522 results about "Energy resources" patented technology

The invention discloses a cross-regional virtual power plant cooperative scheduling method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: acquiring real-time characteristic data such as space-time positions, output / demand prediction and the like of distributed energy resources and loads, and determining dynamic weights of characteristic dimensions based on a global optimization target and data of a current scheduling period; generating a dynamic resource cluster division instruction containing a member list and a coordination constraint condition according to the dynamic weight and the real-time data, and sending an initial cross-regional coordination scheduling instruction containing a net exchange power target value and the like and a compensation price signal to each dynamic resource cluster local agent; after aggregation response boundary information returned by the local agent is received, an instruction and a signal are updated, a target collaborative scheduling instruction is obtained and finally sent to each dynamic resource cluster for execution, and effective control over cross-regional virtual power plant resources is achieved. According to the method, the problem that the adaptability of the cross-regional virtual power plant collaborative scheduling instruction and the actual resource capacity is insufficient can be relieved.

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

The invention relates to the technical field of carbon footprint evaluation, and discloses a carbon footprint optimization method and system based on LCA-GLSS coupling. The carbon footprint optimization method comprises the following steps: calculating carbon footprints of all stages of a product in a full life cycle, carbon footprint contribution ratios corresponding to materials, energy sources, resources and wastes involved in all process units in the full life cycle, and carbon footprint sensitivities corresponding to the materials, the energy sources, the resources and the wastes; a high-carbon emission link found in the carbon footprint evaluation process is optimized, waste and variation in the carbon emission process are recognized and eliminated, a carbon footprint prediction model is established, the carbon emission trend in the future is predicted and simulated, and the prediction accuracy is improved through model optimization and parameter adjustment; the evaluation result is comprehensively analyzed and explained, main contribution factors and key links of the carbon footprint are identified, and emission reduction measures and optimization suggestions are formulated. The method has both quantitative analysis and process improvement, and can effectively improve the efficiency and accuracy of carbon footprint management.

The invention relates to the technical field of virtual power plant scheduling, and discloses a distributed collaborative optimization scheduling method for a virtual power plant. The method includes collecting an operating state data set of a target virtual power plant. And performing distributed collaborative model construction processing on the operation state data set to generate collaborative scheduling features covering power distribution balance degree, constraint matching closeness and interactive response sensitivity. And calling a pre-trained optimization scheduling model to carry out multi-target collaborative optimization processing on the collaborative scheduling features to obtain an optimization scheduling result and a key collaborative region identifier. And based on the association relationship between the load demand fluctuation sequence and the equipment adjustment capability, performing operation environment compensation correction processing on the optimization scheduling result, and generating a corrected result. And generating a virtual power plant scheduling strategy set including a power transfer path adjustment scheme and an energy storage equipment configuration processing scheme according to the key cooperative region identifier. According to the method, distributed energy resources are effectively integrated through multi-dimensional collaborative optimization and dynamic correction.

The invention discloses a source network load storage oriented regional collaborative scheduling method and system, and relates to the technical field related to power resource scheduling, and the method comprises the steps: traversing target region source network load storage to carry out real-time data collection, and constructing a source network load storage multi-dimensional data set to carry out multi-scale layered optimization; operation constraint conditions are set, limitation is carried out in combination with a power grid topological structure, a cooperative scheduling instruction set is determined, cooperative control feedback is carried out, and regional power scheduling feedback parameters are generated; and multi-channel dynamic correction is carried out, and a cooperative scheduling optimization strategy is generated to carry out energy balance cooperative scheduling on the source network load storage in the target area. The technical problems that in the prior art, intermittent fluctuation of new energy is difficult to consume, the collaboration of all links of source network load storage is poor, the dispatching flexibility of a power system is insufficient, and the resource configuration efficiency is low are solved. The technical effects of optimizing regional energy resource allocation and improving the clean energy consumption level, the power grid operation stability and the source grid load storage cooperative response capability are achieved.

