337 results about "Cost optimization" patented technology

The invention relates to the technical field of data processing, and discloses a method and system for monitoring the running state of reflow soldering equipment. The method comprises the steps of collecting data through a multi-point sensor, extracting temperature gradient, tension fluctuation and gas concentration characteristics, constructing a state recognition model, calculating a health index and setting an early warning threshold value, constructing a fault precursor extraction model to predict a future state, and finally establishing a maintenance strategy optimization system and generating an equipment maintenance plan based on fault early warning information. Through multi-dimensional data fusion and intelligent analysis, early accurate identification of the abnormal state of the equipment, accurate prediction of the fault development trend and active maintenance decision based on quality influence and cost optimization are realized, so that the welding quality stability is improved, the non-planned downtime is shortened, and the maintenance cost is reduced.

The invention discloses a cost optimization method for resource scheduling management of a cloud data center, and relates to the technical field of cloud computing, and the method comprises the following steps: S1, collecting and modeling a multi-dimensional resource state of the cloud data center, and generating a resource change trend based on a sliding time window and a prediction model; and S2, constructing a multi-target game scheduling model taking calculation, storage, bandwidth and energy consumption as participants, outputting a scheduling game solution in combination with task modal adaptability parameters, and forming task-resource optimal matching. According to the method, through multi-dimensional resource state collection, a sliding time window and an advanced prediction model, resource dynamic changes and future trends can be captured more accurately, more reliable input is provided for scheduling decisions, resource waste or performance bottlenecks caused by information lag are avoided, calculation, storage, bandwidth and energy consumption are modeled as multi-party game participants, and the game efficiency is improved. Nash equilibrium is solved in combination with task modal adaptability parameters, and an optimal scheduling scheme giving consideration to resource utilization rate, performance and cost can be found.

The invention relates to the technical field of modular assembly production lines, in particular to a modular flexible digital twin layout optimization method and system. Comprising the following steps: constructing multi-dimensional constraint modeling, constructing a multi-objective optimization model containing route length, equipment cost and human resource quantity, and generating an initial layout scheme based on three-dimensional geometric attributes and logistics paths of production line equipment; dynamic weight distribution is carried out, according to priority parameters input by a user, a route optimization weight # imgabs0 #, a cost optimization weight # imgabs1 # and a human resource weight # imgabs2 # are adjusted in real time, and a dynamic weight combination matrix is generated; digital twinborn simulation is carried out, the initial layout scheme is mapped to a digital twinborn platform, and the actual equipment utilization rate, the material carrying efficiency and the worker operation path are calculated through logistics path simulation; according to the scheme, dynamic balance multiple targets can be provided based on a simulation result, simulation and optimization real-time linkage can be realized, and a layout optimization technology of a multi-dimensional verifiable scheme is provided.

A device for time-based tracking and cost optimization in construction projects, the device comprising the following: a robust housing suitable for use on construction sites; a processing unit located inside the housing, configured to perform real-time time-stamping, data acquisition and preprocessing tasks; a multimodal sensor unit that is operationally coupled with the processing unit, wherein the sensor unit comprises at least a motion sensor, an RFID reader, sensors for environmental conditions and a vision module with optical character recognition; a real-time clock module that is operationally connected to the processing unit to provide time synchronization for all sensor data streams; a wireless communication module that supports the Wi-Fi, LoRa and LTE protocols and is configured for transmitting time-stamped data to a central project server; a storage module that is operationally coupled with the processing unit to locally buffer time series data of construction activity during offline operation; a housing-mounted, touchscreen-based human-machine interface configured to allow site personnel to enter activity updates and confirm the status of construction tasks; a cost optimization engine running on the central server, the engine being configured to receive time-synchronized sensor data from multiple such devices and dynamically calculate time-cost trade-offs using a predictive planning technique that incorporates the principles of the critical path and the power value; furthermore, the device is configured to be integrated into a digital twin environment of the building under construction in order to provide real-time visualization of progress and to generate suggestions for resource reallocation based on a time-cost-benefit analysis.

