258 results about "Cost optimization" patented technology

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.

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 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 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.

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 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 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 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 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 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.

The invention discloses a multi-target cost optimization scheduling system for a wind-light-gas storage micro-grid, and relates to the technical field of micro-grid scheduling, and the system comprises an equipment health parameter collection module which collects the operation parameters of wind-light-gas storage equipment through a sensor, and transmits the operation parameters to a health state evaluation module after preprocessing; the module constructs a multi-dimensional model to calculate a health index and predict an attenuation trend; an aging loss cost quantification module constructs a dynamic model to calculate the cost; the multi-target optimization scheduling module sets multiple targets and searches an optimal strategy; the strategy execution and feedback module executes the instruction and returns data; according to the invention, by constructing a linkage mechanism of the equipment health parameter acquisition module and the health state evaluation module, real-time perception and quantitative analysis of the state of the wind-light-gas storage micro-grid equipment are realized, and a multi-dimensional stress-fatigue accumulation model is adopted to calculate the health index of the equipment and divide the health level. The problems that in a traditional method, health assessment is high in subjectivity, and stress accumulation influences cannot be quantified are solved.

An improved computerized e-commerce system, optimized for smaller merchants such as florists who often provide seasonal perishable gifts that require local delivery, and who often desire to form cooperative networks with local merchants offering related gift services, as well as other related merchants such as other florists in more distant locations. The web server based system allows merchants to easily set up non-static (time variable) websites that automatically provide schedule driven promotions. New products can be quickly uploaded from smartphones, and sophisticated time and location aware algorithms can compute accurate delivery costs and make such costs transparent to customers. The system may also include backend software configured to assist in product production, inventory control, product cost optimization, and product delivery optimization, management, and tracking.

The invention provides an adjustable resource real-time control method suitable for a port shore power energy system. The method comprises the following steps: step (1), modeling a key energy sub-module in the system; (2) constructing a prediction model by adopting a generalized regression neural network; (3) an economic layer is used for dynamically constructing an economic cost optimization model according to the real-time electricity price, the battery state and the photovoltaic output condition after the short-term prediction load curve is obtained, and generating an optimal reference charging and discharging track of the energy storage system; and step (4), a control layer is an execution layer in the energy management system, a model prediction control method is adopted, an optimal control strategy is calculated in real time according to the optimal state generated by the economic layer, and accurate tracking and power balance adjustment of the state of the energy storage system are realized. The invention provides the adjustable resource real-time control method suitable for the port shore power energy system, and the method has good prediction precision, economic optimization capability and control responsiveness.

The invention relates to the technical field of network security asset risk pricing and management, in particular to a business influence-oriented network security asset risk pricing and management method and system. Historical business data, infrastructure resource data and external environment data are integrated to construct a business-resource portrait library; a potential interruption event prediction model is trained based on a random forest or a long short-term memory (LSTM) network, a multi-target cost optimization function including resource preset cost, service interruption loss cost and resource idle penalty cost is constructed, and an optimal resource preset strategy is solved by adopting a non-dominated sorting genetic algorithm NSGA-II or a particle swarm optimization algorithm. And after execution, feedback data update models and functions form closed-loop optimization. According to the method, accurate risk prediction, dynamic cost balance and continuous strategy adaptation are realized, the service continuity is effectively guaranteed, the overall operation cost of an enterprise is reduced, and the pertinence and effectiveness of network security asset risk pricing and management are improved.

The invention provides a school canteen procurement cost actuarial system based on image recognition, and relates to the technical field of image recognition, and the system comprises a time-sharing image group recognition module which is used for determining the image collection frequency according to the dining busy degree, and carrying out the recognition of the characteristics of a crowd, and obtaining different people groups. And the group consumption actuarial module is used for establishing a consumption prediction model, inputting a personnel dining image for prediction to obtain personnel consumption predicted amount, and calculating predicted consumption cost in combination with the purchase cost parameter. And the consumption difference correlation analysis module is used for analyzing the comparison consumption by using a consumption difference multi-dimensional attribution method to obtain a difference reason and finding out a correlation difference relationship. And the cost optimization decision module is used for making a purchase decision according to the optimized consumption cost scheme. According to the method, the accuracy of crowd identification and the accuracy of food material consumption prediction are improved, the association relationship between the consumption and the difference reason is clearly compared, a targeted basis is provided for optimizing the purchase cost, and the cost accounting precision is improved.

The invention discloses an online temporary structure intelligent aided design method and system based on a big language model, and the method comprises the steps: receiving a user natural language temporary structure design demand, a big language model recognition intention, extracting parameters, complementing missing parameters in combination with a specification and a knowledge base, verifying the reasonability, and supporting the interaction adjustment. And forming a standardized design parameter set. And synchronously generating a BIM model and an FEM model based on the parameter set, submitting the FEM model to a calculation engine for analysis, and associating the BIM model through a homologous ID to realize result visualization. If the result reaches the standard, an optimization suggestion is given in combination with multi-source data, and a user selects whether to optimize or not; and if not reaching the standard or receiving a cost optimization instruction, directly optimizing. A large model is utilized to generate a scheme analysis text, a standardized calculation book is automatically assembled, and multi-level auditing is started and traces are reserved. And storing the approved scheme into a knowledge base in a structured manner to form a knowledge closed loop. And the efficiency, accuracy and standardization level of temporary structure design are improved.

