191 results about "Configuration optimization" patented technology

Disclosed in the present invention are a shared energy storage configuration optimization method and system for a wind farm cluster. The method comprises: constructing a shared energy storage double-layer programming model; on the basis of the output of a wind farm cluster, an electricity price in an electric energy market and a compensation price in a frequency-modulation auxiliary service market, obtaining multiple uncertainty sets, using an improved K-means clustering method to perform joint clustering on the multiple uncertainty sets so as to obtain a typical scenario, and on the basis of the annual occurrence frequency of the typical scenario, calculating a typical scenario probability; and using a distributionally robust optimization method to model the multiple uncertainty sets on a short-time scale so as to obtain an extreme uncertainty scenario, using the typical scenario probability and the extreme uncertainty scenario to update the shared energy storage double-layer programming model so as to obtain an updated shared energy storage double-layer programming model, and using a particle swarm algorithm to perform solving so as to obtain a configuration result. By means of the method, shared energy storage configuration optimization is performed by means of taking various uncertainty factors into consideration, thereby improving the accuracy of shared energy storage configuration optimization.

The invention discloses an energy storage system capacity configuration optimization method and device and a storage medium, and relates to the technical field of energy storage system capacity configuration.The energy storage system capacity configuration optimization method comprises the following steps that daily total electricity utilization power information of a factory is continuously collected, corresponding weather information is obtained, and factory power weather data is obtained; denoising processing is carried out, an electricity consumption power prediction model is constructed, and the daily total electricity consumption power of the factory is predicted; calculating a prediction error, carrying out error rule analysis, and correcting the predicted total power consumption power to obtain corrected power prediction data; calculating predicted electricity consumption, and performing configuration optimization on the capacity of the energy storage system of the factory according to the predicted electricity consumption; the method is used for solving the problems that when an existing energy storage system capacity configuration technology configures the capacity of an energy storage system of a factory through a power consumption prediction model, the rule of prediction deviation of the model cannot be analyzed, meanwhile, a prediction result cannot be corrected, and the accuracy of capacity configuration of the energy storage system cannot be improved.

The invention belongs to the technical field of cold and heat source configuration methods, and particularly relates to a cold and heat source configuration optimization method and system based on a full life cycle, and the method comprises the steps: S1, building use characteristic analysis; s2, designing a staged unit configuration scheme; s3, optimizing an intelligent control strategy; s4, carrying out full-life-cycle cost-benefit analysis; s5, optimal scheme decision making and dynamic adjustment; according to the method, a dynamic prediction model of a time sequence analysis and Transform-TCN hybrid architecture is adopted, historical load data can be deeply mined through time sequence analysis, and time characteristics and periodic rules in the data are captured; the long sequence modeling capability of the Transform and the local feature extraction capability of the TCN are combined by the Transform-TCN hybrid architecture, so that the long and short term dependency relationship in the load data can be better processed, and the prediction accuracy is improved.

The invention discloses a capacity configuration optimization method for an energy storage system, relates to the field of energy storage planning, solves the problem of energy waste of the energy storage system, and comprises the following steps: collecting historical environmental data of a region where a power generation facility corresponding to the energy storage system is located and historical power consumption of a power grid corresponding to the energy storage system; analyzing the power generation performance of the power generation facility of the energy storage system and the power consumption performance of a power grid corresponding to the energy storage system to obtain a peak value operation time period, an average value operation time period and a valley value operation time period corresponding to the energy storage system; setting a system capacity configuration scheme of the energy storage system corresponding to the peak value operation time period and the valley value operation time period; analyzing the operation state of the energy storage system corresponding to the average operation time period, and setting a system capacity configuration scheme of the energy storage system in the average operation time period; the actual energy storage mode of the energy storage system is adjusted based on the system capacity configuration schemes corresponding to the peak value operation time period, the average value operation time period and the valley value operation time period, and stable operation of the energy storage system is guaranteed by improving the energy utilization rate.