The invention discloses a reinforcement learning-based mobile vehicle charging and storage dynamic scheduling method and system, and solves the problems of insufficient scheduling flexibility and low peak-valley electricity price utilization rate of a fixed charging facility of an existing parking lot. A dynamic environment model is constructed, the real-time SOC of the mobile charging and storage vehicle, the position topological relation and the charging demand space-time distribution are integrated, and a deep reinforcement learning algorithm is adopted to train an intelligent body to generate a multi-dimensional collaborative optimization strategy. According to the method, a charging / discharging time sequence, a task path and energy distribution are autonomously planned, a reward function mechanism fusing dynamic path cost and energy constraint is innovatively designed, a multi-vehicle asynchronous collaborative decision framework is established, and dual targets of charging demand response efficiency and operation cost optimization are achieved. According to the method, an MCSV hardware embedded system which supports an ROS2 communication protocol and has a real-time sensor data processing capability is deployed, so that effective transition from a theoretical strategy to actual application is realized.

The invention provides a cost-aware efficient tool planning method based on a large model. According to the method, efficiency and cost optimization of tool scheduling is realized by constructing a CATP-LLM framework. The method comprises the steps that a tool planning language TPL is designed, a non-linear multi-branch parallel scheme is generated through structured Token support, and the task execution efficiency is remarkably improved; in combination with a cost-aware offline reinforcement learning CAORL algorithm, a tool selection strategy is dynamically optimized based on historical data, and task performance and resource consumption are balanced; and the balance relationship between the performance and the cost is quantified through the QoP of the scheme quality index, and dynamic adjustment is realized to output an efficient and low-cost planning scheme. Compared with the prior art, parallel tool calling, dynamic cost optimization and complex task adaptability are supported, the execution cost is remarkably reduced while the task quality is ensured, and an efficient solution is provided for multi-scene task planning.

The invention discloses a multi-microgrid system distributed optimization scheduling method based on an electric power-carbon market, and the method comprises the steps: constructing an energy consumption equipment model and a carbon quota transaction model based on the energy flow and carbon quota transaction process in a microgrid; a dynamic collaborative pricing model is constructed based on the power and carbon quota market supply-demand relationship; constructing an operation cost optimization model of a single micro-grid system based on the above models, and constructing a multi-micro-grid collaborative optimization scheduling problem with the goal of minimizing the total operation cost of all micro-grids based on a Nash bargaining game framework; and solving by adopting an accelerated prediction-correction alternating direction multiplier method algorithm to obtain an optimal scheduling scheme based on power-carbon market coupling, thereby realizing energy operation scheduling of the multi-microgrid. Through power-carbon market coupling, dynamic pricing, game theory optimization and an efficient distributed algorithm, the operation cost of the micro-grid system is reduced, and the reduction of the operation cost assists in improving the operation income of the micro-grid system.

The invention discloses a source-grid-load-storage cooperative scheduling method and system based on multi-time scale cost optimization, and the method comprises the steps: outputting a dynamic coupling map containing short-term and long-term cost evolution paths according to the source-side power generation cost, the grid-side transmission loss, the load-side load demand and the storage-side life attenuation data of an energy system; based on the dynamic coupling map, outputting an uncertainty quantization parameter containing probability distribution; according to the uncertainty quantization parameters, outputting a collaborative scheduling framework containing space-time correlation constraints; and based on the collaborative scheduling framework, performing multi-objective optimization solution by adopting a hybrid algorithm, and outputting a global optimal scheduling strategy. By utilizing the embodiment of the invention, the global optimal decision can be realized to balance the economy and safety of the system and the service life of the equipment.

