10591results about "Multi-objective optimisation" patented technology

The invention provides a new material production process parameter optimization method and system based on artificial intelligence, and relates to the technical field of parameter optimization, and the method comprises the steps: constructing a multi-dimensional process parameter feature space, collecting and preprocessing process parameters, extracting correlation features through a deep convolutional neural network, and building a parameter feature space model; generating a technological parameter collaborative optimization model, constructing a multi-objective optimization function, and performing iterative optimization by using reinforcement learning and an improved ant colony algorithm; and performing process parameter adaptive control, constructing a dynamic control interval, collecting parameters in real time and performing adaptive adjustment to realize closed-loop control and model dynamic update.

The invention discloses a multi-dimensional intelligent management method and system for whole-process cost, and the method comprises the steps: generating a time-space associated structured cost data cube according to heterogeneous cost data of the stages of project planning, design, construction and completion; outputting a dynamic cost prediction curve and deviation sensitive nodes based on the structured cost data cube; according to the dynamic cost prediction curve, performing multi-party task allocation optimization by using a block chain enabled BIM / CIM collaboration platform, and generating a collaboration instruction set of smart contract coding; outputting a risk probability matrix and an advanced early warning signal based on the collaborative instruction set and the real-time engineering data flow; and according to the risk probability matrix, adopting a multi-objective optimization algorithm to generate an anti-interference decision scheme set, and outputting an optimal cost control strategy after digital twinborn simulation verification. By using the embodiment of the invention, the cost data of each stage of the project can be efficiently integrated, dynamic cost prediction is realized, and collaborative decision and effective risk management are optimized.

The invention discloses a communication engineering construction dynamic optimization method based on multi-dimensional perception, and the method comprises the steps: collecting the multi-dimensional perception data of a construction site in real time through a multi-source heterogeneous sensor network, including environment parameters, equipment operation states, construction progress and personnel behavior data; performing space-time alignment processing on the multi-dimensional sensing data, and constructing a space-time associated dynamic construction digital twinborn body; establishing a dynamic optimization model based on a reinforcement learning algorithm, taking construction efficiency maximization, resource loss minimization and safety risk minimization as target optimization functions, and embedding risk constraint conditions; inputting the dynamic construction digital twin into the dynamic optimization model, and outputting a multi-dimensional parameter optimization instruction set comprising equipment scheduling parameters, construction path parameters and resource configuration parameters; and performing real-time performance evaluation on the multi-dimensional parameter optimization instruction set through edge computing nodes, and dynamically adjusting weight parameters of the model to form closed-loop feedback control. The purpose of cooperatively improving the construction efficiency, the safety and the economical efficiency is achieved.

The invention relates to the technical field of engineering progress management, in particular to an engineering construction digital project management method and system, and the method comprises the steps: database building, real-time data collection, and model building: generating a visual construction progress model; progress deviation judgment: calculating progress deviation and performing judgment; deviation analysis: calculating a resource gap of an affected process, and generating a resource allocation priority list; resource adjustment: pushing an adjustment instruction to the construction terminal according to the priority list; simulation: simulating the adjusted construction progress, and if the deviation is not eliminated, executing a redistribution step; redistribution: executing the deviation analysis step again; and optimization: optimizing subsequent project progress plan generation logic. The system comprises a database building module, a real-time data acquisition module, a model building module, a progress deviation judgment module, a deviation analysis module, a simulation module, a redistribution module and an optimization module. The method and the device have the effect of facilitating fine management of the engineering project.

The invention relates to the technical field of logistics transportation, in particular to a logistics transportation route optimization method and system based on digital twinning. The method comprises the following steps: constructing a digital twin logistics base through multi-source heterogeneous data fusion and dynamic logistics network modeling; carrying out regional logistics distribution feature extraction and dynamic time sequence prediction based on a digital twinborn substrate to obtain a predicted regional transportation demand; performing real-time state evaluation on the line, generating a dynamic line performance matrix, and performing vehicle resource allocation in combination with the order task priority; and carrying out multi-objective solution and dynamic adjustment on the transportation scheduling scheme to finally obtain an optimal transportation scheduling scheme. And performing iterative optimization on the digital twin model through real-time deviation analysis and a feedback mechanism, and dynamically adjusting a transportation plan to form a closed-loop optimization process. Through the logistics transportation route optimization technology, the logistics transportation efficiency is remarkably improved, the cost is reduced, and the toughness of the system is enhanced.

