42 results about "Hybrid genetic algorithms" patented technology

The invention relates to the technical field of engineering management, and discloses an engineering management resource optimization configuration platform, which comprises the following modules: a data acquisition and management module used for acquiring real-time state data of manpower, equipment and materials in an engineering project through Internet of Things equipment and sensors; the resource pooling and sharing module is used for carrying out centralized management on resources in the engineering projects, constructing a cross-project resource pool and carrying out resource sharing according to project requirements and resource states; and the intelligent scheduling and optimizing module is used for dynamically scheduling resources based on project requirements, task priorities and task dependency relationships. By adopting an intelligent scheduling method combining a hybrid genetic algorithm and a particle swarm optimization algorithm and a deep reinforcement learning dynamic scheduling module, resources can be dynamically allocated according to project requirements and task priorities, the utilization rate of the resources can be maximized, the phenomenon that the resources are idle or reused is reduced, and efficient configuration of the resources is ensured.

The invention relates to a joint estimation method for SOC and SOH of a power battery, in particular to a joint estimation method for SOC and SOH of a lithium ion power battery based on FOASEKF-EKF, comprising fractional order equivalent circuit models of two parallel fractional order CPE branches, and providing a hybrid genetic algorithm HGA fusing a differential evolution strategy and an adaptive variation mechanism. Accurate estimation of SOC and terminal voltage under a fast time scale is realized by introducing a sliding-mode observer and an FOASEKF, periodic online correction is performed on model parameters and battery capacity based on an EKF under a slow time scale, and high-precision and high-robustness battery SOC and SOH joint estimation is realized. The method is suitable for complex industrial environments such as electric automobiles and rail transit, does not need to set a large number of hyper-parameters, does not excessively depend on the quality and quantity of data, has good interpretability, adaptability and engineering practicability, can still achieve high-precision cooperative estimation of SOC and SOH especially under the working conditions of frequent start and stop and unsteady operation, and has good application prospects. And misjudgment and drift estimation risks are obviously reduced.

The invention discloses a private generation type large model optimization system based on a hybrid genetic algorithm, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of how to compress the model scale, simplify the feature dimension to improve the reasoning efficiency and maintain the model precision as much as possible. Comprising a multi-source heterogeneous data fine adjustment module, a large model construction module, a hybrid genetic algorithm optimization module, a diffusion type encoder feature extraction module, a course type noise robustness improvement module, an activation function optimization module and a feature dimension reduction and low-rank compression module, the large model is gradually and deeply optimized from the aspects of data, structure, training, robustness, activation function and model compression, and a generative large model which can fully learn financial data of the new energy vehicle enterprise and is excellent in performance, reliable in robustness and friendly to resources is produced.

The invention provides a multi-layer cloud architecture intelligent traffic resource allocation method, a storage medium and an electronic device, the multi-layer cloud architecture comprises an edge cloud, a region cloud and a center cloud, and the method comprises the following steps: the edge cloud performs resource allocation on a received edge task by adopting a genetic algorithm; the regional cloud adopts a particle swarm optimization algorithm to carry out resource allocation on the received regional tasks; the central cloud performs resource allocation on the received central task by adopting a hybrid genetic algorithm and a simulated annealing algorithm; and the central cloud monitors the resource allocation results of the edge cloud and the regional cloud, and dynamically adjusts the resource allocation of the edge cloud and the regional cloud according to the monitoring results. According to the technical scheme, the resource utilization rate is effectively improved, the task completion time is shortened, and the real-time response capability and the overall performance of the intelligent traffic system are remarkably improved.

