475 results about "Configuration optimization" patented technology

The invention relates to the technical field of energy scheduling, in particular to an energy system capacity optimization and mobile resource space-time decoupling cooperative scheduling method, system and device under extreme disaster disturbance and a medium. Load change parameters are coupled to form a disaster-causing response database; renewable energy output fluctuation and user load demand deviation are converted into fuzzy variables, and a capacity configuration optimization model is established in combination with a disaster-causing response database to generate multi-energy complementary capacity configuration; mobile resources are introduced to establish an elastic lifting scheduling model, and an elastic lifting scheme is generated by implementing damaged road network topology reconstruction of the mobile resources, space-time transfer chain modeling and action state coordinated regulation and control. According to the method, collaborative decision-making of capacity optimization of equipment and dynamic scheduling of mobile resources is driven through the disaster-causing response database, and dual elastic gains of fixed resource optimization and after-disaster quick response of the mobile resources under extreme disaster events are achieved.

The invention relates to the technical field of power data processing, in particular to an energy storage configuration optimization method, which comprises the following steps: acquiring new energy output time sequence data and computing power load characteristic data; generating a space-time correlation coupling evaluation result of the new energy output volatility and the computing power load volatility; inputting a result into a hybrid power supply double-layer optimization model, recursively correcting parameters through a two-stage collaborative solution algorithm, and outputting a Pareto optimal solution set; a computing power task elastic regulation and control mechanism is embedded, and the task priority is dynamically adjusted according to the energy storage charge state and the new energy output level to generate a scheduling strategy; finally, an energy storage configuration scheme and a dynamic scheduling strategy are output, and collaborative optimization of cost effectiveness and power supply reliability is achieved. The method breaks through the coupling conflict of the economic target and the robust constraint in the traditional bilevel planning, remarkably reduces the energy storage configuration cost, and improves the system stability.

The invention discloses a machine room energy-saving operation and maintenance management method and system, and particularly relates to the technical field of machine room management. Computing resources are integrated in combination with a virtualization technology, virtual machine allocation is dynamically adjusted, migration flow is optimized by adopting an intelligent network bandwidth scheduling mechanism, and migration efficiency is improved; by combining a temperature and humidity control system and equipment operation temperature anomaly detection, the operation states of the air conditioner and the cooling equipment are dynamically optimized, energy consumption is reduced, early warning is performed based on the equipment operation temperature and the network load condition through a fault prediction model, equipment faults are prevented, and fuzzy logic control is adopted; based on the energy consumption efficiency and the fault prediction result, resource configuration is dynamically adjusted, the energy efficiency of the whole machine room is optimized, the utilization rate of computing resources is effectively improved, the influence of network migration on services is reduced, the energy consumption of the machine room is reduced, the equipment stability is improved, the management cost is optimized, and efficient, energy-saving and intelligent machine room operation and maintenance management is achieved.

The invention belongs to the field of complex equipment system design and verification, and discloses a model-based complex equipment system design and verification integrated method, which takes four layers of'demand-behavior-structure-parameter 'as a framework, combines task scene demands, and constructs a full-process design and analysis method of a reusable space launch vehicle system. The method comprises the following steps: decomposing a complex system from demand analysis, performing parameter calibration and performance verification on a model by combining theoretical and experimental data, constructing a reusable space launch vehicle system model, and performing comprehensive analysis and verification of the system; in combination with a three-dimensional virtual environment and a configuration optimization algorithm of the model, performing rapid optimization on various configurations of the aerospace vehicle, and evaluating feasibility and performance of a design scheme; through bidirectional interaction of design and verification models and communication of system-level design and professional-level design, the problems of design and verification separation, insufficient model collaboration and the like in the prior art are solved, and the efficiency and precision of design and verification of a complex equipment system are improved.

The invention relates to the technical field of resource configuration optimization, in particular to a virtual power plant resource optimal configuration method and system. The method comprises the following steps: obtaining energy load storage resource data corresponding to each power generation unit in a virtual power plant, and carrying out power generation power prediction analysis and energy storage equipment charge and discharge state analysis on each power generation unit in the virtual power plant; the energy power generation power corresponding to each power generation unit and the charging and discharging operation state data of the energy storage equipment are obtained; performing resource operation load demand prediction analysis on the corresponding power generation units to obtain resource operation load demand prediction quantities corresponding to the power generation units; and carrying out resource configuration optimization target analysis on each power generation unit in the virtual power plant to obtain a virtual power plant power generation unit resource configuration optimization target and resource optimization configuration processing so as to generate a virtual power plant power generation unit resource optimal configuration scheme. According to the invention, efficient optimal configuration of each power generation unit resource in the virtual power plant can be realized.