The invention provides a municipal heat supply pipe network working condition twinborn modeling method, system and device and a medium. A pipe network twinborn model is constructed; according to the correlation coefficient matrix of the working condition response characteristics between the monitoring nodes and the pipe network structure parameters of the monitoring nodes, determining the boundary constraint quantity of the position of each monitoring node under the variable working condition; extracting temperature-pressure-flow coupling response characteristics of each monitoring node in the pipe network twinborn model under the variable working condition, and further determining the coupling response loss of twinborn modeling simulation under the variable working condition; and constructing an error cost function of pipe network twinborn simulation according to all the boundary constraint quantities and the coupling response loss, performing parameter optimization on model parameters of the pipe network twinborn model in combination with the error cost function to obtain optimal model parameters suitable for variable working conditions, and updating simulation control parameters of the pipe network twinborn model based on the optimal model parameters. By means of the scheme, cost optimization of simulation parameters in twin modeling of the municipal heat supply pipe network under the variable working conditions can be achieved.

The invention is applicable to the technical field of fuel cells, and provides a power-heat cooperative control method for a network-connected fuel cell vehicle, and the method comprises the steps: constructing a speed prediction model and a vehicle dynamics system model; establishing a power battery system model; establishing a fuel cell thermal management system model; establishing a passenger compartment thermal model; analyzing the coupling relationship between the energy management system and the fuel cell-passenger compartment coupling thermal management system; and designing a power-heat hierarchical optimization framework, and realizing efficient energy utilization and real-time control through a hierarchical collaboration mechanism. According to the method, the problem of comprehensive operation cost optimization of the integrated heat management system considering energy and heat coupling can be effectively solved, and efficient energy utilization and real-time system response are realized. The real-time responsiveness and stability of the system are improved, and it is ensured that efficient operation can be kept under various conditions.

The invention discloses a self-adaptive cloud edge-end cooperative task unloading method based on multi-agent reinforcement learning, and belongs to the technical field of cloud edge cooperative task unloading. The method comprises the following steps: constructing a cloud edge-end three-layer cooperation model; the terminal user optimizes the edge device selected to cooperate and the unloading task amount, and the edge end server comprises a fixed edge server and an unmanned aerial vehicle server; the unmanned aerial vehicle server optimizes a flight path according to the scheduling position of the cloud; the cloud data center optimizes and dispatches the unmanned aerial vehicle position according to the load condition; mobile equipment energy consumption and task completion time delay in the cloud side end cooperative task unloading process are modeled into a comprehensive system cost optimization problem, and the comprehensive system cost optimization problem is further converted into a Markov decision process; designing a multi-agent reinforcement learning algorithm to optimize a task unloading strategy; on the premise of meeting various constraints, the task execution efficiency can be effectively improved, meanwhile, the energy consumption of the mobile equipment is reduced to the maximum extent, and particularly, the service duration and quality of the unmanned aerial vehicle are improved.

The invention provides a truck-unmanned aerial vehicle cooperative dynamic scheduling method and system in emergency logistics, and relates to the technical field of unmanned aerial vehicle intelligent scheduling. Constructing a truck-unmanned aerial vehicle cooperative distribution network, wherein each truck is equipped with a cooperative unit comprising a large unmanned aerial vehicle and a small unmanned aerial vehicle; the network comprises warehouses, hub points and demand points; establishing a mixed integer programming model by taking minimization of the total operation cost as a target and taking electric quantity management, service distribution, a time window and collaborative feasibility as constraints; and solving the model by adopting a randomized greedy algorithm, and synchronously deciding a truck path and an unmanned aerial vehicle task in single iteration to obtain a truck path scheme, an unmanned aerial vehicle service distribution scheme and a dynamic electric quantity scheduling scheme. Therefore, the cost optimization and dynamic coordination of emergency logistics scheduling are realized, and the efficiency and feasibility of emergency distribution are effectively improved.

The invention provides a cost-oriented civil aircraft fleet planned maintenance scheme optimization method, relates to the field of regular maintenance plan and cost optimization of an aircraft fleet, and aims to improve the maintenance efficiency of the aircraft fleet and reduce resource waste in task intervals. According to the method, the optimal maintenance frequency and interval are realized by integrating the regular inspection plans and combining the balanced maintenance strategy. According to the method, a discrete event simulation model is adopted, a fleet maintenance plan is subjected to multi-dimensional simulation and optimization, and an efficient maintenance task allocation scheme is formulated according to constraint conditions such as fleet scale, flight hours, task types and hangar resources. Through optimization of a maintenance task distribution mode and fine adjustment of task intervals, the maintenance cost of a fleet can be effectively reduced, and the airworthiness and the utilization rate of aircrafts are improved. The method provides a scientific basis for an airline company to make a maintenance decision, and has an important application value for optimizing the operation and maintenance cost of an aviation fleet.

