49 results about "Capacity optimization" patented technology

The invention relates to the technical field of production management, and particularly discloses a bearing processing productivity optimization and production scheduling system, which comprises a process state sensing module, a dynamic bottleneck identification module, a self-adaptive buffer control module, a collaborative scheduling decision module and a productivity optimization execution module. Through cooperative work of the dynamic bottleneck identification module and the adaptive buffer control module, a static management mode of a traditional fixed buffer area is thoroughly changed. The system can sense the change of a production flow in real time, accurately identify a bottleneck, and dynamically adjust the target inventory level of a buffer area based on a fuzzy control algorithm, so that a buffer strategy can adaptively match a current production state. The problem that upstream and downstream processes wait for each other due to improper arrangement of a buffer area is solved, and passive blockage and material interruption between the processes are converted into active and flexible rhythm cooperation, so that systematic equipment waiting time is remarkably shortened, hidden capacity is released, and the equipment comprehensive utilization rate and output efficiency of a whole production line are improved.

The invention discloses an AGC frequency modulation capacity optimization method and system based on similar day and source load disturbance decoupling, and relates to the technical field of automatic power generation control of a power system. The method comprises the steps of obtaining running day prediction data and historical sample data, and screening a similar day set; based on the similar day set, performing new energy disturbance time-sharing statistics and load disturbance decomposition to obtain disturbance sub-item data; calculating time-sharing AGC capacity according to the disturbance sub-item data; aGC capacity allocation optimization is carried out in combination with resource constraints, and an AGC capacity allocation instruction is obtained; and performing rolling calibration and updating on the distribution instruction based on real-time data. The method is used for solving the problems of strong capacity reservation subjectivity and excessive / insufficient reservation caused by the existing fixed proportion or empirical method.

The invention provides a hydrogen-electricity hybrid energy storage capacity optimization method and device applied to an integrated energy system, and belongs to the field of integrated energy configuration optimization. The method provided by the invention comprises the following steps: adopting a self-adaptive time sequence compression algorithm to perform self-adaptive grouping, weighted fusion and iterative updating processing on annual original time sequence data of the integrated energy system, and compressing to obtain representative time sequence data; based on the representative time sequence data, constructing a capacity planning model, and solving to obtain a capacity pre-planning value of the system core equipment; the capacity pre-planning value is substituted into the original time sequence data, the capacity planning model is reused, the hydrogen energy storage capacity is checked and corrected through full-time sequence energy balance simulation, and the final capacity configuration of the system core equipment is obtained; and based on the final capacity configuration, simulating system operation, extracting an electric and thermal power unbalance residual sequence, extracting a residual fluctuation scene and a corresponding probability, respectively constructing independent optimization models of electric energy storage and thermal energy storage, and solving to obtain the optimal capacity of the electric energy storage and the thermal energy storage.

The invention discloses an SOP locating and sizing optimal configuration system and method based on sensitivity analysis. Comprising a source network data acquisition unit, a sensitivity analysis and calculation unit, an SOP locating and sizing optimization unit, a configuration scheme evaluation unit, a data storage unit and a visual monitoring platform. Synchronously acquiring and processing power distribution network data; selecting candidate branches according to sensitivity analysis; establishing a planning model, embedding multiple types of constraint conditions, obtaining an SOP optimal capacity through a particle swarm optimization algorithm, and determining an SOP configuration scheme; a multi-dimensional evaluation index system is constructed, after simulation verification, an analytic hierarchy process is adopted for weighted scoring, a comprehensive score is output, and an evaluation report is generated and sent to a data storage unit and a visual monitoring platform; through cooperative application of sensitivity analysis and an intelligent optimization algorithm, the SOP locating and sizing problem is efficiently solved, the operation economy, reliability and stability of the power distribution network can be remarkably improved, and powerful support is provided for scientific configuration of the SOP.