The invention discloses a large model training resource configuration optimization method for a cloud heterogeneous GPU cluster. The method comprises heterogeneous GPU resource configuration and resource perception stage deployment. A cost tree pruning mechanism is provided for resource configuration, a plan with low cost is searched preferentially, a configuration scheme is expressed as a tree path, meanwhile, a multi-dimensional pruning method is adopted to reduce unnecessary branches, and efficient GPU configuration is achieved. According to stage deployment, through defining a Compute-Memory Ratio (Compute-Memory Ratio, CMR), the requirements of each stage on the GPU computing power and memory capacity are considered in a combined manner, the communication and computing efficiency in the training process is optimized, and the large model training time is remarkably shortened. And finally, a large model training resource configuration optimization method Espresso oriented to the cloud heterogeneous GPU cluster is provided in combination with resource configuration and stage deployment strategies, and the user cost is reduced to the maximum extent while the training performance is ensured.

The invention discloses a workload configuration optimization method and system under a multi-instance GPU, and the method comprises the steps: obtaining a set of undistributed but distributable regions of each GPU, obtaining an undistributed feasible partition of each GPU, and obtaining the current workload of each GPU; for the non-allocated feasible partition and the current workload of each GPU, determining a GPU allocation scheme of workload placement with maximum income and minimum cost according to a preset objective function and constraint conditions, the objective function including the income for maximizing the workload; the constraints are used to ensure that the workload placed on the GPU does not exceed its available compute instances and memory while minimizing GPU usage costs, re-partitioning costs, workload migration costs, and resource waste costs. According to the method, the partition function of the multi-instance GPU is efficiently utilized, the optimal workload placement and migration are realized, and the LLM reasoning task is more efficiently operated.

The invention provides a virtual power plant capacity configuration optimization method and system considering an accurate capacity market, and the method comprises the steps: building a virtual power plant model according to the related parameters of a virtual power plant; according to the preprocessed historical demand data and price data of the capacity market, establishing a capacity market model; based on the virtual power plant model and the capacity market model, taking maximization of the total revenue of the virtual power plant in the capacity market as a target, considering system reliability, and establishing an optimal configuration model; the optimal configuration model is solved, a global optimal solution is output, and an optimal configuration scheme of the virtual power plant in the capacity market is obtained; by implementing the method provided by the invention, the virtual power plant can participate in the power capacity market more flexibly, the economic benefit of the virtual power plant can be improved, the stability and reliability of the power system can be enhanced, and powerful support is provided for healthy development of the power market.

The invention relates to the technical field of design service, in particular to a roof waterproof structure intelligent design optimization system which comprises a node contour extraction module, a contact segment recognition module, a close boundary evaluation module, a disturbance area labeling module and a waterproof configuration reconstruction module. According to the invention, by extracting the direction vector change of the boundary point cloud sequence, the continuity and stability of the turning trend in the contour structure can be accurately obtained, the boundary of the roof structure can be identified, and the contact type of the boundary joint can be determined in combination with the direction change rate distribution of the contact surface, so that the boundary sealing state has quantitative index output; in addition, local positioning information of a fitting path and a structure defect can be provided, a node contour reconstruction path with an adjustment space is provided based on a pressing path continuity judgment and simulation edge shift strategy, and the adaptability of a node structure under the action of complex stress and the configuration optimization capability of the overall waterproof performance of a roof are improved.

The invention discloses a deep-sea mining VTS configuration optimization design method based on data driving, and belongs to the technical field of deep-sea mining engineering and optimizing.The method comprises the steps that firstly, a mechanical response model of a VTS is constructed through a VFIFE method, and mechanical response is calculated in combination with VTS structure parameters and borne external force; then generating a prediction agent model based on CPO-BP, and realizing relation modeling between the configuration design variable and the mechanical response of the VTS; and finally, the prediction agent model is embedded into the MODA-DE, the VTS configuration is optimized based on the MODA-DE, and VTS configuration design variables meeting the multi-objective optimization requirements are finally obtained through multi-generation iteration. According to the scheme, the three links of VFIFE, CPO-BP and MODA-DE are closely combined, an efficient multi-target optimization technology is formed, collaborative improvement from high-precision prediction to global optimization is achieved in mining pipeline configuration design, and the method has high actual popularization and application value.