The invention discloses an electricity meter metering error correction method and system based on time sequence anomaly detection and an electricity meter, relates to the technical field of electricity meter metering error correction, and provides an intelligent anomaly detection method integrating multi-source data preprocessing, online learning and federation cooperation for a power system in which distributed energy and diversified loads coexist. The method comprises the following steps of: 1, dynamically filtering and caching original data by adopting a suspicious degree function, and outputting high-quality input; secondly, online learning is carried out through multi-algorithm fusion and expert knowledge constraint, and accurate judgment of traditional and emerging modes is achieved; 3, realizing multi-region cooperative gain based on difference parameter sharing and global aggregation; and 4, deploying a lightweight model at the edge end by using feedback evaluation and model distillation, thereby improving the real-time performance and reliability. In conclusion, the false alarm rate and the missing report rate can be remarkably reduced, various changes of load behaviors are effectively adapted, the operation and maintenance efficiency of a power grid is improved, and meanwhile data privacy safety and collaboration are guaranteed.

The invention discloses a source-grid-load-storage multidirectional cooperation method and system in a new energy high-permeability power grid, and belongs to the technical field of source-grid-load-storage collaboration.The method comprises the steps that a historical data set and social economic data of a power system in a region are obtained, and a prediction model is constructed to obtain a load prediction result; acquiring solar radiation quantity, wind energy resource distribution data and historical meteorological data in the region to obtain a power supply prediction result; obtaining a load prediction correction value based on the power supply prediction result; constructing a power balance model based on the load prediction correction value and the power supply prediction result; solving the established power balance model based on a genetic algorithm, and determining an operation scheme; and evaluating the determined operation scheme, and if the operation scheme meets requirements, outputting the operation scheme for source-network-load-storage multidirectional collaboration. According to the method, the load change rule can be comprehensively captured, and the problem of inaccurate load prediction caused by single data in the prior art is effectively solved.

The invention relates to the technical field of industrial park green low-carbon development, and discloses an industrial park green low-carbon multi-dimensional performance evaluation index system construction method. Setting evaluation dimensions of energy, resources, economy, environment, management and the like, determining a core index, and calculating a comprehensive weight based on objective and subjective weights; after multi-source data are collected and processed, a TOPSIS method and a grey correlation analysis method are adopted for comprehensive evaluation, and an industry comparison report is generated; establishing a real-time monitoring system to track, evaluate and display key data and indexes; meanwhile, an index system and a weight distribution mechanism are periodically optimized in combination with a machine learning technology, core indexes are screened through clustering analysis, variance contribution evaluation, correlation matrix construction and dynamic threshold adjustment, and dynamic adaptive optimization of the indexes and the weights is realized by utilizing a feature importance analysis and reinforcement learning algorithm. According to the invention, scientific evaluation and intelligent management of park green low-carbon performance are realized, and the accuracy, adaptability and continuous optimization capability of evaluation are improved.

The invention relates to the technical field of power systems, and discloses an active power distribution network intra-day economic optimization scheduling method, system, device, medium and product. The method takes the comprehensive operation cost minimization, the voltage fluctuation minimization, the load peak-valley difference minimization and the energy storage life loss minimization of an active power distribution network as multiple optimization objectives; and determining constraint conditions of the multiple optimization targets according to a reducible load balance state, a transferable load balance state and a power distribution network operation balance state, constructing a power distribution network optimization scheduling model, optimizing and solving the power distribution network optimization scheduling model, and determining an intra-day scheduling optimal strategy of the active power distribution network according to an optimal solution. Therefore, the comprehensive operation cost, the voltage fluctuation, the load peak-valley difference and the energy storage life loss are comprehensively considered, continuously changing load requirements and energy resources can be better dealt with, the resource utilization rate and the flexibility of the optimal scheduling strategy of the active power distribution network are improved, and the stability and the economical efficiency of power supply are ensured.

