8 results about "Operational cost reduction" patented technology

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 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 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 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 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 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.