The invention relates to the technical field of power system energy storage and automatic control, and discloses a capacity optimization method, system and device for a multi-time-scale hybrid energy storage system, and the method comprises the steps: building and solving an initial capacity optimization model based on a multi-time-scale decomposition result of new energy power fluctuation, obtaining an initial capacity configuration parameter and a multi-time scale division parameter; controlling the hybrid energy storage system to participate in power grid frequency modulation based on the initial parameters, and collecting operation performance and equipment state data; generating a performance feedback index according to the data, dynamically correcting an adjustable parameter of the capacity optimization model based on the index, re-solving the model, and processing a division parameter and a capacity configuration parameter as associated variables in the process to obtain an updated parameter; and applying the updated parameters to the system and returning to the frequency modulation operation step to form closed-loop optimization. According to the invention, collaborative dynamic optimization of the energy storage capacity and the labor division strategy is realized, the system economy is improved, the service life of equipment is prolonged, and the operation robustness is improved.

The invention relates to the technical field of wireless charging assembly line automatic control, in particular to an intelligent cooperative control system for a wireless charging assembly line, and adopts the technical scheme that a central scheduling module and a beat cooperative algorithm unit can construct a dynamic state model based on global real-time data; a digital twin sub-module is introduced to perform simulation deduction verification on an instruction in a virtual environment, a beat redistribution strategy can automatically adjust a material flow when a fault occurs, and a visual positioning and compensation module can identify a material position and attitude offset in real time and generate compensation data. The man-machine interaction interface can visualize the whole line state, prediction information and alarm information in real time, the historical database and optimization learning unit can store data and iteratively optimize algorithm parameters, uncertain interference is analyzed through Monte Carlo simulation, and a Pareto optimal solution set is solved through an NSGA-II algorithm. The production takt of the three stages of feeding, dispensing assembling and baking discharging is dynamically matched, and dynamic takt synchronization and productivity optimization of the whole production line are achieved.

The invention discloses a distributed resource adjustment capability assessment method and system, and belongs to the field of capability assessment, and the method comprises the steps: obtaining multi-dimensional real-time data and external environment dynamic elements of a power distribution network and distributed resources, analyzing and extracting dynamic response characteristic parameters under multiple time scales through employing a preset correlation model, and carrying out the analysis of the dynamic response characteristic parameters under multiple time scales; and forming a resource characteristic parameter matrix. And then, inputting the matrix into a double-layer adjustable capability optimization model constructed based on a system scheduling target and an economic cost function, calculating adjustable space and response time of each distributed resource under different time scales, and outputting an adjustable capability index. And then, carrying out aggregation calculation on the indexes to obtain a comprehensive evaluation index set under multiple time scales, and carrying out comprehensive evaluation on the adjustment capability of the distributed resources according to the comprehensive evaluation index set. According to the invention, the problem that the adjustment capability of each distributed resource of the power grid cannot be accurately and efficiently evaluated in the prior art is effectively solved.

The invention discloses a micro power supply capacity optimization configuration and cooperative control method for a weak power grid, and belongs to the technical field of power systems, and the method comprises the steps: generating a capacity matching and day-ahead output plan containing each micro power supply based on a multi-objective optimization algorithm; monitoring system operation data in real time, and calculating a multi-energy complementary coupling coefficient representing a system adjustment margin; according to the wind and light permeability monitored in real time, control parameters of a virtual synchronous machine of the net-building type energy storage device are dynamically corrected so as to adjust system equivalent damping in a self-adaptive mode; energy storage output is controlled according to the corrected virtual synchronous machine control parameters so as to support the bus voltage frequency, and when the multi-energy complementary coupling coefficient is lower than a preset threshold value or the frequency deviation exceeds the limit, the micro pumped storage device is cooperatively triggered to carry out power compensation, and electric quantity balance and stable control of the micro-grid are completed. Through parameter dynamic correction and multi-energy cooperative compensation, the frequency stability, the power supply reliability and the clean energy consumption rate of the weak interconnection micro-grid are effectively improved.

This invention discloses an adaptive optimization method for SMT production line process parameters based on digital twins, belonging to the field of surface mount technology (SMT) data control technology. It includes constructing a digital twin to map SMT production line elements, reconstructing a model to calculate the SMT production line process status, deriving an SMT production line capacity optimization scheme, dynamically correcting process parameters, eliminating model overfitting faults caused by batch variations in incoming materials, and optimizing the joint simulation of equipment status and process parameters. This invention upgrades SMT production line process optimization from traditional reactive optimization to predictive optimization, reducing quality defects at the source and achieving a fundamental upgrade in the optimization model. Through a four-dimensional hierarchical digital twin and reduced-order model virtual sensing technology, it achieves real-time acquisition of key physical quantities that traditional methods cannot measure, such as thermal field distribution, stencil deformation, and dynamic gaps. This eliminates decision-making blind spots in process optimization, improves the accuracy and timeliness of optimization decisions, and thus fills the data dimension gap in process optimization.