The invention provides an energy complex flexible dynamic aggregation method and system based on reinforcement learning, and the method comprises the steps: collecting the real-time operation parameters of electric, gas, heat and multi-energy coupling equipment in an energy complex through an industrial Ethernet protocol, and constructing a continuous time dynamic system model through employing a Shenchang differential equation process modeling technology; and the nonlinear dynamic characteristics of various energy resources are accurately described. Modeling an aggregation process into a Markov decision process, and training and optimizing by adopting an improved twin delay depth deterministic strategy gradient algorithm to generate a reinforcement learning aggregation strategy model. The system realizes self-adaptive dynamic optimization through an online learning updating mechanism, and the renewable energy consumption rate and the system operation efficiency are remarkably improved.

A meter socket adapter (MSA) for connecting a battery energy storage system (BESS) to a household microgrid through a meter combo load center. The MSA includes: a housing matching an electricity meter and a meter socket in the meter combo load center; a plurality of power conductors that connect among the electricity meter, the meter socket, and BESS; a meter interconnection device (MID) switch for connecting a utility grid or one or more distribution energy resource (DER) devices to supply power to household loads in the household microgrid; a MID driving circuit for driving the MID switch to close or open; and a MID connection detection circuit to verify an electrical connection of a closed MID switch.

The invention discloses a space-time collaborative optimization-based virtual power plant adjustable capability evaluation method and system. The method comprises the following steps of S10, obtaining operation parameters of distributed energy resources in a virtual power plant; s20, performing space-time analysis on the operation parameters, extracting space distribution characteristics and time change characteristics of the distributed energy resources, and generating a space-time distribution matrix; s30, constructing a joint scheduling model of the virtual power plant based on the spatial-temporal distribution matrix, and obtaining an adjustable capability evaluation result of the virtual power plant by solving the joint scheduling model; and S40, generating a regulation and control instruction according to the adjustable capability evaluation result, and sending the regulation and control instruction to the distributed energy resources to realize collaborative optimization scheduling. According to the method, the operation parameters of the time dimension and the space dimension of the distributed energy resources are jointly scheduled, and the dynamic data of the virtual power plant are acquired in real time for analysis and evaluation, so that the regulation and control process is more comprehensive, and the evaluation result of the adjustable capability of the virtual power plant is optimized.

One aspect of the present disclosure relates to a distributed energy data platform process, and more particularly, to a distributed energy data platform process capable of increasing efficiency of energy resources and ensuring flexibility in management by configuring an AIoT process based on a distributed energy data platform. The distributed energy data platform process includes a data collection unit for collecting IoT sensor data of an IoT sensor installed in an energy-consuming device in real time; a database for loading the IoT sensor data collected in real time from the data collection unit; a visualization unit for interacting with the database so as to visualize the IoT sensor data to monitor the IoT sensor data in real time; and a training unit for configuring an artificial intelligence life cycle operation for the IoT sensor data.

A building energy system includes equipment configured to consume, store, or discharge one or more energy resources purchased from a utility supplier. At least one of the energy resources is subject to a demand charge. The system further includes a controller configured to determine an optimal allocation of the energy resources across the equipment over a demand charge period. The controller includes a stochastic optimizer configured to obtain representative loads and rates for the building or campus for each of a plurality of scenarios, generate a first objective function comprising a cost of purchasing the energy resources over a portion of the demand charge period, and perform a first optimization to determine a peak demand target for the optimal allocation of the energy resources. The peak demand target minimizes a risk attribute of the first objective function over the plurality of the scenarios.

The invention discloses a U-Net-based multi-mode wind energy resource monthly scale prediction correction method and system, belongs to the technical field of wind energy resource evaluation and climate numerical value prediction crossing, and is used for correcting monthly scale wind speed output by a climate mode. According to the method, a climate state wind speed field is constructed by utilizing ERA5 reanalysis, and a monthly-scale 10m wind speed anomaly is calculated to serve as a correction reference; monthly-scale historical return data of a plurality of dynamic climate modes are obtained, 10m wind speed and multilayer meteorological elements are extracted, unified interpolation and standardization are carried out, and a sample set is formed; a U-Net correction model with a coding and decoding structure is constructed based on a sample set, feature combination and hyper-parameters are optimized through cross validation, and nonlinear mapping from a multi-mode forecast field to an ERA5 distance flat field is learned. And correcting future monthly scale forecast by using the optimal model, generating a wind speed product of which space structure and amplitude distribution are closer to observation, and providing high-credibility wind energy climate information for wind power planning, power generation planning and power grid dispatching.

