24 results about "Operational cost reduction" patented technology

The invention relates to a warehouse inventory management and optimization method based on artificial intelligence, and the method comprises the steps: constructing a novel mathematical model through comprehensive consideration of the storage position of a commodity, tray partition, order priority, system state and other factors, solving through employing a deep reinforcement learning DQN network, achieving the intelligent decision of a commodity pickup position, and generating an optimal picking path; according to the method provided by the invention, the order processing time can be effectively shortened, aiming at the requirements of small-batch and multi-category orders, the storage efficiency is improved, the movement distance of a stacker and the number of goods taking times are reduced, the operation cost is reduced, and flexible scheduling can be performed according to the priority to adapt to the real-time state change of the system; compared with a traditional method, after the optimization strategy is adopted, the number of times of taking out the trays is remarkably reduced, and the method has the remarkable beneficial effects, can be widely applied to the field of intelligent warehousing, improves the automation, informatization and intelligentization levels of warehousing management and meets the requirements of the modern logistics industry for an efficient warehousing system.

The invention provides a relay core component double-layer production line AGV scheduling strategy considering task completion time and energy consumption optimization, and aims to optimize task completion time and AGV energy consumption distribution of a double-layer relay welding production line and improve the operation efficiency of an AGV in a complex production environment. The method comprises the following steps: firstly, constructing an environment model of a double-layer production line, extracting operation constraints and task requirements of an AGV, and planning an initial path by using an A * algorithm; secondly, whether dynamic or static position conflicts exist is judged according to the real-time position of the AGV, obstacle avoidance is performed in combination with an improved BUG2 algorithm and a priority strategy, and an AGV route after obstacle avoidance is updated; and finally, combined optimization is carried out on the path planning and task allocation of the AGV by using mixed integer programming in combination with an adaptive weight adjustment strategy so as to ensure that the AGV can complete the established task under the lowest energy consumption. The method can be widely applied to the fields of intelligent manufacturing, automatic logistics, unmanned warehousing and the like, the production efficiency is improved, and the operation cost is reduced.

The invention relates to the technical field of supply chain management and control, in particular to a supply chain intelligent management and control system and method based on multi-source data fusion, the system comprises a data acquisition module, a data fusion processing module, a digital twin deduction module, a multi-target decision module and a strategy execution and feedback module, and the method completes corresponding management and control steps according to the system. The method comprises the following steps: firstly collecting full-chain multi-source heterogeneous data of a supply chain, generating unified multi-dimensional state data through space-time alignment and multi-modal fusion, then constructing a digital twin model to carry out multi-scene deduction to output a candidate strategy, screening out a comprehensive optimal strategy through a multi-target decision, and finally analyzing and issuing the strategy and collecting execution effect feedback. And closed-loop iterative optimization is formed. According to the method, the problems of isolated data, subjective decision, no deduction system and lack of closed-loop optimization of traditional supply chain management and control are effectively solved, the management and control intelligence and automation level is improved, the supply chain toughness is enhanced, the operation efficiency is optimized, the operation cost is reduced, and full-chain collaborative management and control are realized.

The invention discloses an automatic implementation method and system for contract product contract extension, and belongs to the technical field of intelligent data analysis, and the method comprises the steps of intelligent recommendation, intelligent auditing, automatic contract solution, user self-service contract signing and batch processing, and achieves whole-process closed-loop management from personalized recommendation list generation to user online ordering and related report intelligent generation. The intelligent recommendation is based on the large model capability, a collaborative filtering algorithm, a decision tree algorithm and a personalized recommendation algorithm are applied, and data recommendation generation is efficiently completed; and the intelligent auditing is used for dynamically verifying the contract extension data based on a rule base to ensure that the contract extension data conform to specifications. According to the method, the operation cost can be remarkably reduced, the compliance and the user experience are improved, and the business process is promoted to be transformed to intelligence and greenization; the method is suitable for telecommunications, finance and other industries requiring high-frequency contract products.