The invention discloses a receiving end power system adjustable resource pool optimization scheduling method and device and a storage medium, and relates to the technical field of power system reserve capacity optimization. A receiving end power system adjustable resource pool optimization scheduling method comprises the following steps: obtaining historical data, obtaining a local net load prediction value based on an AE model, inputting the local net load prediction value into a CGAN model to generate a prediction error multi-element scene, and superposing the prediction error multi-element scene to obtain a local net load day-ahead scene; constructing a direct current locking probability distribution model, fusing a local net load day-ahead scene and clustering to obtain a credible scene set; analyzing operation characteristics of the adjustable resource pool to obtain power up-down adjustable boundaries of the adjustable resources; establishing a power balance constraint during adjustment by combining various resource operation characteristics; and establishing a two-stage distribution robust optimization model based on the trusted scene set and the power balance constraint, and solving a scheduling strategy with the minimum operation risk total cost in the worst scene. According to the invention, the adjustment advantages of different resources can be exerted, and the optimal scheduling of the resources is realized.

The present application relates to the technical field of financial management, in particular to a digital one-network financial marketing management method and system, comprising the following steps: acquiring node data contrast queuing delay task sequence, reading dependent chain comparison time length analysis and splitting link extraction, extracting standby capacity contrast residual time weight distribution task, matching regional distribution order according to business currency time limit, reading branch comparison delay insertion position to obtain execution scheme. In the present application, the processing delay, the queuing quantity and the capacity of the transaction node are compared in real time, the task entering order and the node position are optimized, the dependent chain comparison and the link information extraction are combined, the split task is reorganized under the condition of maintaining the dependent order, the resource utilization rate is improved by matching the standby channel capacity, the task residual time and the dependent weight, the priority is adjusted according to the business type, the currency type and the time limit across regions, and the overall waiting time is shortened by inserting the branch of the confluence node according to the delay optimization.

The invention provides a coverage and capacity optimization method, device and system, and relates to the field of mobile communication. The first wireless access network equipment receives an artificial intelligence / machine learning model sent by the operation management maintenance system; acquiring data of at least one of the first wireless access network equipment, the second wireless access network equipment and the user equipment as input data for training and / or reasoning of the artificial intelligence / machine learning model; and reasoning based on the artificial intelligence / machine learning model and one or more items of the input data to obtain prediction information related to coverage and capacity optimization. Therefore, the coverage and capacity optimization information can be foreseen in advance, and the coverage and capacity optimization of the wireless network can be improved.

The invention relates to the technical field of wind and light storage load capacity matching, in particular to a wind and light storage load capacity optimization matching method of a source network load storage integrated system, which comprises the following steps of: collecting and preprocessing data; determining an optimization target; establishing a wind-solar-storage-load integrated model; determining a capacity optimization variable; constructing an optimization model; optimization calculation: operating an optimization algorithm to solve the constructed optimization model; analyzing an optimization calculation result, and analyzing the optimization calculation result based on economy, reliability and environmental protection to obtain an optimal capacity ratio of the wind-light storage and load integrated system; verifying a result; and outputting an optimal wind and light storage load capacity matching scheme. According to the method, through the design of the particle swarm optimization algorithm and the fitness function, the optimal capacity matching scheme can be found in a complex system environment, and it can be ensured that the optimization result reaches the expected target in the aspects of economic benefits, system reliability and environmental protection performance.

The invention relates to an adjustable capacity optimization method and system for a data center participating in a peak shaving auxiliary service market, and the method comprises the steps: obtaining a data center computing power task plan, power load prediction data, power grid peak shaving auxiliary service demand information and external environment information, and carrying out the preprocessing; according to the preprocessed data, constructing a computing power-electric power cooperative regulation and control cost model; with maximization of computing power user satisfaction and minimization of data center operation cost as targets, according to the computing power-electric power cooperative regulation and control cost model, constructing a double-target optimization model of the adjustable capacity of the data center participating in the peak shaving auxiliary service market; solving the dual-objective optimization model by adopting a multi-objective particle swarm optimization algorithm to obtain an optimal load scheduling scheme; and obtaining the optimal adjustable capacity according to the optimal load scheduling scheme. According to the invention, the efficiency and economy of the data center participating in the peak regulation auxiliary service can be improved.