According to the transition prediction method suitable for the wide Mach domain of the high-speed aircraft provided by the invention, temperature and Mach number coupling correction is performed on key parameters in time scale modeling of a second mode of a transition mode, and simplified processing of a flow compression effect and a heat transfer process by traditional single parameter association is broken through; the influence of an incoming flow disturbance effect is embedded in the mode, and dynamic association between test environment parameters and transition triggering criteria is achieved; according to the improved mode, accurate prediction results are obtained on a plurality of examples with the Mach number range of 4.7-14, the research results provide a more reliable theoretical tool for aerodynamic configuration optimization of a high-speed aircraft with a wider speed range, and the method has important engineering guiding significance for thermal protection system design of a new-generation reusable aircraft.

The invention discloses a server configuration optimization method and device, a storage medium and electronic equipment, and relates to the technical field of computers.The method comprises the steps that firstly, configuration data of a server is obtained; configuration data of the servers are input into a reinforcement learning model, change benefits corresponding to different configuration change operations of the servers are predicted, and the reinforcement learning model is obtained by grouping server clusters and training based on sample data generated in the running process of multiple groups of servers; and generating a configuration optimization strategy of the server according to the change income. Compared with the prior art, the method has the advantages that grouping management and operation and maintenance monitoring are performed on the server cluster, reinforcement learning modeling is combined, the reinforcement learning model is utilized to generate the intelligent server configuration optimization strategy, a traditional server operation and maintenance configuration mode is optimized, and the server operation and maintenance efficiency and benefits are effectively improved.

The invention relates to the technical field of power system automation, and particularly discloses a Bayesian optimization setting method for virtual inertia control parameters of a wind turbine generator, and the method comprises the following steps: constructing a disturbed performance index after a power system suffers from an expected fault; constructing a virtual inertia configuration optimization model of the new energy power system by taking the disturbed performance index as an optimization objective function; introducing a constraint condition into a target function in a penalty function form, and converting the virtual inertia configuration optimization model of the new energy power system into an unconstrained optimization model; and iteratively solving an optimal solution of the unconstrained optimization model by adopting a Bayesian optimization algorithm based on a Gaussian process. The method has the advantages that by optimizing the virtual inertia control parameters of the new energy unit, the power angle stability and the frequency stability of the new energy power system under anticipated faults such as an alternating current fault and generator off-grid are improved, and safe and stable operation of the novel power system with new energy as a main body is powerfully guaranteed.

The invention discloses an energy storage optimal configuration method and system based on data-driven model fusion. The method comprises the following steps: extracting feature data with important influence in load data and operation and maintenance data for data fusion; constructing a load prediction model for predicting the load state of the energy storage equipment and a predictive maintenance model for predicting the health state and the remaining service life state of the energy storage equipment; training a load prediction model and a predictive maintenance model of the energy storage system through iteration; and the load state, the health state and the remaining service life state of the energy storage equipment are predicted in real time, an energy storage system configuration optimization model is constructed, and the overall configuration of the energy storage system is optimized. The method is not only used for short-term power load prediction, but also used for health state prediction and residual service life prediction of the energy storage system, through the accurately predicted energy storage equipment state, an energy storage system configuration optimization model is cooperatively constructed in a complex power grid scene, a maintenance plan is reasonably arranged, and efficient and stable operation of the energy storage system is ensured; and the reliability and the economical efficiency of power supply are improved.

The invention discloses a smart park platform planning and designing system, and belongs to the technical field of smart city construction. Multi-source static and dynamic data are integrated through the data acquisition and integration module, and the space function planning module and the dynamic resource allocation optimization module construct an optimization model based on core function requirements and resource requirements, so that scientific planning of space layout and resource allocation is realized; the simulation evaluation display module dynamically evaluates the traffic flow, the environmental influence and the resource efficiency by using a three-dimensional digital park model and a multi-dimensional simulation technology, provides a visual and quantitative evaluation result for a planning scheme, effectively reduces the design risk, combines the planning intelligent auxiliary decision-making module and the user participation feedback module, and improves the design efficiency. Through a rule engine and a multi-dimensional comparison model, an optimal design strategy is recommended for the dimensions of cost, efficiency, environment and the like, meanwhile, user requirements are collected and converted into optimization suggestions, a closed-loop optimization process is formed, and the applicability of a planning scheme and the user satisfaction degree are improved.