The invention discloses a new energy access power distribution network low-carbon economic regulation and control method, and the method comprises the following steps: S1, data collection and analysis: carrying out the real-time monitoring of the productivity of all renewable energy sources in a region, and building a power generation prediction model in combination with historical data; s2, formulating an optimization scheduling strategy: formulating a scheduling strategy capable of meeting the power consumption demand of a user and maximizing the utilization of renewable energy sources based on a data analysis result in the step S1, and establishing a total cost function model; s3, establishing a dynamic adjustment mechanism: monitoring a system state in real time and automatically adjusting power distribution according to a preset adjustment rule; and S4, implementation and feedback: continuously performing data collection to evaluate an effect, and continuously optimizing model parameters according to feedback information. The energy utilization efficiency is improved, the operation cost and the environmental influence are reduced, and the system stability and reliability are enhanced.

The invention discloses an intelligent prediction method of a human resource system operation management budget system. Comprising an intelligent data collecting and processing module which is connected with each data source through an API interface, automatically collects employee attendance records and salary information, and performs cleaning and standardization processing on data through a data preprocessing algorithm to ensure the accuracy of the data; the multi-dimensional data analysis and modeling module realizes accurate prediction of human resource management budget by combining a random forest algorithm and a near-end strategy optimization PPO algorithm; the random forest algorithm is used for preliminarily analyzing and extracting data features, and the PPO algorithm is used for further optimizing a prediction strategy, so that the accuracy of model prediction in a dynamic environment is improved; the dynamic budget adjustment mechanism automatically adjusts the budget scheme according to market changes and real-time data, the human resource management efficiency and decision scientificity can be effectively improved, the operation cost is reduced, the data safety is guaranteed, and reliable management tools and technical supports are provided for enterprises.

The invention provides a multi-energy complementary energy supply system and a control method. The system comprises PV / T equipment, a gas turbine and an energy storage system. The control method comprises the following steps: acquiring initial parameters of a system, and setting an initial operation strategy based on a load demand; constructing a system model, establishing a multi-objective optimization model taking a cost objective, an environment objective and a primary energy saving rate as optimization objectives, and generating a Pareto frontier solution set through a non-dominated sorting genetic algorithm so as to screen out an optimal solution; and obtaining a system operation strategy through the optimal solution. According to the system, the building cost is low, a multi-energy complementary system coupled with multiple energy sources is built, the life and production energy consumption requirements of residents can be met, the energy utilization efficiency can be effectively improved, and pollutant emission is reduced; the corresponding control method is low in execution cost, and based on a multi-objective optimization framework, the method can effectively balance cost, environment and multi-party benefits, so that the system efficiency is improved, the operation cost is reduced, and the utilization rate of renewable energy sources is improved.

The invention discloses a thermal power plant enterprise management system, which comprises a resource allocation module, an equipment maintenance module, an energy efficiency management module, an environmental protection monitoring module, a cost control module, a data management platform and the like, and relates to the technical field of thermal power plant enterprise management. According to the enterprise management system for the thermal power plant, the comprehensive and intelligent management of the thermal power plant is realized by integrating a plurality of modules such as resource allocation, equipment maintenance, energy efficiency management, environmental protection monitoring and cost control, the production efficiency and the resource utilization rate are improved, the real-time monitoring and fault early warning capabilities of equipment are enhanced, and the system is suitable for popularization and application. And meanwhile, the system also has remarkable advantages in the aspects of energy consumption management and environmental protection monitoring, the management efficiency of the thermal power plant is effectively improved, remarkable advantages are shown in the aspects of reducing the operation cost, ensuring the environmental protection compliance and improving the economic benefits, and the system has a good market application prospect.

The invention discloses an efficient low-vibration strip mine deep hole step blasting method and a pre-splitting hole charging structure. The method comprises the following steps that A, main blasting holes which are located in the front row and arranged in a staggered mode and presplitting holes located in the rear row are drilled; b, after the main blasting hole and the presplitting hole are drilled, explosive filling operation is conducted, explosive filling is continuous, filling is continuous and compact, and after explosive filling is completed, the explosive filling condition and the filling condition are checked through a measuring rope; c, safety disposal; and D, blasting operation is carried out, a signal is sent out before blasting, and blasting is started after personnel in the dangerous area are removed to a safe place. The method has the advantages that by optimizing blasting parameters (such as aperture and spacing) and adopting a brand-new pre-split hole charging structure, slope quality can be improved from the source, follow-up repair investment is reduced, indirect loss caused by safety accidents is avoided, core requirements of enterprise cost management and control are met, technical reference is provided for industry operation cost reduction, and the method is worthy of popularization and application. The method is an important path for promoting economic and efficient operation of mines.