A multi-objective optimized evaluation method of anti-seismic performance of a slope reinforced by a pile-anchor system is provided. The method includes: training an initial three-dimensional slope numerical calculation model to obtain an target three-dimensional slope numerical calculation model; determining numerical values to be imported into the target three-dimensional slope numerical calculation model according to deformation differences, to obtain a model analysis result; obtaining a simulation operation result according to a reinforcement scheme working condition table of the pile-anchor system; based on the model analysis result and the simulation operation result, evaluating anti-seismic reinforcing performance of the pile-anchor system to obtain comprehensive evaluation values, and then optimizing and evaluating the reinforcement schemes of an overall slope to be reinforced.

The invention discloses a building energy consumption dynamic optimization method and system based on BIM and reinforcement learning, and belongs to the technical field of building energy management and intelligent control, and the method comprises the steps: building a BIM containing building component physical attribute parameters, and generating a building digital twinborn body with dynamic thermal attribute evolution; extracting the spatial topological relation and the physical property parameters of the components, and constructing a multi-dimensional state space of a preset reinforcement learning model; embedding physical constraint conditions, and training the reinforcement learning model to generate a multi-objective optimization strategy of the energy equipment; and analyzing the multi-objective optimization strategy into an equipment control instruction set, and feeding back the equipment control instruction set to the building digital twin for real-time physical attribute simulation. According to the method, the physical accuracy of the BIM and the self-adaptive decision-making ability of reinforcement learning are combined, adversarial training under physical constraints is introduced, an energy consumption optimization strategy which conforms to actual operation limitation and dynamically adapts to environmental changes can be generated, and the energy utilization efficiency and the system response speed are remarkably improved.

The invention relates to the technical field of power systems and automation thereof, in particular to a distribution network auxiliary decision-making method and system considering source load fluctuation relevance and a medium. The method comprises the following steps: firstly, collecting related information of a distribution network area, quantifying a synchronization and hysteresis association rule of multi-source heterogeneous data fluctuation, and constructing a composite feature vector and a standardized risk perception data set; defining a state space and an action space of a reinforcement learning algorithm based on the composite feature vector, and realizing auxiliary decision-making optimization of the distribution network; constructing a scene feature library, calculating the fluctuation relevance similarity between a new scene and a historical scene, and multiplexing a deep reinforcement learning model architecture and carrying out transfer learning; building a power grid digital twinborn simulation platform, designing evaluation indexes, generating candidate schemes, deducing the candidate schemes, selecting recommendation strategies and storing the recommendation strategies in a strategy knowledge base.

A power grid planning method and system adapted to multiple uncertainties and multi-objective requirements, which relate to the technical field of power grid planning. The method comprises: constructing a deterministic multi-objective coordinated planning model with the objectives of minimizing economic cost, reliability risks and environmental impact; on the basis of an IGDT, constructing a multi-uncertainty model considering wind power, photovoltaic power and load; converting a model into a second-order cone programming model by using variable substitution, absolute value linearization, second-order cone relaxation and McCormick envelopes relaxation methods; and solving the second-order cone programming model, in order to obtain a power grid planning scheme that adapts to uncertainties in wind and photovoltaic power output and load demand growth and meets the requirements for the economy, reliability and environmental friendliness of power grid operation. Thus, economic benefits of power grid planning are improved, carbon emissions from power grid operation are reduced, the reliability of a power grid is enhanced, and an obtained planning scheme has high robustness within expected objectives of planners.

According to the optical storage direct flexible system scheduling method based on multi-objective optimization and an adaptive scheduling strategy, monitoring devices are installed on a photovoltaic array, an energy storage unit and a load side, a data sensing network covering the whole link of'source-storage-load-network 'is constructed, and key data are collected in real time. A multi-objective optimization model with maximization of economical efficiency, reliability and clean energy consumption rate as objectives is established, and a hybrid optimization mechanism of a genetic algorithm and particle swarm optimization is adopted to generate a day-ahead scheduling reference scheme. And designing an adaptive scheduling algorithm and a dynamic parameter adjustment mechanism based on fuzzy logic, and combining a rolling optimization window to realize real-time optical storage coordination control and power real-time balance. According to the invention, the operation efficiency, stability and flexibility of the optical storage direct-flexible system are effectively improved, the capability of coping with emergencies is enhanced, and the optimization of the overall performance of the system is realized.