The invention discloses a space target monitoring constellation configuration optimization method and system considering complex constraints, and belongs to the technical field of aerospace mission design and optimization. The method comprises the following steps: firstly, constructing a discrete orbit modal library meeting regression orbit dynamic resonance conditions; constructing generalized hybrid design variables consisting of integer indexes and real number fine tuning quantities on the basis of the library; establishing an optimization model taking the space volume coverage rate, the maximum revisit time and the system construction cost as targets and taking illumination, dynamics and maneuvering capability as constraints; performing rapid analysis and coarse screening on invalid links through track geometry; and finally, performing global optimization by using an improved non-dominated sorting genetic algorithm to obtain a Pareto optimal constellation configuration set. According to the method, accurate evaluation of the three-dimensional space target coverage efficiency is realized, the physical feasibility and long-term stability of a configuration scheme are ensured, and a high-dimensional multi-target optimization problem is efficiently solved.

The invention discloses a civil aviation element configuration optimization method based on mixed integer dynamic programming. The method comprises the following steps: S1, constructing a Chinese civil aviation core element and carbon emission spatial-temporal characteristic database; s2, establishing a mixed integer dynamic programming model with the purpose of minimizing the total cost of the system in the programming period; and S3, establishing decision variables, constraint conditions and a fusion solution algorithm framework required by the mixed integer dynamic programming model. And S4, based on the fusion solution algorithm framework, solving the mixed integer dynamic programming model by using a mathematical programming solver integrated with a robust optimization module so as to obtain a civil aviation core element dynamic configuration scheme which is optimal in total system cost in multiple periods in the future and meets the uncertainty scene. According to the method provided by the invention, a multi-source data fusion algorithm is optimized, a carbon emission measuring and calculating method is deepened, and solver parameters are adjusted, so that the collaborative requirements of improving the operation efficiency and reducing the carbon emission from the perspective of carbon cost are met.

The invention belongs to the field of product top-layer system design and modeling, and discloses an interactive feedback-oriented product top-layer system design method, which comprises the following steps of: constructing a multi-layer model based on a demand R, a function F, a behavior B and a structure S, establishing a knowledge graph for representing a semantic relationship between layers and between nodes on the same layer by using weighted directed edges, forming a layer-by-layer mapping matrix by using edge weights, and constructing a layer-by-layer mapping model; realizing weight decomposition from the demand to the structural unit; a constraint hypergraph is constructed on the node set, and structural unit combination constraints are depicted through mutual exclusion, dependency, collaboration and conflict hyperedges; physical parameters of the structural units are normalized into performance vectors, weighted aggregation is carried out according to weights, legality judgment and performance correction are carried out in combination with a hypergraph, a system-level performance evaluation result is obtained, candidate configuration schemes are compared and optimized according to the result, and a product configuration scheme meeting requirements and constraint conditions is formed. The method provides a model basis for subsequent product structure configuration optimization based on interactive feedback.

The invention relates to the field of power system energy configuration, in particular to a photovoltaic station power generation and energy storage unit configuration optimization method and related equipment, and the method comprises the steps: obtaining photovoltaic station data; establishing a full-cycle hour model according to the photovoltaic station data; calculating a monthly granularity average value according to the photovoltaic station data, establishing a full-cycle monthly model by combining the monthly granularity average value and the full-cycle hour model, and solving the full-cycle monthly model to obtain an optimal configuration solution; according to the photovoltaic station data and the optimal configuration solution, a whole-year hour model is created for the photovoltaic station in the whole cycle every year based on the whole-cycle hour model; a two-stage coupling optimization model is established in combination with the full-period monthly model and N full-year hour models, and N represents the year number of the full period of the photovoltaic station; and iteratively solving the two-stage coupling optimization model to obtain an optimal configuration scheme of the photovoltaic station power generation and energy storage unit. According to the method, energy configuration can be optimized, and the feasibility of an hour-level station operation plan can be ensured.