The invention belongs to the field of computational lithography, and particularly discloses a lithography model calibration method based on a proxy model assisted hybrid genetic algorithm, which comprises the following steps: determining to-be-optimized parameters of a lithography model, and generating an initial population according to the to-be-optimized parameters; performing parallel simulation on the current population to obtain a photoetching model simulation result of the current population; performing parallel evaluation on the current population to obtain photoetching model evaluation indexes and fitness distribution of the current population; screening excellent individuals in the current population to construct an agent model, and performing optimal prediction to obtain a first type of individuals; screening excellent individuals in the current population and carrying out crossover and variation to obtain a second type of individuals; and combining the first type of individuals and the second type of individuals, carrying out parallel simulation and evaluation on the next-generation population, and carrying out loop iteration updating until a preset termination condition is met, so as to obtain a photoetching model parameter set of the optimal individual. According to the invention, the calibration process of the photoetching model can be simplified, and the automation, precision efficiency and global search capability are improved.

The invention relates to a hybrid flow shop scheduling robust optimization method and device based on a hybrid genetic algorithm, which can realize the improvement of the robustness of a hybrid flow shop scheduling scheme, emphasizes the mean value performance while paying attention to the worst situation, and has relatively strong capabilities of global search and jumping out of a locally optimal solution. The method comprises the following steps: determining a hybrid flow shop scheduling problem mathematical model based on robust optimization; constructing a first optimization algorithm based on a genetic algorithm according to the constraint of the hybrid flow shop scheduling problem; constructing a second optimization algorithm based on a simulated annealing algorithm framework; constructing a scheduling scheme evaluation method based on the stochastic simulation algorithm and considering the worst situation and the average performance; and fusing the first optimization algorithm and the second optimization algorithm to obtain a target optimization algorithm, and performing scheduling scheme optimization based on the target optimization algorithm and the scheduling scheme evaluation method to obtain an optimal scheduling scheme.

The invention discloses a low-carbon supply chain management method, relates to the technical field of supply chain management, and solves the problem that an existing cold-chain logistics scheme does not consider carbon emission and enterprise development demands. Calculating user dissatisfaction, carbon emission and total cost; determining a constraint function by taking user dissatisfaction, carbon emission and total cost minimization as objective functions, and constructing a low-carbon logistics model; solving the low-carbon logistics model according to an improved hybrid genetic algorithm to obtain an excellent logistics scheme set; obtaining a target function weight matrix, adaptively adjusting the target function weight matrix according to the target function mean value, and screening through the adjusted target function weight matrix to obtain an optimal logistics scheme; and building a low-carbon logistics model by comprehensively considering user dissatisfaction, carbon emission and total cost, and screening an optimal logistics scheme meeting enterprise development requirements from an excellent logistics scheme set based on a set target function weight matrix.

The invention relates to the field of logistics distribution, and particularly provides a shared electric bicycle battery distribution path planning method based on an improved hybrid genetic algorithm. In order to solve the problems of low distribution efficiency, high operation cost, unreasonable resource scheduling and the like of the existing shared electric bicycle system (EBSS) battery management, the invention constructs a battery distribution optimization model considering the demand urgency degree. According to the method, firstly, through data analysis and literature research, an evaluation index system of the battery demand urgency degree is established, and the evaluation index system comprises the number of power-shortage electric bicycles, the human traffic, the regional population density and the number of available vehicles at a station; and quantifying the demand urgency degree of each station by adopting an entropy weight TOPSIS method, and determining the battery distribution priority according to the demand urgency degree. In the aspect of optimization scheduling, the invention provides an improved hybrid genetic algorithm (SVGA), and by combining a simulated annealing criterion and a variable neighborhood search strategy, the global search capability of the algorithm is improved, and local optimum is avoided. Experimental results show that compared with a traditional genetic algorithm (GA) and a simulated annealing improved genetic algorithm (SAGA), the SVGA has remarkable advantages in the aspects of solution cost, calculation time and solution stability. According to the invention, intelligent optimization of shared electric bicycle battery distribution is realized, the resource utilization rate is improved, the operation cost is reduced, and an innovative solution is provided for efficient management of EBSS.