A method of constructing a digital twin physical field of a computer numerical control machine tool with double order reduction is provided. A physical field finite element simulation sample data set in a working state sampling space has an order preliminarily reduced to sparse grid node data via an improved K Nearest Neighbor (KNN) algorithm, so that the number of point sets is kept within the real-time rendering capability range of graphic rendering software. A digital twin physical field proxy model is trained using a radial basis function (RBF) to realize reconstruction of the physical field. The proxy model is iteratively optimized using a genetic algorithm to solve the problem of deviation between a proxy physical field model and a real physical model. Finally, the proxy model is deployed to a digital twin system, and the physical field distribution is solved in real time.

Disclosed in the present invention is a method for constructing a double-order-reduced digital twin physical field of a numerically controlled machine tool. The method comprises: performing preliminary order reduction on finite element simulation sample data sets of a physical field in a working state sampling space to form sparse grid node data by means of an improved KNN algorithm, so that the number of nodes sets is kept within a real-time rendering capability of graphics rendering software; by using a radial basis function (RBF), obtaining a digital twin physical field proxy model by means of training, to implement reconstruction of the physical field; on the basis of a genetic algorithm, iteratively optimizing the digital twin physical field proxy model, to solve the problem of deviation between a proxy physical field model and a real physical model; and finally, deploying the digital twin physical field proxy model to a digital twin system and solving a physical field distribution in real time. According to the present invention, reflecting of the spatial distribution of a related structural field and a temperature field in the digital twin system is implemented, so that simulation of a virtual-real mirror mapped physical field driven by real-time operation data is implemented.

The present application relates to the field of wire laying control technology, and discloses a composite material automatic wire laying forming optimization method, system, medium and wire laying machine. The method comprises: inputting dynamic tension control data into a fuzzy neural network calculation engine to obtain a dynamic compensation model for the retraction and release speed; based on the dynamic compensation model for the retraction and release speed, using a reinforcement learning engine to calculate the shearing sequence of multiple bundles and generate an intelligent shearing instruction set; constructing a re-delivery trajectory mapping relationship based on the intelligent shearing instruction set, and forming a re-delivery path matrix after iterative calculation by a digital twin engine; establishing a temperature field distribution prediction model based on the re-delivery path matrix, and constructing a temperature field dynamic control sequence after finite element analysis and calculation; substituting the temperature field dynamic control sequence into a multi-objective constraint equation, and outputting a laying strategy data packet after deep reinforcement learning iterative optimization. The present application realizes the precise coordination of multiple process parameters such as tension control, speed compensation, shear control, and temperature control.

A multi-objective optimization method and system for a shield construction parameter. The method comprises: acquiring an initial shield construction parameter; and optimizing the shield construction parameter by using a multi-objective optimization algorithm, during the optimization of the shield construction parameter, on the basis of the shield construction parameter, obtaining a tunneling speed by means of performing prediction on the basis of random forest, calculating a shield tunneling energy consumption ratio on the basis of the tunneling speed and the shield construction parameter, and taking the maximization of the tunneling speed and the minimization of the shield tunneling energy consumption ratio as two fitness functions of the multi-objective optimization algorithm. The multi-objective optimization method and system for a shield construction parameter take construction efficiency into consideration and reduce the construction cost while ensuring construction safety.

The invention discloses an electromechanical construction method and system based on a BIM technology, and relates to the field of electromechanical construction, and the method comprises the steps: collecting a three-dimensional point cloud of a construction site; a construction site buult BIM model is constructed; constructing an electromechanical BIM model of the construction site; obtaining a logic dependency relationship between electromechanical tasks in the electromechanical BIM model by utilizing a judgment matrix method DSM, and extracting attribute parameters of task nodes; constructing a directed acyclic graph (DAG) for task collaborative optimization according to the logic dependency relationship between the electromechanical tasks and the time sequence and resource parameters of the nodes; obtaining task nodes on the critical path according to a directed acyclic graph (DAG) of task collaborative optimization; establishing a multi-objective optimization model according to task nodes on the critical path, and solving through a multi-objective particle swarm optimization algorithm MOPSO by taking the shortest construction period and the highest resource utilization rate as optimization objectives and taking process logic and resource occupation as constraint conditions to obtain a resource optimization configuration set; and the collaboration of electromechanical construction tasks is improved.