This invention discloses a method, system, and storage medium for electric vehicle charging scheduling based on dynamic data fusion, relating to the field of data processing technology. The method includes the following steps: acquiring the status monitoring parameters of all standby charging interfaces and filtering out available charging piles that meet the requirements; classifying vehicles into charging-priority vehicles and cost-priority vehicles based on the real-time status data and environmental parameters of the electric vehicles requesting charging; for charging-priority vehicles, adjusting the charging pile search range, introducing output power to determine the target charging pile and performing a locking operation; for cost-priority vehicles, calculating the cost optimization coefficient based on arrival time prediction and electricity price fluctuations within the time period, and generating a preferred charging pile sequence. The user finally selects and locks the charging pile, while dynamically adjusting the locking time according to road congestion. This method achieves efficient allocation of charging pile resources, optimizing user experience and economic costs.

The invention provides a cross-day two-stage random scheduling method for an industrial park integrated energy system, and the method comprises the steps: fitting the output fluctuation characteristics of new energy power generation equipment in a cross-day time scale based on historical data, and constructing an uncertainty scene set containing a new energy prediction error; establishing a cross-day two-stage stochastic programming model; according to the two-stage model, an optimal scheduling scheme is solved so as to minimize the overall operation cost, and the operation cost comprises the demand electric charge, the electricity purchase electric charge, the new energy power abandoning cost, the unit start-stop cost and the standby penalty cost; and on the basis of tie line power constraint and dynamic response characteristics of the multi-energy coupling equipment, feasibility verification and rolling optimization adjustment are performed on the scheduling scheme. According to the method, the new energy consumption capability of the industrial park integrated energy system under the cross-day time scale can be effectively improved, the total operation cost is reduced, and collaborative scheduling of demand cost optimization and spot market participation is realized.

The invention provides a low-carbon integrated energy system modeling method based on a hybrid energy storage system. And constructing a multi-dimensional constraint and cost optimization objective function, and establishing a global optimization framework of the integrated energy system. Wherein the balance constraint and the assembly constraint ensure dynamic matching of electric / hot / cold / gas multi-energy flow and cooperative operation of equipment, and the problem of unbalanced supply and demand of a traditional system is solved. The carbon emission constraint is divided through a dynamic carbon valence interval, the carbon emission and the carbon cost are in nonlinear correlation, a low-carbon technology path is guided, the green certificate constraint converts excess renewable energy power generation into economic benefits, and dual excitation of environmental protection and economy is formed. And the target function comprehensively optimizes equipment investment, maintenance cost and market income, and drives multi-energy complementation and full life cycle cost optimization. The system is adaptive to renewable energy fluctuation, load change and energy price fluctuation through optimal configuration of system output and equipment capacity, and economical and efficient operation under the low-carbon target is achieved.

The invention provides a learning performance precise teaching management method based on time sequence behavior modeling. The learning performance precise teaching management method comprises the following steps: S1, carrying out multi-modal learning behavior data acquisition; s2, aligning and segmenting the time sequence data; s3, performing knowledge retention rate differential modeling; s4, carrying out time sequence feature extraction; s5, constructing a mixed time sequence model; s6, performing dynamic learning ability evaluation; s7, carrying out adaptive resource recommendation; s8, carrying out cross-correction knowledge diffusion optimization; the method has the following advantages: the method is real-time and accurate; the data is acquired to acquire the intervened closed-loop delay lt; compared with the traditional method, the time is increased by 40 times. And cost optimization: the workload of teachers is reduced by 58% and the resource purchase cost is reduced by 42% through an automation strategy. The scale effect is that federal learning supports ten-thousand-person-level concurrence, and the model updating period is shortened to the hour level from the quarter level. Education fairness: cross-school knowledge diffusion enables the superior rate of weak schools to be improved by 29%.