This invention relates to a process architecture for optimizing ship hydrodynamic configurations based on artificial intelligence technology. The architecture includes modules such as expert experience, historical sample data of ship hydrodynamic configurations, design input for ship hydrodynamic configurations, a knowledge base for ship hydrodynamic configuration design and evaluation, a parametric model of ship hydrodynamic configurations, knowledge-driven reconstruction of the ship hydrodynamic model, AI-based optimization design of ship hydrodynamic configurations, rapid evaluation of ship hydrodynamic performance, and a comprehensive evaluation index model for ship hydrodynamic performance. This invention fully leverages the design experience contained in historical sample data, extracting the experience of numerous experts into design knowledge and integrating it into the ship hydrodynamic configuration optimization design process. The process architecture drives intelligent reconstruction of the ship hydrodynamic model through design knowledge, and improves upon the shortcomings of traditional ship hydrodynamic performance prediction methods, such as time consumption and low efficiency, by establishing a rapid prediction proxy model for hydrodynamic performance based on machine learning algorithms.

The invention provides a rope-driven parallel mechanism configuration optimization method for adjusting the position of a tail end connection point, and the method comprises the steps: determining a plurality of rope-driven parallel mechanism configurations meeting constraint conditions based on the installation space needed by a target motion simulator, the shape and load of a tail end actuator, and the motion freedom degree and target motion range of the tail end actuator; respectively calculating a force closed working space of each configuration and screening out at least two to-be-selected configurations; respectively calculating a force helix feasible working space and an attitude rotation range of each to-be-selected configuration; respectively comparing the attitude rotation range of each to-be-selected configuration with the target motion range, and determining whether the to-be-selected configuration is a basic target configuration or not; on the basis of the selected basic target configuration, traversing and calculating a force spiral feasible working space when different connection positions on the rope and the end effector are fixed; and determining a rope connection point on the end effector according to the position and posture number distribution relationship between the connection position and the force spiral feasible working space. And the optimal configuration meeting the requirements is obtained.

The application relates to a modular magnetic reconfiguration parallel robot joint system for on-orbit autonomous assembly of a spacecraft, and belongs to the technical field of space equipment. The system is aimed at solving the problems of poor flexibility, low reconfiguration efficiency, difficulty in balancing high precision and high stiffness, and low reliability of existing space parallel robots. The technical scheme comprises a Halbach array magnetic ball and an electromagnetic locking ring of a magnetic joint module, a carbon fiber telescopic connecting rod micro servo motor and a harmonic reducer of a distributed drive branch chain, a multi-sensor data fusion unit configuration optimization algorithm module and a real-time control unit of an intelligent control system. The system can realize rapid joint mode switching, improve task response and execution efficiency, autonomously switch between precision mode and high stiffness mode to meet different operation scene requirements, adopt non-contact magnetic transmission to avoid friction and wear and pollution, improve system service life and reliability, and realize modular design for convenient maintenance and upgrading.

The invention provides an energy storage configuration optimization method and system for a flexible interconnection power distribution network, and belongs to the technical field of energy storage configuration of the power distribution network, and the method comprises the steps: building a dynamic cost accounting mechanism which is coupled with a network physical state in real time; constructing a short-term two-stage robust optimization model which can fully schedule various flexible resources and takes minimization of the total cost of the system as a target based on a dynamic cost accounting mechanism, and obtaining an energy storage optimal operation strategy; a long-term two-stage robust optimization model is established, the objective of the main problem is to minimize the upper bound of the sum of investment cost and worst-case operation cost, and the sub problem is to evaluate the worst performance of the investment scheme in all possible uncertainty scenes under a given investment scheme; and a robust optimal configuration scheme is obtained through iterative solution of the main problem and the sub-problems.