The invention relates to a building design scheme multi-objective optimization comparison and selection method and device, equipment and a medium. The method comprises the steps of generating a multi-dimensional design parameter set by obtaining building information model data and parameterized design data; performing multi-dimensional target analysis and evaluation by using a multi-field joint simulation platform to generate a multi-dimensional evaluation index; a dynamic multi-objective optimization model is constructed through a dynamic weight adaptive algorithm in combination with project stage demands and user interaction data; carrying out iterative optimization by adopting an improved non-dominated sorting genetic algorithm to obtain an optimized design scheme gene sequence result, and introducing a spatial topology connectivity constraint to generate a Pareto optimal solution set; and according to the Pareto optimal solution set, generating an optimization scheme through user weight adjustment and scheme screening. According to the method, the optimal design scheme set meeting the project requirements can be quickly and efficiently generated and screened out, the project stage requirements and user preferences are met, and the design efficiency and the scheme quality are improved.

The invention relates to the technical field of tunnel engineering, in particular to a tunnel surrounding rock grading method and system, comprising intelligent sensing and data acquisition, multi-source data fusion and modeling, hybrid model dynamic grading, real-time decision and support optimization, online learning and dynamic feedback, and risk early warning and emergency response. Compared with the prior art that a geological data acquisition mode combining manual drilling coring and low-resolution geophysical prospecting is adopted, efficiency is low, subjective errors are large, and a complex geological structure is difficult to cover, unmanned aerial vehicle LiDAR scanning, intelligent rock core image analysis and a high-density IoT sensor network work cooperatively, and the working efficiency is greatly improved. Real-time dynamic acquisition of full-section geological information is achieved, manual intervention errors are eliminated in combination with a multi-source data fusion algorithm, the automation level and three-dimensional space representation precision of data acquisition are remarkably improved, and a high-resolution holographic data base is provided for surrounding rock classification.

The invention provides a tunnel safety-oriented safety step pitch monitoring method and system, and the method comprises the steps: deploying an edge calculation module at a sensor node, carrying out the preprocessing and compression of collected monitoring data, reducing the data transmission amount through data dimension reduction and feature extraction, relieving the data transmission pressure, and improving the data transmission efficiency and real-time performance. An incremental learning algorithm is adopted to carry out online analysis on monitoring data, an anomaly detection model is dynamically updated according to a newly collected data sample, an early warning threshold value is adaptively adjusted, the accuracy and the real-time performance of anomaly recognition are improved, and misinformation and missing report are reduced; a multi-sensor data fusion technology is comprehensively applied, correlation analysis is carried out on sensor data of different types and different positions, the reliability and accuracy of monitoring data are improved through data redundancy and complementation, and comprehensive evaluation of the tunnel construction safety state is achieved.

The invention belongs to the field of industrial automation and intelligent manufacturing, particularly provides a metallurgical fan multi-physics field coupling and dynamic performance optimization method based on digital twinning, and aims to solve the technical problems of performance optimization and fault prediction of a metallurgical fan under complex operation conditions. The method comprises the following steps: acquiring multi-physical field data such as fluid mechanics, thermodynamics and structural mechanics through a distributed sensing network and a real-time data acquisition technology, and performing cleaning, denoising and feature extraction on the data; the method comprises the following steps: constructing a high-precision multi-physics field coupling digital twin model based on a geometric structure and material characteristics of a metallurgical fan, and carrying out model calibration in combination with historical data and real-time sensing data; the dynamic performance of the draught fan under different operation conditions is simulated through the co-simulation technology, the operation parameters of the draught fan are dynamically adjusted through a data-driven optimization algorithm, real-time optimization of the performance is achieved, and the purposes of improving the operation efficiency, reducing energy consumption and prolonging the service life of equipment are achieved.

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a meteorological data fused water-saving irrigation control method, device and equipment and a medium. According to the method, a historical meteorological data set is constructed through multi-source meteorological data fusion, a dynamic water demand table is generated in combination with a crop water demand characteristic database, and a crop water demand model is established based on soil moisture content data. A grid irrigation unit division and growth period coupling soil moisture content response matrix construction technology is adopted, a cooperative constraint is established between a water demand threshold value and a water saving benefit through a multi-objective optimization learning algorithm, and a personalized irrigation scheme is generated. A soil moisture content dynamic evaluation matrix containing a dynamic time warping operator is designed, and dynamic matching of the soil layered soil moisture content and a standard template is achieved. The contradiction between meteorological response lag and low water resource utilization rate in traditional irrigation is effectively solved, accurate irrigation decision is realized through multi-dimensional data fusion and an intelligent optimization algorithm, and the water-saving benefit and the agricultural water resource utilization efficiency are improved.