The invention relates to the technical field of e-commerce logistics and supply chain management, and discloses a multi-channel e-commerce platform order processing optimization system and method, and the method comprises the steps: correcting physical inventory data through a Lagrange dual variable fed back in a previous period, calculating a virtual inventory, and receiving a discrete order flow; constructing a space-time hypergraph model of the to-be-processed order according to the commodity homogeneous attribute and the spatial neighborhood attribute; performing constraint optimization solution on the hypergraph model based on a Lagrangian relaxation algorithm, and generating an optimal order allocation matrix meeting productivity constraints and a Lagrangian dual variable of a current period; and analyzing the distribution matrix to generate an operation instruction, issuing the operation instruction to the physical warehouse, and feeding back the dual variables to the inventory calculation module. According to the method, collaborative operation benefits are mined through the space-time hypergraph, and a negative feedback closed loop with physical capacity pointing to front-end sales is constructed by using dual variables, so that performance cost optimization and flow adaptive control are realized.

The invention discloses a dynamic management method and system for optimizing the full life cycle cost of equipment, and belongs to the technical field of equipment cost management, and the method specifically comprises the steps: collecting equipment operation data in real time, carrying out the preprocessing, generating first collection data, collecting equipment cost data in real time, carrying out the preprocessing, and generating second collection data, and performing evaluation and fault prediction on the health state of the equipment based on the first collection data, and constructing an equipment full life cycle cost model based on the second collection data and the health state evaluation and fault prediction result of the equipment to obtain the operation cost of the equipment in the full life cycle. Dynamically optimizing the life cycle cost of the equipment by using a multi-objective optimization algorithm according to the health condition evaluation and fault prediction result of the equipment and the operation cost of the equipment in the full life cycle; according to the invention, the cost optimization strategy can be dynamically adjusted according to the real-time health condition of the equipment, the fault prediction information and the change of the operation environment.

The invention discloses a 9Ni steel process optimization method based on artificial intelligence, and relates to the technical field of steel production, and the 9Ni steel process optimization method comprises the following steps: collecting multi-source process parameters and optimization target parameters; performing feature processing on the collected data to generate target feature data; constructing a 9Ni steel performance multi-target prediction model based on the target feature data, and training the model until convergence; and constructing a multi-objective optimization function by utilizing the converged 9Ni steel performance multi-objective prediction model, and solving the multi-objective optimization function by adopting a stochastic optimization algorithm under an operation constraint condition and outputting an optimal process parameter combination. According to the method, deep learning modeling, cluster cleaning, feature engineering construction, deep learning and a random optimization strategy are comprehensively applied, a data-driven intelligent process optimization system for the 9Ni steel manufacturing process is formed, the achievement rate of target performance indexes can be remarkably increased, the parameter trial and error period is shortened, and the implementation efficiency of the 9Ni steel manufacturing process is improved. And intelligent adjustment and cost optimization of a process path can be realized.

The invention provides a pipeline route design method based on a single-pipe mathematical model. The method comprises the following steps that a three-dimensional vertical section and a UCS coordinate system are determined according to a live-action three-dimensional model of a pipeline laying target area; establishing a single-tube mathematical model according to the three-dimensional longitudinal section and a UCS coordinate system; according to the single-pipe mathematical model, micro-piping laying is carried out on the pipeline; in the process of laying the micro-piping on the pipeline, calculating a direction similarity index according to the similarity between the pipeline shape and the pipe ditch shape; calculating a stress accumulation index according to the elastic modulus and the inertia moment; and constructing a total cost calculation model based on pipeline laying, and performing cost optimization based on the direction similarity index and the stress accumulation index so as to optimize the pipeline laying micro-piping. The method has the technical effects that the laying design precision and efficiency are remarkably improved, the cost is reduced, and the safety of the system can be enhanced.