The present invention relates to the technical field of resource sharing, and specifically to an entity resource sharing economic system and its method. The system includes a resource monitoring module, a resource flow analysis module, a demand response strategy module, a cost assessment module, a periodic analysis module, and a stability assessment module. In the present invention, by collecting and analyzing the usage and flow data of entity resources, the dynamic adaptability and accuracy of resource allocation are effectively improved. Through the data recorded in real time with geographical location and time tags, the periodic changes and flows of resources are analyzed. This dynamic monitoring and analysis not only helps to predict peak resource demands, but also enables the design of more effective sharing and allocation strategies for shortage areas. By simulating various allocation schemes, the response strategy can be optimized, the resource utilization efficiency can be enhanced, and at the same time, the operating cost can be reduced, ensuring the long-term benefits and sustainable development of the resource sharing economic system, improving cooperation and interaction among communities, and supporting environmental sustainability.

The application provides a kind of modular energy storage shelter operation control method and system, wherein, the comprehensive operation data is obtained by collecting the operation parameters of each energy storage module in the embodiment of the application, the health state evaluation model of energy storage module is established by machine learning algorithm, real-time power demand data is obtained from grid company API interface, user end intelligent electric meter and historical data and current environmental parameter analysis, the charging and discharging strategy of each energy storage module is dynamically adjusted by using genetic algorithm to obtain charging and discharging plan, the prediction result is generated by combining deep learning technology to predict the change trend of power supply and demand, the optimal energy storage module scheduling scheme is confirmed by using mixed integer linear programming algorithm to obtain scheduling instruction, the decision-making process is continuously optimized by online learning, and the best control instruction is generated by using the adaptive control strategy of reinforcement learning.The technical scheme provided by the application effectively reduces energy waste, prolongs service life, reduces maintenance and replacement, thereby reducing operating cost and improving economic benefit.

The invention relates to the technical field related to load scheduling, in particular to an adjustable load management method and device based on multi-target random modeling. The method comprises the following steps: based on the structure and characteristics of an energy hub, establishing an operation model of energy hub equipment; according to system operation characteristics, an uncertain model of loads and prices based on an adjustable load management strategy is established; establishing a multi-target random model, converting a plurality of preset targets into a single target, and optimizing a load management strategy by taking the maximum single target as a target based on the uncertain model and the operation model of the load and the price; wherein the plurality of preset targets comprise operation cost reduction and carbon emission reduction.

The invention provides a multi-agent virtual power plant cooperative scheduling method and system based on P2P transaction driving, and relates to the technical field of power system optimization scheduling. The method comprises the following steps: constructing a VPP-producer and consumer double-layer P2P transaction framework, and establishing a producer and consumer multi-energy complementary optimization model and a VPP auxiliary service bidding model; a multi-market transaction problem is modeled as an observable Markov game, and an asynchronous multi-agent near-end strategy optimization (MAPPO) algorithm is adopted for solving. The producer and the consumer are stimulated to participate through a P2P transaction mechanism, the flexibility potential of electricity-heat-cold multi-energy complementation is fully excavated, and the VPP operation cost is reduced. Test results show that the method can stimulate the generation and disappearance personnel to participate in VPP scheduling, reduces the risk of privacy disclosure, and adapts to dynamic environments such as auxiliary service demand change, electricity price fluctuation and generation and disappearance personnel equipment update.

The invention aims to provide an assembly line performance change demand driven reverse variant network modeling method. The method comprises the following steps: constructing a five-dimensional association model matched with a to-be-changed assembly line; establishing a mapping and constraint relationship among the elements of each dimension; identifying a key assembly link and a change transmission mechanism, and constructing a dynamic feedback model among assembly process parameters, production line configuration parameters and assembly line performance; obtaining a topological index of any node in a network corresponding to the five-dimensional association model, and quantifying propagation risks from an initial node to a potential influence range; establishing an optimization function; solving the optimization function, and outputting a group of optimal solution sets; any solution in the optimal solution set represents an optimal change strategy which balances risks, time and cost. The production efficiency can be improved, the operation cost can be reduced, and the market competitiveness can be enhanced.

