418 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.

The invention discloses a safe trajectory tracking method and system for a ground differential drive robot, and particularly relates to the technical field of robot operation management. Comprehensive means of multi-source data fusion, dynamic safe distance setting, trajectory prediction uncertainty quantification and trajectory deviation cost optimization are introduced; the path planning capability of the robot in a complex dynamic environment is improved; the behavior characteristics and environment complexity of the dynamic obstacle are evaluated in real time, the safe distance is dynamically adjusted, and the dynamic obstacle area is accurately quantified in combination with the trajectory prediction model, so that the problems of low trajectory tracking precision and insufficient obstacle avoidance robustness in the background technology are solved; the path optimization is realized through the minimization strategy of the trajectory deviation cost function, the operation safety of the robot in diversified scenes is ensured, and meanwhile, the efficiency and stability of path planning are greatly improved.

The invention relates to the technical field of construction industry project cost, and provides a project cost management system based on big data, the core of the system is to integrate a big data technology and comprehensively manage project cost, data management covers links of acquisition, storage, analysis and maintenance, data quality and safety are ensured, and a foundation is built for cost management; the accurate cost analysis uses multiple algorithm models to deeply mine data association, so that accurate prediction and fluctuation reason analysis are realized, and cost optimization is assisted; the efficient business logic layer and the user interaction layer cooperatively operate, the business function is complete, the process is smooth, the interface is friendly, the visualization is strong, and a feedback channel is arranged to promote optimization; data acquisition is wide, storage is efficient, and the method is far better than a traditional scheme; the analysis depth and the visualization effect are prominent, and a more valuable decision basis is provided; the system is complete in business function and scientific in management, improves enterprise cost control and decision-making capability, and is good in user experience and high in safety.

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 provides a business travel itinerary recommendation and cost optimization method and system based on big data, and relates to the technical field of big data, and the method comprises the steps: obtaining the real-time traffic information and hotel information of a destination city; a machine learning model is utilized to analyze user travel characteristics, traffic information is intelligently screened, and a traffic scheme conforming to enterprise travel standards is generated. And determining an accommodation time interval according to the traffic scheme and the business activity time, and screening candidate hotels. And performing time sequence analysis on the air ticket price and the house fee, predicting a price fluctuation trend, determining the optimal reservation time, and generating a business travel itinerary initial scheme. And finally, pushing a travel confirmation request to a related contact person, dynamically optimizing the initial scheme according to feedback information, and outputting a final travel travel scheme with the lowest total cost. According to the invention, big data analysis, machine learning and time sequence analysis technologies are combined to intelligently recommend the business travel scheme which accords with enterprise business travel standards and has the lowest cost, so that the business travel efficiency is improved, and the business travel cost is reduced.

The collection, communication, storage, monitoring, and analysis of environmental, vehicle, charger, and charging session data over a single EV or over a fleet of EVs is used to enhance or optimize existing services as well as deliver new services based on analysis of the collected data. One such service is reduction in total cost of travel. The cost minimization is achieved with near-real-time data, models populated using past collected data, and knowledge of energy costs and of chargers located in different geographic areas with potentially limited power for charging available at each charger site or station. A fleet management application used for cost minimization optimizes cost of travel for a single EV or for an entire fleet. Using individual EVs cost optimization as a goal, each EV may be optimized for potential local minimum cost of travel based on predictions using data collected from the fleet or from multiple fleets.

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 multi-microgrid operation optimization method considering supply and demand flexible double responses. The method comprises the following steps: establishing a cost optimization model of multi-microgrid electric energy cooperation sharing; a gas turbine, a waste heat boiler and an organic Rankine cycle power generation device are introduced to construct a supply side response model for flexible output of electricity and heat energy of the cogeneration unit; a demand side establishes a demand side flexible response model by considering transferability and replaceability of the electric heating load; and a multi-microgrid electric energy sharing cooperative operation model is established based on the Nash negotiation theory, and then the model is decomposed into a microgrid alliance benefit maximization sub-problem and a benefit distribution sub-problem. And in the cooperative income distribution sub-problem, the micro-grids negotiate with each other by taking the electric energy contribution and the marginal contribution degree of the micro-grids in cooperation as bargaining capabilities, so that fair distribution of cooperative income is realized. According to the optimization method provided by the invention, the advantages of flexible supply and demand and resource coordination and complementation can be brought into full play, and the condition of unreasonable income distribution of each micro-grid in the multi-micro-grid cooperation system is improved.

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.

The invention provides an energy storage capacity optimal configuration method based on multiple time scales, which solves the coordination problem of power dispatching supply and demand balance and energy storage system investment decision under multiple time scales through a designed multi-target double-layer optimization model, and optimizes and synthesizes the stability and economy of a renewable energy system. According to the method, the problem of renewable energy fluctuation under long and short time scales is considered firstly, an inner-layer model with minimum power fluctuation under the short time scale is constructed by taking the minimum real-time scheduling error as a target function, and supply and demand stability is ensured in hour-level short time scale scheduling. In day-level medium and long term time scale scheduling, an outer layer regulation and control model under a medium and long term time scale is constructed by taking intra-day scheduling power balance and minimum economic cost as objective functions, and renewable energy fluctuation stabilization and energy storage system cost optimization under multiple time scales are ensured.