The invention relates to the technical field of intelligent optimization of discrete combination problems, in particular to a distributed heterogeneous machining workshop order allocation method and system based on simulation, workshops are accurately described by constructing simulation models of the machining workshops, and a hybrid genetic algorithm is combined with simulation to find order allocation schemes meeting requirements, so that the optimization of the machining workshops is realized. According to the method, a factory delivery date relaxation calculation mode is improved, maximization and minimum factory delivery date relaxation are taken as a target, a high-quality order allocation scheme is efficiently generated, whether each order can be completed in time or not is verified through production simulation of each machining workshop, an order transfer operation is designed to adjust overdue order allocation, and the order allocation efficiency is improved. Compared with a traditional method, the efficiency of processing heterogeneous workshops is higher, order allocation of simulation verification is more real and reliable, the resource utilization rate and the on-time completion rate can be improved in a complex distributed manufacturing scene, the order delay risk is reduced, and the method is suitable for the field of multi-factory collaborative production scheduling.

The invention belongs to the technical field of power system operation and planning, and discloses a power grid net load fluctuation scene generation method, system and device based on an ARIMA and Copula combined model and a medium, so as to solve the problem of poor scene generation accuracy. The method comprises the following steps: decomposing and reconstructing an original time sequence of the net load of the power grid by using discrete wavelet transform; taking permutation entropy minimization as an optimization target, adopting a variable chromosome length hybridization genetic algorithm to divide time segments for the low-frequency linear subsequences, and respectively establishing ARIMA models to generate linear trend scenes; establishing a joint probability distribution model of the high-frequency fluctuation subsequences at adjacent moments based on a Copula function, and deducing conditional probability distribution in combination with a Bayesian formula to generate a fluctuation scene; and the linear trend scene and the fluctuation scene are superposed to form an initial net load scene set, and a k-means clustering algorithm is adopted to reduce the initial net load scene set to obtain a representative scene set.

The invention discloses a medical image feature selection method based on population tree search and hybrid intelligent heredity. The method comprises the following steps: acquiring a to-be-processed medical image feature data set; initializing a population tree; evaluating and calculating the fitness; and executing branch evolution, starting from the current tree node, and generating a plurality of sub-generation branches in parallel. Each branch generates a new population by applying a hybrid genetic algorithm to the parent medical image feature subset population; analyzing statistical summary information of all filial generation branches by using a decision model; and performing depth-first search on the population tree until a preset termination condition is met. And finally, outputting a medical image feature subset with the highest fitness found in the whole search process as an optimal feature selection result. According to the method, the problems that a traditional feature selection method is prone to falling into local optimum in high-dimensional data and lacks intelligent guidance are solved, and the global search capability, the convergence speed and the solution quality of the feature selection process are improved.

The present invention discloses a computing offloading method based on a hybrid genetic algorithm in mobile edge computing, including: S1. Establishing a system model to obtain the computing delay of subtask sets on each processor and the transmission delay between processors, and determining the task layer values of each task in the subtask set according to the constraint relationship of the subtask set; S2. Initializing the population according to the determined task layer values and random strategy to obtain the initial population individuals of the subtask set, and performing symbolic encoding to obtain a task scheduling sequence, and optimizing the individuals in the initial population; S3. Constructing a fitness evaluation function and performing a selection operation on the individuals in the optimized initial population; S4. Constructing a crossover mechanism and performing crossover on the individuals in the new population using a crossover operation based on a tabu list search algorithm; S5. Performing a mutation operation on the individuals in the new population using a mutation operation based on a simulated annealing algorithm; S6. Judging whether the iteration step size is reached. If not, repeat steps S3-S5; if so, output the global optimal solution.