The invention discloses an optimization algorithm for capacity configuration of a phase modifier of an offshore wind power plant, and the optimization process is divided into two stages, the first stage aims at improving the short-circuit ratio of the offshore wind power plant, and the second stage aims at suppressing overvoltage. The method comprises the following steps: firstly, establishing objective functions and constraint conditions of two stage targets; and solving the target function by adopting an eagle group intelligent optimization algorithm to obtain the capacity of the phase modifier which should be configured for each sub-wind power plant in the offshore wind power plant.

The invention discloses a hierarchical database system tuning method and system based on a large language model, and relates to the technical field of database system tuning. The method comprises the steps that expert knowledge in the database field is processed based on a large language model, a cue word integration algorithm is used for converting the knowledge into structured knowledge capable of being used by a computer so as to optimize a search space, and a coarse granularity-fine granularity Bayesian optimization algorithm is used for determining optimal configuration, so that configuration optimization of a system level is completed; a parameter screening algorithm, a space pruning algorithm and a context Bayesian optimization algorithm are utilized, and an optimal execution plan is generated by selecting an appropriate optimization prompt for each query in the complete workload, so that configuration optimization of query levels is completed. According to the method, the search space is optimized by utilizing expert knowledge, the optimized space is explored by utilizing a Bayesian optimization algorithm, multi-level optimization is performed on the database system from a system configuration level and a query level, and the optimization efficiency and the system performance are improved.

The invention provides a configuration optimization method before capturing of a space double-arm robot, and the method comprises the steps: constructing a kinematics and dynamics model of the space double-arm robot according to a three-dimensional model of the space double-arm robot; constructing a kinetic model of the to-be-captured target by using the motion parameters of the to-be-captured target in the space; according to the dynamic model of the space double-arm robot, the relative mass of the space double-arm robot under non-centering collision is obtained, and a maximum pre-contact collision force performance evaluation index is constructed; further constructing a double-arm closed chain inertia matching index performance evaluation index; constructing a configuration optimization model before the space double-arm robot captures the target; and through a multi-target optimization algorithm, the optimal configuration of the space double-arm robot before capturing the target is solved. The pre-contact collision force peak value of the double-arm robot during target capturing can be effectively reduced, and meanwhile the operability of a closed chain system formed after capturing is guaranteed.

The invention relates to the technical field of integrated energy system planning and optimization, and discloses an electricity-hydrogen cooperative system planning and configuration optimization method based on an LBBD algorithm. Firstly, data are collected, and basic operation parameters of the electricity-hydrogen cooperation system are determined; secondly, establishing an electricity-hydrogen collaborative planning model: on the basis of the electricity-hydrogen collaborative planning model, taking the minimum investment cost and operation cost of an electricity-hydrogen collaborative system in all stages and the maximum ammonia selling amount as optimization targets, and constructing a target function of joint planning; and finally, solving the power-hydrogen collaborative planning model by using an LBBD algorithm to obtain the site selection and capacity configuration of a hydrogen production station, an energy storage station and a hydrogen storage station of the power-hydrogen collaborative system, and the connection topology of a power transmission line and a hydrogen transmission pipe network. According to the method, the power-hydrogen collaborative planning model is constructed, power-hydrogen system collaborative optimization is comprehensively considered, resource waste is avoided, the asset utilization efficiency of each resource can be fully improved, and the method has relatively high precision and relatively high solving speed.

The invention relates to an energy system configuration optimization method considering equipment variable working condition characteristics and energy storage life attenuation, which comprises the following steps: establishing a mathematical model of an energy system, and calculating equipment cycle aging indexes in the mathematical model of power storage equipment by considering the energy storage life attenuation, in mathematical models of the cold-heat-electricity combined supply system, the gas-fired boiler and the electric refrigerating unit, the corresponding efficiency is calculated by considering the variable working condition characteristics of the equipment; and based on the established mathematical model, resource configuration optimization of the energy system is carried out by using a double-layer optimization method, the upper layer optimizes equipment capacity design by taking minimization of annual cost as a target, and the lower layer carries out operation optimization by taking the sum of minimization of operation cost, battery life degradation cost and demand electricity price as a target. The battery life degradation cost is calculated based on the equipment cycle aging index. Compared with the prior art, battery life attenuation and equipment variable working condition operation are considered at the same time, and the overall efficiency and reliability of the system are improved.