The application discloses a building integrated energy system operation strategy and declaration capacity optimization method considering demand response effectiveness requirements. First, the basic information of the building user side and the power grid side is collected; second, a building integrated energy system optimization scheduling model is established; then, an optimization solving problem of the building integrated energy system optimization scheduling model considering the demand response effectiveness requirements is determined; finally, a mixed integer linear programming algorithm is used to solve the optimization solving problem of the building integrated energy system optimization scheduling model, and the optimal declaration capacity value scheme and the operation strategy scheme are obtained. By using the application, the requirements of the power grid on the user information declaration can be met when the building user participates in the invitation type demand response, the declaration capacity value recommendation scheme and the response strategy recommendation scheme are provided for the user; and based on the response effectiveness auditing mechanism formulated by the power grid, the actual response effect of the user is improved, and the target of further reducing the operation cost of the user is achieved.

The invention discloses an RIS-based capacity optimization method for a composite frame short packet cooperative sensing system, and the method comprises the steps: building an optimization function in a multi-base-station cooperative short packet sensing system by taking the maximization of the reachable capacity of a communication user and a detection target as a target; adopting a one-dimensional search method to obtain an allocation scheme of pilot frequency block length and data block length in the composite frame structure; a first-order Taylor estimation method, a logarithm transformation method and a secondary conversion method are adopted to obtain an active and passive beam forming joint design scheme of the sensing base station and the RIS; and after iterative optimization, obtaining the optimal solution of the block length allocation and the active and passive beam forming scheme, and realizing the maximization of the total reachable capacity of the system. Based on the designed composite frame structure, under the assistance of the RIS, a block length allocation and active and passive beam forming combined design scheme is provided, ultra-reliable and low-delay communication and sensing services can be provided for a 6G vertical application scene, and the overall communication performance of a short packet cooperation sensing system is effectively improved.

The present disclosure provides a system and method for facilitating tracking area code (TAC) engine to prepare dynamic network polygons based on actual configuration, allocation of TAC to new cell sites, identification of various discrepancies and anomalies in existing planned network, capacity optimization through TAC Division, merge and network polygon edit, and auto work order generation and execution of changes in respective systems, MME Pool and eNB / SCs. Tracking Area Code is a vital parameter for new sites planning and rollout. The method complements network rollout speed with highest level of accuracy of planning and better network perform nave and accuracy. Auto planning engine makes TAC planning zero touch engine with manual review and action through manual edit, audit and work order creation features. Auto planning and configuration help faster rollout, speedy correction and implementation in live network to enhance network performance and user experience.

The invention discloses an alternating current and direct current micro-grid hybrid energy storage system capacity optimization configuration method based on an improved black-wing algorithm. The method comprises the following steps: S1, constructing an alternating current and direct current micro-grid hybrid energy storage system capacity optimization configuration model; s2, setting parameters of a Heiwen optimization algorithm, generating a position vector of an initial population by utilizing Logistic chaotic mapping, and mapping each individual into a capacity configuration scheme of the hybrid energy storage system; s3, calculating a fitness value of each individual in the population, determining a current global optimal individual, executing an attack behavior for each individual in the population based on a nonlinear balance factor, and updating an individual position; s4, carrying out migration behaviors on non-optimal individuals in the population, updating positions through interaction with other individuals, and completing a round of basic iteration; and S5, executing a mirror surface reverse learning strategy based on random scaling on the current population to generate a mirror image population, and reserving better individuals to enter the next generation by adopting a survival mechanism according to the fitness value.

The application provides a data center micro power supply capacity optimization method and system, the method comprises the following steps: constructing a corresponding three-dimensional constraint parameter group in real time according to real-time working parameters; obtaining a first correlation coefficient between power balance parameters and equipment capacity parameters through linear correlation, a second correlation coefficient between equipment capacity parameters and voltage limit parameters through nonlinear correlation, and a third correlation coefficient between voltage limit parameters and power balance parameters through mapping correlation; obtaining a power output prediction value of a photovoltaic micro-grid, and combining the first correlation coefficient, the second correlation coefficient and the third correlation coefficient to construct a corresponding capacity optimization function in real time; optimizing a preset particle swarm algorithm to generate a corresponding target optimization algorithm, and solving the capacity optimization function through the target optimization algorithm to output a corresponding photovoltaic capacity optimization value in real time. The application can objectively and dynamically optimize the capacity of the photovoltaic micro power supply, and improves the optimization efficiency.

