626 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 discloses a mold parameter adjustment method and system based on a multi-objective optimization algorithm, and relates to the technical field of mold manufacturing. The method comprises the following steps: calling a production database, carrying out production state division, and establishing a production state division result; executing process data acquisition, and synchronizing a process data acquisition result to a data processing center through an MQTT protocol; matching a process data acquisition result with a production state division result by using a data processing center, and constructing a real-time dynamic weight; updating the multi-objective evaluation function by using the real-time dynamic weight, and establishing a configuration optimization priority; adjusting and optimizing the mold parameters according to the multi-objective evaluation function and the optimizing priority, and optimizing the mold parameters based on an adjusting and optimizing result. The technical problems that in the prior art, the die production efficiency is low, and the quality is unstable are solved, the die parameters are adjusted through the multi-objective optimization algorithm and the real-time dynamic weight, and the technical effect of improving the die production efficiency and the manufacturing quality is 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 information, and discloses a parameter configuration optimization method and system for an operation module of flexible equipment, and the method comprises the steps: carrying out the hierarchical division of the operation module in the flexible equipment through a hierarchical management method, and forming a multi-layer operation module structure; acquiring historical operation related data of each level of operation module, and determining a performance evaluation threshold value, an operation state threshold value and configuration parameter sensitivity of each level of operation module; the method comprises the following steps: monitoring the operation state and performance evaluation data of each level of operation module in real time, generating a configuration parameter table when the operation state and performance evaluation data exceed a threshold value, analyzing the correlation between the operation state and performance, extracting and calculating the importance of configuration parameters in the configuration parameter table, and generating a configuration parameter set; according to the method, each level of operation module is subjected to sensitivity grouping, the mapping relation between the configuration parameter adjustment amount and the performance of each level of operation module is constructed, a progressive adjustment rule is generated to optimize a configuration parameter set, fine adjustment of the configuration parameters of the operation modules in the flexible equipment is achieved, and the operation efficiency and stability are improved.

The invention relates to the technical field of energy, in particular to a capacity configuration method of an off-grid hydrogen-electricity hybrid energy storage system. The capacity configuration method of the system comprises the following steps: 1, establishing a mathematical function of the off-grid hydrogen-electricity hybrid energy storage system; 2, establishing a target optimization function by taking the lowest total cost of the system as an economical efficiency target and taking the maximum self-power generation rate and the minimum power abandoning rate as operation targets of the hydrogen-electricity coupling direct-current micro-grid system; 3, establishing a capacity configuration constraint function by taking system power balance, thermodynamic balance, grid-connected interaction power balance, equipment capacity upper limit and equipment power upper and lower limits as constraint conditions; and 4, establishing a capacity configuration optimization model of the system based on the mathematical function, the target optimization function and the capacity configuration constraint function of the off-grid hydrogen-electricity hybrid energy storage system, and solving through an NSGA-II algorithm to obtain an optimal capacity configuration scheme. The economical efficiency, stability and efficiency of system operation are considered at the same time, and reasonable capacity configuration of the system is achieved.

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 relates to the technical field of micro-grid capacity configuration, in particular to a micro-grid capacity configuration optimization method and system. According to the method, a total cost objective function is constructed by taking the minimization of the total cost of all main bodies in the wind-solar storage micro-grid as a target, a wind-abandoning and light-abandoning objective function is constructed by taking the minimization of the penalty cost of wind-abandoning and light-abandoning as a target, and an ACG wind-solar frequency modulation objective function is constructed by taking the maximization of ACG wind-solar frequency modulation benefits as a target. Setting a power balance constraint and a wind curtailment and light curtailment constraint as constraint conditions, and establishing a micro-grid capacity non-cooperative game model; and based on the micro-grid capacity non-cooperative game model and the constraint conditions thereof, solving the micro-grid capacity non-cooperative game model by adopting an improved particle swarm algorithm to obtain a capacity target configuration result. According to the method, the power output of each main body is optimized by considering the wind curtailment and light curtailment factors, the Nash equilibrium of the system is achieved, the system income is maximized, and the overall performance and economical efficiency of the micro-grid system are effectively improved.