The invention discloses a graph anomaly detection method and system based on graph contrast learning, and relates to the technical field of graph neural networks. The method comprises the following steps: constructing a graph encoder DP-GNN, and obtaining effective node representation in an attribute graph for a subsequent graph anomaly detection task; a cross-scale contrast learning model is constructed, and joint detection of attribute anomaly and structure anomaly is realized by maximizing semantic distances of positive and negative samples in an attribute space and a structure space; and constructing a feature screening framework of global search and local optimization, and realizing screening of key feature subsets through a hybrid genetic algorithm. According to the method, joint detection of the node attribute anomaly and the structure anomaly can be realized, and the anomaly detection accuracy is improved.

The invention relates to the field of agricultural machinery intelligent operation, and discloses a multi-type agricultural plot agricultural machinery collaborative operation simulation system which comprises a plot data input module, an agricultural machinery parameter setting module, an operation task distribution module, a multi-agent collaborative scheduling module, a simulation operation module and a result evaluation module. Firstly, information of agricultural plots is collected through the plot data input module; then agricultural machine performance parameters are managed through an agricultural machine parameter setting module, and a random forest regression model is used for self-adaptive optimization; performing operation task allocation through a hybrid genetic algorithm and a particle swarm optimization algorithm, and optimizing operation time and energy consumption; the multi-agent system simulates and cooperates with operation of multiple agricultural machines, and an A * algorithm and a reinforcement learning algorithm are used to optimize a path; and finally, the operation efficiency, the energy consumption and the soil influence are evaluated through a result evaluation module, and a detailed evaluation report is generated. The working efficiency of the agricultural machine is improved, energy is saved, and soil is protected.

The present invention relates to the field of data processing technology, and in particular to a method, device, equipment and storage medium for generating an optical cable laying plan. The method comprises: processing engineering information to extract road section information; converting the road section information into a feature vector and inputting the feature vector into a laying method decision model to determine the laying method; determining whether to correct the laying method based on feature labels included in the road section information, and obtaining a cost correction coefficient if no correction is required; constructing an objective function and constraint conditions; solving the objective function using a hybrid genetic algorithm to obtain optical fiber information; integrating road section information, laying methods and optical fiber information to generate an optical cable laying plan; the method disclosed in the present application can automatically process engineering information to quickly obtain road section information, significantly shorten the design cycle to several days, reduce manual intervention, and improve planning efficiency; in addition, by constructing feature vectors and decision models, personalized optimization of road section laying is achieved to ensure accurate adaptation of the plan.

The invention provides an automatic guided vehicle scheduling method and device, computer equipment and a storage medium, and belongs to the technical field of production scheduling, and the method comprises the steps: obtaining the number of workpieces transported by an automatic guided vehicle, the number of procedures and the number of parallel machines for machining the workpieces; according to the number of the workpieces, the number of the working procedures and the number of the parallel machines, the priority of workpiece transportation and the parallel machine selection of each workpiece in each working procedure are coded, and an initial scheduling scheme is obtained; for any workpiece, determining a transportation plan of the workpiece in the current working procedure, corresponding to the state of the parallel machine and the position of the automatic guided vehicle in the idle state, and determining a transportation plan of the workpiece in the next working procedure; constructing a parallel multi-population hybrid genetic algorithm, and carrying out iterative optimization on the initial scheduling scheme according to a transportation plan of any workpiece transportation in the next process to obtain an optimal scheduling scheme; and scheduling the automated guided vehicle according to the optimal scheduling scheme. Therefore, the utilization efficiency of AGV scheduling can be effectively improved.