The invention relates to the technical field of financial management, in particular to a digital one-network financial marketing management method and system, and the method comprises the following steps: obtaining a node data comparison time delay queuing arrangement task sequence, reading a dependency chain comparison time length, analyzing, splitting and extracting a link, extracting a spare capacity comparison remaining time weight distribution task, and obtaining a task sequence; and according to a business currency time limit matching region distribution sequence, reading branch comparison delay insertion positions to obtain an execution scheme. According to the method, the processing time delay, the queuing number and the capacity of the transaction nodes are compared in real time, the task entering sequence and the node positions are optimized, and the reorganization of the detachable tasks is completed under the condition that the dependency sequence is kept in combination with dependency chain comparison and link information extraction; the resource utilization rate is improved through matching of standby channel capacity, task remaining time and dependency weights, the priority is adjusted in a cross-region mode according to service types, currency types and time limits, and confluence nodes shorten the overall waiting time according to delay optimization branch insertion.

The invention relates to an energy storage capacity optimal configuration method and system based on flexibility resource prediction, and the method comprises the steps: screening three modes of a base load, adjustability and impact of a precise stripping load through improving variational mode decomposition and Pearson correlation, and constructing an upper layer capacity-lower layer operation double-layer coupling frame; rapid global optimization is realized through an improved honeypot algorithm driven by a load mode, the operation is ensured to be feasible in a planning stage in cooperation with an adaptive penalty mechanism and a closed-loop verification system, and finally an energy storage capacity optimization configuration scheme considering the lowest cost of the whole life cycle and the efficient consumption of renewable energy sources is output; the method has the advantages that the double-layer coupling framework is constructed, the improved honeypot algorithm is adopted, the self-adaptive penalty function is constructed, multi-target dynamic balance is achieved, and a closed-loop verification system is established.

The present application provides a network capacity optimization method, an apparatus, and a system, and relates to the field of communication technology. The solution is used to adjust the network capacity of an area based on a network capacity optimization requirement, so that the network capacity performance of the area satisfies the requirement. The solution includes the steps of: obtaining network capacity optimization requirement information including at least a network capacity optimization objective, the network capacity optimization objective being used to describe the requirement of the network capacity performance of a first area; determining, for the first area, a network capacity optimization solution used to address at least a network capacity problem existing in the first area based on the network capacity optimization requirement information; and optimizing the network capacity of the first area by using the network capacity optimization solution. The solution can satisfy the network capacity optimization objective, i.e., the network capacity performance of the area after optimization is enabled to satisfy the requirement as soon as possible. When a network capacity optimization constraint is included, it helps to know in a timely manner whether the network capacity performance of the first area satisfies the network capacity optimization objective.

The present disclosure relates to Coverage and Capacity Optimization (CCO) in communication systems using Artificial Intelligence / Machine Learning (AI / ML). In one embodiment, the present disclosure discloses a method which comprises predicting, using a proactive CCO mechanism, a first CCO issue potentially occurring at a first time instance within a first Radio Access Network (RAN) node. The method comprises determining, based at least on the predicted first CCO issue, a first CCO configuration for the first RAN node to be applied at a second time instance for mitigating the predicted first CCO issue. The method comprises transmitting a notification to a second RAN node, about the first CCO configuration for the first RAN node to be applied at the second time instance.

The invention relates to a coverage capacity optimization method and system, a network node, user equipment and a medium. The coverage capacity optimization method comprises the steps that a first network node sends a first message to a second network node, and the first message comprises at least one of statistical information, prediction information, real information and time information related to at least one of coverage and capacity. According to the invention, an optimization solution of at least one of coverage and capacity based on AI / ML can be realized.