The application discloses a network type energy storage power station capacity planning and configuration optimization method based on multi-source data, relates to the technical field of configuration optimization, and comprises the following steps: calculating the cavity length and cavity height of a subsurface cavity based on the sound wave response signal of a corridor ground surface structure; calculating the cavity risk coefficient of a target corridor based on the cavity length and cavity height; calculating the updated discharge depth based on the cavity risk coefficient and the planning discharge depth in an electric station design parameter table; generating a shadow band section of the target corridor, marking the energy storage units of the energy storage power station as high-risk units or low-risk units based on the shadow band section; calculating the high-risk power sum of all the high-risk units, and calculating the high-risk capacity sum of all the high-risk units based on the updated discharge depth. The application can improve the reliability of the operation safety of the power station.

The application discloses a kind of energy storage configuration optimization method and system combined with power grid adaptive regulation, is specifically related to energy storage configuration optimization field, including the authentication behavior snapshot based on the authentication behavior of energy storage node generation, the authentication behavior snapshot includes authentication initiator identification, target identity label, role switching flag, authentication timestamp and authentication type label;According to the authentication behavior snapshot, construct authentication trajectory graph structure, the trajectory graph includes multiple authentication nodes and its authentication jump path.By constructing the trajectory graph structure based on authentication behavior snapshot, combined with double endorsement mechanism and path continuity judgment, whether " jump pseudo-legal " abnormal chain exists in authentication path can be identified, so that potential counterfeit authentication energy storage node is frozen and isolated in time, to prevent attackers from tampering with edge device control path to realize role fraud takeover.

The invention discloses a winter energy-saving operation method for a direct air cooling unit. The winter energy-saving operation method comprises the following steps that S1, unit configuration optimization is conducted; s2, constructing a monitoring network; s3, establishing operation parameters; and S4, winter operation regulation and control. On the premise that the anti-freezing requirement is met, the low-temperature advantage of the environment in winter is fully utilized, the back pressure of the air-cooled condenser is optimized, coal consumption of a unit is reduced, and the safety and economical efficiency of unit operation are improved.

The invention relates to the field of agricultural resource allocation, in particular to an agricultural digital intelligence resource allocation management system and method based on dynamic prediction, and the system comprises a region evaluation module which is used for periodically determining the type of each allocation analysis region in an allocation management range, the category of the configuration analysis area is determined based on an area demand parameter and an area demand conflict parameter; the configuration analysis module is used for performing configuration coordination analysis or configuration optimization analysis on the configuration management range based on the first-class configuration proportion index; the coordination analysis module is used for determining whether to carry out matching call analysis or related call analysis on one type of configuration analysis region based on the related configuration proportion index so as to determine an execution optimization target; and the optimization analysis module is used for determining whether configuration optimization is carried out on each configuration analysis region or not based on the related demand target parameters so as to determine an execution optimization target. According to the invention, the distribution quality and the utilization efficiency of agricultural machinery resources in an agricultural planting range are improved.

The invention discloses an intelligent driving Linux system multi-mode dynamic adjustment method based on a multi-core processor, relates to the technical field of intelligent driving, and aims to solve the problems that in existing intelligent driving system multi-core scheduling, calculation power and power consumption are difficult to balance, real-time performance is insufficient, reliability is limited, and performance is reduced due to resource competition. The real-time performance is guaranteed by adopting a Linux system, multi-core distribution is optimized through task CPU affinity configuration, three dynamic switching modes of low power consumption, triple modular redundancy and high performance are designed, and on-demand distribution of computing power, dynamic optimization of power consumption and rapid fault tolerance are realized in combination with a module dependency analysis and partition scheduling strategy. According to the method, the real-time response capability, the endurance performance and the reliability of the intelligent driving system are remarkably improved, the resource waste is reduced, and the method is suitable for various multi-core processor vehicle-mounted intelligent driving platforms.