The invention relates to a concrete working performance measurement method and system based on a multi-modal visual large model, and solves the problem that rapid detection of concrete working performance parameters is troublesome, and the method comprises the steps: based on the spatial semantic understanding capability of the multi-modal visual large model, combining a multi-view stereoscopic vision and structured light scanning technology, and calculating the working performance of concrete; reconstructing a three-dimensional geometric structure of the concrete slurry, extracting morphological characteristic parameters, and forming characteristic vectors; inputting the feature vectors into a pre-trained multi-task neural network, fusing the spatial-temporal features and combining a rheological algorithm to identify various working performance parameters; integrating identification results for at least three times by adopting integrated learning, and verifying parameters based on a fluid dynamics basic equation through a fluid simulation platform; and based on the verification result, generating a mix proportion optimization suggestion containing the material components. The method has the advantages that non-contact rapid measurement of concrete working performance parameters is achieved, precision and efficiency are improved, and mix proportion optimization suggestions are provided.

The invention relates to the technical field of power system optimization scheduling, and discloses a multi-load equipment energy consumption management method and device and computer equipment, and the method comprises the steps: obtaining electricity price data, and the energy consumption data, the operation state and the load demand of electric equipment, based on the electricity price data, the energy consumption data, the operation state and the load demand, the priorities of the electric equipment are classified; adopting a preset multi-task learning model to predict the load demand of the electric equipment in a future preset time period; generating a multi-target optimization model based on the priority, the load demand and the electricity price data of the electric equipment, and generating a dynamic power distribution scheme of the electric equipment based on the multi-target optimization model; and obtaining the load demand and the external environment of the electric equipment in real time, and adjusting the dynamic power distribution scheme of the electric equipment based on the load demand and the external environment. According to the method, the energy consumption optimization and the operation efficiency are always kept balanced, and the problem that the energy consumption optimization and the operation efficiency are not balanced is solved.

The invention relates to the technical field of factory energy consumption management, in particular to a smart factory dynamic optimization management system based on digital twinning and big data analysis. Comprising a data acquisition and fusion module, a digital twinning construction module, a data analysis module, a dynamic optimization decision module and an anomaly diagnosis module. Constructing a digital twinborn model of a factory physical entity according to the collected data; constructing an energy consumption prediction model based on deep learning frameworks such as LTSM; when the energy consumption deviation exceeds the limit, abnormal root causes are positioned; and generating an energy consumption scheduling scheme based on a multi-objective optimization algorithm, and issuing an instruction to realize dynamic energy consumption adjustment. Through deep fusion of digital twinning and big data technologies, comprehensive and accurate simulation, multi-target collaborative optimization, rapid abnormality diagnosis and dynamic control of factory energy consumption are realized, the energy utilization efficiency is effectively improved, the cost is reduced, the intelligent level is improved, and the method has remarkable economic benefits and environmental benefits.

The invention relates to a coal mine safety production intelligent decision-making system based on digital twinning, and the system comprises a physical sensing layer which collects coal mine environment parameters, equipment states and personnel positioning data through the deployment of a multi-mode sensor network, and generates a structured data flow; the edge calculation layer is used for operating an incremental multi-objective evolutionary algorithm, quickly generating a cache strategy in combination with a strategy cache pool preloading mechanism, uploading the processed data to the digital twinborn layer, receiving a global instruction of the intelligent decision-making layer and decomposing the global instruction into a device-level control signal; the digital twinborn layer is used for receiving the real-time data uploaded by the edge calculation layer, updating the state of a digital twinborn body and feeding back an optimization demand to the intelligent decision-making layer; and the intelligent decision-making layer is used for generating a global strategy by means of digital twin-guided hybrid optimization and a special FPGA acceleration card for a coal mine, and fusing the cache strategy of the edge calculation layer and the global strategy of the intelligent decision-making layer to generate a global instruction.

The invention discloses an intelligent building design generation system based on multi-source heterogeneous data and an AI large model, and the system comprises a multi-source heterogeneous data collection and processing module which is responsible for automatically collecting the multi-source heterogeneous data related to a project, and carrying out the data processing; the knowledge graph module is used for carrying out structured storage and association on multi-source heterogeneous data by constructing a knowledge network covering upper planning, historical culture, architectural design standards and law and regulation contents; the AI large model fine tuning module is used for performing fine tuning on the AI large model by using data in the special architectural design field; the scheme design module is responsible for generating various design schemes, providing choices of different design styles and function layouts and optimizing the schemes; and the visual interaction and feedback module is used for providing an interaction interface with a user. The method is remarkably superior to the prior art in the aspects of design substantial content processing, multi-source heterogeneous data integration, design generation and optimization, user interaction experience and the like, and a more comprehensive, intelligent and efficient solution is provided for building design.

