According to the invention, through database standardization design and a modularized model builder, the pain point of parameter management and combination in new energy economic evaluation is solved, innovativeness and practicability are achieved, and the evaluation efficiency and accuracy of complex energy projects are significantly improved.

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 provides a micro-grid regulation and control 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 and control method comprises the following steps: acquiring electric energy original time sequence data of a micro-grid; decomposing the electric energy original time sequence data by using a discrete wavelet transform algorithm to obtain a low-frequency component of the electric energy original time sequence data; performing clustering processing on the low-frequency component to determine a data analysis model corresponding to the type of the low-frequency component as a target analysis model; predicting the original time series data by using the target analysis model to obtain electric energy prediction data of the micro-grid; and generating a regulation and control scheme of the micro-grid according to the electric energy prediction data, and controlling the micro-grid to execute the regulation and control scheme. According to the scheme, the analysis model conforms to the overall operation rule of the micro-grid, the prediction accuracy is high, the electric energy utilization efficiency, economy and safety of the micro-grid can be improved, and the micro-grid can adapt to the complex energy environment.

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.