The invention discloses an industrial park source network load storage, insurance and supply method, system and device and a storage medium, and the method comprises the steps: collecting the multi-source data of a power system, and recognizing the operation mode of the power system based on the multi-source data; based on risk constraints of different operation modes of the power system, taking minimization of economic cost as a target function, and establishing a source network load storage regulation resource planning model; and dynamically adjusting a planning strategy generated by the source network load storage adjustment resource planning model according to the actually occurring extreme weather event and the real-time operation state of the power system so as to optimize the source network load storage resource configuration. According to a risk constraint optimization mechanism, insufficient standby capacity and voltage out-of-limit risks are avoided, seamless connection of risk dynamic constraint and optimization planning is realized through a collaborative planning framework, the new energy consumption capability can be improved, the operation cost is reduced, and the adaptability of a power grid to high-proportion new energy access is enhanced.

The invention provides an inventory management method based on an ERP (Enterprise Resource Planning) system, and relates to the technical field of inventory management.The method comprises the steps of setting multiple upper and lower limit levels and corresponding control stock according to a change rule of historical stock-in and stock-out data, achieving dynamic management of the stock, flexibly coping with changes of market demands, avoiding the situation of excessive stock or stockout, and secondly, improving the efficiency of stock management. The method comprises the following steps: constructing a prediction model through time sequence analysis, predicting warehouse-in and warehouse-out data in a period of time in the future, evaluating the validity of a strategy according to the comparison between a prediction result and the current inventory control strategy, determining the adjustment degree of the current control strategy through setting an evaluation threshold section and an evaluation grade, and carrying out detailed management adjustment. Fund occupancy and loss caused by stock overstock and excessive stock can be avoided, the operation cost can be reduced, and the profit of an enterprise can be increased.

The invention relates to the technical field of port logistics automation control, in particular to an integrated cooperative control method for port whole-process operation heterogeneous equipment. The method comprises the following steps: acquiring operation data of port equipment in real time, and constructing an equipment information model and a standardized data model; obtaining the data information of the whole-process operation, and preliminarily screening the workflow according to the position information of the starting point and the ending point of the whole-process operation; judging a current work flow, and deciding an optimal work flow; according to the optimal operation process and the equipment starting sequence, self-checking operation is executed in sequence; according to the optimal operation process, each device is controlled to complete the whole-process operation; and generating a job log based on the completion state of the full-process job, and realizing full-cycle tracing of the full-process job. According to the invention, the technology of unified management and cooperative control of heterogeneous equipment can be realized, the overall operation efficiency of a port is improved, seamless connection and cooperative operation between the equipment are realized, the operation precision is enhanced, the operation cost is reduced, and the resource configuration is optimized.

The invention discloses a cloud side end adaptive scheduling method and device based on cognitive decision, and a medium, and relates to the technical field of data processing. The method comprises the following steps: carrying out high-fidelity simulation on a cloud side end system by utilizing a digital twin technology, simulating resource use conditions in various service scenes before actual deployment, and finding potential bottlenecks in advance. According to the method, resource configuration can be dynamically adjusted through preset hard constraints in combination with real-time monitoring and simulation deduction, and it is ensured that the service quality requirement can be met even in peak hours. The MADDPG algorithm allows a plurality of agents to carry out collaborative optimization for different scheduling dimensions, a Pareto optimal solution is searched, and the operation cost and the energy consumption are reduced to the maximum extent. By continuously monitoring the deviation between the actual operation data and the simulation predicted value, once a significant difference is found, a model fine tuning mechanism is triggered. In combination with the mechanism, the efficiency of service scheduling under the cloud side-end collaborative architecture is improved.

The invention relates to an intelligent economic evaluation method for a pumping and injecting unit based on a Lie group. The method comprises the following steps: S1, inputting parameters; s2, data preprocessing; s3, dividing a data set; s4, performing Lie group space mapping; s5, constructing a dynamic scoring model; s6, performing a Lie group optimization algorithm; and S7, judging by combining a static threshold value with a dynamic correction factor. According to the economic intelligent evaluation method driven by the Lie group, the cost change trend can be predicted more accurately, resource allocation can be optimized, the operation cost can be reduced, and the economic benefit of the mine can be improved. Meanwhile, the method supports the multi-objective decision of the mining scheme, evaluates the long-term economic influence of the irreversible mining decision, and provides powerful support for the sustainable development of the mine.