The invention provides a wafer-level test yield prediction and process optimization method and system based on big data, and relates to the technical field of wafer-level tests.The wafer-level test yield prediction and process optimization method comprises the steps that wafer manufacturing process data is obtained, a time-sequence-sensitive multi-modal data fusion network is constructed, a LSTM and GNN combined mixed architecture is adopted, and a multi-modal data fusion network is constructed; self-adaptive weight distribution is carried out through an attention mechanism to predict the yield; and when the predicted yield is lower than a threshold value, triggering a layered progressive process optimization system, and adjusting process parameters through an equipment parameter optimization layer and a process level optimization layer. According to the invention, the accurate prediction of the wafer test yield and the automatic optimization of the process parameters are realized, the production efficiency is improved, and the manufacturing cost is reduced.

The invention relates to the field of shale oil and gas, and discloses a method for identifying a shale oil and gas reservoir dessert, which comprises the following steps: acquiring a core CT image, three-dimensional seismic data and production dynamic data, and carrying out cross-scale preprocessing; constructing a fractional-order non-local seepage field model to represent nano-to-kilometer-level flow characteristics; predicting seepage field parameters through a Lie group symmetry constrained neural network; establishing a cross-scale coupling model of the quantum adsorption effect and the macroscopic seepage law; dynamically updating model parameters based on real-time monitoring data; and executing multi-target collaborative optimization to generate a sweet spot three-dimensional distribution and development scheme. According to the method, a fractal dimension dynamic constraint cross-scale data fusion technology is adopted, the effect of accurate mapping of nanopore and macroscopic fracture network parameters is achieved, and the problem of misalignment of CT scanning and seismic inversion data space registration is solved through pore communication fractal analysis.

The invention discloses a low-altitude aircraft take-off and landing platform site selection optimization method. The method comprises the steps that real-time dynamic data and static GIS data including urban traffic flow data, meteorological data, landform data, environment data and POI data are acquired; constructing an urban three-dimensional digital model according to the static GIS data, and constructing a digital twin model according to the three-dimensional digital model and the real-time dynamic data; constructing environment constraint conditions and safety constraint conditions of candidate take-off and landing platform positions in the digital twin model; performing multi-objective optimization on the digital twin model through a particle swarm optimization algorithm, and generating an optimal candidate take-off and landing platform site selection scheme set meeting environment constraint conditions and safety constraint conditions; and dynamically updating the digital twin model according to real-time data feedback, and dynamically adjusting the site selection scheme of the take-off and landing platform. According to the method, the virtual city model is constructed through the digital twin, an accurate simulation environment and real-time feedback are provided for site selection optimization, and the accuracy and feasibility of a site selection scheme are improved.

The invention relates to the technical field of battery energy storage, and discloses an energy storage operation optimization method and system considering battery life loss, and the method comprises the steps: initializing battery energy storage electrical parameters, constructing a battery life loss model, and obtaining the health state change of a battery; constructing a battery operation cost model, defining a multi-objective optimization algorithm and constraint conditions, and optimizing the battery operation cost; and carrying out adaptive scheduling through a model prediction control and reinforcement learning algorithm, and determining an optimal charging and discharging strategy of the energy storage system. By constructing the battery life loss model and the multi-target optimization algorithm, economical operation of the energy storage system and prolonging of the battery life are achieved, the charging and discharging strategy of the energy storage system can be dynamically adjusted according to the power market cost fluctuation, the load requirement and the battery health state, and the operation cost of the energy storage system is reduced.

The invention relates to the technical field of crustal stress field data processing, in particular to a crustal stress field three-dimensional dynamic inversion method based on a multi-scale adaptive algorithm. The method comprises the following steps: acquiring a geological data set of a target area; constructing a crustal stress field three-dimensional initial model based on the geological data set, and performing geologic body space division and mesh generation to obtain crustal stress field three-dimensional mesh model data; performing multi-scale region division on the crustal stress field three-dimensional grid model data, and establishing a multi-scale weighting function to obtain multi-scale partition mapping information; and constructing a cross-scale boundary adaptive transmission mechanism, and establishing a stress tensor continuity constraint model at a multi-scale partition boundary to obtain cross-scale stress boundary coupling data. Through a multi-scale adaptive algorithm and dynamic closed-loop optimization, high-precision, dynamic and continuous inversion of a crustal stress field in a complex geologic structure is realized.