The invention discloses a mine transportation scheduling method and system based on a hybrid genetic algorithm, and belongs to the technical field of intelligent optimization scheduling. The method comprises the following steps: S100, inputting mine tunnel network, facility and equipment parameters; s200, a Dijkstra algorithm and breadth-first search are combined, and an optimal path set containing a shortest path and a secondary short path is generated for each pair of ore unloading fields and mining sites so as to disperse traffic flows; s300, establishing a multi-objective optimization model for minimizing waste time and fuel consumption and maximizing transportation volume; s400, a non-dominated sorting genetic algorithm is adopted for solving, the core of the non-dominated sorting genetic algorithm is that a special triple coding mode is adopted, an execution vehicle is allocated for each transportation task, a going route is selected for each transportation task, a return route is selected for each transportation task, and complex constraints are fused into a chromosome structure; s500, performing dynamic simulation on each scheduling scheme, updating the vehicle state and speed based on the real-time road congestion degree, and accurately evaluating the performance of the scheme; s600, selecting a final scheme from the Pareto optimal solution set by using a multi-attribute decision-making method; and S700, when a fault occurs, automatically re-evaluating and allocating tasks. According to the method, the problems of single path, inaccurate model and uncomfortable coding of a traditional method are effectively solved, and high efficiency, energy conservation and high robustness of vehicle scheduling in a complex mine tunnel network are realized.

The application provides a full-line grounding state evaluation method based on an improved hybrid genetic algorithm, belongs to the grounding resistance monitoring problem of a power system, and comprises the following steps: measuring each grounding resistance; establishing an objective function of the improved hybrid genetic algorithm; constructing a suitable BP neural network; training the BP neural network; and judging whether the error reaches the specified requirement or not.The application improves the grounding resistance measurement mode, realizes real-time monitoring of the grounding resistance, performs trend analysis and early warning, reduces the workload of manual measurement, and improves the work efficiency.

The application provides an automatic guided vehicle scheduling method and device, computer equipment and a storage medium, and belongs to the technical field of production scheduling. The method comprises the following steps: acquiring the workpiece quantity, the process quantity and the parallel machine quantity for processing the workpiece of the automatic guided vehicle; encoding the priority of the workpiece transportation and the parallel machine selection of each workpiece in each process according to the workpiece quantity, the process quantity and the parallel machine quantity, to obtain an initial scheduling scheme; for any workpiece, determining the transportation plan of the workpiece in the current process, the state of the corresponding parallel machine and the position of the automatic guided vehicle in the idle state, and determining the transportation plan of the workpiece in the next process; constructing a parallel multi-population hybrid genetic algorithm, iteratively optimizing the initial scheduling scheme according to the transportation plan of any workpiece in the next process, to obtain an optimal scheduling scheme; and scheduling the automatic guided vehicle according to the optimal scheduling scheme. In this way, the utilization efficiency of AGV scheduling can be effectively improved.

The invention discloses a large model coding agent system of a hybrid genetic algorithm and an implementation method. The system comprises a user interaction and configuration layer, an evolution core layer, a large model coding agent layer and a data storage and result layer. The invention provides a system framework for deeply coupling a global population evolution mechanism of a genetic algorithm and deep code understanding and intelligent generation capability of a large language model coding agent, and realizes a novel intelligent code generation and optimization system capable of efficiently discovering, generating and optimizing high-quality and innovative codes and algorithms.

The invention relates to the field of power plant energy saving, and discloses a power plant energy-saving optimization control method, which comprises the steps of calling historical efficiency data, calculating deviation based on an optimized actual energy efficiency value, quantifying energy-saving potential, generating a potential evaluation report, updating an optimized equipment parameter set on the basis of the potential evaluation report, and giving an adjustment suggestion. According to the method, global search and local optimization can be considered through combination of the hybrid genetic algorithm and the particle swarm algorithm, and limitation of a single algorithm, such as premature convergence of the genetic algorithm or falling into local optimization of the particle swarm algorithm, is avoided, so that optimization precision and efficiency are improved. According to the method, the optimization direction is accurately positioned, the defect that a key loss item is neglected in a traditional single balance method is overcome, the energy-saving potential is continuously excavated through closed-loop adjustment, it is ensured that the energy efficiency value continuously approaches the target, the optimization stagnation phenomenon is avoided, and the continuity and operability of energy-saving optimization of the power plant are improved.