The present application relates to a kind of based on artificial intelligence warehouse inventory management and optimization method, by comprehensively considering commodity storage location, tray partition, order priority and system state etc., construct new mathematical model, and use deep reinforcement learning DQN network solution, realize intelligent decision commodity pick-up location and generate optimal picking path;The method provided by the present application can effectively shorten order processing time, for the demand of small batch, multi-category order, improve warehouse efficiency, reduce stacker moving distance and pick-up frequency, reduce operating cost, and can be flexibly scheduled according to priority, adapt to system real-time state change;Compared with traditional method, after adopting the optimization strategy provided by the present application, the number of tray extraction is significantly reduced, has significant beneficial effect, can be widely applied in intelligent warehousing field, improve the automation, informatization and intelligent level of warehousing management, meet the demand of modern logistics industry to efficient warehousing system.

The invention provides a computing energy collaborative service planning method and device and related equipment, and is applied to the technical field of computers. Comprising the following steps: acquiring equipment configuration information, service configuration information and electricity price information; based on the equipment configuration information and the service configuration information, predicting the equipment performance information and the service performance information by using a time sequence prediction model, and outputting multi-index prediction results corresponding to the equipment performance information and the service performance information respectively; determining a correlation between GPU power and GPU load of each device based on the device configuration information; constructing a corresponding objective function and constraint conditions of the objective function according to the multi-index prediction results corresponding to the equipment performance information and the business performance information, the correlation between the GPU power and the GPU load of each piece of equipment and the electricity price information; and under the constraint of the constraint condition, solving the target function to obtain a business scheduling strategy when the operation cost is minimum. Resource waste can be avoided, and operation cost can be reduced.

The invention provides a simulation-based factory queuing theory optimization method. The method comprises the steps of obtaining an order quantity, a service counter quantity, an order emergency degree grade, order waiting time and a preset initial weight ratio; inputting the number of orders, the number of service desks, the order emergency degree levels and a preset initial weight ratio into simulation software, and obtaining the average order number, the average queue length, the average stay time and the average waiting time of the orders under different order emergency degree levels based on the order emergency degree levels; according to the order emergency degree grade and the average waiting time and the average staying time corresponding to the order emergency degree grade, adjusting a preset initial weight ratio to obtain an adjusted weight ratio; and calculating according to the order emergency degree grade, the order waiting time and the adjusted weight ratio to obtain an order processing priority corresponding to the order. According to the simulation method, a factory manager can more accurately optimize the production line, the production efficiency is improved, and the operation cost is reduced.

The invention provides a virtual power plant optimal scheduling method based on multi-time scale collaborative decision, and belongs to the technical field of power supply, and the method comprises the steps: 1, obtaining a power market mechanism, a distributed energy set, power data, energy storage data, user power consumption data and historical operation data; 2, determining a power time vector, a first time scale vector, a second time scale vector, a virtual time scale vector and a scheduling target vector; 3, constructing a plurality of optimization scheduling models; and 4, determining the hierarchy of each optimization scheduling model, and realizing the coordinated optimization scheduling of the virtual power plant based on the virtual time scale vector. According to the method, an optimal scheduling model and a hierarchical system can be constructed in a layered manner, accurate collaborative decision-making under multiple time scales is realized, market price fluctuation and energy output change are quickly responded, the resource configuration efficiency of the virtual power plant is improved, the operation cost is reduced, and the competitiveness and flexibility in a complex power market are enhanced.

The invention provides a production operation management and control method and device used in a storage lease scene, and belongs to the technical field of Internet of Things. The method comprises the steps of obtaining warehousing operation data, and establishing a data view based on the warehousing operation data; dividing role responsibilities and role permissions of the warehousing operation personnel based on the data view, and determining an operation collaboration process based on the role responsibilities and the role permissions; and generating job scheduling information based on the job collaboration process, and monitoring the storage resource utilization rate and the storage operation efficiency in combination with the data view. According to the invention, intelligent, collaborative and efficient management of multi-role production operation in a warehouse lease scene can be realized, so that the operation efficiency is improved, the resource configuration is optimized, the operation cost is reduced, and the data security is guaranteed.