The invention relates to the technical field of optical line terminal configuration, in particular to a method and system for automatically configuring optical line terminals of different manufacturers based on artificial intelligence. The method comprises the steps of collecting multi-source heterogeneous data to train a deep learning model, constructing a Transform or RNN-based architecture of a TensorFlow framework, performing cross validation and model compression, then performing optimization through a TensorRT inference engine, developing an intelligent configuration application system, deploying the intelligent configuration application system to a control host, and providing algorithm support for automatic configuration. By constructing an automatic configuration system based on artificial intelligence, the problems of heterogeneous compatibility and intelligent configuration of OLT equipment of multiple manufacturers are systematically solved. In a configuration optimization link, a virtual simulation environment is constructed based on a real network topology, a configuration effect is evaluated through multi-dimensional indexes such as throughput and time delay, potential conflicts are detected in combination with an AI algorithm, an optimization scheme is generated, and the configuration error rate of manual operation is reduced.

The invention discloses a novel telescopic pipe structure of a high-temperature and high-pressure well and an optimal configuration method of a pipe column of the novel telescopic pipe structure, belongs to the technical field of oil and gas exploitation, and aims to solve the problem of buckling failure of the pipe column caused by piston force load of an existing telescopic pipe under high-temperature and high-pressure working conditions. The novel telescopic pipe is composed of an inner cylinder mechanism, an outer cylinder mechanism and a balance chamber mechanism. The inner cylinder mechanism and the outer cylinder mechanism are connected through threads, and structural integrity and reliable connection are guaranteed. The balance chamber mechanism comprises inner and outer pressure cavities and a hydraulic hole which are respectively communicated with the inner and outer pressure of the oil pipe to dynamically balance the piston force. The optimized configuration of the pipe column comprises the following three steps: S1, analyzing the mechanical behavior of the telescopic pipe column to provide a mechanical basis for configuration optimization; s2, the position of the telescopic pipe is designed, the well depth range where the telescopic pipe works is determined, and the optimal position is locked through iterative calculation; and S3, designing the number of the telescopic pipes, and determining the optimal number of the telescopic pipes meeting the compensation requirement. The stability of the high-temperature and high-pressure well pipe column can be remarkably improved, and technical support is provided for safe and efficient development of ultra-deep oil and gas wells.

The invention provides a grid-connected control method and system based on configuration optimization, and relates to the technical field of grid-connected control, and the method comprises the steps: obtaining the operation data of a power grid, carrying out the time sequence analysis, and obtaining the initial state of the power grid; determining filtering parameters of the power grid operation data; adjusting the power grid state according to the deviation of the filtered power grid operation data, and generating a correction state prediction result; performing dynamic frequency adjustment and pressure adjustment, simulating a power grid operation feedback optimization adjustment strategy, and obtaining a power grid adjustment strategy; analyzing a power grid structure and flow efficiency, optimizing configuration of power grid nodes and connections, simulating and verifying a configuration adjustment effect of the power grid nodes and connections, and generating an optimized power grid design scheme; data transmission and electric energy efficiency of cross-regional connection are analyzed, a power grid connection path is configured, and a cross-regional connection optimization result is generated; and adjusting a grid-connected control strategy, and evaluating a grid-connected control result. The operation efficiency and the operation stability of the power grid can be improved.

The invention relates to the technical field of electric storage capacity configuration, in particular to a capacity configuration method and system based on source network load storage integration. The method comprises the following steps: acquiring power utilization data, power generation data and environment temperature, and analyzing and predicting a load demand by using an ARI MA model in combination with a linear regression model; preliminarily calculating and analyzing energy storage capacity configuration based on actual load requirements, and establishing a source network load storage capacity configuration optimization model in which the source network load storage capacity and the energy storage capacity are mutually restrained; introducing an environment temperature change factor, and redesigning an energy storage capacity configuration strategy; and evaluating the stability of the capacity configuration strategy by using a phase space analysis algorithm, and introducing the Lyapunov index into the source network load storage capacity configuration optimization model to determine the capacity configuration strategy. According to the capacity configuration method and system based on source-network load storage integration, an energy storage capacity configuration strategy is adjusted based on dynamic changes of actual load requirements and environmental factors, and energy storage capacity configuration is stably optimized by combining a Lyapunov index analysis system.