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 discloses a batch interrupt task processing method and device, equipment, a medium and a product, and relates to the field of distributed technologies. The method comprises the following steps: determining service types of batch services to be handled by a user; acquiring historical interrupt data of the service type; inputting the historical interruption data into a pre-trained interruption prediction model to obtain interruption prediction data of the service type in a preset duration after the current time; and performing interruption processing on the batch services to be handled based on the interruption prediction data. According to the technical scheme provided by the invention, the service interruption risk in the future preset duration can be pre-judged in advance, efficient resource configuration and cost optimization can be realized, the satisfaction degree of a user on batch task execution is improved, and the service continuity and stability are enhanced.

The invention belongs to the technical field of electric power system analysis, and mainly relates to a new energy consumption-oriented high-capacity energy storage partition optimization configuration method, which comprises the following steps of: firstly, dividing a power grid model into a plurality of regions according to geographic and electrical characteristics of the selected power grid model, and determining regional energy storage requirements after determining partitions of the power grid model; further constructing a total cost optimization scheduling model of the maximum new energy consumption and the minimum energy storage system, determining constraint conditions of the target according to a load balance constraint, an energy storage system capacity constraint and a new energy processing limit, processing by using a mixed integer linear investment and operation cost planning method, and solving an optimal solution through iteration; more importantly, after the optimization scheduling model is solved preliminarily, the energy storage configuration function of each region is adjusted in real time according to time, system load and new energy consumption amount by updating the energy storage configuration step by step, and finally, the precision of energy storage configuration parameters is guaranteed.

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 discloses a repetitive construction project cost optimization method, and particularly relates to the field of project construction. Comprising the following steps: S1, parameter setting: initializing the starting time of a time window and the number of algorithm iterations; s2, generating an initial construction plan; s3, if the starting time of the time window is larger than the difference between the construction period of the initial construction plan and the length of the time window, the starting time of the time window is set to be 0; s4, generating a corresponding relaxation problem by adopting a rolling time domain method; s5, solving the relaxation problem in the step S4 by using a constraint programming method; s6, updating the number of iterations, and updating the starting time of the time window; and S7, repeating the steps S3-S6 until an iteration condition is met, and outputting an optimal solution in an iteration process. By the adoption of the technical scheme, the problem that an existing construction plan compilation method with cost as an optimization target cannot be suitable for different construction scenes is solved, and meanwhile, when medium and large-scale cases are solved, an approximate optimal solution can be obtained within reasonable time.

The invention discloses a deep learning-based batching model collaboration method and equipment for two-step stainless steel smelting and a medium. Comprising the following steps: S1, acquiring historical raw and auxiliary material usage amount data, historical energy medium usage amount, tapping molten steel components, tapping molten steel temperature, raw and auxiliary material cost and energy medium cost data, constructing a data set, and dividing the data set into a training set, a test set and a verification set; s2, obtaining calculation data of raw and auxiliary materials and an energy medium of the batching model; s3, constructing batching model collaboration based on cost optimization; s4, inputting the calibrated batching model calculation data into a batching model based on cost optimization for cooperation, and outputting predicted tapping molten steel components, tapping molten steel temperature and batching prediction cost; s5, collecting current raw and auxiliary materials, energy medium usage amount data and cost in real time; and S6, based on deep learning, realizing cost-optimal batching model cooperation, and performing operation according to the corrected calculation data of the raw and auxiliary materials and the energy medium.

The invention discloses a design cost optimization method, system and device for a real estate project and a medium. The method comprises the steps that a plurality of product modular units are generated in a target engineering project; associating the product modular unit with a project unit identifier; in the target engineering project, obtaining a corresponding project product list library and a contract planning list; taking each product modular unit as a basic unit, determining a corresponding design cost influence factor according to the corresponding project product list library and the contract planning list, and performing engineering project cost budgeting to obtain a project cost budgeting result; in the planning and construction process, obtaining project construction data and actual construction cost of each product modular unit; obtaining a corresponding budget cost early warning interval based on the project unit identifier, judging whether the actual construction cost is within the corresponding budget cost early warning interval, and obtaining a budget cost evaluation result; according to the invention, the cost budgeting accuracy of the real estate project is improved.

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 discloses a design method for a steel bar component. According to the method, a large amount of historical design data can be quickly processed and analyzed through the machine learning model, and key factors influencing axial force reinforcement calculation are automatically identified, so that the calculation efficiency is improved. The machine learning model automatically completes calculation through an algorithm, and errors in the calculation process are reduced. The fully trained machine learning model can accurately predict the reinforcing steel bar area based on the input parameters, and the design accuracy is improved. A machine learning model can discover a hidden relationship between data by analyzing historical data, which is particularly important for reinforcement calculation caused by axial force because the axial force is related to various factors, such as structure size, material characteristics and the like. Machine learning is applied to the reinforcement calculation process caused by the axial force, the intelligent level of design can be remarkably improved, the design efficiency and quality are improved, and meanwhile powerful technical support is provided for cost optimization.

The invention relates to the technical field of inventory optimization, and discloses a dynamic inventory optimization method, which is used for dynamically determining an industrial optimal inventory. Through real-time data collection and preprocessing, accurate demand prediction and a dynamic replenishment strategy, an enterprise can quickly respond to market changes, and the risks of stock overstock and stockout are effectively avoided. Meanwhile, the inventory cost is further reduced through the dynamic safe inventory strategy and economic order quantity optimization calculation, and the fund utilization efficiency is improved. In addition, the method further pays attention to inventory minimization and cost optimization, and effective control of the total cost is achieved by optimizing a supply chain and formulating an inventory minimization strategy. And the accuracy of inventory data is ensured through periodic inventory and difference analysis, and the transparency and credibility of inventory management are improved.