A method and system for testing an electrical device in an energy system are provided. The method includes: selecting a test suite to run against the electrical device based on a type of the electrical device; in response to determining that an output power of the electrical device is greater than a preselected minimum threshold: determining that the electrical device is energized, and running the test suite; in response to determining that the electrical device has passed the test suite: attaching the electrical device to a designated service point in a topology structure, marking a state of the electrical device to active, and activating the electrical device in the energy system; and in response to determining that the electrical device has not passed the test suite: attaching the electrical device to a limited node in the topology structure, and limiting the electrical device to function within a default control limit.

A microgrid controller of a microgrid includes a communication interface configured to receive load information corresponding to a current load demand of a plurality of loads connected to the microgrid and output control signals for controlling energy resource systems associated with the microgrid, which include a non-stabilizing group and a stabilizing group; and one or more processors, coupled to the one or more memories, configured to execute a load stabilization algorithm to generate the one or more control signals based on the load information. Executing the load stabilization algorithm includes generating, based on the load information, one or more first control signals to dynamically control an amount of total output power provided by the stabilizing group to a power distribution network of the microgrid in order to stabilize one or more cyclic loads on the power distribution network and to maintain the non-stabilizing group at a substantially constant load.

The invention discloses novel wind-gathering wind power generation equipment, relates to the technical field of wind power generation, and solves the technical problems of low wind energy utilization efficiency, huge structure and limited application scene in the existing technical scheme. The novel wind-gathering wind power generation equipment comprises a main base, a stand column is arranged on the upper wall surface of the main base, and a wind gathering barrel is arranged on the stand column; according to the wind power generation device, the wind gathering cover and the wind gathering barrel with the input end area far larger than the output end area are arranged, incoming flow air can be effectively gathered and accelerated, the wind speed acting on the wind power fan blades is remarkably increased, and the wind power generation device has the advantages that the wind power generation device is high in wind power generation efficiency; the utilization rate of wind energy resources is greatly improved; the whole structure is very compact, the occupied area is small, the device can be flexibly applied to a mobile carrier with limited space, for example, the device is installed on a vehicle roof to generate power through driving wind or installed on a ship deck to provide auxiliary power, and a brand new application field of mobile energy regeneration is developed.

The invention relates to the technical field of wind energy resource assessment, and discloses a wind resource assessment method based on a millimeter wave wind finding radar. The method comprises the steps of collecting wind field data of a target area through a millimeter wave wind measurement radar and performing preprocessing to generate a standardized data set; inputting the data into a prediction model to obtain a wind energy distribution characteristic graph; generating a parameter set by adopting threshold segmentation based on the feature map, and dynamically constructing an evaluation function for simulation evaluation; synchronously collecting actual wind energy data, and calculating a deviation matrix between the actual wind energy data and a predicted value to quantify an evaluation error; performing parameter correction on the prediction model by using the error to obtain an optimized model; and finally, regenerating a feature map by adopting the optimization model and completing resource evaluation. According to the method, through online model correction and dynamic evaluation function construction, closed-loop optimization and data driving in the evaluation process are realized, and the accuracy of an evaluation result and the adaptability to a specific site are improved.

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 relates to an optimal scheduling method for port system electricity-hydrogen energy resource allocation, and belongs to the field of resource optimal scheduling. The method comprises the following steps: establishing a mapping relation between a logistics operation organization mode and an energy resource demand, and forming a unified collaborative information model; based on the collaborative information model, a configuration decision evaluation model considering electrochemical energy storage and hydrogen energy resources is constructed and used for generating evaluation indexes corresponding to a resource configuration scheme under the condition that the resource configuration scheme and a logistics operation organization scheme are given; constructing a three-dimensional decision-making space taking the operation cost, the carbon emission and the average ship in-port time as dimensions, constructing an observation grid on a preset discrete value of a corresponding index, solving a boundary state point set point by point, and constructing an envelope surface of the three-dimensional decision-making space by using a three-dimensional convex hull algorithm; and screening a configuration scheme based on decision boundary characteristics of the three-dimensional decision space. According to the invention, the applicability of an electricity-hydrogen energy resource configuration result in a complex operation scene can be improved.