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 a user demand-driven distributed energy supply system capacity configuration method based on a large language model. The method comprises the following steps: collecting user demand information; performing semantic analysis and understanding on the acquired user demand information by utilizing a large language model, and outputting structured and quantitative user demand description; parameterizing the structured and quantized user demand description to obtain a mathematical model for capacity configuration optimization; and solving a mathematical model for capacity configuration optimization by adopting a numerical optimization algorithm, generating a plurality of optimization schemes, and performing performance evaluation and visual display to assist a user in selection. According to the method, the accuracy and the intelligent level of user demand understanding are improved through the large language model, automatic processing and personalized capacity configuration of unstructured demands are achieved, the capacity configuration efficiency and the user satisfaction degree of the distributed energy supply system are remarkably improved, and the system performance is effectively improved.

The invention relates to the technical field of control and adjustment, in particular to an energy storage configuration point-based power consumption cost calculation method, which comprises the following steps of: acquiring power supply data, renewable energy output fluctuation data and computing power load fluctuation data, and generating a coupling influence feature vector; splicing the electricity price features and the coupling features to form configuration optimization input features; the input features are processed through a dual-objective optimization model, and robustness configuration parameters and economical efficiency operation parameters are output; calculating the reserve cost compensation amount of the extreme scene based on the robustness parameter, and calculating the power purchase cost and maintenance cost of the power grid based on the economical efficiency parameter; and aggregating the cost component and outputting a power consumption cost calculation result. According to the method, the interactive influence of wind-light fluctuation and load mutation is quantified through coupling features, the target conflict between robustness and economy is coordinated by adopting the Pareto algorithm, the risk compensation amount is generated in combination with a pressure test, a feature updating mechanism is fed back in a closed loop mode, and the problem that the operation cost deviates from expectation due to quantization deviation of an existing model is solved.

The invention discloses a divertor structure of a Tokamak device and a design optimization method of the divertor structure, and relates to the technical field of magnetic confinement nuclear fusion. Based on a compact fusion reactor, the design of the physical structure of the divertor is carried out, and the physical structure of the divertor is optimized in combination with numerical simulation and evaluation technologies; according to the optimal divertor structure, the thermal load can be effectively controlled, and meanwhile the defects, limitation and the like caused by a configuration optimization technology and an external impurity injection technology are overcome; besides, in the design optimization process, the impurity injection effect, the impurity removal effect and the like are further considered, and therefore the optimal divertor structure further has the good impurity removal level and impurity shielding capacity.

The invention relates to the technical field of micro-grid configuration, in particular to a wind and light storage micro-grid capacity optimization configuration method based on a PGWO algorithm, and the method comprises the following steps: building a wind and light storage micro-grid capacity optimization configuration model according to the output power model of each distributed power supply in a micro-grid; setting a micro-grid operation strategy; under the micro-grid operation strategy, solving the capacity optimization configuration model through a PGWO algorithm to obtain a micro-grid optimal configuration scheme; according to the method, the PGWO algorithm combining Bernoulli chaotic mapping initialization, individual historical memory and a border crossing processing strategy is provided, the method has higher global optimization capacity, capacity configuration optimization can be carried out on the premise of stable operation of the microgrid, and the operation cost is reduced.

The invention discloses a collaborative configuration optimization method for an intelligent self-reconfiguration robot. The invention relates to the field of modular robot configuration-motion collaborative optimization in extraterrestrial planet surface inspection and detection tasks, constructs a multi-constraint dynamic graph optimization model for unified representation of configuration and motion control, and aims to improve the configuration reconstruction efficiency of a modular robot and obtain an optimal configuration-motion reconstruction scheme. The method comprises the following steps: carrying out restrictive configuration characterization on a robot by utilizing a rooted tree structure; constructing a sensing-driving integrated neural network to perform robot motion control representation; constructing a multi-constraint dynamic graph optimization model to uniformly represent configuration and motion control; constructing a fitness function for reconstruction optimization according to the evaluation function corresponding to the task; and performing configuration-motion optimization solution by using a non-dominated sorting genetic algorithm with an elitist strategy.