The application provides a virtual power plant backup capacity optimization configuration management method and system, relates to the technical field of power system management, and comprises the following steps: obtaining distributed energy unit parameters and power grid demand information, clustering and grouping based on response time delay-energy state, constructing a double-layer optimization framework, adopting an intra-cluster multi-agent non-cooperative game model to solve a Nash equilibrium solution, and obtaining a final backup capacity configuration scheme through upper and lower layer configuration amount deviation convergence. The scheme realizes effective coordination of heterogeneous resources in the virtual power plant, improves backup capacity configuration efficiency, and reduces system operation cost.

A method is described. The method includes profiling a model for a learning network having a plurality of layers and associated memory. The layers include weight layers and activation layers. The plurality of weight layers including weights. The method also includes determining, based on the profiling, a training technique for the model on the learning network. The determination of the training technique includes optimizing a latency for at least one training iteration for a capacity of the associated memory. The training iteration(s) include at least one update of the weights for the weight layers.

The application relates to the technical field of power system energy storage and automatic control, and discloses a capacity optimization method, system and device of a multi-time-scale hybrid energy storage system, which comprises the following steps: based on a multi-time-scale decomposition result of new energy power fluctuation, an initial capacity optimization model is established and solved to obtain initial capacity configuration parameters and multi-time-scale division parameters; based on the initial parameters, the hybrid energy storage system is controlled to participate in grid frequency modulation, and operation performance and equipment state data are collected; performance feedback indexes are generated according to the data, and adjustable parameters of the capacity optimization model are dynamically corrected based on the indexes, so that the model is solved again, in the process, the division parameters and the capacity configuration parameters are treated as associated variables to obtain updated parameters; the updated parameters are applied to the system and returned to the frequency modulation operation step to form a closed-loop optimization. The application realizes the collaborative dynamic optimization of energy storage capacity and division strategy, and improves the economy, equipment life and operation robustness of the system.

The invention discloses a capacity optimization method and device for a solid state disk, electronic equipment and a storage medium, and relates to the technical field of storage systems.According to the capacity optimization method and device for the solid state disk, due to the cooperative operation of logical channel expansion and random data aggregation sorting and the combination of an independent hot plug and data recovery mechanism of a storage module, the capacity of the solid state disk can be optimized; the technical problems of storage capacity expansion bottleneck caused by physical channel number limitation of an existing QLC-based solid state disk, write-in performance reduction and flash memory service life shortening caused by a random write-in mode, and low component utilization rate and high maintenance cost caused by fixed design of the whole disk can be solved. The technical effects that the single-disk storage capacity and the data writing efficiency are remarkably improved under a limited physical interface, the durability of the QLC flash memory is optimized, and independent maintenance of the storage module and efficient utilization of resources are achieved are achieved.

The invention provides a regional integrated energy system adjustable capacity optimization method and system, and belongs to the technical field of regional integrated energy system optimization. The method comprises the following steps: determining upper and lower limits of adjustable capacity of equipment to determine total upward and downward adjustment capacity of the system; according to the upward and downward adjustment total capacity of the system, the total transmission loss and the available transmission capacity of the line, solving the upward and downward adjustable capacity of the region; upward and downward capacity prediction errors, wind and light output errors and load fluctuation errors are determined, and probability feature vectors of up-regulation and down-regulation directions are obtained through Bayesian reasoning modeling; calculating the attention weight of the historical window through a time sequence attention mechanism; and determining a dynamic safety margin to dynamically correct the upward and downward adjustable capacities of the current region to obtain the final upward and downward adjustable capacities of the region. According to the method, the precision of the regional integrated energy system capacity and the system operation reliability are improved, and the regional adjustable capacity is more suitable for the actual network state.

The invention discloses a micro-grid capacity optimization method based on an improved sparrow search algorithm, and the method comprises the following steps: S1, building a micro-grid capacity optimization model according to a mathematical model of a micro-grid distributed power supply and a price-type demand response mathematical model with the minimum equivalent value comprehensive cost as an objective function, and constructing a micro-grid hybrid energy storage system; s2, proposing a micro-grid operation control model according to the leveling cost of kilowatt-hour electricity and a time-of-use electricity price theory; s3, providing an improved sparrow search algorithm fusing a chaos reverse learning strategy, a golden sine formula, a dynamic weight factor, Levy flight, a mixed variation disturbance strategy and a greedy strategy; and S4, reasonably configuring the number of each distributed power supply of the micro-grid by using the ISSA. The algorithm provided by the invention has higher convergence precision and faster convergence speed.