The invention provides a method for constructing a container cloud agent based on a MiniMax-Text-01 large model, a medium and equipment, belongs to the technical field of automatic operation and maintenance, and can perform fault detection, flow prediction and resource elastic expansion and contraction according to real monitoring data. The intelligent agent constructed by the invention can automatically pre-judge and give an optimal scheme for capacity expansion or migration when finding that the load of the server is abnormal, so that the frequency and the duration of manual intervention are reduced. In a multi-cloud and hybrid cloud architecture, an intelligent agent can cooperatively manage calculation, storage and network resources on a heterogeneous platform, and cross-cloud load balancing and cost optimization are realized. In addition, the intelligent agent can help a data analysis and machine learning team to better utilize a cloud GPU / TPU cluster and automatically select a proper computing power and storage scheme, so that the training and prediction efficiency of mass data is remarkably improved. In the scene of Internet of Things and edge computing, the intelligent agent can link the cloud and the edge nodes, intelligently dispatch the computing power and bandwidth of each node, and find and correct abnormal data streams in time.

The invention provides a building energy system optimization scheduling method based on active and passive energy storage efficient utilization, and belongs to the technical field of energy management and intelligent control. By integrating various flexible resources, efficient utilization of energy, cost optimization and thermal comfort guarantee are realized. And establishing a four-end prediction model, and accurately predicting energy supply and demand in future 24 hours by combining weather forecast and operation data. Double-stage optimization scheduling is adopted, the heat pump heating water temperature is optimized in the first stage, and equipment such as an energy storage battery is finely scheduled in the second stage. According to the method, multi-objective collaborative optimization is realized, flexible resource potential is fully excavated, calculation complexity is reduced, practical application is facilitated, energy utilization efficiency is effectively improved, operation cost is reduced, thermal comfort is guaranteed, intelligent and efficient development of a building energy management system is promoted, and the method has remarkable economic and social benefits.

The invention discloses a flexible job shop scheduling method and system based on an improved genetic algorithm, and the method comprises the steps: 1, problem modeling: defining parameters and constraint conditions of a flexible job shop scheduling problem, the parameters comprising a machine set, a workpiece set, process information, processing time and decision variables, 2, improved genetic algorithm design, and step 3, executing the scheduling scheme. According to the method, the initial population generation strategy and the multi-target fitness function of the genetic algorithm are improved, so that the global search capability and the convergence speed are improved, the maximum completion time is shortened, the target is optimized in combination with machine load balancing and cost, the resource utilization rate is improved, and the production cost is reduced; the system has flexibility and expansibility, can adapt to flexible workshops of different scales, and realizes real-time adjustment of a scheduling scheme through dynamic monitoring.

The invention belongs to the technical field of wind power operation and maintenance, and discloses a wind turbine generator intelligent maintenance system combined with component residual life evaluation, which comprises a sensing edge layer, a station cooperation layer, a cloud intelligent center, an execution and safety interlocking layer and a credible evidence storage layer. The system collects unit operation and environment data through the edge layer, gathers the data through the station cooperation layer, inputs the data into the cloud intelligent center for multi-domain degradation modeling and life prediction, and determines a maintenance window and a resource scheduling scheme based on a comprehensive cost optimization model. The cloud center realizes cross-station model sharing and self-learning through a federated learning mechanism, the execution layer completes maintenance tasks and safety locking control, and the trusted evidence storage layer performs block chain abstract evidence storage on key data. According to the system, health state evaluation, intelligent maintenance decision making and whole-process credible tracing of the wind turbine generator are realized, and the operation and maintenance safety and economy are improved.

The invention provides an operator-level data consanguinity automatic generation method based on a large model, and relates to the field of electric digital data processing, and the method comprises the steps of S1, heterogeneous script preprocessing and semantic normalization, S2, symbol enhanced LLM consanguinity reasoning, S3, causal logic verification and consanguinity correction, S4, dynamic adaptation, and S5, consanguinity intelligent analysis and visualization. The method is suitable for multi-source, multi-format and multi-language data processing scenes, the automation degree, the refinement capability and the intelligent level of data blood relationship construction can be improved, and more accurate and efficient technical support is provided for data governance, data security, system migration / reconstruction, data quality management and control, cost optimization and the like.