The invention discloses a clothing multi-mode collaborative design method driven by small-number ornaments. The clothing multi-mode collaborative design method specifically comprises the steps of receiving an ornament plane image uploaded by a user, extracting an ornament main body contour through an image segmentation network, executing three-level attribute classification in parallel based on contour features, and generating a structured feature matrix M; inputting the feature matrix M into a copyright filtering module, and outputting a costume design constraint set; encoding the constraint set C into embedded vectors with the same dimension as the feature matrix M, performing LayerNorm standardization and splicing, inputting the embedded vectors into a generative adversarial network, and outputting a matching degree score S; performing process decision based on the matching degree score S, including: generating a cost optimization model in combination with the version complexity and the fabric unit price; the metal ornament is associated with anti-wrinkle fabric, and a laser cutting process is started for a special-shaped outline; and outputting a cutting scheme and a bill of materials which can be directly put into production.

The invention discloses a power enterprise agent platform based on a large language model, and relates to the technical field of artificial intelligence and power systems, and the platform comprises a data governance module, a model deployment module, a platform building module and an application development module. Through the technical path of large model enabling, agent landing and electric power scene deep adaptation, the core pain points of a traditional electric power enterprise in the aspects of efficiency, safety, data utilization, service quality and the like are systematically solved, four dimensions of intelligent operation and maintenance, data-driven decision, user experience upgrading and cost optimization are covered, and the method has the advantages of being high in practicability and easy to popularize. The digital competitiveness of a power enterprise is remarkably improved, and the requirements of a novel power system for high automation, high robustness and agile response are met; the intelligent agent and the workflow are effectively combined together, a single automation mode is replaced, complex task processing time is shortened, conventional task efficiency is improved, and flexibility and execution stability are balanced.

The invention discloses a self-pick-up distribution method based on customer self-pick-up behavior dynamic modeling and a space-time replenishment strategy, and the method comprises the steps: obtaining customer self-pick-up behavior data, building a model based on truncation normal distribution, and determining a probability density function and a probability function; establishing a self-pickup cabinet available capacity and replenishment model, calculating the residual capacity at each moment, and determining replenishment time and vehicle waiting time and cost; a self-pickup distribution mathematical model with the minimum operation cost as a target function is constructed, and self-pickup cabinet site selection, capacity planning, replenishment decision making and vehicle path optimization are carried out; and designing a two-stage heuristic algorithm for solving, generating self-pickup cabinet site selection, capacity grade configuration, replenishment opportunity decision and vehicle path planning schemes, and realizing operation cost optimization. Through dynamic modeling and a space-time replenishment strategy, the space-time utilization rate of the self-service cabinet is improved, the operation cost is reduced, and an efficient solution is provided for express terminal distribution.

The present invention discloses an intelligent switching system for an LNG and CNG dual-use pressure regulating box, which relates to the technical field of LNG and CNG pressure regulating boxes. The present invention will ensure the stability of gas supply and cost optimization based on the data and market price information provided by the sensor module. At the same time, multiple sensors comprehensively monitor the gas source and environment, provide comprehensive data support, and greatly improve the system perception capability. The remote communication unit allows operators and maintenance personnel to monitor the system operation status in real time, and can remotely control and make decisions to improve the response speed and efficiency of the system. The system has a built-in optimization algorithm, which automatically adjusts the gas supply strategy according to different objective functions and constraints, including switching time, switching frequency, and pressure regulation parameters, to reduce costs, improve benefits or maintain system stability. The data analysis and optimization module is then used to accurately formulate the gas supply strategy, maximize economic benefits, and ensure the stability and safety of the system.

The application discloses a cost-optimized out-of-season clothing renovation design method and an intelligent processing system, relates to the field of clothing intelligent design systems, and has the technical scheme as follows: a data acquisition module, which collects original data of out-of-season clothing and popular trend matching accurate marketing landing; a data analysis and prediction module, which evaluates potential risks and feasibility in the renovation process based on the collected data; a design scheme generation module, which automatically generates multiple feasible renovation design schemes according to the data analysis results and personalized requirements input by a user, and determines the best design scheme; and the application integrates a complete closed-loop process from data acquisition to evaluation, combines an artificial intelligence deep learning algorithm to complete demand prediction, design scheme generation and optimization, and significantly improves production efficiency and reduces artificial dependence.

This invention discloses a method for optimizing the cost management of flue gas pollution control, comprising: Step 1: generating a set of operating units for control equipment; Step 2: generating a sequence of combined energy consumption paths; Step 3: inputting the combined energy consumption path sequence into an improved Koopa model; Step 4: generating initial path features through a path embedding module and generating steady-state evolution features through a steady-state evolution coding module; Step 5: generating rearranged path features through a disturbance track rearrangement module using a smoke load tidal migration mechanism and generating a path cost evolution sequence through a cost trend output module; Step 6: performing weighted splitting of path affiliation to generate single-path control cost units; Step 7: performing emission contribution mapping to generate a path contribution sequence and using the MCTS algorithm for tree search to generate a control path adjustment set; Step 8: outputting the cost optimization results. This invention combines the improved Koopa model and the MCTS algorithm to achieve dynamic optimization of flue gas control paths.

The invention belongs to the technical field of enterprise management, and particularly relates to a green transformation cost optimization method and system based on green premium, and the method comprises the steps: collecting the specified parameters of a target enterprise, so as to determine the green premium cost, the green premium cost comprises self-generation and self-use cost, special line direct supply cost, green electricity direct purchase cost, green electricity transaction participation cost, green certificate cost and green electricity investment cost; and formulating or optimizing an existing green transformation method according to the green premium cost so as to minimize the green transformation cost. Collecting specified parameters of the target enterprise to determine green premium cost; and according to the green premium cost, an existing green transformation method is formulated or optimized, so that the green transformation cost is minimized, reasonable suggestions can be provided for transformation of enterprises, and the transformation efficiency is improved.