A microgrid controller of a microgrid includes one or more memories configured to store a power distribution schedule comprising a plurality of time segments; a communication interface configured to receive energy resource information corresponding to a plurality of energy resource systems connected to the microgrid, and output control signals for controlling an operation of each energy resource system of the plurality of energy resource systems; and one or more processors, coupled to the one or more memories, configured to: monitor a current time to determine a current time segment among the plurality of time segments; and generate the control signals based on the current time segment within the power distribution schedule to dynamically control the operation of each energy resource system of the plurality of energy resource systems.

A method for testing an electrical device installed in an energy system includes: receiving an identifier and a test identification (testID) associated with the electrical device; validating the identifier and the testID; in response to determining that the electrical device is registered and communicating: setting and storing a polling rate and a posting rate of the electrical device, setting a first plurality of pre-conditions associated with a first test, and in response to determining that the first plurality of pre-conditions is met, performing the first test by: executing a first set of test commands associated with the first test, receiving responses to the first set of test commands of the electrical device, determining first test results based on the responses to the first set of test commands, storing the first test results, and determining whether the electrical device has passed the first test.

The invention relates to the technical field of power equipment, in particular to an adjustable blade supporting frame of a vertical axis wind turbine, which comprises a base, a central shaft is rotatably connected to the base through a rotating seat, two groups of fixed discs are fixedly arranged on the outer wall of the central shaft, and a plurality of brackets are mounted on the fixed discs; one end of the support is movably connected with the inner end face of the outer blade through a first hinge block, a centrifugal driving mechanism is fixedly arranged on the outer wall of the center shaft, and a plurality of angle adjusting mechanisms are in transmission engagement with the outer side of the centrifugal driving mechanism. According to the adjustable blade supporting frame of the vertical-axis wind turbine, the problem that a traditional fixed blade is difficult to adapt to variable wind conditions is effectively solved, active speed limiting can be achieved when the rotating speed is too high, damage to equipment caused by power overload can be avoided, it can be guaranteed that an outer blade always operates in a high-efficiency interval, and the service life of the equipment is prolonged. And it can be ensured that the draught fan can continuously and stably operate when the wind speed is low, the applicable wind speed range of equipment is widened, and the utilization rate of wind energy resources is increased.

The invention provides a load storage scheduling method and system for a double-layer source network containing an electric vehicle, and relates to the field of power system scheduling optimization, and the method comprises the steps: collecting the travel behavior data of the electric vehicle, and combining a Monte Carlo method to generate a plurality of electric vehicle load scenes; based on the load scene, constructing an upper-layer scheduling optimization model of the source network load storage system; based on the upper-layer scheduling optimization model and the developed transactional real-time electricity price signal, constructing a lower-layer scheduling optimization model containing the electric vehicle; introducing a quadratic function to quantify the energy scheduling deviation between the upper-layer scheduling optimization model and the lower-layer scheduling optimization model; coordinated optimization is realized through iterative interaction between an upper layer and a lower layer, an energy scheduling scheme is obtained, and load storage scheduling of the double-layer source network containing the electric vehicle is completed. According to the technical scheme, efficient coordination of multi-energy resources is achieved, the flexible capacity of the electric vehicle is fully released, and the operation cost of the system is remarkably reduced.