The invention discloses a cloud service cost optimization and prediction analysis system based on artificial intelligence, and relates to the technical field of cloud service cost optimization, and the system comprises a cost feature deep analysis module which accesses a cloud service full life cycle standardized cost data set, and generates a cost efficiency evaluation matrix and an abnormal attribution report; the multi-modal prediction engine module is used for constructing a multi-scene cost prediction model; the dynamic optimization decision module is used for generating a resource dynamic scheduling strategy, a service type selection optimization scheme and a cost budget dynamic allocation plan, and calculating an expected cost saving rate and a risk coefficient of each scheme; and the closed-loop iteration upgrading module tracks the implementation effect of the optimization scheme in real time and updates the three-dimensional characteristic spectrum and prediction model parameters. According to the invention, through a three-dimensional characteristic spectrum, multi-algorithm fusion prediction, SLA constraint verification and a closed loop iteration mechanism, and by matching with an intelligent interaction visualization and early warning module, intelligent transformation of the cloud service cost from passive accounting to active prediction and from experience optimization to scientific decision is realized.

The invention discloses a switching power supply intelligent aided design method and system based on RAG and AI Agent. Comprising the steps that S1, an RAG knowledge base is constructed, domain data collection and data preprocessing are included, and domain knowledge constraint is carried out on a large language model LLM; s2, constructing a switching power supply AI intelligent agent, and combining and constructing a complete cue word project by utilizing an input demand to cooperate with the RAG knowledge base; each step of the switching power supply AI intelligent agent interacts with the RAG, the basis of large model reasoning is enhanced, and the illusion effect of a large model can be remarkably reduced; the design result is audited and checked according to the item-by-item rules through the large model, and it is ensured that the design result meets the standard and the product design specification; the design of the switching power supply is constrained through domain knowledge and environment knowledge so as to solve the illusion influence of a large model; and outputting a BOM list compatible with purchase feasibility and cost optimization by using ERP data and component mall data.

The invention discloses a global user and manufacturer oriented end-to-end online customization manufacturing platform system and a method thereof, which realize the whole process from creative input to finished product delivery based on ai-driven design generation and global manufacturing resource intelligent scheduling. The platform comprises an AI design generation module, a CAD modeling and optimization module, a manufacturing rule verification module, a material intelligent recommendation module, a global factory matching and pricing module, a tax and logistics estimation module, a one-key ordering and delivery module, an intellectual property protection module and a user and factory authentication mechanism module. And a multi-part structure decomposition and remote part division type cost optimization scheduling module. A user can generate a product drawing through language or image input, a platform automatically outputs a CAD model and performs structure optimization and manufacturability verification, and materials are recommended in combination with a purpose scene. The system intelligently matches global manufacturers based on factors such as features, manufacturing difficulty, regions, tax and productivity, estimates the tax and logistics cost, and provides a one-key ordering path.

The invention relates to the technical field of green electricity transaction industry chain optimization, in particular to a green electricity transaction industry chain dynamic optimization method based on multi-factor evaluation, which realizes accurate description of a green electricity transaction industry chain network structure through multi-dimensional node attribute definition and two-way relation edge construction. By distinguishing the quantitative logic of the transaction relation edge and the influence edge and combining with the comprehensive association strength, the unified adjacency matrix is constructed, the economic cooperation and implicit decision conduction relation between the nodes in the industrial chain can be comprehensively captured, a network model basis fitting an actual service scene is provided for subsequent optimization, and the economic cooperation and implicit decision conduction relation between the nodes in the industrial chain can be comprehensively captured. According to the method, efficiency and fair targets are creatively integrated into a dynamic optimization framework, collaborative balance of industrial chain efficiency improvement and fair guarantee is realized by scientifically designing a target function and introducing a multi-target optimization algorithm, the efficiency target focuses on resource configuration and cost optimization, and the fair target pays attention to edge node accessibility and association balance.