The invention discloses a power grid equipment dynamic configuration method and system based on multi-objective optimization, and relates to the technical field of power system planning. The method comprises the following steps: calculating a historical fund distribution proportion according to historical investment data and an asset net value of power grid equipment; constructing a configuration optimization model by taking maximum benefit and minimum risk as objective functions and taking total configuration fund constraint, equipment fund allocation proportion constraint and equipment fund upper and lower limits as constraint conditions, and solving the configuration optimization model to obtain a first configuration demand; adjusting the first configuration demand based on the equipment configuration annual change rate to obtain a second configuration demand of each equipment; and after the weight of each subitem of the target function is updated according to the second configuration requirement, the configuration optimization model is solved again, the requirement is corrected according to policy requirements, and a final equipment fund configuration scheme is obtained. The method is used for solving the problems that traditional experience distribution is high in subjectivity, response to interannual changes is lagged, and historical continuity and risk management are difficult to consider at the same time.

The invention provides a transformer substation intelligent configuration optimization method and system based on modular design, and relates to the technical field of configuration optimization, and the method comprises the steps: obtaining an initial module set of a transformer substation; constructing a three-dimensional configuration model based on the initial module set; generating a module dependency tree according to the three-dimensional configuration model, and obtaining an initial module configuration scheme based on the module dependency tree; based on the initial module configuration scheme and a genetic algorithm, generating a first-generation population containing reference individuals and variation individuals, executing a composite verification condition to obtain an effective population, generating a next-generation population according to the effective population, and executing multiple iterations to obtain an optimal configuration scheme. The improvement of the genetic algorithm is converted into a computer-driven intelligent optimization method, so that the compactness and rationality of the layout of the transformer substation module are greatly improved.

The application discloses a kind of granule sensor shell systems and its configuration optimization and preparation method, belong to infrastructure monitoring technical field.The shell system includes the empty shell of the real shape of the replica broken stone, the fixed support and counterweight block of inner setting.Through three-dimensional scanning, the target broken stone shape is obtained, and the shell digital model is established;Design the fixed support including sensor cavity, multiple counterweight cavities and support shaft;Establish the optimization model with the size of counterweight block as variable, the minimum matching error of system total mass and centroid as target, and obtain the optimal size of counterweight block by solving;According to the optimization result, adjust the size of counterweight cavity and iterate optimization until the accuracy meets the threshold;Finally, the shell preparation is completed through additive manufacturing and assembly.The application realizes the high-precision matching of shell geometric shape, mass and centroid with real broken stone, and optimizes signal transmission through material selection, wall thickness thinning and cambered surface transition, which is suitable for in-situ high-precision monitoring of broken stone, aggregate and other bulk materials.

The invention discloses a configuration optimization method and device for a flexible interconnection device of a power distribution network, a terminal and a medium, and the method comprises the steps: firstly obtaining the topological structure information of the power distribution network, collecting the data of the power distribution network, and calculating the load moment parameter of each node; the method comprises the following steps: acquiring a load moment parameter, constructing a load moment correlation model of a correlation relationship between the load moment parameter and a power distribution network operation parameter based on the load moment parameter, then constructing a multi-target optimization model based on load moment parameter weighting based on the load moment correlation model, and solving the model to obtain an SOP configuration optimization scheme. According to the scheme, the load moment parameter is introduced, the load moment correlation model and the load moment parameter weighting-based multi-objective optimization model are constructed, and the spatial distribution characteristics of the load in the power distribution network and the influence of the spatial distribution characteristics on the system operation are fully considered, so that in the active power distribution network containing the high-proportion distributed power supply, the power distribution efficiency is improved. The optimization result can be prevented from falling into local optimum, so that the configuration of the flexible interconnection device of the power distribution network can be optimized more accurately.