The invention relates to a system energy management optimization method and system based on an improved genetic algorithm, and the method comprises the following steps: 1, taking a hydrogen-containing carrier energy storage region integrated energy system as a research object, taking the local consumption maximization of renewable energy power generation as a target function, building a comprehensive energy system management optimization model, and carrying out the optimization of the management of the comprehensive energy system; day-ahead coordination and optimization control of the energy system are carried out; and step 2, solving the integrated energy system management optimization model by using a hybrid genetic algorithm, and further completing energy optimization management of the integrated energy system. According to the invention, the problems of volatility and randomness of wind power and photovoltaic power generation can be solved.

The invention relates to the technical field of data processing, in particular to an optical cable laying scheme generation method and device, equipment and a storage medium, and the method comprises the steps: processing engineering information to extract road section information; converting the road section information into feature vectors, and inputting the feature vectors into a laying mode decision model to determine a laying mode; determining whether to correct the laying mode according to a feature tag included in the road section information, and if not, obtaining a cost correction coefficient; constructing a target function and a constraint condition; solving the objective function by using a hybrid genetic algorithm to obtain optical fiber information; integrating the road section information, the laying mode and the optical fiber information to generate an optical cable laying scheme; according to the method disclosed by the invention, the engineering information can be automatically processed to quickly obtain the road section information, the design period is remarkably shortened to several days, manual intervention is reduced, and the planning efficiency is improved; in addition, by constructing a feature vector and a decision model, personalized optimization of road section laying is realized, and accurate adaptation of the scheme is ensured.

The application discloses a kind of based on big data analysis's automobile sealing production resource intelligent scheduling method, specifically related to production resource scheduling field, for solving the problem that it is difficult to match between resource allocation and assembly quality in the process of existing multi-sealing assembly production scheduling;Scheme by constructing sealing assembly coupling topological relation network, identifying coupling sealing assembly, establishing component collaborative size distribution domain and assembly stable sub-domain, constructing size collaborative deviation index, and combining historical production resource allocation to build associated prediction model, on this basis, with the minimum size collaborative deviation cumulative amount as the goal, using hybrid genetic algorithm to jointly optimize equipment resources, material resources and process path resources, generate production scheduling strategy for assembly stability, so as to realize the systematization, refinement and intelligent scheduling of sealing production resources.

A general assembly project scheduling optimization method based on importance and a resource unavailable time window comprises the following steps: analyzing topological characteristics and information transfer capability of a process in a complex equipment general assembly network through a process importance evaluation method, and after the process importance is obtained, generating a global approximate optimal scheduling scheme according to a hybrid genetic-variable neighborhood search algorithm. According to the method, through an optimization module for process importance quantification, global search of a hybrid genetic algorithm and local search of an improved variable neighborhood search algorithm, heuristic information mining and scheduling optimization of a complex equipment general assembly system are integrated, so that the algorithm search efficiency is remarkably improved, and meanwhile, premature convergence to a local optimal solution is avoided; and a new thought is provided for solving a production scheduling optimization problem in a complex production environment.

The invention belongs to the technical field of water supply systems, and particularly relates to an intelligent water supply revenue system. Comprising a data collection module for collecting data such as environment and water by using an intelligent water meter and transmitting the data to a central data processor; the intelligent scheduling module carries out demand prediction on water supply related factors and schedules resources by using a multi-layer adaptive neural network, the neural network comprises three sub-network layers, and the layers have different functions and can be adaptively adjusted; and the dynamic pricing and charging module adopts an improved hybrid genetic algorithm to optimize and dynamically adjust the water price based on the collected data, and relates to the steps of fitness function multi-dimensional optimization, nonlinear crossing, high-order nonlinear variation, particle swarm optimization and the like. Multiple factors can be integrated, the water supply management efficiency, the resource utilization rate and the service quality are improved, and sustainable development of the water supply industry is promoted.