The invention relates to the technical field of adaptive control, and discloses a heterogeneous energy resource multi-agent aggregation and adaptive control method, which comprises the following steps of: firstly, acquiring state information of energy equipment in real time through a sensor; based on the equipment state information, a multi-objective optimization problem is constructed, and the optimization objective is to maximize the comprehensive power generation efficiency of the system, minimize the operation cost and maintain load balance; according to the method, through a Nash equilibrium model based on the game theory, a collaboration strategy between agents is constructed, the agents are adjusted according to local information, and finally global optimal power output is achieved; and then performing adaptive control on each device, processing external disturbance by using a robust control algorithm, and enabling the system to adjust device operation parameters in real time when the environment changes. By introducing a self-adaptive dynamic optimization strategy based on an augmented Lagrangian multiplier method, real-time optimal scheduling of a multi-source system under a dynamic constraint condition is realized, and the system can still keep stable operation and global optimal control effects in a complex disturbance environment.

A microgrid controller of a microgrid includes a communication interface configured to receive load information corresponding to a plurality of loads connected to the microgrid, receive energy resource information corresponding to a plurality of energy resource systems connected to the microgrid, and output control signals for controlling an operation of each energy resource system; and one or more processors configured to perform at least two dispatch operations selected from a load balance dispatch operation, an export dispatch operation, or a charge / discharge dispatch operation according to a prioritization dispatch scheme. The load balance dispatch operation, the export dispatch operation, and the charge / discharge dispatch operation are assigned different dispatch priorities that define a sequence of dispatch operations of the prioritization dispatch scheme. The plurality of energy resource systems includes energy storage systems (ESSs), unidirectional non-ESSs, and bidirectional non-ESSs.

The invention provides a virtual power plant-power distribution network voltage dynamic cooperative control method and device, and the method comprises the steps: obtaining multi-source heterogeneous data to construct a mixed data set, and mapping the mixed data set to a quantum state space for feature extraction; fusing the quantum features, the satellite cloud picture and the voltage waveform data, and modeling by adopting quantum probability; generating a DERs output scene based on a quantum state superposition principle, searching and quantifying the voltage out-of-limit probability amplitude of each node, and generating a power distribution network risk thermodynamic diagram; constructing a hierarchical control decision; the hierarchical control decision is executed, then a causal logic chain of a voltage out-of-limit event is generated through an invariant graph reasoning network, the contribution degree of features to the control decision is quantified, and iterative optimization of model parameters is achieved in combination with a meta-learning mechanism. Based on the method, the invention further provides a voltage dynamic cooperative control device, self-adaptive response of distributed energy uncertainty is achieved, and a solution is provided for voltage stabilization of the power distribution network in a high-proportion new energy access scene.

The present inventive concepts comprise a connected, intelligent transfer switch system that permits remote metering, monitoring and control of energy sources connected to a device both by hardwired and wireless connection, and the method for operating this system is disclosed. The inventive concepts represent a significant improvement upon existing transfer switch systems by incorporating advanced monitoring and control capabilities of all energy resources connected to a building, such as fossil-fuel powered generators, battery storage systems, solar photovoltaic arrays, wind turbines, utility grid connections, controllable loads, or other technologies which generate, store or consume energy. The inventive concepts further provide means for flexible and intelligent operation of these resources through a dedicated network communication connection which enables advanced operational decision-making to determine optimal switching actions and real-time interaction through user-facing digital interfaces.

The invention relates to the technical field of material performance testing, and discloses a multi-parameter simulation salt spray environment test system and method, and the system comprises a multi-parameter cooperative control module, a bionic fog drop generation and salt spray adjustment module, an energy recovery and resource circulation module, a salt spray environment adaptive compensation module, and a test data intelligent analysis module. The modules are in closed-loop communication through an industrial Ethernet; the method comprises the steps of test preparation, model initialization, dynamic simulation and compensation, energy resource circulation and data processing ending, specifically, a salt mist concentration-temperature-humidity-wind speed coupling model is constructed, uniform salt mist is generated through bionic atomization and electrostatic adsorption, self-adaptive compensation is achieved in combination with partition sensing and corrosion current detection, and the salt mist concentration-temperature-humidity-wind speed coupling model is obtained. And heat is recovered, and condensate water and non-atomized saline water are recycled. The problems of multi-parameter coupling interference, resource waste and low data value in the prior art are solved, the simulation precision and the resource utilization rate are improved, and the method is suitable for multi-field material corrosion resistance testing.