The invention discloses a resident air conditioner load multi-mode configuration method for various auxiliary service requirements. The resident air conditioner load multi-mode configuration method comprises the steps that a matching mapping relation between differential control modes and auxiliary service types is constructed; establishing a resident user participation willingness response model based on the incentive price; building a physical aggregation power calculation model of the resident air conditioning load in multiple modes; the theoretical aggregation power interval is corrected, and an air conditioner load reliable aggregation power interval is generated; and based on the matching mapping relation, the resident user participation willingness response model and the air conditioner load reliable aggregation power interval, constructing and solving a collaborative configuration optimization model considering both reliability and economy, and outputting an optimal resource configuration scheme. According to different requirements of secondary frequency modulation, peak regulation and spinning reserve, the configuration combination of the three modes of the intelligent control switch, the intelligent temperature controller and non-intrusive reminding can be differentially optimized, and the operation cost of the system is minimized on the premise that the aggregation reliability is guaranteed.

The invention discloses a high-voltage cable sheath protector configuration optimization method and system based on twinborn simulation, and relates to the technical field of high-voltage cables, and the method comprises the steps: collecting the voltage environment characteristics of a high-voltage cable, simulating the voltage environment characteristics through twinborn simulation, extracting the voltage impact characteristics, combining with preset protector configuration parameters, building a matching relation set, and carrying out the configuration optimization of the high-voltage cable sheath protector. The matching relation set is optimized through a protection quantification target, and an optimal protector configuration parameter strategy is obtained; and performing overvoltage simulation verification on the target configuration strategy through the twin simulation space, and determining a final protector configuration scheme. The technical problems that an existing high-voltage cable sheath protector configuration method depends on experience and trial and error, complex and changeable voltage environments are difficult to accurately match, and the protection effect is unstable are solved, the voltage environment is simulated based on twinborn simulation, protector configuration parameters are accurately optimized, and the protection effect is improved. And the effectiveness and the reliability of the protector under the complex overvoltage condition are improved.

The application relates to the technical field of design and control of restraint release devices in spacecraft launch systems, and provides an integrated design method of mechanism type restraint release devices based on configuration optimization and impedance control. The method simultaneously optimizes the slope of the slow-release characteristic curve and the amplification coefficient of the mechanism type restraint release device, improves the air pressure transmission efficiency, and minimizes the impact response in the slow-release process; an impedance controller for the mechanism type restraint release device is constructed, which can realize accurate displacement tracking and force compliance control, improve the effectiveness of the restraint arm and the adaptability to the environment; an integrated optimization method for configuration parameters and impedance control coefficients is provided, which maximizes the amplification coefficient, slow-release performance and control compliance, and can more comprehensively realize the optimization design of the mechanism type restraint release device.

This invention relates to the field of power system technology and discloses a capacity configuration optimization method, apparatus, equipment, medium, and program product. By acquiring multiple operating models and multiple installed capacity values, this invention establishes a synthetic ammonia production capacity relationship, enabling a comprehensive understanding of the operating characteristics and possible configurations of each part of the system. This, in turn, allows for the quantification of the relationship between synthetic ammonia production capacity and various system factors. Furthermore, by establishing total investment cost and total operation and maintenance cost relationships, the cost composition of the system in terms of investment and operation and maintenance can be clearly understood. Moreover, by establishing an objective function with the goal of minimizing the sales value of synthetic ammonia and solving it using a genetic algorithm, the capacity configuration of the system can be optimized as a whole. Therefore, the capacity configuration optimization result obtained in this way can achieve optimal economic efficiency for a renewable energy hydrogen production, synthetic ammonia, and hydrogen power generation system while ensuring system safety and reliability.

The invention discloses a two-stage wind-light storage collaborative planning method and system with wind-light ratio optimization, and the method comprises the steps: obtaining wind-light resource time sequence data and load curve data, and carrying out the building of a production simulation model based on the wind-light resource time sequence data and the load curve data; setting a wind-light capacity matching scheme set, and performing time sequence production simulation to obtain a system net load curve corresponding to each wind-light capacity matching scheme; evaluating each wind-light capacity matching scheme based on a system net load curve, and screening out a candidate matching interval capable of maximally stabilizing fluctuation from a source and enabling the net load curve to be smoother according to an evaluation result; performing configuration optimization of the energy storage capacity based on the candidate matching intervals, and determining an optimal energy storage capacity configuration scheme matched with each candidate matching interval; and calculating the total cost or comprehensive benefit of each candidate ratio interval and the corresponding optimal energy storage capacity configuration scheme, and selecting the scheme with the lowest total cost or the highest comprehensive benefit as a recommended scheme.