The present invention relates to the VRP field of logistics. The present invention provides a crowdsourcing scheduling method and system based on a hybrid genetic algorithm and a position allocation algorithm, obtaining a customer set, a self-owned vehicle set, a warehouse set and a crowdsourcing vehicle set; constructing a self-owned vehicle route optimization model, solving it through a hybrid genetic algorithm, obtaining an optimal solution that achieves the optimization goal and satisfies the preset constraints; the optimal solution represents the customer set served by each self-owned vehicle, the optimal route plan of each self-owned vehicle and the customer set that needs to be served by the crowdsourcing vehicle; constructing a crowdsourcing task allocation model, solving it through a position update allocation algorithm, obtaining an optimal solution that achieves the optimization goal and satisfies the constraints, the optimal solution represents the customer set served by each crowdsourcing vehicle and the service order; dispatching self-owned vehicles and crowdsourcing vehicles to serve customers according to the optimal solution of the self-owned vehicle route optimization model and the crowdsourcing task allocation model.

The invention discloses a dynamic task planning method based on a hybrid genetic algorithm, belongs to the technical field of unmanned aerial vehicle dynamic task planning, and is used for realizing dynamic task distribution in a search and reconnaissance task scene. The method comprises the following steps: pre-planning according to task information and unmanned aerial vehicle capability information by using a hybrid genetic algorithm to obtain a pre-planning scheme; the hybrid genetic algorithm is formed by combining an original genetic algorithm and a variable neighborhood search algorithm, the basic genetic algorithm is used for ensuring the exploration capability of the algorithm, and the variable neighborhood search algorithm is used for ensuring the development capability of the algorithm; loading the dynamic task information, and sorting the dynamic tasks according to the occurrence time of the dynamic task information; then, a dynamic allocation mechanism is utilized to allocate dynamic tasks, and four dynamic adjustment strategies are designed for different task scenes, namely a random allocation strategy, an allocation strategy based on clustering center allocation, a nearby allocation strategy and an allocation strategy based on fuel surplus; and finally, the unmanned aerial vehicle updates the task sequence.

This invention discloses a dual-tunnel scheduling optimization method based on a hybrid genetic algorithm and rolling time domain, belonging to the field of tunnel engineering construction management technology. It includes: constructing a multi-objective scheduling model for dual-tunnel construction with the objectives of minimizing the total project duration and minimizing the weighted resource peak value; using a constraint method to transform the multi-objective scheduling model into a resource-balanced single-objective optimization model under a fixed project duration constraint; using a hybrid genetic algorithm to perform static global optimization on the single-objective optimization model; establishing a dynamic monitoring mechanism based on rolling time domain through multi-dimensional IoT sensing; updating the parameters or status of affected processes according to the type of interference event when interference events are detected; using a heuristic local scheduling strategy to reschedule unscheduled tasks after interference, predicting the final project state, and generating a dynamically adjusted construction scheduling scheme. This invention combines static global optimization with dynamic real-time adjustment to achieve dual optimization of project duration and resources.

The embodiment of the invention discloses a liner path and navigational speed collaborative optimization method and system considering an emission control area. The method comprises the steps that S1, assumed conditions corresponding to liner path selection and navigational speed optimization problems of containers considering the emission control area and perishable products are defined; s2, creating a liner path selection and navigational speed optimization model which corresponds to the optimization target and considers emission control area limitation and perishable product transportation; and S3, solving the liner path selection and navigational speed optimization model through an adaptive hybrid genetic algorithm to generate a corresponding liner path and navigational speed collaborative optimization scheme. According to the method, the policy constraint of an emission control area (ECA) and the transportation time requirement of perishable products are analyzed, a mixed integer nonlinear programming model with the minimum total operation cost as the target is constructed, and an improved self-adaptive hybrid genetic algorithm is adopted for solving; and an optimal ship path and speed cooperation scheme is dynamically generated based on multiple factors such as an ECA region boundary, a fuel price and a port time window.