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 discloses a network construction power supply capacity configuration optimization method and system, and belongs to the technical field of power system planning. The method comprises the following steps: on the basis of constraint conditions of new energy grid-connected stability, associating technical indexes corresponding to a network construction power supply, and estimating and giving an initial configuration capacity of the network construction power supply; selecting a high-voltage side and a low-voltage side of a step-up transformer of the new energy station as a network construction power supply access position; according to the new energy machine end and grid-connected point multi-station short-circuit ratio constraint, static security constraint and transient stability constraint conditions, verifying whether the network construction power supply meets the constraint requirements, and if not, adjusting the access capacity; if yes, calculating the cost required by the current capacity according to the comprehensive unit price; and comparing the total costs of the corresponding capacities of the two access positions, and selecting the economic optimal capacity as the final configuration capacity. According to the method, the optimal stability and economical efficiency of the new energy station are taken as the target, the optimal access position and capacity of a network construction power supply during grid connection of the new energy single station are determined, and the stability of a power grid in a new energy grid connection scene is improved.

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.

The invention discloses a wind and light storage mobile power supply optimization configuration method, system and device in a complex environment, and relates to the technical field of power systems. The method comprises the following steps: configuring a sensor network, performing environmental data time sequence acquisition, and establishing an environmental data set; performing environment prediction by using the data set, and establishing a prediction result; load demand fitting is executed, and a calibration demand is established; constructing a configuration model by using a calibration demand, and establishing a power generation fitting result based on a prediction result; synchronizing the power generation fitting result and the environment steady-state identifier to a configuration model, executing power supply configuration optimization, and establishing a configuration result; and performing optimal configuration management on the wind-solar storage power supply based on a configuration result. The technical problems of resource instability and load electricity demand fluctuation of a wind power generation and photovoltaic power generation system in a complex environment are solved, and the technical effect of improving the power supply continuity and reliability of the wind and light storage mobile power supply system is achieved through real-time acquisition and prediction of environmental data and dynamic adjustment of demand fitting and power generation fitting.

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 production line configuration optimization method and device based on digital twinborn and dual-population genetic algorithm collaboration. The method comprises the following steps: constructing a PCB production line twinborn model; constructing a single-row layout model of the PCB production line, and constructing and forming a multi-target planning model by taking the maximum production efficiency, the maximum equipment utilization rate and the minimum logistics cost as targets; the method comprises the following steps: acquiring twinborn data in real time, acquiring a priority sequence of each process unit of a PCB production line according to the twinborn data, and encoding the priority sequence of each process unit and spacing distances among the process units by using a double-population genetic algorithm, carrying out iterative solution by using a fitness function constructed based on a multi-objective planning model to obtain an optimal configuration scheme; and mapping an optimal configuration scheme obtained by iterative solution to the twin model, and evaluating an optimization effect. According to the invention, an accurate and reliable PCB production line resource configuration optimization scheme can be efficiently generated, and the production line efficiency and the equipment utilization rate are improved.

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 discloses an efficient distributed machine learning training system based on a server-free architecture, and the system comprises a modeling module which is used for obtaining an input workload for modeling; the resource configuration module is configured with an optimized mathematical model and used for obtaining optimal parameter configuration based on the optimized mathematical model on the basis that the modeling module completes modeling; optimizing the relation between double-layer parameters and system-level parameters of mathematical model modeling server-free distributed training and training delay, cost and convergence efficiency; and the K-REDUCE training framework is used for triggering the server-free function, and the server-free function carries out distributed training according to the K-REDUCE framework. According to the method, a mathematical model is constructed to describe the relationship between parameters and performance, and optimal parameter configuration is searched by adopting a heuristic search algorithm based on pruning, so that the training performance is effectively improved, the economic cost is reduced, system resources are dynamically adjusted, and the optimal training performance is realized.

The invention discloses an ITSS operation and maintenance resource configuration optimization method and system. The method comprises the following steps: firstly, establishing a network node model of operation and maintenance resources to be allocated; constructing a constraint model of the operation and maintenance resources to be allocated according to the constraint conditions of the operation and maintenance resources to be allocated; determining a to-be-allocated operation and maintenance resource configuration model based on the to-be-allocated operation and maintenance resource network node model and the to-be-allocated operation and maintenance resource constraint model; according to the to-be-allocated operation and maintenance resource configuration model, obtaining an initial configuration scheme of optimal configuration of to-be-allocated operation and maintenance resources and a resource configuration deviation matrix; and according to the initial configuration scheme and the resource configuration deviation matrix, obtaining a target configuration scheme for optimal configuration of the operation and maintenance resources to be allocated. Through the systematized and modeled operation and maintenance resource configuration optimization method, efficient, accurate and flexible configuration of the operation and maintenance resources is realized, and the method has remarkable effects of improving the operation and maintenance service quality, reducing the cost and enhancing the enterprise competitiveness.

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.