This invention belongs to the field of metal additive manufacturing technology, specifically relating to a structural design and manufacturing method for endogenous heterogeneous topology toughening using laser additive manufacturing. The method includes the following steps: S1. Selection of matrix material and testing of heterogeneous performance ranges; S2. Design of endogenous heterogeneous topology toughening structure; S3. Additive forming of the endogenous heterogeneous topology toughening structure. This invention improves material strength and toughness by using topology toughening units to achieve crack deflection, branching, strain distribution, and stress concentration mitigation. It actively designs heterogeneous topologies by simulating the stress distribution of components, and uses simulation to predict the performance of heterogeneous components in advance. Integrated forming shortens the component manufacturing cycle by more than 30%, and simulation-aided design and configuration optimization shorten the R&D cycle by more than 40%.

The application provides a kind of method and system for intelligent dynamic configuration of river-sea intermodal terminal resources based on deep learning, first, the resource type of terminal, ship demand and historical configuration result set are obtained, then the time sequence association of ship demand and resource use is captured by using deep learning model, resource demand feature sequence is generated, multiple sets of resource configuration initial schemes are generated according to resource type and feature sequence, potential conflicts are pre-resolved through interaction module, multiple sets of resource configuration initial schemes are input into evaluation module to generate evaluation results in combination with historical configuration results, resource configuration optimization scheme is generated according to evaluation results, terminal resource allocation operation is triggered and application results are recorded and fed back to model update logic, thereby realizing efficient and intelligent dynamic configuration of terminal resources and improving terminal operation efficiency.

The application provides a MaaS platform large model inference parameter automatic optimization acceleration method, comprising the following steps: detecting hardware configuration, constructing a standardized container environment, and preparing pressure test data; using an intelligent search algorithm to traverse a multi-dimensional parameter space, automatically discovering optimal inference parameters of a specific hardware and model combination through pressure testing and a performance scoring formula; persistently storing optimal parameter combinations corresponding to various hardware models and model versions to a structured database to form a reusable parameter knowledge base; automatically identifying a current hardware environment during model deployment, intelligently matching and loading corresponding optimal parameter configurations from the parameter base, and realizing zero-configuration optimization deployment. Through a parameter combination feasibility verification mechanism, the application can timely discover and exclude parameter combinations that may cause system errors or memory overflow, ensuring that the finally selected parameters can both improve performance and guarantee stable operation of the system.

The invention discloses a hybrid energy storage configuration optimization method and device based on clustering analysis, equipment and a storage medium, and relates to the technical field of power systems, and the method comprises the steps: carrying out the feature extraction of a target automatic power generation control instruction, obtaining a target multi-dimensional feature vector, carrying out the standardization processing of the target multi-dimensional feature vector, and obtaining a target multi-dimensional feature vector; obtaining a standardized target multi-dimensional feature vector; determining an instruction mode type corresponding to the standardized target multi-dimensional feature vector based on the standardized target multi-dimensional feature vector and the obtained mapping relation; based on the instruction mode type, determining an initial hybrid energy storage configuration scheme; and based on the initial hybrid energy storage configuration scheme, the output of the thermal power generating unit and the target function, optimizing the initial hybrid energy storage configuration scheme to obtain a final hybrid energy storage configuration scheme. According to the method, feature extraction is performed on the target automatic power generation control instruction, so that the instruction mode type can be accurately determined, and the adjustment precision of the hybrid energy storage system is improved.